diff --git a/ANALYSIS_REQUEST.md b/ANALYSIS_REQUEST.md new file mode 100644 index 0000000..ac84b1b --- /dev/null +++ b/ANALYSIS_REQUEST.md @@ -0,0 +1,199 @@ +# LAM_Audio2Expression 解析・実装依頼 + +## 依頼の背景 + +Audio2ExpressionサービスをGoogle Cloud Runにデプロイしようと48時間以上、40回以上試行したが、モデルが「mock」モードのままで正しく初期化されない。対症療法的な修正を繰り返しても解決できないため、根本的なアプローチの見直しが必要。 + +## 前任AIの反省点 + +**重要**: 前任AI（Claude）は以下の問題を抱えていた： + +1. **古い知識ベースからの推論に依存** + - 一般的な「Cloud Runデプロイ」パターンを適用しようとした + - LAM_Audio2Expression固有の設計思想を理解できていなかった + +2. **表面的なコード理解** + - コードを読んだが、なぜそのように設計されているかを理解していなかった + - 元々どのような環境・ユースケースを想定したコードなのかを考慮しなかった + +3. **対症療法の繰り返し** + - ログからエラーを見つけ→修正→デプロイ→また別のエラー、の無限ループ + - 根本原因を特定せず、見えている症状だけを修正し続けた + +4. **思い込み** + - 「モデルの読み込みや初期化がうまくいっていない」と決めつけていた + - 問題はそこではなく、もっと根本的なアプローチの誤りである可能性がある + +**この解析を行う際は、上記の落とし穴にハマらないよう注意してください。** + +## 解析対象コード + +### 主要ファイル + +**1. audio2exp-service/app.py** (現在のサービス実装) +- FastAPI を使用したWebサービス +- `/health`, `/debug`, `/api/audio2expression`, `/ws/{session_id}` エンドポイント +- `Audio2ExpressionEngine` クラスでモデル管理 + +**2. LAM_Audio2Expression/engines/infer.py** +- `InferBase` クラス: モデル構築の基底クラス +- `Audio2ExpressionInfer` クラス: 音声→表情推論 +- `infer_streaming_audio()`: リアルタイムストリーミング推論 + +**3. LAM_Audio2Expression/models/network.py** +- `Audio2Expression` クラス: PyTorchニューラルネットワーク +- wav2vec2 エンコーダー + Identity Encoder + Decoder構成 + +**4. LAM_Audio2Expression/engines/defaults.py** +- `default_config_parser()`: 設定ファイル読み込み +- `default_setup()`: バッチサイズ等の設定計算 +- `create_ddp_model()`: 分散データ並列ラッパー + +## 具体的な解析依頼 + +### Q1: モデル初期化が完了しない根本原因 + +```python +# app.py での初期化 +self.infer = INFER.build(dict(type=cfg.infer.type, cfg=cfg)) +self.infer.model.eval() +``` + +この処理がCloud Run環境で正常に完了しない理由を特定してください。 + +考えられる原因: +- [ ] メモリ不足 (8GiBで足りない?) +- [ ] CPU環境での動作制限 +- [ ] 分散処理設定が単一インスタンスで問題を起こす +- [ ] ファイルシステムの書き込み権限 +- [ ] タイムアウト (コールドスタート時間) +- [ ] その他 + +### Q2: default_setup() の問題 + +```python +# defaults.py +def default_setup(cfg): + world_size = comm.get_world_size() # Cloud Runでは1 + cfg.num_worker = cfg.num_worker if cfg.num_worker is not None else mp.cpu_count() + cfg.num_worker_per_gpu = cfg.num_worker // world_size + assert cfg.batch_size % world_size == 0 # 失敗する可能性? +``` + +推論時にこの設定が問題を起こしていないか確認してください。 + +### Q3: ロガー設定の問題 + +```python +# infer.py +self.logger = get_root_logger( + log_file=os.path.join(cfg.save_path, "infer.log"), + file_mode="a" if cfg.resume else "w", +) +``` + +Cloud Runのファイルシステムでログファイル作成が失敗する可能性を確認してください。 + +### Q4: wav2vec2 モデル読み込み + +```python +# network.py +if os.path.exists(pretrained_encoder_path): + self.audio_encoder = Wav2Vec2Model.from_pretrained(pretrained_encoder_path) +else: + config = Wav2Vec2Config.from_pretrained(wav2vec2_config_path) + self.audio_encoder = Wav2Vec2Model(config) # ランダム重み! +``` + +- wav2vec2-base-960h フォルダの構成は正しいか? +- HuggingFaceからのダウンロードが必要なファイルはないか? + +### Q5: 適切なデプロイ方法 + +Cloud Runが不適切な場合、以下の代替案を検討: +- Google Compute Engine (GPU インスタンス) +- Cloud Run Jobs (バッチ処理) +- Vertex AI Endpoints +- Kubernetes Engine + +## 期待する成果 + +### 1. 分析結果 +- 根本原因の特定 +- なぜ40回以上の試行で解決できなかったかの説明 + +### 2. 修正されたコード +``` +audio2exp-service/ +├── app.py # 修正版 +├── Dockerfile # 必要なら修正 +└── cloudbuild.yaml # 必要なら修正 +``` + +### 3. 動作確認方法 +```bash +# ヘルスチェック +curl https:///health +# 期待する応答: {"model_initialized": true, "mode": "inference", ...} + +# 推論テスト +curl -X POST https:///api/audio2expression \ + -H "Content-Type: application/json" \ + -d '{"audio_base64": "...", "session_id": "test"}' +``` + +## 技術スペック + +### モデル仕様 +| 項目 | 値 | +|------|-----| +| 入力サンプルレート | 24kHz (API) / 16kHz (内部) | +| 出力フレームレート | 30 fps | +| 出力次元 | 52 (ARKit blendshape) | +| モデルファイルサイズ | ~500MB (LAM) + ~400MB (wav2vec2) | + +### デプロイ環境 +| 項目 | 値 | +|------|-----| +| プラットフォーム | Cloud Run Gen 2 | +| リージョン | asia-northeast1 | +| メモリ | 8GiB | +| CPU | 4 | +| max-instances | 4 | + +### 依存関係 (requirements.txt) +``` +torch==2.0.1 +torchaudio==2.0.2 +transformers==4.30.2 +librosa==0.10.0 +fastapi==0.100.0 +uvicorn==0.23.0 +numpy==1.24.3 +scipy==1.11.1 +pydantic==2.0.3 +``` + +## ファイルの場所 + +```bash +# プロジェクトルート +cd /home/user/LAM_gpro + +# メインサービス +cat audio2exp-service/app.py + +# 推論エンジン +cat audio2exp-service/LAM_Audio2Expression/engines/infer.py + +# ニューラルネットワーク +cat audio2exp-service/LAM_Audio2Expression/models/network.py + +# 設定 +cat audio2exp-service/LAM_Audio2Expression/engines/defaults.py +cat audio2exp-service/LAM_Audio2Expression/configs/lam_audio2exp_config_streaming.py +``` + +--- + +以上、よろしくお願いいたします。 diff --git a/LAM_Audio2Expression_HANDOFF.md b/LAM_Audio2Expression_HANDOFF.md new file mode 100644 index 0000000..c109d4c --- /dev/null +++ b/LAM_Audio2Expression_HANDOFF.md @@ -0,0 +1,199 @@ +# LAM_Audio2Expression 引継ぎ・解析依頼文 + +## 1. プロジェクト概要 + +### 目的 +Audio2Expressionサービスを Google Cloud Run にデプロイし、音声からARKit 52 blendshape係数をリアルタイムで生成するAPIを提供する。 + +### リポジトリ構成 +``` +/home/user/LAM_gpro/ +├── audio2exp-service/ +│ ├── app.py # FastAPI サービス本体 +│ ├── Dockerfile # Dockerイメージ定義 +│ ├── cloudbuild.yaml # Cloud Build設定 +│ ├── requirements.txt # Python依存関係 +│ ├── start.sh # 起動スクリプト +│ ├── models/ # モデルファイル格納 +│ │ ├── LAM_audio2exp_streaming.tar # LAMモデル重み +│ │ └── wav2vec2-base-960h/ # wav2vec2事前学習モデル +│ └── LAM_Audio2Expression/ # LAMモデルソースコード +│ ├── configs/ +│ │ └── lam_audio2exp_config_streaming.py +│ ├── engines/ +│ │ ├── defaults.py # 設定パーサー・セットアップ +│ │ └── infer.py # 推論エンジン (Audio2ExpressionInfer) +│ ├── models/ +│ │ ├── __init__.py +│ │ ├── builder.py # モデルビルダー +│ │ ├── default.py # DefaultEstimator +│ │ ├── network.py # Audio2Expression ニューラルネットワーク +│ │ └── utils.py # 後処理ユーティリティ +│ └── utils/ +│ ├── comm.py # 分散処理ユーティリティ +│ ├── config.py # 設定管理 +│ ├── env.py # 環境設定 +│ └── logger.py # ロギング +``` + +## 2. コア技術アーキテクチャ + +### Audio2Expression モデル (network.py) + +```python +# 入力 → 出力フロー +input_audio_array (24kHz or 16kHz) + → wav2vec2 audio_encoder (768次元特徴) + → feature_projection (512次元) + → identity_encoder (話者特徴 + GRU) + → decoder (Conv1D + LayerNorm + ReLU) + → output_proj (52次元) + → sigmoid + → ARKit 52 blendshape coefficients (0-1) +``` + +### 重要なパラメータ +- **内部サンプルレート**: 16kHz +- **出力フレームレート**: 30 fps +- **出力次元**: 52 (ARKit blendshape) +- **identity classes**: 12 (話者ID用) + +### wav2vec2の読み込みロジック (network.py:40-44) +```python +if os.path.exists(pretrained_encoder_path): + self.audio_encoder = Wav2Vec2Model.from_pretrained(pretrained_encoder_path) +else: + # 警告: この場合、ランダム重みで初期化される + config = Wav2Vec2Config.from_pretrained(wav2vec2_config_path) + self.audio_encoder = Wav2Vec2Model(config) +``` + +### ストリーミング推論 (infer.py) + +`infer_streaming_audio()` メソッド: +1. コンテキスト管理 (`previous_audio`, `previous_expression`, `previous_volume`) +2. 64フレーム最大長でバッファリング +3. 16kHzへリサンプリング +4. 後処理パイプライン: + - `smooth_mouth_movements()` - 無音時の口動き抑制 + - `apply_frame_blending()` - フレーム間ブレンディング + - `apply_savitzky_golay_smoothing()` - 平滑化フィルタ + - `symmetrize_blendshapes()` - 左右対称化 + - `apply_random_eye_blinks_context()` - 瞬き追加 + +## 3. 現在の問題 + +### 症状 +- Cloud Runへのデプロイは成功する +- ヘルスチェック応答: + ```json + { + "model_initialized": false, + "mode": "mock", + "init_step": "...", + "init_error": "..." + } + ``` +- 48時間以上、40回以上のデプロイ試行で解決できていない + +### 試行した解決策（全て失敗） +1. gsutil でモデルダウンロード +2. Python GCSクライアントでモデルダウンロード +3. Cloud Storage FUSE でマウント +4. Dockerイメージにモデルを焼き込み +5. max-instances を 10 → 5 → 4 に削減（quota対策） +6. ステップ別エラー追跡を追加 + +### 重要な指摘 +ユーザーからの指摘: +> 「キミは、モデルの読み込みや、初期化が上手く行ってないと、思い込んでるでしょ？そうじゃなく、根本的にやり方が間違ってるんだよ！」 +> 「LAM_Audio2Expressionのロジックを本質的に理解できてないでしょ？」 + +つまり、問題は単なる「ファイルが見つからない」「初期化エラー」ではなく、**アプローチ自体が根本的に間違っている**可能性がある。 + +## 4. 解析依頼事項 + +### 4.1 根本原因の特定 +1. **LAM_Audio2Expressionの設計思想** + - このモデルは元々どのような環境で動作することを想定しているか？ + - GPU必須か？CPU動作可能か？ + - リアルタイムストリーミング vs バッチ処理の制約は？ + +2. **Cloud Run適合性** + - コールドスタート時間の問題はないか？ + - メモリ8GiBで十分か？ + - CPUのみで実用的な速度が出るか？ + +3. **初期化プロセス** + - `default_setup(cfg)` のバッチサイズ計算が問題を起こしていないか？ + - `create_ddp_model()` がシングルプロセス環境で正しく動作するか？ + - ロガー設定がCloud Run環境で問題を起こしていないか？ + +### 4.2 app.py の問題点 +現在の `app.py` の初期化フローを確認: +```python +# lifespan内で非同期初期化 +loop = asyncio.get_event_loop() +await loop.run_in_executor(None, engine.initialize) +``` + +- この初期化方法は正しいか？ +- エラーが正しくキャッチ・伝播されているか？ + +### 4.3 設定ファイルの問題 +`lam_audio2exp_config_streaming.py`: +```python +num_worker = 16 # Cloud Runで問題になる？ +batch_size = 16 # 推論時も必要？ +``` + +## 5. 期待する成果物 + +1. **根本原因の分析レポート** + - なぜ現在のアプローチが機能しないのか + - Cloud Runでこのモデルを動作させることは可能か + +2. **正しい実装方針** + - 必要な場合、代替デプロイメント方法の提案 + - app.py の正しい実装 + +3. **動作する実装コード** + - モデル初期化が成功する + - `/health` エンドポイントで `model_initialized: true` を返す + - `/api/audio2expression` でリアルタイム推論が機能する + +## 6. 関連ファイル一覧 + +### 必読ファイル +| ファイル | 説明 | +|---------|------| +| `audio2exp-service/app.py` | FastAPIサービス本体 | +| `LAM_Audio2Expression/engines/infer.py` | 推論エンジン | +| `LAM_Audio2Expression/models/network.py` | ニューラルネットワーク定義 | +| `LAM_Audio2Expression/engines/defaults.py` | 設定パーサー | +| `LAM_Audio2Expression/configs/lam_audio2exp_config_streaming.py` | ストリーミング設定 | + +### 補助ファイル +| ファイル | 説明 | +|---------|------| +| `LAM_Audio2Expression/models/utils.py` | 後処理ユーティリティ | +| `LAM_Audio2Expression/utils/comm.py` | 分散処理ユーティリティ | +| `LAM_Audio2Expression/models/builder.py` | モデルビルダー | + +## 7. デプロイ環境 + +- **Cloud Run Gen 2** +- **メモリ**: 8GiB +- **CPU**: 4 +- **max-instances**: 4 +- **コンテナポート**: 8080 +- **リージョン**: asia-northeast1 + +## 8. Git情報 + +- **ブランチ**: `claude/implementation-testing-w2xCb` +- **最新コミット**: `4ba662c Simplify deployment: bake models into Docker image` + +--- + +作成日: 2026-02-07 diff --git a/audio2exp-service/.gcloudignore b/audio2exp-service/.gcloudignore new file mode 100644 index 0000000..28d1cdd --- /dev/null +++ b/audio2exp-service/.gcloudignore @@ -0,0 +1,12 @@ +# Cloud Build ignore file +# Unlike .gitignore, we INCLUDE LAM_Audio2Expression for the build + +exp/ +models/ +__pycache__/ +*.pyc +.git/ +.gitignore +*.md +*.txt +!requirements.txt diff --git a/audio2exp-service/.gitignore b/audio2exp-service/.gitignore new file mode 100644 index 0000000..8739a60 --- /dev/null +++ b/audio2exp-service/.gitignore @@ -0,0 +1,4 @@ +exp/ +models/ +__pycache__/ +*.pyc diff --git a/audio2exp-service/Dockerfile b/audio2exp-service/Dockerfile new file mode 100644 index 0000000..db7eead --- /dev/null +++ b/audio2exp-service/Dockerfile @@ -0,0 +1,32 @@ +FROM python:3.10-slim + +WORKDIR /app + +# System dependencies +RUN apt-get update && apt-get install -y --no-install-recommends \ + ffmpeg \ + && rm -rf /var/lib/apt/lists/* + +# Copy and install Python dependencies +COPY requirements.txt . +RUN pip install --no-cache-dir -r requirements.txt + +# Copy LAM_Audio2Expression code +COPY LAM_Audio2Expression/ ./LAM_Audio2Expression/ + +# Copy models directory (downloaded during Cloud Build, fallback for FUSE) +COPY models/ ./models/ + +# Copy application code +COPY app.py . +COPY start.sh . +RUN chmod +x start.sh + +# Fixed paths - models served via GCS FUSE mount (primary), Docker-baked (fallback) +ENV LAM_A2E_PATH=/app/LAM_Audio2Expression +ENV LAM_WEIGHT_PATH=/app/models/lam_audio2exp_streaming.pth +ENV WAV2VEC_PATH=/app/models/wav2vec2-base-960h + +ENV PORT=8080 + +CMD ["./start.sh"] diff --git a/audio2exp-service/LAM_Audio2Expression/.gitignore b/audio2exp-service/LAM_Audio2Expression/.gitignore new file mode 100644 index 0000000..73c532f --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/.gitignore @@ -0,0 +1,18 @@ +image/ +__pycache__ +**/build/ +**/*.egg-info/ +**/dist/ +*.so +exp +weights +data +log +outputs/ +.vscode +.idea +*/.DS_Store +TEMP/ +pretrained/ +**/*.out +Dockerfile \ No newline at end of file diff --git a/audio2exp-service/LAM_Audio2Expression/LICENSE b/audio2exp-service/LAM_Audio2Expression/LICENSE new file mode 100644 index 0000000..f49a4e1 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/LICENSE @@ -0,0 +1,201 @@ + Apache License + Version 2.0, January 2004 + http://www.apache.org/licenses/ + + TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION + + 1. 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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. \ No newline at end of file diff --git a/audio2exp-service/LAM_Audio2Expression/README.md b/audio2exp-service/LAM_Audio2Expression/README.md new file mode 100644 index 0000000..7f9e2c2 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/README.md @@ -0,0 +1,123 @@ +# LAM-A2E: Audio to Expression + +[![Website](https://raw.githubusercontent.com/prs-eth/Marigold/main/doc/badges/badge-website.svg)](https://aigc3d.github.io/projects/LAM/) +[![Apache License](https://img.shields.io/badge/📃-Apache--2.0-929292)](https://www.apache.org/licenses/LICENSE-2.0) +[![ModelScope Demo](https://img.shields.io/badge/%20ModelScope%20-Space-blue)](https://www.modelscope.cn/studios/Damo_XR_Lab/LAM-A2E) + +## Description +#### This project leverages audio input to generate ARKit blendshapes-driven facial expressions in ⚡real-time⚡, powering ultra-realistic 3D avatars generated by [LAM](https://github.com/aigc3d/LAM). +To enable ARKit-driven animation of the LAM model, we adapted ARKit blendshapes to align with FLAME's facial topology through manual customization. The LAM-A2E network follows an encoder-decoder architecture, as shown below. We adopt the state-of-the-art pre-trained speech model Wav2Vec for the audio encoder. The features extracted from the raw audio waveform are combined with style features and fed into the decoder, which outputs stylized blendshape coefficients. + +
+Architecture +
+ +## Demo + +
+ +
+ +## 📢 News + +**[May 21, 2025]** We have released a [Avatar Export Feature](https://www.modelscope.cn/studios/Damo_XR_Lab/LAM_Large_Avatar_Model), enabling users to generate facial expressions from audio using any [LAM-generated](https://github.com/aigc3d/LAM) 3D digital humans.
+**[April 21, 2025]** We have released the [ModelScope](https://www.modelscope.cn/studios/Damo_XR_Lab/LAM-A2E) Space !
+**[April 21, 2025]** We have released the WebGL Interactive Chatting Avatar SDK on [OpenAvatarChat](https://github.com/HumanAIGC-Engineering/OpenAvatarChat) (including LLM, ASR, TTS, Avatar), with which you can freely chat with our generated 3D Digital Human ! 🔥
+ +### To do list +- [ ] Release Huggingface space. +- [x] Release [Modelscope demo space](https://www.modelscope.cn/studios/Damo_XR_Lab/LAM-A2E). You can try the demo or pull the demo source code and deploy it on your own machine. +- [ ] Release the LAM-A2E model based on the Flame expression. +- [x] Release Interactive Chatting Avatar SDK with [OpenAvatarChat](https://www.modelscope.cn/studios/Damo_XR_Lab/LAM-A2E), including LLM, ASR, TTS, LAM-Avatars. + + + +## 🚀 Get Started +### Environment Setup +```bash +git clone git@github.com:aigc3d/LAM_Audio2Expression.git +cd LAM_Audio2Expression +# Create conda environment (currently only supports Python 3.10) +conda create -n lam_a2e python=3.10 +# Activate the conda environment +conda activate lam_a2e +# Install with Cuda 12.1 +sh ./scripts/install/install_cu121.sh +# Or Install with Cuda 11.8 +sh ./scripts/install/install_cu118.sh +``` + + +### Download + +``` +# HuggingFace download +# Download Assets and Model Weights +huggingface-cli download 3DAIGC/LAM_audio2exp --local-dir ./ +tar -xzvf LAM_audio2exp_assets.tar && rm -f LAM_audio2exp_assets.tar +tar -xzvf LAM_audio2exp_streaming.tar && rm -f LAM_audio2exp_streaming.tar + +# Or OSS Download (In case of HuggingFace download failing) +# Download Assets +wget https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/aigc3d/data/LAM/LAM_audio2exp_assets.tar +tar -xzvf LAM_audio2exp_assets.tar && rm -f LAM_audio2exp_assets.tar +# Download Model Weights +wget https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/aigc3d/data/LAM/LAM_audio2exp_streaming.tar +tar -xzvf LAM_audio2exp_streaming.tar && rm -f LAM_audio2exp_streaming.tar + +Or Modelscope Download +git clone https://www.modelscope.cn/Damo_XR_Lab/LAM_audio2exp.git ./modelscope_download +``` + + +### Quick Start Guide +#### Using Gradio Interface: +We provide a simple Gradio demo with **WebGL Render**, and you can get rendering results by uploading audio in seconds. + +[//]: # (teaser) +
+ +
+ + +``` +python app_lam_audio2exp.py +``` + +### Inference +```bash +# example: python inference.py --config-file configs/lam_audio2exp_config_streaming.py --options save_path=exp/audio2exp weight=pretrained_models/lam_audio2exp_streaming.tar audio_input=./assets/sample_audio/BarackObama_english.wav +python inference.py --config-file ${CONFIG_PATH} --options save_path=${SAVE_PATH} weight=${CHECKPOINT_PATH} audio_input=${AUDIO_INPUT} +``` + +### Acknowledgement +This work is built on many amazing research works and open-source projects: +- [FLAME](https://flame.is.tue.mpg.de) +- [FaceFormer](https://github.com/EvelynFan/FaceFormer) +- [Meshtalk](https://github.com/facebookresearch/meshtalk) +- [Unitalker](https://github.com/X-niper/UniTalker) +- [Pointcept](https://github.com/Pointcept/Pointcept) + +Thanks for their excellent works and great contribution. + + +### Related Works +Welcome to follow our other interesting works: +- [LAM](https://github.com/aigc3d/LAM) +- [LHM](https://github.com/aigc3d/LHM) + + +### Citation +``` +@inproceedings{he2025LAM, + title={LAM: Large Avatar Model for One-shot Animatable Gaussian Head}, + author={ + Yisheng He and Xiaodong Gu and Xiaodan Ye and Chao Xu and Zhengyi Zhao and Yuan Dong and Weihao Yuan and Zilong Dong and Liefeng Bo + }, + booktitle={arXiv preprint arXiv:2502.17796}, + year={2025} +} +``` diff --git a/audio2exp-service/LAM_Audio2Expression/app_lam_audio2exp.py b/audio2exp-service/LAM_Audio2Expression/app_lam_audio2exp.py new file mode 100644 index 0000000..56c2339 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/app_lam_audio2exp.py @@ -0,0 +1,313 @@ +""" +Copyright 2024-2025 The Alibaba 3DAIGC Team Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + https://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. +""" +import os +import base64 + +import gradio as gr +import argparse +from omegaconf import OmegaConf +from gradio_gaussian_render import gaussian_render + +from engines.defaults import ( + default_argument_parser, + default_config_parser, + default_setup, +) +from engines.infer import INFER +from pathlib import Path + +try: + import spaces +except: + pass + +import patoolib + +h5_rendering = True + + +def assert_input_image(input_image,input_zip_textbox): + if(os.path.exists(input_zip_textbox)): + return + if input_image is None: + raise gr.Error('No image selected or uploaded!') + + +def prepare_working_dir(): + import tempfile + working_dir = tempfile.TemporaryDirectory() + return working_dir + +def get_image_base64(path): + with open(path, 'rb') as image_file: + encoded_string = base64.b64encode(image_file.read()).decode() + return f'data:image/png;base64,{encoded_string}' + + +def do_render(): + print('WebGL rendering ....') + return + +def audio_loading(): + print("Audio loading ....") + return "None" + +def parse_configs(): + parser = argparse.ArgumentParser() + parser.add_argument("--config", type=str) + parser.add_argument("--infer", type=str) + args, unknown = parser.parse_known_args() + + cfg = OmegaConf.create() + cli_cfg = OmegaConf.from_cli(unknown) + + # parse from ENV + if os.environ.get("APP_INFER") is not None: + args.infer = os.environ.get("APP_INFER") + if os.environ.get("APP_MODEL_NAME") is not None: + cli_cfg.model_name = os.environ.get("APP_MODEL_NAME") + + args.config = args.infer if args.config is None else args.config + + if args.config is not None: + cfg_train = OmegaConf.load(args.config) + cfg.source_size = cfg_train.dataset.source_image_res + try: + cfg.src_head_size = cfg_train.dataset.src_head_size + except: + cfg.src_head_size = 112 + cfg.render_size = cfg_train.dataset.render_image.high + _relative_path = os.path.join( + cfg_train.experiment.parent, + cfg_train.experiment.child, + os.path.basename(cli_cfg.model_name).split("_")[-1], + ) + + cfg.save_tmp_dump = os.path.join("exps", "save_tmp", _relative_path) + cfg.image_dump = os.path.join("exps", "images", _relative_path) + cfg.video_dump = os.path.join("exps", "videos", _relative_path) # output path + + if args.infer is not None: + cfg_infer = OmegaConf.load(args.infer) + cfg.merge_with(cfg_infer) + cfg.setdefault( + "save_tmp_dump", os.path.join("exps", cli_cfg.model_name, "save_tmp") + ) + cfg.setdefault("image_dump", os.path.join("exps", cli_cfg.model_name, "images")) + cfg.setdefault( + "video_dump", os.path.join("dumps", cli_cfg.model_name, "videos") + ) + cfg.setdefault("mesh_dump", os.path.join("dumps", cli_cfg.model_name, "meshes")) + + cfg.motion_video_read_fps = 30 + cfg.merge_with(cli_cfg) + + cfg.setdefault("logger", "INFO") + + assert cfg.model_name is not None, "model_name is required" + + return cfg, cfg_train + + +def create_zip_archive(output_zip='assets/arkitWithBSData.zip', base_dir=""): + if os.path.exists(output_zip): + os.remove(output_zip) + print(f"Remove previous file: {output_zip}") + + try: + # 创建压缩包 + patoolib.create_archive( + archive=output_zip, + filenames=[base_dir], # 要压缩的目录 + verbosity=-1, # 静默模式 + program='zip' # 指定使用zip格式 + ) + print(f"Archive created successfully: {output_zip}") + except Exception as e: + raise ValueError(f"Archive creation failed: {str(e)}") + + +def demo_lam_audio2exp(infer, cfg): + def core_fn(image_path: str, audio_params, working_dir, input_zip_textbox): + + if(os.path.exists(input_zip_textbox)): + base_id = os.path.basename(input_zip_textbox).split(".")[0] + output_dir = os.path.join('assets', 'sample_lam', base_id) + # unzip_dir + if (not os.path.exists(os.path.join(output_dir, 'arkitWithBSData'))): + run_command = 'unzip -d '+output_dir+' '+input_zip_textbox + os.system(run_command) + rename_command = 'mv '+os.path.join(output_dir,base_id)+' '+os.path.join(output_dir,'arkitWithBSData') + os.system(rename_command) + else: + base_id = os.path.basename(image_path).split(".")[0] + + # set input audio + cfg.audio_input = audio_params + cfg.save_json_path = os.path.join("./assets/sample_lam", base_id, 'arkitWithBSData', 'bsData.json') + infer.infer() + + output_file_name = base_id+'_'+os.path.basename(audio_params).split(".")[0]+'.zip' + assetPrefix = 'gradio_api/file=assets/' + output_file_path = os.path.join('./assets',output_file_name) + + create_zip_archive(output_zip=output_file_path, base_dir=os.path.join("./assets/sample_lam", base_id)) + + return 'gradio_api/file='+audio_params, assetPrefix+output_file_name + + with gr.Blocks(analytics_enabled=False) as demo: + logo_url = './assets/images/logo.jpeg' + logo_base64 = get_image_base64(logo_url) + gr.HTML(f""" +
+
+

LAM-A2E: Audio to Expression

+
+
+ """) + + gr.HTML( + """

Notes: This project leverages audio input to generate ARKit blendshapes-driven facial expressions in ⚡real-time⚡, powering ultra-realistic 3D avatars generated by LAM.

""" + ) + + # DISPLAY + with gr.Row(): + with gr.Column(variant='panel', scale=1): + with gr.Tabs(elem_id='lam_input_image'): + with gr.TabItem('Input Image'): + with gr.Row(): + input_image = gr.Image(label='Input Image', + image_mode='RGB', + height=480, + width=270, + sources='upload', + type='filepath', # 'numpy', + elem_id='content_image', + interactive=False) + # EXAMPLES + with gr.Row(): + examples = [ + ['assets/sample_input/barbara.jpg'], + ['assets/sample_input/status.png'], + ['assets/sample_input/james.png'], + ['assets/sample_input/vfhq_case1.png'], + ] + gr.Examples( + examples=examples, + inputs=[input_image], + examples_per_page=20, + ) + + with gr.Column(): + with gr.Tabs(elem_id='lam_input_audio'): + with gr.TabItem('Input Audio'): + with gr.Row(): + audio_input = gr.Audio(label='Input Audio', + type='filepath', + waveform_options={ + 'sample_rate': 16000, + 'waveform_progress_color': '#4682b4' + }, + elem_id='content_audio') + + examples = [ + ['assets/sample_audio/Nangyanwen_chinese.wav'], + ['assets/sample_audio/LiBai_TTS_chinese.wav'], + ['assets/sample_audio/LinJing_TTS_chinese.wav'], + ['assets/sample_audio/BarackObama_english.wav'], + ['assets/sample_audio/HillaryClinton_english.wav'], + ['assets/sample_audio/XitongShi_japanese.wav'], + ['assets/sample_audio/FangXiao_japanese.wav'], + ] + gr.Examples( + examples=examples, + inputs=[audio_input], + examples_per_page=10, + ) + + # SETTING + with gr.Row(): + with gr.Column(variant='panel', scale=1): + input_zip_textbox = gr.Textbox( + label="Input Local Path to LAM-Generated ZIP File", + interactive=True, + placeholder="Input Local Path to LAM-Generated ZIP File", + visible=True + ) + submit = gr.Button('Generate', + elem_id='lam_generate', + variant='primary') + + if h5_rendering: + gr.set_static_paths(Path.cwd().absolute() / "assets/") + with gr.Row(): + gs = gaussian_render(width=380, height=680) + + working_dir = gr.State() + selected_audio = gr.Textbox(visible=False) + selected_render_file = gr.Textbox(visible=False) + + submit.click( + fn=assert_input_image, + inputs=[input_image,input_zip_textbox], + queue=False, + ).success( + fn=prepare_working_dir, + outputs=[working_dir], + queue=False, + ).success( + fn=core_fn, + inputs=[input_image, audio_input, + working_dir, input_zip_textbox], + outputs=[selected_audio, selected_render_file], + queue=False, + ).success( + fn=audio_loading, + outputs=[selected_audio], + js='''(output_component) => window.loadAudio(output_component)''' + ).success( + fn=do_render(), + outputs=[selected_render_file], + js='''(selected_render_file) => window.start(selected_render_file)''' + ) + + demo.queue() + demo.launch(inbrowser=True) + + + +def launch_gradio_app(): + os.environ.update({ + 'APP_ENABLED': '1', + 'APP_MODEL_NAME':'', + 'APP_INFER': 'configs/lam_audio2exp_streaming_config.py', + 'APP_TYPE': 'infer.audio2exp', + 'NUMBA_THREADING_LAYER': 'omp', + }) + + args = default_argument_parser().parse_args() + args.config_file = 'configs/lam_audio2exp_config_streaming.py' + cfg = default_config_parser(args.config_file, args.options) + cfg = default_setup(cfg) + + cfg.ex_vol = True + infer = INFER.build(dict(type=cfg.infer.type, cfg=cfg)) + + demo_lam_audio2exp(infer, cfg) + + +if __name__ == '__main__': + launch_gradio_app() diff --git a/audio2exp-service/LAM_Audio2Expression/assets/images/framework.png b/audio2exp-service/LAM_Audio2Expression/assets/images/framework.png new file mode 100644 index 0000000..210a975 Binary files /dev/null and b/audio2exp-service/LAM_Audio2Expression/assets/images/framework.png differ diff --git a/audio2exp-service/LAM_Audio2Expression/assets/images/logo.jpeg b/audio2exp-service/LAM_Audio2Expression/assets/images/logo.jpeg new file mode 100644 index 0000000..6fa8d78 Binary files /dev/null and b/audio2exp-service/LAM_Audio2Expression/assets/images/logo.jpeg differ diff --git a/audio2exp-service/LAM_Audio2Expression/assets/images/snapshot.png b/audio2exp-service/LAM_Audio2Expression/assets/images/snapshot.png new file mode 100644 index 0000000..8fc9bc9 Binary files /dev/null and b/audio2exp-service/LAM_Audio2Expression/assets/images/snapshot.png differ diff --git a/audio2exp-service/LAM_Audio2Expression/assets/images/teaser.jpg b/audio2exp-service/LAM_Audio2Expression/assets/images/teaser.jpg new file mode 100644 index 0000000..8c7c406 Binary files /dev/null and b/audio2exp-service/LAM_Audio2Expression/assets/images/teaser.jpg differ diff --git a/audio2exp-service/LAM_Audio2Expression/configs/lam_audio2exp_config.py b/audio2exp-service/LAM_Audio2Expression/configs/lam_audio2exp_config.py new file mode 100644 index 0000000..a1e4abb --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/configs/lam_audio2exp_config.py @@ -0,0 +1,92 @@ +weight = 'pretrained_models/lam_audio2exp.tar' # path to model weight +ex_vol = True # Isolates vocal track from audio file +audio_input = './assets/sample_audio/BarackObama.wav' +save_json_path = 'bsData.json' + +audio_sr = 16000 +fps = 30.0 + +movement_smooth = True +brow_movement = True +id_idx = 153 + +resume = False # whether to resume training process +evaluate = True # evaluate after each epoch training process +test_only = False # test process + +seed = None # train process will init a random seed and record +save_path = "exp/audio2exp" +num_worker = 16 # total worker in all gpu +batch_size = 16 # total batch size in all gpu +batch_size_val = None # auto adapt to bs 1 for each gpu +batch_size_test = None # auto adapt to bs 1 for each gpu +epoch = 100 # total epoch, data loop = epoch // eval_epoch +eval_epoch = 100 # sche total eval & checkpoint epoch + +sync_bn = False +enable_amp = False +empty_cache = False +find_unused_parameters = False + +mix_prob = 0 +param_dicts = None # example: param_dicts = [dict(keyword="block", lr_scale=0.1)] + +# model settings +model = dict( + type="DefaultEstimator", + backbone=dict( + type="Audio2Expression", + pretrained_encoder_type='wav2vec', + pretrained_encoder_path='facebook/wav2vec2-base-960h', + wav2vec2_config_path = 'configs/wav2vec2_config.json', + num_identity_classes=5016, + identity_feat_dim=64, + hidden_dim=512, + expression_dim=52, + norm_type='ln', + use_transformer=True, + num_attention_heads=8, + num_transformer_layers=6, + ), + criteria=[dict(type="L1Loss", loss_weight=1.0, ignore_index=-1)], +) + +dataset_type = 'audio2exp' +data_root = './' +data = dict( + train=dict( + type=dataset_type, + split="train", + data_root=data_root, + test_mode=False, + ), + val=dict( + type=dataset_type, + split="val", + data_root=data_root, + test_mode=False, + ), + test=dict( + type=dataset_type, + split="val", + data_root=data_root, + test_mode=True + ), +) + +# hook +hooks = [ + dict(type="CheckpointLoader"), + dict(type="IterationTimer", warmup_iter=2), + dict(type="InformationWriter"), + dict(type="SemSegEvaluator"), + dict(type="CheckpointSaver", save_freq=None), + dict(type="PreciseEvaluator", test_last=False), +] + +# Trainer +train = dict(type="DefaultTrainer") + +# Tester +infer = dict(type="Audio2ExpressionInfer", + verbose=True) diff --git a/audio2exp-service/LAM_Audio2Expression/configs/lam_audio2exp_config_streaming.py b/audio2exp-service/LAM_Audio2Expression/configs/lam_audio2exp_config_streaming.py new file mode 100644 index 0000000..3f44b92 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/configs/lam_audio2exp_config_streaming.py @@ -0,0 +1,92 @@ +weight = 'pretrained_models/lam_audio2exp_streaming.tar' # path to model weight +ex_vol = True # extract +audio_input = './assets/sample_audio/BarackObama.wav' +save_json_path = 'bsData.json' + +audio_sr = 16000 +fps = 30.0 + +movement_smooth = False +brow_movement = False +id_idx = 0 + +resume = False # whether to resume training process +evaluate = True # evaluate after each epoch training process +test_only = False # test process + +seed = None # train process will init a random seed and record +save_path = "exp/audio2exp" +num_worker = 16 # total worker in all gpu +batch_size = 16 # total batch size in all gpu +batch_size_val = None # auto adapt to bs 1 for each gpu +batch_size_test = None # auto adapt to bs 1 for each gpu +epoch = 100 # total epoch, data loop = epoch // eval_epoch +eval_epoch = 100 # sche total eval & checkpoint epoch + +sync_bn = False +enable_amp = False +empty_cache = False +find_unused_parameters = False + +mix_prob = 0 +param_dicts = None # example: param_dicts = [dict(keyword="block", lr_scale=0.1)] + +# model settings +model = dict( + type="DefaultEstimator", + backbone=dict( + type="Audio2Expression", + pretrained_encoder_type='wav2vec', + pretrained_encoder_path='facebook/wav2vec2-base-960h', + wav2vec2_config_path = 'configs/wav2vec2_config.json', + num_identity_classes=12, + identity_feat_dim=64, + hidden_dim=512, + expression_dim=52, + norm_type='ln', + use_transformer=False, + num_attention_heads=8, + num_transformer_layers=6, + ), + criteria=[dict(type="L1Loss", loss_weight=1.0, ignore_index=-1)], +) + +dataset_type = 'audio2exp' +data_root = './' +data = dict( + train=dict( + type=dataset_type, + split="train", + data_root=data_root, + test_mode=False, + ), + val=dict( + type=dataset_type, + split="val", + data_root=data_root, + test_mode=False, + ), + test=dict( + type=dataset_type, + split="val", + data_root=data_root, + test_mode=True + ), +) + +# hook +hooks = [ + dict(type="CheckpointLoader"), + dict(type="IterationTimer", warmup_iter=2), + dict(type="InformationWriter"), + dict(type="SemSegEvaluator"), + dict(type="CheckpointSaver", save_freq=None), + dict(type="PreciseEvaluator", test_last=False), +] + +# Trainer +train = dict(type="DefaultTrainer") + +# Tester +infer = dict(type="Audio2ExpressionInfer", + verbose=True) diff --git a/audio2exp-service/LAM_Audio2Expression/configs/wav2vec2_config.json b/audio2exp-service/LAM_Audio2Expression/configs/wav2vec2_config.json new file mode 100644 index 0000000..8ca9cc7 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/configs/wav2vec2_config.json @@ -0,0 +1,77 @@ +{ + "_name_or_path": "facebook/wav2vec2-base-960h", + "activation_dropout": 0.1, + "apply_spec_augment": true, + "architectures": [ + "Wav2Vec2ForCTC" + ], + "attention_dropout": 0.1, + "bos_token_id": 1, + "codevector_dim": 256, + "contrastive_logits_temperature": 0.1, + "conv_bias": false, + "conv_dim": [ + 512, + 512, + 512, + 512, + 512, + 512, + 512 + ], + "conv_kernel": [ + 10, + 3, + 3, + 3, + 3, + 2, + 2 + ], + "conv_stride": [ + 5, + 2, + 2, + 2, + 2, + 2, + 2 + ], + "ctc_loss_reduction": "sum", + "ctc_zero_infinity": false, + "diversity_loss_weight": 0.1, + "do_stable_layer_norm": false, + "eos_token_id": 2, + "feat_extract_activation": "gelu", + "feat_extract_dropout": 0.0, + "feat_extract_norm": "group", + "feat_proj_dropout": 0.1, + "feat_quantizer_dropout": 0.0, + "final_dropout": 0.1, + "gradient_checkpointing": false, + "hidden_act": "gelu", + "hidden_dropout": 0.1, + "hidden_dropout_prob": 0.1, + "hidden_size": 768, + "initializer_range": 0.02, + "intermediate_size": 3072, + "layer_norm_eps": 1e-05, + "layerdrop": 0.1, + "mask_feature_length": 10, + "mask_feature_prob": 0.0, + "mask_time_length": 10, + "mask_time_prob": 0.05, + "model_type": "wav2vec2", + "num_attention_heads": 12, + "num_codevector_groups": 2, + "num_codevectors_per_group": 320, + "num_conv_pos_embedding_groups": 16, + "num_conv_pos_embeddings": 128, + "num_feat_extract_layers": 7, + "num_hidden_layers": 12, + "num_negatives": 100, + "pad_token_id": 0, + "proj_codevector_dim": 256, + "transformers_version": "4.7.0.dev0", + "vocab_size": 32 +} diff --git a/audio2exp-service/LAM_Audio2Expression/engines/__init__.py b/audio2exp-service/LAM_Audio2Expression/engines/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/audio2exp-service/LAM_Audio2Expression/engines/defaults.py b/audio2exp-service/LAM_Audio2Expression/engines/defaults.py new file mode 100644 index 0000000..488148b --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/engines/defaults.py @@ -0,0 +1,147 @@ +""" +The code is base on https://github.com/Pointcept/Pointcept +""" + +import os +import sys +import argparse +import multiprocessing as mp +from torch.nn.parallel import DistributedDataParallel + + +import utils.comm as comm +from utils.env import get_random_seed, set_seed +from utils.config import Config, DictAction + + +def create_ddp_model(model, *, fp16_compression=False, **kwargs): + """ + Create a DistributedDataParallel model if there are >1 processes. + Args: + model: a torch.nn.Module + fp16_compression: add fp16 compression hooks to the ddp object. + See more at https://pytorch.org/docs/stable/ddp_comm_hooks.html#torch.distributed.algorithms.ddp_comm_hooks.default_hooks.fp16_compress_hook + kwargs: other arguments of :module:`torch.nn.parallel.DistributedDataParallel`. + """ + if comm.get_world_size() == 1: + return model + # kwargs['find_unused_parameters'] = True + if "device_ids" not in kwargs: + kwargs["device_ids"] = [comm.get_local_rank()] + if "output_device" not in kwargs: + kwargs["output_device"] = [comm.get_local_rank()] + ddp = DistributedDataParallel(model, **kwargs) + if fp16_compression: + from torch.distributed.algorithms.ddp_comm_hooks import default as comm_hooks + + ddp.register_comm_hook(state=None, hook=comm_hooks.fp16_compress_hook) + return ddp + + +def worker_init_fn(worker_id, num_workers, rank, seed): + """Worker init func for dataloader. + + The seed of each worker equals to num_worker * rank + worker_id + user_seed + + Args: + worker_id (int): Worker id. + num_workers (int): Number of workers. + rank (int): The rank of current process. + seed (int): The random seed to use. + """ + + worker_seed = num_workers * rank + worker_id + seed + set_seed(worker_seed) + + +def default_argument_parser(epilog=None): + parser = argparse.ArgumentParser( + epilog=epilog + or f""" + Examples: + Run on single machine: + $ {sys.argv[0]} --num-gpus 8 --config-file cfg.yaml + Change some config options: + $ {sys.argv[0]} --config-file cfg.yaml MODEL.WEIGHTS /path/to/weight.pth SOLVER.BASE_LR 0.001 + Run on multiple machines: + (machine0)$ {sys.argv[0]} --machine-rank 0 --num-machines 2 --dist-url [--other-flags] + (machine1)$ {sys.argv[0]} --machine-rank 1 --num-machines 2 --dist-url [--other-flags] + """, + formatter_class=argparse.RawDescriptionHelpFormatter, + ) + parser.add_argument( + "--config-file", default="", metavar="FILE", help="path to config file" + ) + parser.add_argument( + "--num-gpus", type=int, default=1, help="number of gpus *per machine*" + ) + parser.add_argument( + "--num-machines", type=int, default=1, help="total number of machines" + ) + parser.add_argument( + "--machine-rank", + type=int, + default=0, + help="the rank of this machine (unique per machine)", + ) + # PyTorch still may leave orphan processes in multi-gpu training. + # Therefore we use a deterministic way to obtain port, + # so that users are aware of orphan processes by seeing the port occupied. + # port = 2 ** 15 + 2 ** 14 + hash(os.getuid() if sys.platform != "win32" else 1) % 2 ** 14 + parser.add_argument( + "--dist-url", + # default="tcp://127.0.0.1:{}".format(port), + default="auto", + help="initialization URL for pytorch distributed backend. See " + "https://pytorch.org/docs/stable/distributed.html for details.", + ) + parser.add_argument( + "--options", nargs="+", action=DictAction, help="custom options" + ) + return parser + + +def default_config_parser(file_path, options): + # config name protocol: dataset_name/model_name-exp_name + if os.path.isfile(file_path): + cfg = Config.fromfile(file_path) + else: + sep = file_path.find("-") + cfg = Config.fromfile(os.path.join(file_path[:sep], file_path[sep + 1 :])) + + if options is not None: + cfg.merge_from_dict(options) + + if cfg.seed is None: + cfg.seed = get_random_seed() + + cfg.data.train.loop = cfg.epoch // cfg.eval_epoch + + os.makedirs(os.path.join(cfg.save_path, "model"), exist_ok=True) + if not cfg.resume: + cfg.dump(os.path.join(cfg.save_path, "config.py")) + return cfg + + +def default_setup(cfg): + # scalar by world size + world_size = comm.get_world_size() + cfg.num_worker = cfg.num_worker if cfg.num_worker is not None else mp.cpu_count() + cfg.num_worker_per_gpu = cfg.num_worker // world_size + assert cfg.batch_size % world_size == 0 + assert cfg.batch_size_val is None or cfg.batch_size_val % world_size == 0 + assert cfg.batch_size_test is None or cfg.batch_size_test % world_size == 0 + cfg.batch_size_per_gpu = cfg.batch_size // world_size + cfg.batch_size_val_per_gpu = ( + cfg.batch_size_val // world_size if cfg.batch_size_val is not None else 1 + ) + cfg.batch_size_test_per_gpu = ( + cfg.batch_size_test // world_size if cfg.batch_size_test is not None else 1 + ) + # update data loop + assert cfg.epoch % cfg.eval_epoch == 0 + # settle random seed + rank = comm.get_rank() + seed = None if cfg.seed is None else cfg.seed * cfg.num_worker_per_gpu + rank + set_seed(seed) + return cfg diff --git a/audio2exp-service/LAM_Audio2Expression/engines/hooks/__init__.py b/audio2exp-service/LAM_Audio2Expression/engines/hooks/__init__.py new file mode 100644 index 0000000..1ab2c4b --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/engines/hooks/__init__.py @@ -0,0 +1,5 @@ +from .default import HookBase +from .misc import * +from .evaluator import * + +from .builder import build_hooks diff --git a/audio2exp-service/LAM_Audio2Expression/engines/hooks/builder.py b/audio2exp-service/LAM_Audio2Expression/engines/hooks/builder.py new file mode 100644 index 0000000..e0a121c --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/engines/hooks/builder.py @@ -0,0 +1,15 @@ +""" +The code is base on https://github.com/Pointcept/Pointcept +""" + +from utils.registry import Registry + + +HOOKS = Registry("hooks") + + +def build_hooks(cfg): + hooks = [] + for hook_cfg in cfg: + hooks.append(HOOKS.build(hook_cfg)) + return hooks diff --git a/audio2exp-service/LAM_Audio2Expression/engines/hooks/default.py b/audio2exp-service/LAM_Audio2Expression/engines/hooks/default.py new file mode 100644 index 0000000..57150a7 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/engines/hooks/default.py @@ -0,0 +1,29 @@ +""" +The code is base on https://github.com/Pointcept/Pointcept +""" + + +class HookBase: + """ + Base class for hooks that can be registered with :class:`TrainerBase`. + """ + + trainer = None # A weak reference to the trainer object. + + def before_train(self): + pass + + def before_epoch(self): + pass + + def before_step(self): + pass + + def after_step(self): + pass + + def after_epoch(self): + pass + + def after_train(self): + pass diff --git a/audio2exp-service/LAM_Audio2Expression/engines/hooks/evaluator.py b/audio2exp-service/LAM_Audio2Expression/engines/hooks/evaluator.py new file mode 100644 index 0000000..c0d2717 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/engines/hooks/evaluator.py @@ -0,0 +1,577 @@ +""" +The code is base on https://github.com/Pointcept/Pointcept +""" + +import numpy as np +import torch +import torch.distributed as dist +from uuid import uuid4 + +import utils.comm as comm +from utils.misc import intersection_and_union_gpu + +from .default import HookBase +from .builder import HOOKS + + +@HOOKS.register_module() +class ClsEvaluator(HookBase): + def after_epoch(self): + if self.trainer.cfg.evaluate: + self.eval() + + def eval(self): + self.trainer.logger.info(">>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>") + self.trainer.model.eval() + for i, input_dict in enumerate(self.trainer.val_loader): + for key in input_dict.keys(): + if isinstance(input_dict[key], torch.Tensor): + input_dict[key] = input_dict[key].cuda(non_blocking=True) + with torch.no_grad(): + output_dict = self.trainer.model(input_dict) + output = output_dict["cls_logits"] + loss = output_dict["loss"] + pred = output.max(1)[1] + label = input_dict["category"] + intersection, union, target = intersection_and_union_gpu( + pred, + label, + self.trainer.cfg.data.num_classes, + self.trainer.cfg.data.ignore_index, + ) + if comm.get_world_size() > 1: + dist.all_reduce(intersection), dist.all_reduce(union), dist.all_reduce( + target + ) + intersection, union, target = ( + intersection.cpu().numpy(), + union.cpu().numpy(), + target.cpu().numpy(), + ) + # Here there is no need to sync since sync happened in dist.all_reduce + self.trainer.storage.put_scalar("val_intersection", intersection) + self.trainer.storage.put_scalar("val_union", union) + self.trainer.storage.put_scalar("val_target", target) + self.trainer.storage.put_scalar("val_loss", loss.item()) + self.trainer.logger.info( + "Test: [{iter}/{max_iter}] " + "Loss {loss:.4f} ".format( + iter=i + 1, max_iter=len(self.trainer.val_loader), loss=loss.item() + ) + ) + loss_avg = self.trainer.storage.history("val_loss").avg + intersection = self.trainer.storage.history("val_intersection").total + union = self.trainer.storage.history("val_union").total + target = self.trainer.storage.history("val_target").total + iou_class = intersection / (union + 1e-10) + acc_class = intersection / (target + 1e-10) + m_iou = np.mean(iou_class) + m_acc = np.mean(acc_class) + all_acc = sum(intersection) / (sum(target) + 1e-10) + self.trainer.logger.info( + "Val result: mIoU/mAcc/allAcc {:.4f}/{:.4f}/{:.4f}.".format( + m_iou, m_acc, all_acc + ) + ) + for i in range(self.trainer.cfg.data.num_classes): + self.trainer.logger.info( + "Class_{idx}-{name} Result: iou/accuracy {iou:.4f}/{accuracy:.4f}".format( + idx=i, + name=self.trainer.cfg.data.names[i], + iou=iou_class[i], + accuracy=acc_class[i], + ) + ) + current_epoch = self.trainer.epoch + 1 + if self.trainer.writer is not None: + self.trainer.writer.add_scalar("val/loss", loss_avg, current_epoch) + self.trainer.writer.add_scalar("val/mIoU", m_iou, current_epoch) + self.trainer.writer.add_scalar("val/mAcc", m_acc, current_epoch) + self.trainer.writer.add_scalar("val/allAcc", all_acc, current_epoch) + self.trainer.logger.info("<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<") + self.trainer.comm_info["current_metric_value"] = all_acc # save for saver + self.trainer.comm_info["current_metric_name"] = "allAcc" # save for saver + + def after_train(self): + self.trainer.logger.info( + "Best {}: {:.4f}".format("allAcc", self.trainer.best_metric_value) + ) + + +@HOOKS.register_module() +class SemSegEvaluator(HookBase): + def after_epoch(self): + if self.trainer.cfg.evaluate: + self.eval() + + def eval(self): + self.trainer.logger.info(">>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>") + self.trainer.model.eval() + for i, input_dict in enumerate(self.trainer.val_loader): + for key in input_dict.keys(): + if isinstance(input_dict[key], torch.Tensor): + input_dict[key] = input_dict[key].cuda(non_blocking=True) + with torch.no_grad(): + output_dict = self.trainer.model(input_dict) + output = output_dict["seg_logits"] + loss = output_dict["loss"] + pred = output.max(1)[1] + segment = input_dict["segment"] + if "origin_coord" in input_dict.keys(): + idx, _ = pointops.knn_query( + 1, + input_dict["coord"].float(), + input_dict["offset"].int(), + input_dict["origin_coord"].float(), + input_dict["origin_offset"].int(), + ) + pred = pred[idx.flatten().long()] + segment = input_dict["origin_segment"] + intersection, union, target = intersection_and_union_gpu( + pred, + segment, + self.trainer.cfg.data.num_classes, + self.trainer.cfg.data.ignore_index, + ) + if comm.get_world_size() > 1: + dist.all_reduce(intersection), dist.all_reduce(union), dist.all_reduce( + target + ) + intersection, union, target = ( + intersection.cpu().numpy(), + union.cpu().numpy(), + target.cpu().numpy(), + ) + # Here there is no need to sync since sync happened in dist.all_reduce + self.trainer.storage.put_scalar("val_intersection", intersection) + self.trainer.storage.put_scalar("val_union", union) + self.trainer.storage.put_scalar("val_target", target) + self.trainer.storage.put_scalar("val_loss", loss.item()) + info = "Test: [{iter}/{max_iter}] ".format( + iter=i + 1, max_iter=len(self.trainer.val_loader) + ) + if "origin_coord" in input_dict.keys(): + info = "Interp. " + info + self.trainer.logger.info( + info + + "Loss {loss:.4f} ".format( + iter=i + 1, max_iter=len(self.trainer.val_loader), loss=loss.item() + ) + ) + loss_avg = self.trainer.storage.history("val_loss").avg + intersection = self.trainer.storage.history("val_intersection").total + union = self.trainer.storage.history("val_union").total + target = self.trainer.storage.history("val_target").total + iou_class = intersection / (union + 1e-10) + acc_class = intersection / (target + 1e-10) + m_iou = np.mean(iou_class) + m_acc = np.mean(acc_class) + all_acc = sum(intersection) / (sum(target) + 1e-10) + self.trainer.logger.info( + "Val result: mIoU/mAcc/allAcc {:.4f}/{:.4f}/{:.4f}.".format( + m_iou, m_acc, all_acc + ) + ) + for i in range(self.trainer.cfg.data.num_classes): + self.trainer.logger.info( + "Class_{idx}-{name} Result: iou/accuracy {iou:.4f}/{accuracy:.4f}".format( + idx=i, + name=self.trainer.cfg.data.names[i], + iou=iou_class[i], + accuracy=acc_class[i], + ) + ) + current_epoch = self.trainer.epoch + 1 + if self.trainer.writer is not None: + self.trainer.writer.add_scalar("val/loss", loss_avg, current_epoch) + self.trainer.writer.add_scalar("val/mIoU", m_iou, current_epoch) + self.trainer.writer.add_scalar("val/mAcc", m_acc, current_epoch) + self.trainer.writer.add_scalar("val/allAcc", all_acc, current_epoch) + self.trainer.logger.info("<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<") + self.trainer.comm_info["current_metric_value"] = m_iou # save for saver + self.trainer.comm_info["current_metric_name"] = "mIoU" # save for saver + + def after_train(self): + self.trainer.logger.info( + "Best {}: {:.4f}".format("mIoU", self.trainer.best_metric_value) + ) + + +@HOOKS.register_module() +class InsSegEvaluator(HookBase): + def __init__(self, segment_ignore_index=(-1,), instance_ignore_index=-1): + self.segment_ignore_index = segment_ignore_index + self.instance_ignore_index = instance_ignore_index + + self.valid_class_names = None # update in before train + self.overlaps = np.append(np.arange(0.5, 0.95, 0.05), 0.25) + self.min_region_sizes = 100 + self.distance_threshes = float("inf") + self.distance_confs = -float("inf") + + def before_train(self): + self.valid_class_names = [ + self.trainer.cfg.data.names[i] + for i in range(self.trainer.cfg.data.num_classes) + if i not in self.segment_ignore_index + ] + + def after_epoch(self): + if self.trainer.cfg.evaluate: + self.eval() + + def associate_instances(self, pred, segment, instance): + segment = segment.cpu().numpy() + instance = instance.cpu().numpy() + void_mask = np.in1d(segment, self.segment_ignore_index) + + assert ( + pred["pred_classes"].shape[0] + == pred["pred_scores"].shape[0] + == pred["pred_masks"].shape[0] + ) + assert pred["pred_masks"].shape[1] == segment.shape[0] == instance.shape[0] + # get gt instances + gt_instances = dict() + for i in range(self.trainer.cfg.data.num_classes): + if i not in self.segment_ignore_index: + gt_instances[self.trainer.cfg.data.names[i]] = [] + instance_ids, idx, counts = np.unique( + instance, return_index=True, return_counts=True + ) + segment_ids = segment[idx] + for i in range(len(instance_ids)): + if instance_ids[i] == self.instance_ignore_index: + continue + if segment_ids[i] in self.segment_ignore_index: + continue + gt_inst = dict() + gt_inst["instance_id"] = instance_ids[i] + gt_inst["segment_id"] = segment_ids[i] + gt_inst["dist_conf"] = 0.0 + gt_inst["med_dist"] = -1.0 + gt_inst["vert_count"] = counts[i] + gt_inst["matched_pred"] = [] + gt_instances[self.trainer.cfg.data.names[segment_ids[i]]].append(gt_inst) + + # get pred instances and associate with gt + pred_instances = dict() + for i in range(self.trainer.cfg.data.num_classes): + if i not in self.segment_ignore_index: + pred_instances[self.trainer.cfg.data.names[i]] = [] + instance_id = 0 + for i in range(len(pred["pred_classes"])): + if pred["pred_classes"][i] in self.segment_ignore_index: + continue + pred_inst = dict() + pred_inst["uuid"] = uuid4() + pred_inst["instance_id"] = instance_id + pred_inst["segment_id"] = pred["pred_classes"][i] + pred_inst["confidence"] = pred["pred_scores"][i] + pred_inst["mask"] = np.not_equal(pred["pred_masks"][i], 0) + pred_inst["vert_count"] = np.count_nonzero(pred_inst["mask"]) + pred_inst["void_intersection"] = np.count_nonzero( + np.logical_and(void_mask, pred_inst["mask"]) + ) + if pred_inst["vert_count"] < self.min_region_sizes: + continue # skip if empty + segment_name = self.trainer.cfg.data.names[pred_inst["segment_id"]] + matched_gt = [] + for gt_idx, gt_inst in enumerate(gt_instances[segment_name]): + intersection = np.count_nonzero( + np.logical_and( + instance == gt_inst["instance_id"], pred_inst["mask"] + ) + ) + if intersection > 0: + gt_inst_ = gt_inst.copy() + pred_inst_ = pred_inst.copy() + gt_inst_["intersection"] = intersection + pred_inst_["intersection"] = intersection + matched_gt.append(gt_inst_) + gt_inst["matched_pred"].append(pred_inst_) + pred_inst["matched_gt"] = matched_gt + pred_instances[segment_name].append(pred_inst) + instance_id += 1 + return gt_instances, pred_instances + + def evaluate_matches(self, scenes): + overlaps = self.overlaps + min_region_sizes = [self.min_region_sizes] + dist_threshes = [self.distance_threshes] + dist_confs = [self.distance_confs] + + # results: class x overlap + ap_table = np.zeros( + (len(dist_threshes), len(self.valid_class_names), len(overlaps)), float + ) + for di, (min_region_size, distance_thresh, distance_conf) in enumerate( + zip(min_region_sizes, dist_threshes, dist_confs) + ): + for oi, overlap_th in enumerate(overlaps): + pred_visited = {} + for scene in scenes: + for _ in scene["pred"]: + for label_name in self.valid_class_names: + for p in scene["pred"][label_name]: + if "uuid" in p: + pred_visited[p["uuid"]] = False + for li, label_name in enumerate(self.valid_class_names): + y_true = np.empty(0) + y_score = np.empty(0) + hard_false_negatives = 0 + has_gt = False + has_pred = False + for scene in scenes: + pred_instances = scene["pred"][label_name] + gt_instances = scene["gt"][label_name] + # filter groups in ground truth + gt_instances = [ + gt + for gt in gt_instances + if gt["vert_count"] >= min_region_size + and gt["med_dist"] <= distance_thresh + and gt["dist_conf"] >= distance_conf + ] + if gt_instances: + has_gt = True + if pred_instances: + has_pred = True + + cur_true = np.ones(len(gt_instances)) + cur_score = np.ones(len(gt_instances)) * (-float("inf")) + cur_match = np.zeros(len(gt_instances), dtype=bool) + # collect matches + for gti, gt in enumerate(gt_instances): + found_match = False + for pred in gt["matched_pred"]: + # greedy assignments + if pred_visited[pred["uuid"]]: + continue + overlap = float(pred["intersection"]) / ( + gt["vert_count"] + + pred["vert_count"] + - pred["intersection"] + ) + if overlap > overlap_th: + confidence = pred["confidence"] + # if already have a prediction for this gt, + # the prediction with the lower score is automatically a false positive + if cur_match[gti]: + max_score = max(cur_score[gti], confidence) + min_score = min(cur_score[gti], confidence) + cur_score[gti] = max_score + # append false positive + cur_true = np.append(cur_true, 0) + cur_score = np.append(cur_score, min_score) + cur_match = np.append(cur_match, True) + # otherwise set score + else: + found_match = True + cur_match[gti] = True + cur_score[gti] = confidence + pred_visited[pred["uuid"]] = True + if not found_match: + hard_false_negatives += 1 + # remove non-matched ground truth instances + cur_true = cur_true[cur_match] + cur_score = cur_score[cur_match] + + # collect non-matched predictions as false positive + for pred in pred_instances: + found_gt = False + for gt in pred["matched_gt"]: + overlap = float(gt["intersection"]) / ( + gt["vert_count"] + + pred["vert_count"] + - gt["intersection"] + ) + if overlap > overlap_th: + found_gt = True + break + if not found_gt: + num_ignore = pred["void_intersection"] + for gt in pred["matched_gt"]: + if gt["segment_id"] in self.segment_ignore_index: + num_ignore += gt["intersection"] + # small ground truth instances + if ( + gt["vert_count"] < min_region_size + or gt["med_dist"] > distance_thresh + or gt["dist_conf"] < distance_conf + ): + num_ignore += gt["intersection"] + proportion_ignore = ( + float(num_ignore) / pred["vert_count"] + ) + # if not ignored append false positive + if proportion_ignore <= overlap_th: + cur_true = np.append(cur_true, 0) + confidence = pred["confidence"] + cur_score = np.append(cur_score, confidence) + + # append to overall results + y_true = np.append(y_true, cur_true) + y_score = np.append(y_score, cur_score) + + # compute average precision + if has_gt and has_pred: + # compute precision recall curve first + + # sorting and cumsum + score_arg_sort = np.argsort(y_score) + y_score_sorted = y_score[score_arg_sort] + y_true_sorted = y_true[score_arg_sort] + y_true_sorted_cumsum = np.cumsum(y_true_sorted) + + # unique thresholds + (thresholds, unique_indices) = np.unique( + y_score_sorted, return_index=True + ) + num_prec_recall = len(unique_indices) + 1 + + # prepare precision recall + num_examples = len(y_score_sorted) + # https://github.com/ScanNet/ScanNet/pull/26 + # all predictions are non-matched but also all of them are ignored and not counted as FP + # y_true_sorted_cumsum is empty + # num_true_examples = y_true_sorted_cumsum[-1] + num_true_examples = ( + y_true_sorted_cumsum[-1] + if len(y_true_sorted_cumsum) > 0 + else 0 + ) + precision = np.zeros(num_prec_recall) + recall = np.zeros(num_prec_recall) + + # deal with the first point + y_true_sorted_cumsum = np.append(y_true_sorted_cumsum, 0) + # deal with remaining + for idx_res, idx_scores in enumerate(unique_indices): + cumsum = y_true_sorted_cumsum[idx_scores - 1] + tp = num_true_examples - cumsum + fp = num_examples - idx_scores - tp + fn = cumsum + hard_false_negatives + p = float(tp) / (tp + fp) + r = float(tp) / (tp + fn) + precision[idx_res] = p + recall[idx_res] = r + + # first point in curve is artificial + precision[-1] = 1.0 + recall[-1] = 0.0 + + # compute average of precision-recall curve + recall_for_conv = np.copy(recall) + recall_for_conv = np.append(recall_for_conv[0], recall_for_conv) + recall_for_conv = np.append(recall_for_conv, 0.0) + + stepWidths = np.convolve( + recall_for_conv, [-0.5, 0, 0.5], "valid" + ) + # integrate is now simply a dot product + ap_current = np.dot(precision, stepWidths) + + elif has_gt: + ap_current = 0.0 + else: + ap_current = float("nan") + ap_table[di, li, oi] = ap_current + d_inf = 0 + o50 = np.where(np.isclose(self.overlaps, 0.5)) + o25 = np.where(np.isclose(self.overlaps, 0.25)) + oAllBut25 = np.where(np.logical_not(np.isclose(self.overlaps, 0.25))) + ap_scores = dict() + ap_scores["all_ap"] = np.nanmean(ap_table[d_inf, :, oAllBut25]) + ap_scores["all_ap_50%"] = np.nanmean(ap_table[d_inf, :, o50]) + ap_scores["all_ap_25%"] = np.nanmean(ap_table[d_inf, :, o25]) + ap_scores["classes"] = {} + for li, label_name in enumerate(self.valid_class_names): + ap_scores["classes"][label_name] = {} + ap_scores["classes"][label_name]["ap"] = np.average( + ap_table[d_inf, li, oAllBut25] + ) + ap_scores["classes"][label_name]["ap50%"] = np.average( + ap_table[d_inf, li, o50] + ) + ap_scores["classes"][label_name]["ap25%"] = np.average( + ap_table[d_inf, li, o25] + ) + return ap_scores + + def eval(self): + self.trainer.logger.info(">>>>>>>>>>>>>>>> Start Evaluation >>>>>>>>>>>>>>>>") + self.trainer.model.eval() + scenes = [] + for i, input_dict in enumerate(self.trainer.val_loader): + assert ( + len(input_dict["offset"]) == 1 + ) # currently only support bs 1 for each GPU + for key in input_dict.keys(): + if isinstance(input_dict[key], torch.Tensor): + input_dict[key] = input_dict[key].cuda(non_blocking=True) + with torch.no_grad(): + output_dict = self.trainer.model(input_dict) + + loss = output_dict["loss"] + + segment = input_dict["segment"] + instance = input_dict["instance"] + # map to origin + if "origin_coord" in input_dict.keys(): + idx, _ = pointops.knn_query( + 1, + input_dict["coord"].float(), + input_dict["offset"].int(), + input_dict["origin_coord"].float(), + input_dict["origin_offset"].int(), + ) + idx = idx.cpu().flatten().long() + output_dict["pred_masks"] = output_dict["pred_masks"][:, idx] + segment = input_dict["origin_segment"] + instance = input_dict["origin_instance"] + + gt_instances, pred_instance = self.associate_instances( + output_dict, segment, instance + ) + scenes.append(dict(gt=gt_instances, pred=pred_instance)) + + self.trainer.storage.put_scalar("val_loss", loss.item()) + self.trainer.logger.info( + "Test: [{iter}/{max_iter}] " + "Loss {loss:.4f} ".format( + iter=i + 1, max_iter=len(self.trainer.val_loader), loss=loss.item() + ) + ) + + loss_avg = self.trainer.storage.history("val_loss").avg + comm.synchronize() + scenes_sync = comm.gather(scenes, dst=0) + scenes = [scene for scenes_ in scenes_sync for scene in scenes_] + ap_scores = self.evaluate_matches(scenes) + all_ap = ap_scores["all_ap"] + all_ap_50 = ap_scores["all_ap_50%"] + all_ap_25 = ap_scores["all_ap_25%"] + self.trainer.logger.info( + "Val result: mAP/AP50/AP25 {:.4f}/{:.4f}/{:.4f}.".format( + all_ap, all_ap_50, all_ap_25 + ) + ) + for i, label_name in enumerate(self.valid_class_names): + ap = ap_scores["classes"][label_name]["ap"] + ap_50 = ap_scores["classes"][label_name]["ap50%"] + ap_25 = ap_scores["classes"][label_name]["ap25%"] + self.trainer.logger.info( + "Class_{idx}-{name} Result: AP/AP50/AP25 {AP:.4f}/{AP50:.4f}/{AP25:.4f}".format( + idx=i, name=label_name, AP=ap, AP50=ap_50, AP25=ap_25 + ) + ) + current_epoch = self.trainer.epoch + 1 + if self.trainer.writer is not None: + self.trainer.writer.add_scalar("val/loss", loss_avg, current_epoch) + self.trainer.writer.add_scalar("val/mAP", all_ap, current_epoch) + self.trainer.writer.add_scalar("val/AP50", all_ap_50, current_epoch) + self.trainer.writer.add_scalar("val/AP25", all_ap_25, current_epoch) + self.trainer.logger.info("<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<") + self.trainer.comm_info["current_metric_value"] = all_ap_50 # save for saver + self.trainer.comm_info["current_metric_name"] = "AP50" # save for saver diff --git a/audio2exp-service/LAM_Audio2Expression/engines/hooks/misc.py b/audio2exp-service/LAM_Audio2Expression/engines/hooks/misc.py new file mode 100644 index 0000000..52b398e --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/engines/hooks/misc.py @@ -0,0 +1,460 @@ +""" +The code is base on https://github.com/Pointcept/Pointcept +""" + +import sys +import glob +import os +import shutil +import time +import torch +import torch.utils.data +from collections import OrderedDict + +if sys.version_info >= (3, 10): + from collections.abc import Sequence +else: + from collections import Sequence +from utils.timer import Timer +from utils.comm import is_main_process, synchronize, get_world_size +from utils.cache import shared_dict + +import utils.comm as comm +from engines.test import TESTERS + +from .default import HookBase +from .builder import HOOKS + + +@HOOKS.register_module() +class IterationTimer(HookBase): + def __init__(self, warmup_iter=1): + self._warmup_iter = warmup_iter + self._start_time = time.perf_counter() + self._iter_timer = Timer() + self._remain_iter = 0 + + def before_train(self): + self._start_time = time.perf_counter() + self._remain_iter = self.trainer.max_epoch * len(self.trainer.train_loader) + + def before_epoch(self): + self._iter_timer.reset() + + def before_step(self): + data_time = self._iter_timer.seconds() + self.trainer.storage.put_scalar("data_time", data_time) + + def after_step(self): + batch_time = self._iter_timer.seconds() + self._iter_timer.reset() + self.trainer.storage.put_scalar("batch_time", batch_time) + self._remain_iter -= 1 + remain_time = self._remain_iter * self.trainer.storage.history("batch_time").avg + t_m, t_s = divmod(remain_time, 60) + t_h, t_m = divmod(t_m, 60) + remain_time = "{:02d}:{:02d}:{:02d}".format(int(t_h), int(t_m), int(t_s)) + if "iter_info" in self.trainer.comm_info.keys(): + info = ( + "Data {data_time_val:.3f} ({data_time_avg:.3f}) " + "Batch {batch_time_val:.3f} ({batch_time_avg:.3f}) " + "Remain {remain_time} ".format( + data_time_val=self.trainer.storage.history("data_time").val, + data_time_avg=self.trainer.storage.history("data_time").avg, + batch_time_val=self.trainer.storage.history("batch_time").val, + batch_time_avg=self.trainer.storage.history("batch_time").avg, + remain_time=remain_time, + ) + ) + self.trainer.comm_info["iter_info"] += info + if self.trainer.comm_info["iter"] <= self._warmup_iter: + self.trainer.storage.history("data_time").reset() + self.trainer.storage.history("batch_time").reset() + + +@HOOKS.register_module() +class InformationWriter(HookBase): + def __init__(self): + self.curr_iter = 0 + self.model_output_keys = [] + + def before_train(self): + self.trainer.comm_info["iter_info"] = "" + self.curr_iter = self.trainer.start_epoch * len(self.trainer.train_loader) + + def before_step(self): + self.curr_iter += 1 + # MSC pretrain do not have offset information. Comment the code for support MSC + # info = "Train: [{epoch}/{max_epoch}][{iter}/{max_iter}] " \ + # "Scan {batch_size} ({points_num}) ".format( + # epoch=self.trainer.epoch + 1, max_epoch=self.trainer.max_epoch, + # iter=self.trainer.comm_info["iter"], max_iter=len(self.trainer.train_loader), + # batch_size=len(self.trainer.comm_info["input_dict"]["offset"]), + # points_num=self.trainer.comm_info["input_dict"]["offset"][-1] + # ) + info = "Train: [{epoch}/{max_epoch}][{iter}/{max_iter}] ".format( + epoch=self.trainer.epoch + 1, + max_epoch=self.trainer.max_epoch, + iter=self.trainer.comm_info["iter"] + 1, + max_iter=len(self.trainer.train_loader), + ) + self.trainer.comm_info["iter_info"] += info + + def after_step(self): + if "model_output_dict" in self.trainer.comm_info.keys(): + model_output_dict = self.trainer.comm_info["model_output_dict"] + self.model_output_keys = model_output_dict.keys() + for key in self.model_output_keys: + self.trainer.storage.put_scalar(key, model_output_dict[key].item()) + + for key in self.model_output_keys: + self.trainer.comm_info["iter_info"] += "{key}: {value:.4f} ".format( + key=key, value=self.trainer.storage.history(key).val + ) + lr = self.trainer.optimizer.state_dict()["param_groups"][0]["lr"] + self.trainer.comm_info["iter_info"] += "Lr: {lr:.5f}".format(lr=lr) + self.trainer.logger.info(self.trainer.comm_info["iter_info"]) + self.trainer.comm_info["iter_info"] = "" # reset iter info + if self.trainer.writer is not None: + self.trainer.writer.add_scalar("lr", lr, self.curr_iter) + for key in self.model_output_keys: + self.trainer.writer.add_scalar( + "train_batch/" + key, + self.trainer.storage.history(key).val, + self.curr_iter, + ) + + def after_epoch(self): + epoch_info = "Train result: " + for key in self.model_output_keys: + epoch_info += "{key}: {value:.4f} ".format( + key=key, value=self.trainer.storage.history(key).avg + ) + self.trainer.logger.info(epoch_info) + if self.trainer.writer is not None: + for key in self.model_output_keys: + self.trainer.writer.add_scalar( + "train/" + key, + self.trainer.storage.history(key).avg, + self.trainer.epoch + 1, + ) + + +@HOOKS.register_module() +class CheckpointSaver(HookBase): + def __init__(self, save_freq=None): + self.save_freq = save_freq # None or int, None indicate only save model last + + def after_epoch(self): + if is_main_process(): + is_best = False + if self.trainer.cfg.evaluate: + current_metric_value = self.trainer.comm_info["current_metric_value"] + current_metric_name = self.trainer.comm_info["current_metric_name"] + if current_metric_value > self.trainer.best_metric_value: + self.trainer.best_metric_value = current_metric_value + is_best = True + self.trainer.logger.info( + "Best validation {} updated to: {:.4f}".format( + current_metric_name, current_metric_value + ) + ) + self.trainer.logger.info( + "Currently Best {}: {:.4f}".format( + current_metric_name, self.trainer.best_metric_value + ) + ) + + filename = os.path.join( + self.trainer.cfg.save_path, "model", "model_last.pth" + ) + self.trainer.logger.info("Saving checkpoint to: " + filename) + torch.save( + { + "epoch": self.trainer.epoch + 1, + "state_dict": self.trainer.model.state_dict(), + "optimizer": self.trainer.optimizer.state_dict(), + "scheduler": self.trainer.scheduler.state_dict(), + "scaler": self.trainer.scaler.state_dict() + if self.trainer.cfg.enable_amp + else None, + "best_metric_value": self.trainer.best_metric_value, + }, + filename + ".tmp", + ) + os.replace(filename + ".tmp", filename) + if is_best: + shutil.copyfile( + filename, + os.path.join(self.trainer.cfg.save_path, "model", "model_best.pth"), + ) + if self.save_freq and (self.trainer.epoch + 1) % self.save_freq == 0: + shutil.copyfile( + filename, + os.path.join( + self.trainer.cfg.save_path, + "model", + f"epoch_{self.trainer.epoch + 1}.pth", + ), + ) + + +@HOOKS.register_module() +class CheckpointLoader(HookBase): + def __init__(self, keywords="", replacement=None, strict=False): + self.keywords = keywords + self.replacement = replacement if replacement is not None else keywords + self.strict = strict + + def before_train(self): + self.trainer.logger.info("=> Loading checkpoint & weight ...") + if self.trainer.cfg.weight and os.path.isfile(self.trainer.cfg.weight): + self.trainer.logger.info(f"Loading weight at: {self.trainer.cfg.weight}") + checkpoint = torch.load( + self.trainer.cfg.weight, + map_location=lambda storage, loc: storage.cuda(), + ) + self.trainer.logger.info( + f"Loading layer weights with keyword: {self.keywords}, " + f"replace keyword with: {self.replacement}" + ) + weight = OrderedDict() + for key, value in checkpoint["state_dict"].items(): + if not key.startswith("module."): + if comm.get_world_size() > 1: + key = "module." + key # xxx.xxx -> module.xxx.xxx + # Now all keys contain "module." no matter DDP or not. + if self.keywords in key: + key = key.replace(self.keywords, self.replacement) + if comm.get_world_size() == 1: + key = key[7:] # module.xxx.xxx -> xxx.xxx + weight[key] = value + load_state_info = self.trainer.model.load_state_dict( + weight, strict=self.strict + ) + self.trainer.logger.info(f"Missing keys: {load_state_info[0]}") + if self.trainer.cfg.resume: + self.trainer.logger.info( + f"Resuming train at eval epoch: {checkpoint['epoch']}" + ) + self.trainer.start_epoch = checkpoint["epoch"] + self.trainer.best_metric_value = checkpoint["best_metric_value"] + self.trainer.optimizer.load_state_dict(checkpoint["optimizer"]) + self.trainer.scheduler.load_state_dict(checkpoint["scheduler"]) + if self.trainer.cfg.enable_amp: + self.trainer.scaler.load_state_dict(checkpoint["scaler"]) + else: + self.trainer.logger.info(f"No weight found at: {self.trainer.cfg.weight}") + + +@HOOKS.register_module() +class PreciseEvaluator(HookBase): + def __init__(self, test_last=False): + self.test_last = test_last + + def after_train(self): + self.trainer.logger.info( + ">>>>>>>>>>>>>>>> Start Precise Evaluation >>>>>>>>>>>>>>>>" + ) + torch.cuda.empty_cache() + cfg = self.trainer.cfg + tester = TESTERS.build( + dict(type=cfg.test.type, cfg=cfg, model=self.trainer.model) + ) + if self.test_last: + self.trainer.logger.info("=> Testing on model_last ...") + else: + self.trainer.logger.info("=> Testing on model_best ...") + best_path = os.path.join( + self.trainer.cfg.save_path, "model", "model_best.pth" + ) + checkpoint = torch.load(best_path) + state_dict = checkpoint["state_dict"] + tester.model.load_state_dict(state_dict, strict=True) + tester.test() + + +@HOOKS.register_module() +class DataCacheOperator(HookBase): + def __init__(self, data_root, split): + self.data_root = data_root + self.split = split + self.data_list = self.get_data_list() + + def get_data_list(self): + if isinstance(self.split, str): + data_list = glob.glob(os.path.join(self.data_root, self.split, "*.pth")) + elif isinstance(self.split, Sequence): + data_list = [] + for split in self.split: + data_list += glob.glob(os.path.join(self.data_root, split, "*.pth")) + else: + raise NotImplementedError + return data_list + + def get_cache_name(self, data_path): + data_name = data_path.replace(os.path.dirname(self.data_root), "").split(".")[0] + return "pointcept" + data_name.replace(os.path.sep, "-") + + def before_train(self): + self.trainer.logger.info( + f"=> Caching dataset: {self.data_root}, split: {self.split} ..." + ) + if is_main_process(): + for data_path in self.data_list: + cache_name = self.get_cache_name(data_path) + data = torch.load(data_path) + shared_dict(cache_name, data) + synchronize() + + +@HOOKS.register_module() +class RuntimeProfiler(HookBase): + def __init__( + self, + forward=True, + backward=True, + interrupt=False, + warm_up=2, + sort_by="cuda_time_total", + row_limit=30, + ): + self.forward = forward + self.backward = backward + self.interrupt = interrupt + self.warm_up = warm_up + self.sort_by = sort_by + self.row_limit = row_limit + + def before_train(self): + self.trainer.logger.info("Profiling runtime ...") + from torch.profiler import profile, record_function, ProfilerActivity + + for i, input_dict in enumerate(self.trainer.train_loader): + if i == self.warm_up + 1: + break + for key in input_dict.keys(): + if isinstance(input_dict[key], torch.Tensor): + input_dict[key] = input_dict[key].cuda(non_blocking=True) + if self.forward: + with profile( + activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], + record_shapes=True, + profile_memory=True, + with_stack=True, + ) as forward_prof: + with record_function("model_inference"): + output_dict = self.trainer.model(input_dict) + else: + output_dict = self.trainer.model(input_dict) + loss = output_dict["loss"] + if self.backward: + with profile( + activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], + record_shapes=True, + profile_memory=True, + with_stack=True, + ) as backward_prof: + with record_function("model_inference"): + loss.backward() + self.trainer.logger.info(f"Profile: [{i + 1}/{self.warm_up + 1}]") + if self.forward: + self.trainer.logger.info( + "Forward profile: \n" + + str( + forward_prof.key_averages().table( + sort_by=self.sort_by, row_limit=self.row_limit + ) + ) + ) + forward_prof.export_chrome_trace( + os.path.join(self.trainer.cfg.save_path, "forward_trace.json") + ) + + if self.backward: + self.trainer.logger.info( + "Backward profile: \n" + + str( + backward_prof.key_averages().table( + sort_by=self.sort_by, row_limit=self.row_limit + ) + ) + ) + backward_prof.export_chrome_trace( + os.path.join(self.trainer.cfg.save_path, "backward_trace.json") + ) + if self.interrupt: + sys.exit(0) + + +@HOOKS.register_module() +class RuntimeProfilerV2(HookBase): + def __init__( + self, + interrupt=False, + wait=1, + warmup=1, + active=10, + repeat=1, + sort_by="cuda_time_total", + row_limit=30, + ): + self.interrupt = interrupt + self.wait = wait + self.warmup = warmup + self.active = active + self.repeat = repeat + self.sort_by = sort_by + self.row_limit = row_limit + + def before_train(self): + self.trainer.logger.info("Profiling runtime ...") + from torch.profiler import ( + profile, + record_function, + ProfilerActivity, + schedule, + tensorboard_trace_handler, + ) + + prof = profile( + activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA], + schedule=schedule( + wait=self.wait, + warmup=self.warmup, + active=self.active, + repeat=self.repeat, + ), + on_trace_ready=tensorboard_trace_handler(self.trainer.cfg.save_path), + record_shapes=True, + profile_memory=True, + with_stack=True, + ) + prof.start() + for i, input_dict in enumerate(self.trainer.train_loader): + if i >= (self.wait + self.warmup + self.active) * self.repeat: + break + for key in input_dict.keys(): + if isinstance(input_dict[key], torch.Tensor): + input_dict[key] = input_dict[key].cuda(non_blocking=True) + with record_function("model_forward"): + output_dict = self.trainer.model(input_dict) + loss = output_dict["loss"] + with record_function("model_backward"): + loss.backward() + prof.step() + self.trainer.logger.info( + f"Profile: [{i + 1}/{(self.wait + self.warmup + self.active) * self.repeat}]" + ) + self.trainer.logger.info( + "Profile: \n" + + str( + prof.key_averages().table( + sort_by=self.sort_by, row_limit=self.row_limit + ) + ) + ) + prof.stop() + + if self.interrupt: + sys.exit(0) diff --git a/audio2exp-service/LAM_Audio2Expression/engines/infer.py b/audio2exp-service/LAM_Audio2Expression/engines/infer.py new file mode 100644 index 0000000..236671e --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/engines/infer.py @@ -0,0 +1,295 @@ +""" +Copyright 2024-2025 The Alibaba 3DAIGC Team Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + https://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +""" + +import os +import math +import time +import librosa +import numpy as np +from collections import OrderedDict + +import torch +import torch.utils.data +import torch.nn.functional as F + +from .defaults import create_ddp_model +import utils.comm as comm +from models import build_model +from utils.logger import get_root_logger +from utils.registry import Registry +from utils.misc import ( + AverageMeter, +) + +from models.utils import smooth_mouth_movements, apply_frame_blending, apply_savitzky_golay_smoothing, apply_random_brow_movement, \ + symmetrize_blendshapes, apply_random_eye_blinks, apply_random_eye_blinks_context, export_blendshape_animation, \ + RETURN_CODE, DEFAULT_CONTEXT, ARKitBlendShape + +INFER = Registry("infer") + +# Device detection for CPU/GPU support +def get_device(): + """Get the best available device (CUDA or CPU)""" + if torch.cuda.is_available(): + return torch.device('cuda') + else: + return torch.device('cpu') + +class InferBase: + def __init__(self, cfg, model=None, verbose=False) -> None: + torch.multiprocessing.set_sharing_strategy("file_system") + self.device = get_device() + self.logger = get_root_logger( + log_file=os.path.join(cfg.save_path, "infer.log"), + file_mode="a" if cfg.resume else "w", + ) + self.logger.info("=> Loading config ...") + self.logger.info(f"=> Using device: {self.device}") + self.cfg = cfg + self.verbose = verbose + if self.verbose: + self.logger.info(f"Save path: {cfg.save_path}") + self.logger.info(f"Config:\n{cfg.pretty_text}") + if model is None: + self.logger.info("=> Building model ...") + self.model = self.build_model() + else: + self.model = model + + def build_model(self): + model = build_model(self.cfg.model) + n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) + self.logger.info(f"Num params: {n_parameters}") + model = create_ddp_model( + model.to(self.device), + broadcast_buffers=False, + find_unused_parameters=self.cfg.find_unused_parameters, + ) + if os.path.isfile(self.cfg.weight): + self.logger.info(f"Loading weight at: {self.cfg.weight}") + checkpoint = torch.load(self.cfg.weight, map_location=self.device, weights_only=False) + weight = OrderedDict() + for key, value in checkpoint["state_dict"].items(): + if key.startswith("module."): + if comm.get_world_size() == 1: + key = key[7:] # module.xxx.xxx -> xxx.xxx + else: + if comm.get_world_size() > 1: + key = "module." + key # xxx.xxx -> module.xxx.xxx + weight[key] = value + model.load_state_dict(weight, strict=True) + self.logger.info( + "=> Loaded weight '{}'".format( + self.cfg.weight + ) + ) + else: + raise RuntimeError("=> No checkpoint found at '{}'".format(self.cfg.weight)) + return model + + + def infer(self): + raise NotImplementedError + + + +@INFER.register_module() +class Audio2ExpressionInfer(InferBase): + def infer(self): + logger = get_root_logger() + logger.info(">>>>>>>>>>>>>>>> Start Inference >>>>>>>>>>>>>>>>") + batch_time = AverageMeter() + self.model.eval() + + # process audio-input + assert os.path.exists(self.cfg.audio_input) + if(self.cfg.ex_vol): + logger.info("Extract vocals ...") + vocal_path = self.extract_vocal_track(self.cfg.audio_input) + logger.info("=> Extract vocals at: {}".format(vocal_path if os.path.exists(vocal_path) else '... Failed')) + if(os.path.exists(vocal_path)): + self.cfg.audio_input = vocal_path + + with torch.no_grad(): + input_dict = {} + input_dict['id_idx'] = F.one_hot(torch.tensor(self.cfg.id_idx), + self.cfg.model.backbone.num_identity_classes).to(self.device)[None,...] + speech_array, ssr = librosa.load(self.cfg.audio_input, sr=16000) + input_dict['input_audio_array'] = torch.FloatTensor(speech_array).to(self.device)[None,...] + + end = time.time() + output_dict = self.model(input_dict) + batch_time.update(time.time() - end) + + logger.info( + "Infer: [{}] " + "Running Time: {batch_time.avg:.3f} ".format( + self.cfg.audio_input, + batch_time=batch_time, + ) + ) + + out_exp = output_dict['pred_exp'].squeeze().cpu().numpy() + + frame_length = math.ceil(speech_array.shape[0] / ssr * 30) + volume = librosa.feature.rms(y=speech_array, frame_length=int(1 / 30 * ssr), hop_length=int(1 / 30 * ssr))[0] + if (volume.shape[0] > frame_length): + volume = volume[:frame_length] + + if(self.cfg.movement_smooth): + out_exp = smooth_mouth_movements(out_exp, 0, volume) + + if (self.cfg.brow_movement): + out_exp = apply_random_brow_movement(out_exp, volume) + + pred_exp = self.blendshape_postprocess(out_exp) + + if(self.cfg.save_json_path is not None): + export_blendshape_animation(pred_exp, + self.cfg.save_json_path, + ARKitBlendShape, + fps=self.cfg.fps) + + logger.info("<<<<<<<<<<<<<<<<< End Evaluation <<<<<<<<<<<<<<<<<") + + def infer_streaming_audio(self, + audio: np.ndarray, + ssr: float, + context: dict): + + if (context is None): + context = DEFAULT_CONTEXT.copy() + max_frame_length = 64 + + frame_length = math.ceil(audio.shape[0] / ssr * 30) + output_context = DEFAULT_CONTEXT.copy() + + volume = librosa.feature.rms(y=audio, frame_length=min(int(1 / 30 * ssr), len(audio)), hop_length=int(1 / 30 * ssr))[0] + if (volume.shape[0] > frame_length): + volume = volume[:frame_length] + + # resample audio + if (ssr != self.cfg.audio_sr): + in_audio = librosa.resample(audio.astype(np.float32), orig_sr=ssr, target_sr=self.cfg.audio_sr) + else: + in_audio = audio.copy() + + start_frame = int(max_frame_length - in_audio.shape[0] / self.cfg.audio_sr * 30) + + if (context['is_initial_input'] or (context['previous_audio'] is None)): + blank_audio_length = self.cfg.audio_sr * max_frame_length // 30 - in_audio.shape[0] + blank_audio = np.zeros(blank_audio_length, dtype=np.float32) + + # pre-append + input_audio = np.concatenate([blank_audio, in_audio]) + output_context['previous_audio'] = input_audio + + else: + clip_pre_audio_length = self.cfg.audio_sr * max_frame_length // 30 - in_audio.shape[0] + clip_pre_audio = context['previous_audio'][-clip_pre_audio_length:] + input_audio = np.concatenate([clip_pre_audio, in_audio]) + output_context['previous_audio'] = input_audio + + with torch.no_grad(): + try: + input_dict = {} + input_dict['id_idx'] = F.one_hot(torch.tensor(self.cfg.id_idx), + self.cfg.model.backbone.num_identity_classes).to(self.device)[ + None, ...] + input_dict['input_audio_array'] = torch.FloatTensor(input_audio).to(self.device)[None, ...] + output_dict = self.model(input_dict) + out_exp = output_dict['pred_exp'].squeeze().cpu().numpy()[start_frame:, :] + except: + self.logger.error('Error: faided to predict expression.') + output_dict['pred_exp'] = torch.zeros((max_frame_length, 52)).float() + return + + + # post-process + if (context['previous_expression'] is None): + out_exp = self.apply_expression_postprocessing(out_exp, audio_volume=volume) + else: + previous_length = context['previous_expression'].shape[0] + out_exp = self.apply_expression_postprocessing(expression_params = np.concatenate([context['previous_expression'], out_exp], axis=0), + audio_volume=np.concatenate([context['previous_volume'], volume], axis=0), + processed_frames=previous_length)[previous_length:, :] + + if (context['previous_expression'] is not None): + output_context['previous_expression'] = np.concatenate([context['previous_expression'], out_exp], axis=0)[ + -max_frame_length:, :] + output_context['previous_volume'] = np.concatenate([context['previous_volume'], volume], axis=0)[-max_frame_length:] + else: + output_context['previous_expression'] = out_exp.copy() + output_context['previous_volume'] = volume.copy() + + output_context['first_input_flag'] = False + + return {"code": RETURN_CODE['SUCCESS'], + "expression": out_exp, + "headpose": None}, output_context + def apply_expression_postprocessing( + self, + expression_params: np.ndarray, + processed_frames: int = 0, + audio_volume: np.ndarray = None + ) -> np.ndarray: + """Applies full post-processing pipeline to facial expression parameters. + + Args: + expression_params: Raw output from animation model [num_frames, num_parameters] + processed_frames: Number of frames already processed in previous batches + audio_volume: Optional volume array for audio-visual synchronization + + Returns: + Processed expression parameters ready for animation synthesis + """ + # Pipeline execution order matters - maintain sequence + expression_params = smooth_mouth_movements(expression_params, processed_frames, audio_volume) + expression_params = apply_frame_blending(expression_params, processed_frames) + expression_params, _ = apply_savitzky_golay_smoothing(expression_params, window_length=5) + expression_params = symmetrize_blendshapes(expression_params) + expression_params = apply_random_eye_blinks_context(expression_params, processed_frames=processed_frames) + + return expression_params + + def extract_vocal_track( + self, + input_audio_path: str + ) -> str: + """Isolates vocal track from audio file using source separation. + + Args: + input_audio_path: Path to input audio file containing vocals+accompaniment + + Returns: + Path to isolated vocal track in WAV format + """ + separation_command = f'spleeter separate -p spleeter:2stems -o {self.cfg.save_path} {input_audio_path}' + os.system(separation_command) + + base_name = os.path.splitext(os.path.basename(input_audio_path))[0] + return os.path.join(self.cfg.save_path, base_name, 'vocals.wav') + + def blendshape_postprocess(self, + bs_array: np.ndarray + )->np.array: + + bs_array, _ = apply_savitzky_golay_smoothing(bs_array, window_length=5) + bs_array = symmetrize_blendshapes(bs_array) + bs_array = apply_random_eye_blinks(bs_array) + + return bs_array diff --git a/audio2exp-service/LAM_Audio2Expression/engines/launch.py b/audio2exp-service/LAM_Audio2Expression/engines/launch.py new file mode 100644 index 0000000..05f5671 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/engines/launch.py @@ -0,0 +1,135 @@ +""" +Launcher + +modified from detectron2(https://github.com/facebookresearch/detectron2) + +""" + +import os +import logging +from datetime import timedelta +import torch +import torch.distributed as dist +import torch.multiprocessing as mp + +from utils import comm + +__all__ = ["DEFAULT_TIMEOUT", "launch"] + +DEFAULT_TIMEOUT = timedelta(minutes=30) + + +def _find_free_port(): + import socket + + sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + # Binding to port 0 will cause the OS to find an available port for us + sock.bind(("", 0)) + port = sock.getsockname()[1] + sock.close() + # NOTE: there is still a chance the port could be taken by other processes. + return port + + +def launch( + main_func, + num_gpus_per_machine, + num_machines=1, + machine_rank=0, + dist_url=None, + cfg=(), + timeout=DEFAULT_TIMEOUT, +): + """ + Launch multi-gpu or distributed training. + This function must be called on all machines involved in the training. + It will spawn child processes (defined by ``num_gpus_per_machine``) on each machine. + Args: + main_func: a function that will be called by `main_func(*args)` + num_gpus_per_machine (int): number of GPUs per machine + num_machines (int): the total number of machines + machine_rank (int): the rank of this machine + dist_url (str): url to connect to for distributed jobs, including protocol + e.g. "tcp://127.0.0.1:8686". + Can be set to "auto" to automatically select a free port on localhost + timeout (timedelta): timeout of the distributed workers + args (tuple): arguments passed to main_func + """ + world_size = num_machines * num_gpus_per_machine + if world_size > 1: + if dist_url == "auto": + assert ( + num_machines == 1 + ), "dist_url=auto not supported in multi-machine jobs." + port = _find_free_port() + dist_url = f"tcp://127.0.0.1:{port}" + if num_machines > 1 and dist_url.startswith("file://"): + logger = logging.getLogger(__name__) + logger.warning( + "file:// is not a reliable init_method in multi-machine jobs. Prefer tcp://" + ) + + mp.spawn( + _distributed_worker, + nprocs=num_gpus_per_machine, + args=( + main_func, + world_size, + num_gpus_per_machine, + machine_rank, + dist_url, + cfg, + timeout, + ), + daemon=False, + ) + else: + main_func(*cfg) + + +def _distributed_worker( + local_rank, + main_func, + world_size, + num_gpus_per_machine, + machine_rank, + dist_url, + cfg, + timeout=DEFAULT_TIMEOUT, +): + assert ( + torch.cuda.is_available() + ), "cuda is not available. Please check your installation." + global_rank = machine_rank * num_gpus_per_machine + local_rank + try: + dist.init_process_group( + backend="NCCL", + init_method=dist_url, + world_size=world_size, + rank=global_rank, + timeout=timeout, + ) + except Exception as e: + logger = logging.getLogger(__name__) + logger.error("Process group URL: {}".format(dist_url)) + raise e + + # Setup the local process group (which contains ranks within the same machine) + assert comm._LOCAL_PROCESS_GROUP is None + num_machines = world_size // num_gpus_per_machine + for i in range(num_machines): + ranks_on_i = list( + range(i * num_gpus_per_machine, (i + 1) * num_gpus_per_machine) + ) + pg = dist.new_group(ranks_on_i) + if i == machine_rank: + comm._LOCAL_PROCESS_GROUP = pg + + assert num_gpus_per_machine <= torch.cuda.device_count() + torch.cuda.set_device(local_rank) + + # synchronize is needed here to prevent a possible timeout after calling init_process_group + # See: https://github.com/facebookresearch/maskrcnn-benchmark/issues/172 + comm.synchronize() + + main_func(*cfg) diff --git a/audio2exp-service/LAM_Audio2Expression/engines/train.py b/audio2exp-service/LAM_Audio2Expression/engines/train.py new file mode 100644 index 0000000..7de2364 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/engines/train.py @@ -0,0 +1,299 @@ +""" +The code is base on https://github.com/Pointcept/Pointcept +""" + +import os +import sys +import weakref +import torch +import torch.nn as nn +import torch.utils.data +from functools import partial + +if sys.version_info >= (3, 10): + from collections.abc import Iterator +else: + from collections import Iterator +from tensorboardX import SummaryWriter + +from .defaults import create_ddp_model, worker_init_fn +from .hooks import HookBase, build_hooks +import utils.comm as comm +from datasets import build_dataset, point_collate_fn, collate_fn +from models import build_model +from utils.logger import get_root_logger +from utils.optimizer import build_optimizer +from utils.scheduler import build_scheduler +from utils.events import EventStorage +from utils.registry import Registry + + +TRAINERS = Registry("trainers") + + +class TrainerBase: + def __init__(self) -> None: + self.hooks = [] + self.epoch = 0 + self.start_epoch = 0 + self.max_epoch = 0 + self.max_iter = 0 + self.comm_info = dict() + self.data_iterator: Iterator = enumerate([]) + self.storage: EventStorage + self.writer: SummaryWriter + + def register_hooks(self, hooks) -> None: + hooks = build_hooks(hooks) + for h in hooks: + assert isinstance(h, HookBase) + # To avoid circular reference, hooks and trainer cannot own each other. + # This normally does not matter, but will cause memory leak if the + # involved objects contain __del__: + # See http://engineering.hearsaysocial.com/2013/06/16/circular-references-in-python/ + h.trainer = weakref.proxy(self) + self.hooks.extend(hooks) + + def train(self): + with EventStorage() as self.storage: + # => before train + self.before_train() + for self.epoch in range(self.start_epoch, self.max_epoch): + # => before epoch + self.before_epoch() + # => run_epoch + for ( + self.comm_info["iter"], + self.comm_info["input_dict"], + ) in self.data_iterator: + # => before_step + self.before_step() + # => run_step + self.run_step() + # => after_step + self.after_step() + # => after epoch + self.after_epoch() + # => after train + self.after_train() + + def before_train(self): + for h in self.hooks: + h.before_train() + + def before_epoch(self): + for h in self.hooks: + h.before_epoch() + + def before_step(self): + for h in self.hooks: + h.before_step() + + def run_step(self): + raise NotImplementedError + + def after_step(self): + for h in self.hooks: + h.after_step() + + def after_epoch(self): + for h in self.hooks: + h.after_epoch() + self.storage.reset_histories() + + def after_train(self): + # Sync GPU before running train hooks + comm.synchronize() + for h in self.hooks: + h.after_train() + if comm.is_main_process(): + self.writer.close() + + +@TRAINERS.register_module("DefaultTrainer") +class Trainer(TrainerBase): + def __init__(self, cfg): + super(Trainer, self).__init__() + self.epoch = 0 + self.start_epoch = 0 + self.max_epoch = cfg.eval_epoch + self.best_metric_value = -torch.inf + self.logger = get_root_logger( + log_file=os.path.join(cfg.save_path, "train.log"), + file_mode="a" if cfg.resume else "w", + ) + self.logger.info("=> Loading config ...") + self.cfg = cfg + self.logger.info(f"Save path: {cfg.save_path}") + self.logger.info(f"Config:\n{cfg.pretty_text}") + self.logger.info("=> Building model ...") + self.model = self.build_model() + self.logger.info("=> Building writer ...") + self.writer = self.build_writer() + self.logger.info("=> Building train dataset & dataloader ...") + self.train_loader = self.build_train_loader() + self.logger.info("=> Building val dataset & dataloader ...") + self.val_loader = self.build_val_loader() + self.logger.info("=> Building optimize, scheduler, scaler(amp) ...") + self.optimizer = self.build_optimizer() + self.scheduler = self.build_scheduler() + self.scaler = self.build_scaler() + self.logger.info("=> Building hooks ...") + self.register_hooks(self.cfg.hooks) + + def train(self): + with EventStorage() as self.storage: + # => before train + self.before_train() + self.logger.info(">>>>>>>>>>>>>>>> Start Training >>>>>>>>>>>>>>>>") + for self.epoch in range(self.start_epoch, self.max_epoch): + # => before epoch + # TODO: optimize to iteration based + if comm.get_world_size() > 1: + self.train_loader.sampler.set_epoch(self.epoch) + self.model.train() + self.data_iterator = enumerate(self.train_loader) + self.before_epoch() + # => run_epoch + for ( + self.comm_info["iter"], + self.comm_info["input_dict"], + ) in self.data_iterator: + # => before_step + self.before_step() + # => run_step + self.run_step() + # => after_step + self.after_step() + # => after epoch + self.after_epoch() + # => after train + self.after_train() + + def run_step(self): + input_dict = self.comm_info["input_dict"] + for key in input_dict.keys(): + if isinstance(input_dict[key], torch.Tensor): + input_dict[key] = input_dict[key].cuda(non_blocking=True) + with torch.cuda.amp.autocast(enabled=self.cfg.enable_amp): + output_dict = self.model(input_dict) + loss = output_dict["loss"] + self.optimizer.zero_grad() + if self.cfg.enable_amp: + self.scaler.scale(loss).backward() + self.scaler.step(self.optimizer) + + # When enable amp, optimizer.step call are skipped if the loss scaling factor is too large. + # Fix torch warning scheduler step before optimizer step. + scaler = self.scaler.get_scale() + self.scaler.update() + if scaler <= self.scaler.get_scale(): + self.scheduler.step() + else: + loss.backward() + self.optimizer.step() + self.scheduler.step() + if self.cfg.empty_cache: + torch.cuda.empty_cache() + self.comm_info["model_output_dict"] = output_dict + + def build_model(self): + model = build_model(self.cfg.model) + if self.cfg.sync_bn: + model = nn.SyncBatchNorm.convert_sync_batchnorm(model) + n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) + # logger.info(f"Model: \n{self.model}") + self.logger.info(f"Num params: {n_parameters}") + model = create_ddp_model( + model.cuda(), + broadcast_buffers=False, + find_unused_parameters=self.cfg.find_unused_parameters, + ) + return model + + def build_writer(self): + writer = SummaryWriter(self.cfg.save_path) if comm.is_main_process() else None + self.logger.info(f"Tensorboard writer logging dir: {self.cfg.save_path}") + return writer + + def build_train_loader(self): + train_data = build_dataset(self.cfg.data.train) + + if comm.get_world_size() > 1: + train_sampler = torch.utils.data.distributed.DistributedSampler(train_data) + else: + train_sampler = None + + init_fn = ( + partial( + worker_init_fn, + num_workers=self.cfg.num_worker_per_gpu, + rank=comm.get_rank(), + seed=self.cfg.seed, + ) + if self.cfg.seed is not None + else None + ) + + train_loader = torch.utils.data.DataLoader( + train_data, + batch_size=self.cfg.batch_size_per_gpu, + shuffle=(train_sampler is None), + num_workers=0, + sampler=train_sampler, + collate_fn=partial(point_collate_fn, mix_prob=self.cfg.mix_prob), + pin_memory=True, + worker_init_fn=init_fn, + drop_last=True, + # persistent_workers=True, + ) + return train_loader + + def build_val_loader(self): + val_loader = None + if self.cfg.evaluate: + val_data = build_dataset(self.cfg.data.val) + if comm.get_world_size() > 1: + val_sampler = torch.utils.data.distributed.DistributedSampler(val_data) + else: + val_sampler = None + val_loader = torch.utils.data.DataLoader( + val_data, + batch_size=self.cfg.batch_size_val_per_gpu, + shuffle=False, + num_workers=self.cfg.num_worker_per_gpu, + pin_memory=True, + sampler=val_sampler, + collate_fn=collate_fn, + ) + return val_loader + + def build_optimizer(self): + return build_optimizer(self.cfg.optimizer, self.model, self.cfg.param_dicts) + + def build_scheduler(self): + assert hasattr(self, "optimizer") + assert hasattr(self, "train_loader") + self.cfg.scheduler.total_steps = len(self.train_loader) * self.cfg.eval_epoch + return build_scheduler(self.cfg.scheduler, self.optimizer) + + def build_scaler(self): + scaler = torch.cuda.amp.GradScaler() if self.cfg.enable_amp else None + return scaler + + +@TRAINERS.register_module("MultiDatasetTrainer") +class MultiDatasetTrainer(Trainer): + def build_train_loader(self): + from datasets import MultiDatasetDataloader + + train_data = build_dataset(self.cfg.data.train) + train_loader = MultiDatasetDataloader( + train_data, + self.cfg.batch_size_per_gpu, + self.cfg.num_worker_per_gpu, + self.cfg.mix_prob, + self.cfg.seed, + ) + self.comm_info["iter_per_epoch"] = len(train_loader) + return train_loader diff --git a/audio2exp-service/LAM_Audio2Expression/inference.py b/audio2exp-service/LAM_Audio2Expression/inference.py new file mode 100644 index 0000000..37ac22e --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/inference.py @@ -0,0 +1,48 @@ +""" +# Copyright 2024-2025 The Alibaba 3DAIGC Team Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + https://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +""" + +from engines.defaults import ( + default_argument_parser, + default_config_parser, + default_setup, +) +from engines.infer import INFER +from engines.launch import launch + + +def main_worker(cfg): + cfg = default_setup(cfg) + infer = INFER.build(dict(type=cfg.infer.type, cfg=cfg)) + infer.infer() + + +def main(): + args = default_argument_parser().parse_args() + cfg = default_config_parser(args.config_file, args.options) + + launch( + main_worker, + num_gpus_per_machine=args.num_gpus, + num_machines=args.num_machines, + machine_rank=args.machine_rank, + dist_url=args.dist_url, + cfg=(cfg,), + ) + + +if __name__ == "__main__": + main() diff --git a/audio2exp-service/LAM_Audio2Expression/inference_streaming_audio.py b/audio2exp-service/LAM_Audio2Expression/inference_streaming_audio.py new file mode 100644 index 0000000..c14b084 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/inference_streaming_audio.py @@ -0,0 +1,60 @@ +""" +# Copyright 2024-2025 The Alibaba 3DAIGC Team Authors. All rights reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + https://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. + +""" + +import numpy as np + +from engines.defaults import ( + default_argument_parser, + default_config_parser, + default_setup, +) +from engines.infer import INFER +import librosa +from tqdm import tqdm +import time + + +def export_json(bs_array, json_path): + from models.utils import export_blendshape_animation, ARKitBlendShape + export_blendshape_animation(bs_array, json_path, ARKitBlendShape, fps=30.0) + +if __name__ == '__main__': + args = default_argument_parser().parse_args() + args.config_file = 'configs/lam_audio2exp_config_streaming.py' + cfg = default_config_parser(args.config_file, args.options) + + + cfg = default_setup(cfg) + infer = INFER.build(dict(type=cfg.infer.type, cfg=cfg)) + infer.model.eval() + + audio, sample_rate = librosa.load(cfg.audio_input, sr=16000) + context = None + input_num = audio.shape[0]//16000+1 + gap = 16000 + all_exp = [] + for i in tqdm(range(input_num)): + + start = time.time() + output, context = infer.infer_streaming_audio(audio[i*gap:(i+1)*gap], sample_rate, context) + end = time.time() + print('Inference time {}'.format(end - start)) + all_exp.append(output['expression']) + + all_exp = np.concatenate(all_exp,axis=0) + + export_json(all_exp, cfg.save_json_path) \ No newline at end of file diff --git a/audio2exp-service/LAM_Audio2Expression/lam_modal.py b/audio2exp-service/LAM_Audio2Expression/lam_modal.py new file mode 100644 index 0000000..d50f746 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/lam_modal.py @@ -0,0 +1,189 @@ +import os +import sys +import subprocess +import time +import shutil +import modal +import base64 + +# アプリ名を変更 +app = modal.App("lam-final-v33-ui-fix-v2") + +# --- 事前チェック --- +local_assets_path = "./assets/human_parametric_models/flame_assets/flame/flame2023.pkl" +if __name__ == "__main__": + if not os.path.exists(local_assets_path): + print(f"❌ CRITICAL ERROR: Local asset not found at: {local_assets_path}") + sys.exit(1) + +# --- UI修復パッチ (Base64) --- +# 1. GradioのExamplesを無効化 +# 2. サーバーポートを8080に固定 +PATCH_SCRIPT = """ +import re +import os + +path = '/root/LAM/app_lam.py' +if os.path.exists(path): + print("🛠️ Applying UI patch...") + with open(path, 'r') as f: + code = f.read() + + # 1. Examples機能を無効化するコードを注入 + patch_code = ''' +import gradio as gr +# --- PATCH START --- +try: + class DummyExamples: + def __init__(self, *args, **kwargs): pass + def attach_load_event(self, *args, **kwargs): pass + def render(self): pass + gr.Examples = DummyExamples + print("✅ Gradio Examples disabled to prevent UI crash.") +except Exception as e: + print(f"⚠️ Failed to disable examples: {e}") +# --- PATCH END --- +''' + code = code.replace('import gradio as gr', patch_code) + + # 2. 起動設定の強制書き換え + if '.launch(' in code: + code = re.sub(r'\.launch\s*\(', ".launch(server_name='0.0.0.0', server_port=8080, ", code) + print("✅ Server port forced to 8080.") + + with open(path, 'w') as f: + f.write(code) + print("🚀 Patch applied successfully.") +""" + +# スクリプトをBase64化 +patch_b64 = base64.b64encode(PATCH_SCRIPT.encode('utf-8')).decode('utf-8') +patch_cmd = f"python -c \"import base64; exec(base64.b64decode('{patch_b64}'))\"" + + +# --- 1. 環境構築 --- +image = ( + modal.Image.from_registry("nvidia/cuda:11.8.0-devel-ubuntu22.04", add_python="3.10") + .apt_install( + "git", "libgl1-mesa-glx", "libglib2.0-0", "ffmpeg", "wget", "tree", + "libusb-1.0-0", "build-essential", "ninja-build", + "clang", "llvm", "libclang-dev" + ) + + # 1. Base setup + .run_commands( + "python -m pip install --upgrade pip setuptools wheel", + "pip install 'numpy==1.23.5'" + ) + # 2. PyTorch 2.2.0 + .run_commands( + "pip install torch==2.2.0 torchvision==0.17.0 torchaudio==2.2.0 --index-url https://download.pytorch.org/whl/cu118" + ) + + # 3. Build Environment + .env({ + "FORCE_CUDA": "1", + "CUDA_HOME": "/usr/local/cuda", + "MAX_JOBS": "4", + "TORCH_CUDA_ARCH_LIST": "8.6", + "CC": "clang", + "CXX": "clang++" + }) + + # 4. Critical Build (no-build-isolation) + .run_commands( + "pip install chumpy==0.70 --no-build-isolation", + "pip install git+https://github.com/facebookresearch/pytorch3d.git@v0.7.7 --no-build-isolation" + ) + + # 5. Dependencies + .pip_install( + "gradio==3.50.2", + "omegaconf==2.3.0", + "pandas", + "scipy<1.14.0", + "opencv-python-headless", + "imageio[ffmpeg]", + "moviepy==1.0.3", + "rembg[gpu]", + "scikit-image", + "pillow", + "onnxruntime-gpu", + "huggingface_hub>=0.24.0", + "filelock", + "typeguard", + + "transformers==4.44.2", + "diffusers==0.30.3", + "accelerate==0.34.2", + "tyro==0.8.0", + "mediapipe==0.10.21", + + "tensorboard", + "rich", + "loguru", + "Cython", + "PyMCubes", + "trimesh", + "einops", + "plyfile", + "jaxtyping", + "ninja", + "numpy==1.23.5" + ) + + # 6. LAM 3D Libs + .run_commands( + "pip install git+https://github.com/ashawkey/diff-gaussian-rasterization.git --no-build-isolation", + "pip install git+https://github.com/ShenhanQian/nvdiffrast.git@backface-culling --no-build-isolation" + ) + + # 7. LAM Setup with UI Patch + .run_commands( + "mkdir -p /root/LAM", + "rm -rf /root/LAM", + "git clone https://github.com/aigc3d/LAM.git /root/LAM", + + # cpu_nms ビルド + "cd /root/LAM/external/landmark_detection/FaceBoxesV2/utils/nms && " + "echo \"from setuptools import setup, Extension; from Cython.Build import cythonize; import numpy; setup(ext_modules=cythonize([Extension('cpu_nms', ['cpu_nms.pyx'])]), include_dirs=[numpy.get_include()])\" > setup.py && " + "python setup.py build_ext --inplace", + + # ★パッチ適用（UIのサンプル機能を無効化） + patch_cmd + ) +) + +# --- 2. サーバー準備 --- +def setup_server(): + from huggingface_hub import snapshot_download + print("📥 Downloading checkpoints...") + try: + snapshot_download( + repo_id="3DAIGC/LAM-20K", + local_dir="/root/LAM/model_zoo/lam_models/releases/lam/lam-20k/step_045500", + local_dir_use_symlinks=False + ) + except Exception as e: + print(f"Checkpoints download warning: {e}") + +image = ( + image + .run_function(setup_server) + .add_local_dir("./assets", remote_path="/root/LAM/model_zoo", copy=True) +) + +# --- 3. アプリ起動 --- +@app.function( + image=image, + gpu="A10G", + timeout=3600 +) +@modal.web_server(8080) +def ui(): + os.chdir("/root/LAM") + import sys + print(f"🚀 Launching LAM App (Python {sys.version})") + + cmd = "python -u app_lam.py" + subprocess.Popen(cmd, shell=True, stdout=sys.stdout, stderr=sys.stderr).wait() \ No newline at end of file diff --git a/audio2exp-service/LAM_Audio2Expression/models/__init__.py b/audio2exp-service/LAM_Audio2Expression/models/__init__.py new file mode 100644 index 0000000..f4beb83 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/models/__init__.py @@ -0,0 +1,7 @@ +from .builder import build_model + +from .default import DefaultEstimator + +# Backbones +from .network import Audio2Expression + diff --git a/audio2exp-service/LAM_Audio2Expression/models/builder.py b/audio2exp-service/LAM_Audio2Expression/models/builder.py new file mode 100644 index 0000000..eed2627 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/models/builder.py @@ -0,0 +1,13 @@ +""" +Modified by https://github.com/Pointcept/Pointcept +""" + +from utils.registry import Registry + +MODELS = Registry("models") +MODULES = Registry("modules") + + +def build_model(cfg): + """Build models.""" + return MODELS.build(cfg) diff --git a/audio2exp-service/LAM_Audio2Expression/models/default.py b/audio2exp-service/LAM_Audio2Expression/models/default.py new file mode 100644 index 0000000..07655f6 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/models/default.py @@ -0,0 +1,25 @@ +import torch.nn as nn + +from models.losses import build_criteria +from .builder import MODELS, build_model + +@MODELS.register_module() +class DefaultEstimator(nn.Module): + def __init__(self, backbone=None, criteria=None): + super().__init__() + self.backbone = build_model(backbone) + self.criteria = build_criteria(criteria) + + def forward(self, input_dict): + pred_exp = self.backbone(input_dict) + # train + if self.training: + loss = self.criteria(pred_exp, input_dict["gt_exp"]) + return dict(loss=loss) + # eval + elif "gt_exp" in input_dict.keys(): + loss = self.criteria(pred_exp, input_dict["gt_exp"]) + return dict(loss=loss, pred_exp=pred_exp) + # infer + else: + return dict(pred_exp=pred_exp) diff --git a/audio2exp-service/LAM_Audio2Expression/models/encoder/wav2vec.py b/audio2exp-service/LAM_Audio2Expression/models/encoder/wav2vec.py new file mode 100644 index 0000000..f11fc57 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/models/encoder/wav2vec.py @@ -0,0 +1,248 @@ +import numpy as np +from typing import Optional, Tuple + +import torch +import torch.nn as nn +import torch.nn.functional as F +from torch.nn import BCEWithLogitsLoss, CrossEntropyLoss, MSELoss + +from dataclasses import dataclass +from transformers import Wav2Vec2Model, Wav2Vec2PreTrainedModel +from transformers.modeling_outputs import BaseModelOutput +from transformers.file_utils import ModelOutput + + +_CONFIG_FOR_DOC = "Wav2Vec2Config" +_HIDDEN_STATES_START_POSITION = 2 + + +# the implementation of Wav2Vec2Model is borrowed from https://huggingface.co/transformers/_modules/transformers/models/wav2vec2/modeling_wav2vec2.html#Wav2Vec2Model +# initialize our encoder with the pre-trained wav2vec 2.0 weights. +def _compute_mask_indices( + shape: Tuple[int, int], + mask_prob: float, + mask_length: int, + attention_mask: Optional[torch.Tensor] = None, + min_masks: int = 0, +) -> np.ndarray: + bsz, all_sz = shape + mask = np.full((bsz, all_sz), False) + + all_num_mask = int( + mask_prob * all_sz / float(mask_length) + + np.random.rand() + ) + all_num_mask = max(min_masks, all_num_mask) + mask_idcs = [] + padding_mask = attention_mask.ne(1) if attention_mask is not None else None + for i in range(bsz): + if padding_mask is not None: + sz = all_sz - padding_mask[i].long().sum().item() + num_mask = int( + mask_prob * sz / float(mask_length) + + np.random.rand() + ) + num_mask = max(min_masks, num_mask) + else: + sz = all_sz + num_mask = all_num_mask + + lengths = np.full(num_mask, mask_length) + + if sum(lengths) == 0: + lengths[0] = min(mask_length, sz - 1) + + min_len = min(lengths) + if sz - min_len <= num_mask: + min_len = sz - num_mask - 1 + + mask_idc = np.random.choice(sz - min_len, num_mask, replace=False) + mask_idc = np.asarray([mask_idc[j] + offset for j in range(len(mask_idc)) for offset in range(lengths[j])]) + mask_idcs.append(np.unique(mask_idc[mask_idc < sz])) + + min_len = min([len(m) for m in mask_idcs]) + for i, mask_idc in enumerate(mask_idcs): + if len(mask_idc) > min_len: + mask_idc = np.random.choice(mask_idc, min_len, replace=False) + mask[i, mask_idc] = True + return mask + + +# linear interpolation layer +def linear_interpolation(features, input_fps, output_fps, output_len=None): + features = features.transpose(1, 2) + seq_len = features.shape[2] / float(input_fps) + if output_len is None: + output_len = int(seq_len * output_fps) + output_features = F.interpolate(features, size=output_len, align_corners=True, mode='linear') + return output_features.transpose(1, 2) + + +class Wav2Vec2Model(Wav2Vec2Model): + def __init__(self, config): + super().__init__(config) + self.lm_head = nn.Linear(1024, 32) + + def forward( + self, + input_values, + attention_mask=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + frame_num=None + ): + self.config.output_attentions = True + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + hidden_states = self.feature_extractor(input_values) + hidden_states = hidden_states.transpose(1, 2) + + hidden_states = linear_interpolation(hidden_states, 50, 30, output_len=frame_num) + + if attention_mask is not None: + output_lengths = self._get_feat_extract_output_lengths(attention_mask.sum(-1)) + attention_mask = torch.zeros( + hidden_states.shape[:2], dtype=hidden_states.dtype, device=hidden_states.device + ) + attention_mask[ + (torch.arange(attention_mask.shape[0], device=hidden_states.device), output_lengths - 1) + ] = 1 + attention_mask = attention_mask.flip([-1]).cumsum(-1).flip([-1]).bool() + + hidden_states = self.feature_projection(hidden_states)[0] + + encoder_outputs = self.encoder( + hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = encoder_outputs[0] + if not return_dict: + return (hidden_states,) + encoder_outputs[1:] + + return BaseModelOutput( + last_hidden_state=hidden_states, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) + + +@dataclass +class SpeechClassifierOutput(ModelOutput): + loss: Optional[torch.FloatTensor] = None + logits: torch.FloatTensor = None + hidden_states: Optional[Tuple[torch.FloatTensor]] = None + attentions: Optional[Tuple[torch.FloatTensor]] = None + + +class Wav2Vec2ClassificationHead(nn.Module): + """Head for wav2vec classification task.""" + + def __init__(self, config): + super().__init__() + self.dense = nn.Linear(config.hidden_size, config.hidden_size) + self.dropout = nn.Dropout(config.final_dropout) + self.out_proj = nn.Linear(config.hidden_size, config.num_labels) + + def forward(self, features, **kwargs): + x = features + x = self.dropout(x) + x = self.dense(x) + x = torch.tanh(x) + x = self.dropout(x) + x = self.out_proj(x) + return x + + +class Wav2Vec2ForSpeechClassification(Wav2Vec2PreTrainedModel): + def __init__(self, config): + super().__init__(config) + self.num_labels = config.num_labels + self.pooling_mode = config.pooling_mode + self.config = config + + self.wav2vec2 = Wav2Vec2Model(config) + self.classifier = Wav2Vec2ClassificationHead(config) + + self.init_weights() + + def freeze_feature_extractor(self): + self.wav2vec2.feature_extractor._freeze_parameters() + + def merged_strategy( + self, + hidden_states, + mode="mean" + ): + if mode == "mean": + outputs = torch.mean(hidden_states, dim=1) + elif mode == "sum": + outputs = torch.sum(hidden_states, dim=1) + elif mode == "max": + outputs = torch.max(hidden_states, dim=1)[0] + else: + raise Exception( + "The pooling method hasn't been defined! Your pooling mode must be one of these ['mean', 'sum', 'max']") + + return outputs + + def forward( + self, + input_values, + attention_mask=None, + output_attentions=None, + output_hidden_states=None, + return_dict=None, + labels=None, + frame_num=None, + ): + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + outputs = self.wav2vec2( + input_values, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + hidden_states = outputs[0] + hidden_states1 = linear_interpolation(hidden_states, 50, 30, output_len=frame_num) + hidden_states = self.merged_strategy(hidden_states1, mode=self.pooling_mode) + logits = self.classifier(hidden_states) + + loss = None + if labels is not None: + if self.config.problem_type is None: + if self.num_labels == 1: + self.config.problem_type = "regression" + elif self.num_labels > 1 and (labels.dtype == torch.long or labels.dtype == torch.int): + self.config.problem_type = "single_label_classification" + else: + self.config.problem_type = "multi_label_classification" + + if self.config.problem_type == "regression": + loss_fct = MSELoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels) + elif self.config.problem_type == "single_label_classification": + loss_fct = CrossEntropyLoss() + loss = loss_fct(logits.view(-1, self.num_labels), labels.view(-1)) + elif self.config.problem_type == "multi_label_classification": + loss_fct = BCEWithLogitsLoss() + loss = loss_fct(logits, labels) + + if not return_dict: + output = (logits,) + outputs[2:] + return ((loss,) + output) if loss is not None else output + + return SpeechClassifierOutput( + loss=loss, + logits=logits, + hidden_states=hidden_states1, + attentions=outputs.attentions, + ) diff --git a/audio2exp-service/LAM_Audio2Expression/models/encoder/wavlm.py b/audio2exp-service/LAM_Audio2Expression/models/encoder/wavlm.py new file mode 100644 index 0000000..0e39b9b --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/models/encoder/wavlm.py @@ -0,0 +1,87 @@ +import numpy as np +import torch +from transformers import WavLMModel +from transformers.modeling_outputs import Wav2Vec2BaseModelOutput +from typing import Optional, Tuple, Union +import torch.nn.functional as F + +def linear_interpolation(features, output_len: int): + features = features.transpose(1, 2) + output_features = F.interpolate( + features, size=output_len, align_corners=True, mode='linear') + return output_features.transpose(1, 2) + +# the implementation of Wav2Vec2Model is borrowed from https://huggingface.co/transformers/_modules/transformers/models/wav2vec2/modeling_wav2vec2.html#Wav2Vec2Model # noqa: E501 +# initialize our encoder with the pre-trained wav2vec 2.0 weights. + + +class WavLMModel(WavLMModel): + def __init__(self, config): + super().__init__(config) + + def _freeze_wav2vec2_parameters(self, do_freeze: bool = True): + for param in self.parameters(): + param.requires_grad = (not do_freeze) + + def forward( + self, + input_values: Optional[torch.Tensor], + attention_mask: Optional[torch.Tensor] = None, + mask_time_indices: Optional[torch.FloatTensor] = None, + output_attentions: Optional[bool] = None, + output_hidden_states: Optional[bool] = None, + return_dict: Optional[bool] = None, + frame_num=None, + interpolate_pos: int = 0, + ) -> Union[Tuple, Wav2Vec2BaseModelOutput]: + + output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions + output_hidden_states = ( + output_hidden_states if output_hidden_states is not None else self.config.output_hidden_states + ) + return_dict = return_dict if return_dict is not None else self.config.use_return_dict + + extract_features = self.feature_extractor(input_values) + extract_features = extract_features.transpose(1, 2) + + if interpolate_pos == 0: + extract_features = linear_interpolation( + extract_features, output_len=frame_num) + + if attention_mask is not None: + # compute reduced attention_mask corresponding to feature vectors + attention_mask = self._get_feature_vector_attention_mask( + extract_features.shape[1], attention_mask, add_adapter=False + ) + + hidden_states, extract_features = self.feature_projection(extract_features) + hidden_states = self._mask_hidden_states( + hidden_states, mask_time_indices=mask_time_indices, attention_mask=attention_mask + ) + + encoder_outputs = self.encoder( + hidden_states, + attention_mask=attention_mask, + output_attentions=output_attentions, + output_hidden_states=output_hidden_states, + return_dict=return_dict, + ) + + hidden_states = encoder_outputs[0] + + if interpolate_pos == 1: + hidden_states = linear_interpolation( + hidden_states, output_len=frame_num) + + if self.adapter is not None: + hidden_states = self.adapter(hidden_states) + + if not return_dict: + return (hidden_states, extract_features) + encoder_outputs[1:] + + return Wav2Vec2BaseModelOutput( + last_hidden_state=hidden_states, + extract_features=extract_features, + hidden_states=encoder_outputs.hidden_states, + attentions=encoder_outputs.attentions, + ) \ No newline at end of file diff --git a/audio2exp-service/LAM_Audio2Expression/models/losses/__init__.py b/audio2exp-service/LAM_Audio2Expression/models/losses/__init__.py new file mode 100644 index 0000000..782a0d3 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/models/losses/__init__.py @@ -0,0 +1,4 @@ +from .builder import build_criteria + +from .misc import CrossEntropyLoss, SmoothCELoss, DiceLoss, FocalLoss, BinaryFocalLoss, L1Loss +from .lovasz import LovaszLoss diff --git a/audio2exp-service/LAM_Audio2Expression/models/losses/builder.py b/audio2exp-service/LAM_Audio2Expression/models/losses/builder.py new file mode 100644 index 0000000..ec936be --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/models/losses/builder.py @@ -0,0 +1,28 @@ +""" +The code is base on https://github.com/Pointcept/Pointcept +""" + +from utils.registry import Registry + +LOSSES = Registry("losses") + + +class Criteria(object): + def __init__(self, cfg=None): + self.cfg = cfg if cfg is not None else [] + self.criteria = [] + for loss_cfg in self.cfg: + self.criteria.append(LOSSES.build(cfg=loss_cfg)) + + def __call__(self, pred, target): + if len(self.criteria) == 0: + # loss computation occur in model + return pred + loss = 0 + for c in self.criteria: + loss += c(pred, target) + return loss + + +def build_criteria(cfg): + return Criteria(cfg) diff --git a/audio2exp-service/LAM_Audio2Expression/models/losses/lovasz.py b/audio2exp-service/LAM_Audio2Expression/models/losses/lovasz.py new file mode 100644 index 0000000..dbdb844 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/models/losses/lovasz.py @@ -0,0 +1,253 @@ +""" +The code is base on https://github.com/Pointcept/Pointcept +""" + +from typing import Optional +from itertools import filterfalse +import torch +import torch.nn.functional as F +from torch.nn.modules.loss import _Loss + +from .builder import LOSSES + +BINARY_MODE: str = "binary" +MULTICLASS_MODE: str = "multiclass" +MULTILABEL_MODE: str = "multilabel" + + +def _lovasz_grad(gt_sorted): + """Compute gradient of the Lovasz extension w.r.t sorted errors + See Alg. 1 in paper + """ + p = len(gt_sorted) + gts = gt_sorted.sum() + intersection = gts - gt_sorted.float().cumsum(0) + union = gts + (1 - gt_sorted).float().cumsum(0) + jaccard = 1.0 - intersection / union + if p > 1: # cover 1-pixel case + jaccard[1:p] = jaccard[1:p] - jaccard[0:-1] + return jaccard + + +def _lovasz_hinge(logits, labels, per_image=True, ignore=None): + """ + Binary Lovasz hinge loss + logits: [B, H, W] Logits at each pixel (between -infinity and +infinity) + labels: [B, H, W] Tensor, binary ground truth masks (0 or 1) + per_image: compute the loss per image instead of per batch + ignore: void class id + """ + if per_image: + loss = mean( + _lovasz_hinge_flat( + *_flatten_binary_scores(log.unsqueeze(0), lab.unsqueeze(0), ignore) + ) + for log, lab in zip(logits, labels) + ) + else: + loss = _lovasz_hinge_flat(*_flatten_binary_scores(logits, labels, ignore)) + return loss + + +def _lovasz_hinge_flat(logits, labels): + """Binary Lovasz hinge loss + Args: + logits: [P] Logits at each prediction (between -infinity and +infinity) + labels: [P] Tensor, binary ground truth labels (0 or 1) + """ + if len(labels) == 0: + # only void pixels, the gradients should be 0 + return logits.sum() * 0.0 + signs = 2.0 * labels.float() - 1.0 + errors = 1.0 - logits * signs + errors_sorted, perm = torch.sort(errors, dim=0, descending=True) + perm = perm.data + gt_sorted = labels[perm] + grad = _lovasz_grad(gt_sorted) + loss = torch.dot(F.relu(errors_sorted), grad) + return loss + + +def _flatten_binary_scores(scores, labels, ignore=None): + """Flattens predictions in the batch (binary case) + Remove labels equal to 'ignore' + """ + scores = scores.view(-1) + labels = labels.view(-1) + if ignore is None: + return scores, labels + valid = labels != ignore + vscores = scores[valid] + vlabels = labels[valid] + return vscores, vlabels + + +def _lovasz_softmax( + probas, labels, classes="present", class_seen=None, per_image=False, ignore=None +): + """Multi-class Lovasz-Softmax loss + Args: + @param probas: [B, C, H, W] Class probabilities at each prediction (between 0 and 1). + Interpreted as binary (sigmoid) output with outputs of size [B, H, W]. + @param labels: [B, H, W] Tensor, ground truth labels (between 0 and C - 1) + @param classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average. + @param per_image: compute the loss per image instead of per batch + @param ignore: void class labels + """ + if per_image: + loss = mean( + _lovasz_softmax_flat( + *_flatten_probas(prob.unsqueeze(0), lab.unsqueeze(0), ignore), + classes=classes + ) + for prob, lab in zip(probas, labels) + ) + else: + loss = _lovasz_softmax_flat( + *_flatten_probas(probas, labels, ignore), + classes=classes, + class_seen=class_seen + ) + return loss + + +def _lovasz_softmax_flat(probas, labels, classes="present", class_seen=None): + """Multi-class Lovasz-Softmax loss + Args: + @param probas: [P, C] Class probabilities at each prediction (between 0 and 1) + @param labels: [P] Tensor, ground truth labels (between 0 and C - 1) + @param classes: 'all' for all, 'present' for classes present in labels, or a list of classes to average. + """ + if probas.numel() == 0: + # only void pixels, the gradients should be 0 + return probas * 0.0 + C = probas.size(1) + losses = [] + class_to_sum = list(range(C)) if classes in ["all", "present"] else classes + # for c in class_to_sum: + for c in labels.unique(): + if class_seen is None: + fg = (labels == c).type_as(probas) # foreground for class c + if classes == "present" and fg.sum() == 0: + continue + if C == 1: + if len(classes) > 1: + raise ValueError("Sigmoid output possible only with 1 class") + class_pred = probas[:, 0] + else: + class_pred = probas[:, c] + errors = (fg - class_pred).abs() + errors_sorted, perm = torch.sort(errors, 0, descending=True) + perm = perm.data + fg_sorted = fg[perm] + losses.append(torch.dot(errors_sorted, _lovasz_grad(fg_sorted))) + else: + if c in class_seen: + fg = (labels == c).type_as(probas) # foreground for class c + if classes == "present" and fg.sum() == 0: + continue + if C == 1: + if len(classes) > 1: + raise ValueError("Sigmoid output possible only with 1 class") + class_pred = probas[:, 0] + else: + class_pred = probas[:, c] + errors = (fg - class_pred).abs() + errors_sorted, perm = torch.sort(errors, 0, descending=True) + perm = perm.data + fg_sorted = fg[perm] + losses.append(torch.dot(errors_sorted, _lovasz_grad(fg_sorted))) + return mean(losses) + + +def _flatten_probas(probas, labels, ignore=None): + """Flattens predictions in the batch""" + if probas.dim() == 3: + # assumes output of a sigmoid layer + B, H, W = probas.size() + probas = probas.view(B, 1, H, W) + + C = probas.size(1) + probas = torch.movedim(probas, 1, -1) # [B, C, Di, Dj, ...] -> [B, Di, Dj, ..., C] + probas = probas.contiguous().view(-1, C) # [P, C] + + labels = labels.view(-1) + if ignore is None: + return probas, labels + valid = labels != ignore + vprobas = probas[valid] + vlabels = labels[valid] + return vprobas, vlabels + + +def isnan(x): + return x != x + + +def mean(values, ignore_nan=False, empty=0): + """Nan-mean compatible with generators.""" + values = iter(values) + if ignore_nan: + values = filterfalse(isnan, values) + try: + n = 1 + acc = next(values) + except StopIteration: + if empty == "raise": + raise ValueError("Empty mean") + return empty + for n, v in enumerate(values, 2): + acc += v + if n == 1: + return acc + return acc / n + + +@LOSSES.register_module() +class LovaszLoss(_Loss): + def __init__( + self, + mode: str, + class_seen: Optional[int] = None, + per_image: bool = False, + ignore_index: Optional[int] = None, + loss_weight: float = 1.0, + ): + """Lovasz loss for segmentation task. + It supports binary, multiclass and multilabel cases + Args: + mode: Loss mode 'binary', 'multiclass' or 'multilabel' + ignore_index: Label that indicates ignored pixels (does not contribute to loss) + per_image: If True loss computed per each image and then averaged, else computed per whole batch + Shape + - **y_pred** - torch.Tensor of shape (N, C, H, W) + - **y_true** - torch.Tensor of shape (N, H, W) or (N, C, H, W) + Reference + https://github.com/BloodAxe/pytorch-toolbelt + """ + assert mode in {BINARY_MODE, MULTILABEL_MODE, MULTICLASS_MODE} + super().__init__() + + self.mode = mode + self.ignore_index = ignore_index + self.per_image = per_image + self.class_seen = class_seen + self.loss_weight = loss_weight + + def forward(self, y_pred, y_true): + if self.mode in {BINARY_MODE, MULTILABEL_MODE}: + loss = _lovasz_hinge( + y_pred, y_true, per_image=self.per_image, ignore=self.ignore_index + ) + elif self.mode == MULTICLASS_MODE: + y_pred = y_pred.softmax(dim=1) + loss = _lovasz_softmax( + y_pred, + y_true, + class_seen=self.class_seen, + per_image=self.per_image, + ignore=self.ignore_index, + ) + else: + raise ValueError("Wrong mode {}.".format(self.mode)) + return loss * self.loss_weight diff --git a/audio2exp-service/LAM_Audio2Expression/models/losses/misc.py b/audio2exp-service/LAM_Audio2Expression/models/losses/misc.py new file mode 100644 index 0000000..48e26bb --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/models/losses/misc.py @@ -0,0 +1,241 @@ +""" +The code is base on https://github.com/Pointcept/Pointcept +""" + +import torch +import torch.nn as nn +import torch.nn.functional as F +from .builder import LOSSES + + +@LOSSES.register_module() +class CrossEntropyLoss(nn.Module): + def __init__( + self, + weight=None, + size_average=None, + reduce=None, + reduction="mean", + label_smoothing=0.0, + loss_weight=1.0, + ignore_index=-1, + ): + super(CrossEntropyLoss, self).__init__() + weight = torch.tensor(weight).cuda() if weight is not None else None + self.loss_weight = loss_weight + self.loss = nn.CrossEntropyLoss( + weight=weight, + size_average=size_average, + ignore_index=ignore_index, + reduce=reduce, + reduction=reduction, + label_smoothing=label_smoothing, + ) + + def forward(self, pred, target): + return self.loss(pred, target) * self.loss_weight + + +@LOSSES.register_module() +class L1Loss(nn.Module): + def __init__( + self, + weight=None, + size_average=None, + reduce=None, + reduction="mean", + label_smoothing=0.0, + loss_weight=1.0, + ignore_index=-1, + ): + super(L1Loss, self).__init__() + weight = torch.tensor(weight).cuda() if weight is not None else None + self.loss_weight = loss_weight + self.loss = nn.L1Loss(reduction='mean') + + def forward(self, pred, target): + return self.loss(pred, target[:,None]) * self.loss_weight + + +@LOSSES.register_module() +class SmoothCELoss(nn.Module): + def __init__(self, smoothing_ratio=0.1): + super(SmoothCELoss, self).__init__() + self.smoothing_ratio = smoothing_ratio + + def forward(self, pred, target): + eps = self.smoothing_ratio + n_class = pred.size(1) + one_hot = torch.zeros_like(pred).scatter(1, target.view(-1, 1), 1) + one_hot = one_hot * (1 - eps) + (1 - one_hot) * eps / (n_class - 1) + log_prb = F.log_softmax(pred, dim=1) + loss = -(one_hot * log_prb).total(dim=1) + loss = loss[torch.isfinite(loss)].mean() + return loss + + +@LOSSES.register_module() +class BinaryFocalLoss(nn.Module): + def __init__(self, gamma=2.0, alpha=0.5, logits=True, reduce=True, loss_weight=1.0): + """Binary Focal Loss + ` + """ + super(BinaryFocalLoss, self).__init__() + assert 0 < alpha < 1 + self.gamma = gamma + self.alpha = alpha + self.logits = logits + self.reduce = reduce + self.loss_weight = loss_weight + + def forward(self, pred, target, **kwargs): + """Forward function. + Args: + pred (torch.Tensor): The prediction with shape (N) + target (torch.Tensor): The ground truth. If containing class + indices, shape (N) where each value is 0≤targets[i]≤1, If containing class probabilities, + same shape as the input. + Returns: + torch.Tensor: The calculated loss + """ + if self.logits: + bce = F.binary_cross_entropy_with_logits(pred, target, reduction="none") + else: + bce = F.binary_cross_entropy(pred, target, reduction="none") + pt = torch.exp(-bce) + alpha = self.alpha * target + (1 - self.alpha) * (1 - target) + focal_loss = alpha * (1 - pt) ** self.gamma * bce + + if self.reduce: + focal_loss = torch.mean(focal_loss) + return focal_loss * self.loss_weight + + +@LOSSES.register_module() +class FocalLoss(nn.Module): + def __init__( + self, gamma=2.0, alpha=0.5, reduction="mean", loss_weight=1.0, ignore_index=-1 + ): + """Focal Loss + ` + """ + super(FocalLoss, self).__init__() + assert reduction in ( + "mean", + "sum", + ), "AssertionError: reduction should be 'mean' or 'sum'" + assert isinstance( + alpha, (float, list) + ), "AssertionError: alpha should be of type float" + assert isinstance(gamma, float), "AssertionError: gamma should be of type float" + assert isinstance( + loss_weight, float + ), "AssertionError: loss_weight should be of type float" + assert isinstance(ignore_index, int), "ignore_index must be of type int" + self.gamma = gamma + self.alpha = alpha + self.reduction = reduction + self.loss_weight = loss_weight + self.ignore_index = ignore_index + + def forward(self, pred, target, **kwargs): + """Forward function. + Args: + pred (torch.Tensor): The prediction with shape (N, C) where C = number of classes. + target (torch.Tensor): The ground truth. If containing class + indices, shape (N) where each value is 0≤targets[i]≤C−1, If containing class probabilities, + same shape as the input. + Returns: + torch.Tensor: The calculated loss + """ + # [B, C, d_1, d_2, ..., d_k] -> [C, B, d_1, d_2, ..., d_k] + pred = pred.transpose(0, 1) + # [C, B, d_1, d_2, ..., d_k] -> [C, N] + pred = pred.reshape(pred.size(0), -1) + # [C, N] -> [N, C] + pred = pred.transpose(0, 1).contiguous() + # (B, d_1, d_2, ..., d_k) --> (B * d_1 * d_2 * ... * d_k,) + target = target.view(-1).contiguous() + assert pred.size(0) == target.size( + 0 + ), "The shape of pred doesn't match the shape of target" + valid_mask = target != self.ignore_index + target = target[valid_mask] + pred = pred[valid_mask] + + if len(target) == 0: + return 0.0 + + num_classes = pred.size(1) + target = F.one_hot(target, num_classes=num_classes) + + alpha = self.alpha + if isinstance(alpha, list): + alpha = pred.new_tensor(alpha) + pred_sigmoid = pred.sigmoid() + target = target.type_as(pred) + one_minus_pt = (1 - pred_sigmoid) * target + pred_sigmoid * (1 - target) + focal_weight = (alpha * target + (1 - alpha) * (1 - target)) * one_minus_pt.pow( + self.gamma + ) + + loss = ( + F.binary_cross_entropy_with_logits(pred, target, reduction="none") + * focal_weight + ) + if self.reduction == "mean": + loss = loss.mean() + elif self.reduction == "sum": + loss = loss.total() + return self.loss_weight * loss + + +@LOSSES.register_module() +class DiceLoss(nn.Module): + def __init__(self, smooth=1, exponent=2, loss_weight=1.0, ignore_index=-1): + """DiceLoss. + This loss is proposed in `V-Net: Fully Convolutional Neural Networks for + Volumetric Medical Image Segmentation `_. + """ + super(DiceLoss, self).__init__() + self.smooth = smooth + self.exponent = exponent + self.loss_weight = loss_weight + self.ignore_index = ignore_index + + def forward(self, pred, target, **kwargs): + # [B, C, d_1, d_2, ..., d_k] -> [C, B, d_1, d_2, ..., d_k] + pred = pred.transpose(0, 1) + # [C, B, d_1, d_2, ..., d_k] -> [C, N] + pred = pred.reshape(pred.size(0), -1) + # [C, N] -> [N, C] + pred = pred.transpose(0, 1).contiguous() + # (B, d_1, d_2, ..., d_k) --> (B * d_1 * d_2 * ... * d_k,) + target = target.view(-1).contiguous() + assert pred.size(0) == target.size( + 0 + ), "The shape of pred doesn't match the shape of target" + valid_mask = target != self.ignore_index + target = target[valid_mask] + pred = pred[valid_mask] + + pred = F.softmax(pred, dim=1) + num_classes = pred.shape[1] + target = F.one_hot( + torch.clamp(target.long(), 0, num_classes - 1), num_classes=num_classes + ) + + total_loss = 0 + for i in range(num_classes): + if i != self.ignore_index: + num = torch.sum(torch.mul(pred[:, i], target[:, i])) * 2 + self.smooth + den = ( + torch.sum( + pred[:, i].pow(self.exponent) + target[:, i].pow(self.exponent) + ) + + self.smooth + ) + dice_loss = 1 - num / den + total_loss += dice_loss + loss = total_loss / num_classes + return self.loss_weight * loss diff --git a/audio2exp-service/LAM_Audio2Expression/models/network.py b/audio2exp-service/LAM_Audio2Expression/models/network.py new file mode 100644 index 0000000..cdedbed --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/models/network.py @@ -0,0 +1,646 @@ +import math +import os.path + +import torch + +import torch.nn as nn +import torch.nn.functional as F +import torchaudio as ta + +from models.encoder.wav2vec import Wav2Vec2Model +from models.encoder.wavlm import WavLMModel + +from models.builder import MODELS + +from transformers.models.wav2vec2.configuration_wav2vec2 import Wav2Vec2Config + +@MODELS.register_module("Audio2Expression") +class Audio2Expression(nn.Module): + def __init__(self, + device: torch.device = None, + pretrained_encoder_type: str = 'wav2vec', + pretrained_encoder_path: str = '', + wav2vec2_config_path: str = '', + num_identity_classes: int = 0, + identity_feat_dim: int = 64, + hidden_dim: int = 512, + expression_dim: int = 52, + norm_type: str = 'ln', + decoder_depth: int = 3, + use_transformer: bool = False, + num_attention_heads: int = 8, + num_transformer_layers: int = 6, + ): + super().__init__() + + self.device = device + + # Initialize audio feature encoder + if pretrained_encoder_type == 'wav2vec': + if os.path.exists(pretrained_encoder_path): + self.audio_encoder = Wav2Vec2Model.from_pretrained(pretrained_encoder_path) + else: + config = Wav2Vec2Config.from_pretrained(wav2vec2_config_path) + self.audio_encoder = Wav2Vec2Model(config) + encoder_output_dim = 768 + elif pretrained_encoder_type == 'wavlm': + self.audio_encoder = WavLMModel.from_pretrained(pretrained_encoder_path) + encoder_output_dim = 768 + else: + raise NotImplementedError(f"Encoder type {pretrained_encoder_type} not supported") + + self.audio_encoder.feature_extractor._freeze_parameters() + self.feature_projection = nn.Linear(encoder_output_dim, hidden_dim) + + self.identity_encoder = AudioIdentityEncoder( + hidden_dim, + num_identity_classes, + identity_feat_dim, + use_transformer, + num_attention_heads, + num_transformer_layers + ) + + self.decoder = nn.ModuleList([ + nn.Sequential(*[ + ConvNormRelu(hidden_dim, hidden_dim, norm=norm_type) + for _ in range(decoder_depth) + ]) + ]) + + self.output_proj = nn.Linear(hidden_dim, expression_dim) + + def freeze_encoder_parameters(self, do_freeze=False): + + for name, param in self.audio_encoder.named_parameters(): + if('feature_extractor' in name): + param.requires_grad = False + else: + param.requires_grad = (not do_freeze) + + def forward(self, input_dict): + + if 'time_steps' not in input_dict: + audio_length = input_dict['input_audio_array'].shape[1] + time_steps = math.ceil(audio_length / 16000 * 30) + else: + time_steps = input_dict['time_steps'] + + # Process audio through encoder + audio_input = input_dict['input_audio_array'].flatten(start_dim=1) + hidden_states = self.audio_encoder(audio_input, frame_num=time_steps).last_hidden_state + + # Project features to hidden dimension + audio_features = self.feature_projection(hidden_states).transpose(1, 2) + + # Process identity-conditioned features + audio_features = self.identity_encoder(audio_features, identity=input_dict['id_idx']) + + # Refine features through decoder + audio_features = self.decoder[0](audio_features) + + # Generate output parameters + audio_features = audio_features.permute(0, 2, 1) + expression_params = self.output_proj(audio_features) + + return torch.sigmoid(expression_params) + + +class AudioIdentityEncoder(nn.Module): + def __init__(self, + hidden_dim, + num_identity_classes=0, + identity_feat_dim=64, + use_transformer=False, + num_attention_heads = 8, + num_transformer_layers = 6, + dropout_ratio=0.1, + ): + super().__init__() + + in_dim = hidden_dim + identity_feat_dim + self.id_mlp = nn.Conv1d(num_identity_classes, identity_feat_dim, 1, 1) + self.first_net = SeqTranslator1D(in_dim, hidden_dim, + min_layers_num=3, + residual=True, + norm='ln' + ) + self.grus = nn.GRU(hidden_dim, hidden_dim, 1, batch_first=True) + self.dropout = nn.Dropout(dropout_ratio) + + self.use_transformer = use_transformer + if(self.use_transformer): + encoder_layer = nn.TransformerEncoderLayer(d_model=hidden_dim, nhead=num_attention_heads, dim_feedforward= 2 * hidden_dim, batch_first=True) + self.transformer_encoder = nn.TransformerEncoder(encoder_layer, num_layers=num_transformer_layers) + + def forward(self, + audio_features: torch.Tensor, + identity: torch.Tensor = None, + time_steps: int = None) -> tuple: + + audio_features = self.dropout(audio_features) + identity = identity.reshape(identity.shape[0], -1, 1).repeat(1, 1, audio_features.shape[2]).to(torch.float32) + identity = self.id_mlp(identity) + audio_features = torch.cat([audio_features, identity], dim=1) + + x = self.first_net(audio_features) + + if time_steps is not None: + x = F.interpolate(x, size=time_steps, align_corners=False, mode='linear') + + if(self.use_transformer): + x = x.permute(0, 2, 1) + x = self.transformer_encoder(x) + x = x.permute(0, 2, 1) + + return x + +class ConvNormRelu(nn.Module): + ''' + (B,C_in,H,W) -> (B, C_out, H, W) + there exist some kernel size that makes the result is not H/s + ''' + + def __init__(self, + in_channels, + out_channels, + type='1d', + leaky=False, + downsample=False, + kernel_size=None, + stride=None, + padding=None, + p=0, + groups=1, + residual=False, + norm='bn'): + ''' + conv-bn-relu + ''' + super(ConvNormRelu, self).__init__() + self.residual = residual + self.norm_type = norm + # kernel_size = k + # stride = s + + if kernel_size is None and stride is None: + if not downsample: + kernel_size = 3 + stride = 1 + else: + kernel_size = 4 + stride = 2 + + if padding is None: + if isinstance(kernel_size, int) and isinstance(stride, tuple): + padding = tuple(int((kernel_size - st) / 2) for st in stride) + elif isinstance(kernel_size, tuple) and isinstance(stride, int): + padding = tuple(int((ks - stride) / 2) for ks in kernel_size) + elif isinstance(kernel_size, tuple) and isinstance(stride, tuple): + padding = tuple(int((ks - st) / 2) for ks, st in zip(kernel_size, stride)) + else: + padding = int((kernel_size - stride) / 2) + + if self.residual: + if downsample: + if type == '1d': + self.residual_layer = nn.Sequential( + nn.Conv1d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding + ) + ) + elif type == '2d': + self.residual_layer = nn.Sequential( + nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding + ) + ) + else: + if in_channels == out_channels: + self.residual_layer = nn.Identity() + else: + if type == '1d': + self.residual_layer = nn.Sequential( + nn.Conv1d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding + ) + ) + elif type == '2d': + self.residual_layer = nn.Sequential( + nn.Conv2d( + in_channels=in_channels, + out_channels=out_channels, + kernel_size=kernel_size, + stride=stride, + padding=padding + ) + ) + + in_channels = in_channels * groups + out_channels = out_channels * groups + if type == '1d': + self.conv = nn.Conv1d(in_channels=in_channels, out_channels=out_channels, + kernel_size=kernel_size, stride=stride, padding=padding, + groups=groups) + self.norm = nn.BatchNorm1d(out_channels) + self.dropout = nn.Dropout(p=p) + elif type == '2d': + self.conv = nn.Conv2d(in_channels=in_channels, out_channels=out_channels, + kernel_size=kernel_size, stride=stride, padding=padding, + groups=groups) + self.norm = nn.BatchNorm2d(out_channels) + self.dropout = nn.Dropout2d(p=p) + if norm == 'gn': + self.norm = nn.GroupNorm(2, out_channels) + elif norm == 'ln': + self.norm = nn.LayerNorm(out_channels) + if leaky: + self.relu = nn.LeakyReLU(negative_slope=0.2) + else: + self.relu = nn.ReLU() + + def forward(self, x, **kwargs): + if self.norm_type == 'ln': + out = self.dropout(self.conv(x)) + out = self.norm(out.transpose(1,2)).transpose(1,2) + else: + out = self.norm(self.dropout(self.conv(x))) + if self.residual: + residual = self.residual_layer(x) + out += residual + return self.relu(out) + +""" from https://github.com/ai4r/Gesture-Generation-from-Trimodal-Context.git """ +class SeqTranslator1D(nn.Module): + ''' + (B, C, T)->(B, C_out, T) + ''' + def __init__(self, + C_in, + C_out, + kernel_size=None, + stride=None, + min_layers_num=None, + residual=True, + norm='bn' + ): + super(SeqTranslator1D, self).__init__() + + conv_layers = nn.ModuleList([]) + conv_layers.append(ConvNormRelu( + in_channels=C_in, + out_channels=C_out, + type='1d', + kernel_size=kernel_size, + stride=stride, + residual=residual, + norm=norm + )) + self.num_layers = 1 + if min_layers_num is not None and self.num_layers < min_layers_num: + while self.num_layers < min_layers_num: + conv_layers.append(ConvNormRelu( + in_channels=C_out, + out_channels=C_out, + type='1d', + kernel_size=kernel_size, + stride=stride, + residual=residual, + norm=norm + )) + self.num_layers += 1 + self.conv_layers = nn.Sequential(*conv_layers) + + def forward(self, x): + return self.conv_layers(x) + + +def audio_chunking(audio: torch.Tensor, frame_rate: int = 30, chunk_size: int = 16000): + """ + :param audio: 1 x T tensor containing a 16kHz audio signal + :param frame_rate: frame rate for video (we need one audio chunk per video frame) + :param chunk_size: number of audio samples per chunk + :return: num_chunks x chunk_size tensor containing sliced audio + """ + samples_per_frame = 16000 // frame_rate + padding = (chunk_size - samples_per_frame) // 2 + audio = torch.nn.functional.pad(audio.unsqueeze(0), pad=[padding, padding]).squeeze(0) + anchor_points = list(range(chunk_size//2, audio.shape[-1]-chunk_size//2, samples_per_frame)) + audio = torch.cat([audio[:, i-chunk_size//2:i+chunk_size//2] for i in anchor_points], dim=0) + return audio + +""" https://github.com/facebookresearch/meshtalk """ +class MeshtalkEncoder(nn.Module): + def __init__(self, latent_dim: int = 128, model_name: str = 'audio_encoder'): + """ + :param latent_dim: size of the latent audio embedding + :param model_name: name of the model, used to load and save the model + """ + super().__init__() + + self.melspec = ta.transforms.MelSpectrogram( + sample_rate=16000, n_fft=2048, win_length=800, hop_length=160, n_mels=80 + ) + + conv_len = 5 + self.convert_dimensions = torch.nn.Conv1d(80, 128, kernel_size=conv_len) + self.weights_init(self.convert_dimensions) + self.receptive_field = conv_len + + convs = [] + for i in range(6): + dilation = 2 * (i % 3 + 1) + self.receptive_field += (conv_len - 1) * dilation + convs += [torch.nn.Conv1d(128, 128, kernel_size=conv_len, dilation=dilation)] + self.weights_init(convs[-1]) + self.convs = torch.nn.ModuleList(convs) + self.code = torch.nn.Linear(128, latent_dim) + + self.apply(lambda x: self.weights_init(x)) + + def weights_init(self, m): + if isinstance(m, torch.nn.Conv1d): + torch.nn.init.xavier_uniform_(m.weight) + try: + torch.nn.init.constant_(m.bias, .01) + except: + pass + + def forward(self, audio: torch.Tensor): + """ + :param audio: B x T x 16000 Tensor containing 1 sec of audio centered around the current time frame + :return: code: B x T x latent_dim Tensor containing a latent audio code/embedding + """ + B, T = audio.shape[0], audio.shape[1] + x = self.melspec(audio).squeeze(1) + x = torch.log(x.clamp(min=1e-10, max=None)) + if T == 1: + x = x.unsqueeze(1) + + # Convert to the right dimensionality + x = x.view(-1, x.shape[2], x.shape[3]) + x = F.leaky_relu(self.convert_dimensions(x), .2) + + # Process stacks + for conv in self.convs: + x_ = F.leaky_relu(conv(x), .2) + if self.training: + x_ = F.dropout(x_, .2) + l = (x.shape[2] - x_.shape[2]) // 2 + x = (x[:, :, l:-l] + x_) / 2 + + x = torch.mean(x, dim=-1) + x = x.view(B, T, x.shape[-1]) + x = self.code(x) + + return {"code": x} + +class PeriodicPositionalEncoding(nn.Module): + def __init__(self, d_model, dropout=0.1, period=15, max_seq_len=64): + super(PeriodicPositionalEncoding, self).__init__() + self.dropout = nn.Dropout(p=dropout) + pe = torch.zeros(period, d_model) + position = torch.arange(0, period, dtype=torch.float).unsqueeze(1) + div_term = torch.exp(torch.arange(0, d_model, 2).float() * (-math.log(10000.0) / d_model)) + pe[:, 0::2] = torch.sin(position * div_term) + pe[:, 1::2] = torch.cos(position * div_term) + pe = pe.unsqueeze(0) # (1, period, d_model) + repeat_num = (max_seq_len//period) + 1 + pe = pe.repeat(1, repeat_num, 1) # (1, repeat_num, period, d_model) + self.register_buffer('pe', pe) + def forward(self, x): + # print(self.pe.shape, x.shape) + x = x + self.pe[:, :x.size(1), :] + return self.dropout(x) + + +class GeneratorTransformer(nn.Module): + def __init__(self, + n_poses, + each_dim: list, + dim_list: list, + training=True, + device=None, + identity=False, + num_classes=0, + ): + super().__init__() + + self.training = training + self.device = device + self.gen_length = n_poses + + norm = 'ln' + in_dim = 256 + out_dim = 256 + + self.encoder_choice = 'faceformer' + + self.audio_encoder = Wav2Vec2Model.from_pretrained("facebook/wav2vec2-base-960h") # "vitouphy/wav2vec2-xls-r-300m-phoneme""facebook/wav2vec2-base-960h" + self.audio_encoder.feature_extractor._freeze_parameters() + self.audio_feature_map = nn.Linear(768, in_dim) + + self.audio_middle = AudioEncoder(in_dim, out_dim, False, num_classes) + + self.dim_list = dim_list + + self.decoder = nn.ModuleList() + self.final_out = nn.ModuleList() + + self.hidden_size = 768 + self.transformer_de_layer = nn.TransformerDecoderLayer( + d_model=self.hidden_size, + nhead=4, + dim_feedforward=self.hidden_size*2, + batch_first=True + ) + self.face_decoder = nn.TransformerDecoder(self.transformer_de_layer, num_layers=4) + self.feature2face = nn.Linear(256, self.hidden_size) + + self.position_embeddings = PeriodicPositionalEncoding(self.hidden_size, period=64, max_seq_len=64) + self.id_maping = nn.Linear(12,self.hidden_size) + + + self.decoder.append(self.face_decoder) + self.final_out.append(nn.Linear(self.hidden_size, 32)) + + def forward(self, in_spec, gt_poses=None, id=None, pre_state=None, time_steps=None): + if gt_poses is None: + time_steps = 64 + else: + time_steps = gt_poses.shape[1] + + # vector, hidden_state = self.audio_encoder(in_spec, pre_state, time_steps=time_steps) + if self.encoder_choice == 'meshtalk': + in_spec = audio_chunking(in_spec.squeeze(-1), frame_rate=30, chunk_size=16000) + feature = self.audio_encoder(in_spec.unsqueeze(0))["code"].transpose(1, 2) + elif self.encoder_choice == 'faceformer': + hidden_states = self.audio_encoder(in_spec.reshape(in_spec.shape[0], -1), frame_num=time_steps).last_hidden_state + feature = self.audio_feature_map(hidden_states).transpose(1, 2) + else: + feature, hidden_state = self.audio_encoder(in_spec, pre_state, time_steps=time_steps) + + feature, _ = self.audio_middle(feature, id=None) + feature = self.feature2face(feature.permute(0,2,1)) + + id = id.unsqueeze(1).repeat(1,64,1).to(torch.float32) + id_feature = self.id_maping(id) + id_feature = self.position_embeddings(id_feature) + + for i in range(self.decoder.__len__()): + mid = self.decoder[i](tgt=id_feature, memory=feature) + out = self.final_out[i](mid) + + return out, None + +def linear_interpolation(features, output_len: int): + features = features.transpose(1, 2) + output_features = F.interpolate( + features, size=output_len, align_corners=True, mode='linear') + return output_features.transpose(1, 2) + +def init_biased_mask(n_head, max_seq_len, period): + + def get_slopes(n): + + def get_slopes_power_of_2(n): + start = (2**(-2**-(math.log2(n) - 3))) + ratio = start + return [start * ratio**i for i in range(n)] + + if math.log2(n).is_integer(): + return get_slopes_power_of_2(n) + else: + closest_power_of_2 = 2**math.floor(math.log2(n)) + return get_slopes_power_of_2(closest_power_of_2) + get_slopes( + 2 * closest_power_of_2)[0::2][:n - closest_power_of_2] + + slopes = torch.Tensor(get_slopes(n_head)) + bias = torch.div( + torch.arange(start=0, end=max_seq_len, + step=period).unsqueeze(1).repeat(1, period).view(-1), + period, + rounding_mode='floor') + bias = -torch.flip(bias, dims=[0]) + alibi = torch.zeros(max_seq_len, max_seq_len) + for i in range(max_seq_len): + alibi[i, :i + 1] = bias[-(i + 1):] + alibi = slopes.unsqueeze(1).unsqueeze(1) * alibi.unsqueeze(0) + mask = (torch.triu(torch.ones(max_seq_len, + max_seq_len)) == 1).transpose(0, 1) + mask = mask.float().masked_fill(mask == 0, float('-inf')).masked_fill( + mask == 1, float(0.0)) + mask = mask.unsqueeze(0) + alibi + return mask + + +# Alignment Bias +def enc_dec_mask(device, T, S): + mask = torch.ones(T, S) + for i in range(T): + mask[i, i] = 0 + return (mask == 1).to(device=device) + + +# Periodic Positional Encoding +class PeriodicPositionalEncoding(nn.Module): + + def __init__(self, d_model, dropout=0.1, period=25, max_seq_len=3000): + super(PeriodicPositionalEncoding, self).__init__() + self.dropout = nn.Dropout(p=dropout) + pe = torch.zeros(period, d_model) + position = torch.arange(0, period, dtype=torch.float).unsqueeze(1) + div_term = torch.exp( + torch.arange(0, d_model, 2).float() * + (-math.log(10000.0) / d_model)) + pe[:, 0::2] = torch.sin(position * div_term) + pe[:, 1::2] = torch.cos(position * div_term) + pe = pe.unsqueeze(0) # (1, period, d_model) + repeat_num = (max_seq_len // period) + 1 + pe = pe.repeat(1, repeat_num, 1) + self.register_buffer('pe', pe) + + def forward(self, x): + x = x + self.pe[:, :x.size(1), :] + return self.dropout(x) + + +class BaseModel(nn.Module): + """Base class for all models.""" + + def __init__(self): + super(BaseModel, self).__init__() + # self.logger = logging.getLogger(self.__class__.__name__) + + def forward(self, *x): + """Forward pass logic. + + :return: Model output + """ + raise NotImplementedError + + def freeze_model(self, do_freeze: bool = True): + for param in self.parameters(): + param.requires_grad = (not do_freeze) + + def summary(self, logger, writer=None): + """Model summary.""" + model_parameters = filter(lambda p: p.requires_grad, self.parameters()) + params = sum([np.prod(p.size()) + for p in model_parameters]) / 1e6 # Unit is Mega + logger.info('===>Trainable parameters: %.3f M' % params) + if writer is not None: + writer.add_text('Model Summary', + 'Trainable parameters: %.3f M' % params) + + +"""https://github.com/X-niper/UniTalker""" +class UniTalkerDecoderTransformer(BaseModel): + + def __init__(self, out_dim, identity_num, period=30, interpolate_pos=1) -> None: + super().__init__() + self.learnable_style_emb = nn.Embedding(identity_num, out_dim) + self.PPE = PeriodicPositionalEncoding( + out_dim, period=period, max_seq_len=3000) + self.biased_mask = init_biased_mask( + n_head=4, max_seq_len=3000, period=period) + decoder_layer = nn.TransformerDecoderLayer( + d_model=out_dim, + nhead=4, + dim_feedforward=2 * out_dim, + batch_first=True) + self.transformer_decoder = nn.TransformerDecoder( + decoder_layer, num_layers=1) + self.interpolate_pos = interpolate_pos + + def forward(self, hidden_states: torch.Tensor, style_idx: torch.Tensor, + frame_num: int): + style_idx = torch.argmax(style_idx, dim=1) + obj_embedding = self.learnable_style_emb(style_idx) + obj_embedding = obj_embedding.unsqueeze(1).repeat(1, frame_num, 1) + style_input = self.PPE(obj_embedding) + tgt_mask = self.biased_mask.repeat(style_idx.shape[0], 1, 1)[:, :style_input.shape[1], :style_input. + shape[1]].clone().detach().to( + device=style_input.device) + memory_mask = enc_dec_mask(hidden_states.device, style_input.shape[1], + frame_num) + feat_out = self.transformer_decoder( + style_input, + hidden_states, + tgt_mask=tgt_mask, + memory_mask=memory_mask) + if self.interpolate_pos == 2: + feat_out = linear_interpolation(feat_out, output_len=frame_num) + return feat_out \ No newline at end of file diff --git a/audio2exp-service/LAM_Audio2Expression/models/utils.py b/audio2exp-service/LAM_Audio2Expression/models/utils.py new file mode 100644 index 0000000..4b15130 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/models/utils.py @@ -0,0 +1,752 @@ +import json +import time +import warnings +import numpy as np +from typing import List, Optional,Tuple +from scipy.signal import savgol_filter + + +ARKitLeftRightPair = [ + ("jawLeft", "jawRight"), + ("mouthLeft", "mouthRight"), + ("mouthSmileLeft", "mouthSmileRight"), + ("mouthFrownLeft", "mouthFrownRight"), + ("mouthDimpleLeft", "mouthDimpleRight"), + ("mouthStretchLeft", "mouthStretchRight"), + ("mouthPressLeft", "mouthPressRight"), + ("mouthLowerDownLeft", "mouthLowerDownRight"), + ("mouthUpperUpLeft", "mouthUpperUpRight"), + ("cheekSquintLeft", "cheekSquintRight"), + ("noseSneerLeft", "noseSneerRight"), + ("browDownLeft", "browDownRight"), + ("browOuterUpLeft", "browOuterUpRight"), + ("eyeBlinkLeft","eyeBlinkRight"), + ("eyeLookDownLeft","eyeLookDownRight"), + ("eyeLookInLeft", "eyeLookInRight"), + ("eyeLookOutLeft","eyeLookOutRight"), + ("eyeLookUpLeft","eyeLookUpRight"), + ("eyeSquintLeft","eyeSquintRight"), + ("eyeWideLeft","eyeWideRight") + ] + +ARKitBlendShape =[ + "browDownLeft", + "browDownRight", + "browInnerUp", + "browOuterUpLeft", + "browOuterUpRight", + "cheekPuff", + "cheekSquintLeft", + "cheekSquintRight", + "eyeBlinkLeft", + "eyeBlinkRight", + "eyeLookDownLeft", + "eyeLookDownRight", + "eyeLookInLeft", + "eyeLookInRight", + "eyeLookOutLeft", + "eyeLookOutRight", + "eyeLookUpLeft", + "eyeLookUpRight", + "eyeSquintLeft", + "eyeSquintRight", + "eyeWideLeft", + "eyeWideRight", + "jawForward", + "jawLeft", + "jawOpen", + "jawRight", + "mouthClose", + "mouthDimpleLeft", + "mouthDimpleRight", + "mouthFrownLeft", + "mouthFrownRight", + "mouthFunnel", + "mouthLeft", + "mouthLowerDownLeft", + "mouthLowerDownRight", + "mouthPressLeft", + "mouthPressRight", + "mouthPucker", + "mouthRight", + "mouthRollLower", + "mouthRollUpper", + "mouthShrugLower", + "mouthShrugUpper", + "mouthSmileLeft", + "mouthSmileRight", + "mouthStretchLeft", + "mouthStretchRight", + "mouthUpperUpLeft", + "mouthUpperUpRight", + "noseSneerLeft", + "noseSneerRight", + "tongueOut" +] + +MOUTH_BLENDSHAPES = [ "mouthDimpleLeft", + "mouthDimpleRight", + "mouthFrownLeft", + "mouthFrownRight", + "mouthFunnel", + "mouthLeft", + "mouthLowerDownLeft", + "mouthLowerDownRight", + "mouthPressLeft", + "mouthPressRight", + "mouthPucker", + "mouthRight", + "mouthRollLower", + "mouthRollUpper", + "mouthShrugLower", + "mouthShrugUpper", + "mouthSmileLeft", + "mouthSmileRight", + "mouthStretchLeft", + "mouthStretchRight", + "mouthUpperUpLeft", + "mouthUpperUpRight", + "jawForward", + "jawLeft", + "jawOpen", + "jawRight", + "noseSneerLeft", + "noseSneerRight", + "cheekPuff", + ] + +DEFAULT_CONTEXT ={ + 'is_initial_input': True, + 'previous_audio': None, + 'previous_expression': None, + 'previous_volume': None, + 'previous_headpose': None, +} + +RETURN_CODE = { + "SUCCESS": 0, + "AUDIO_LENGTH_ERROR": 1, + "CHECKPOINT_PATH_ERROR":2, + "MODEL_INFERENCE_ERROR":3, +} + +DEFAULT_CONTEXTRETURN = { + "code": RETURN_CODE['SUCCESS'], + "expression": None, + "headpose": None, +} + +BLINK_PATTERNS = [ + np.array([0.365, 0.950, 0.956, 0.917, 0.367, 0.119, 0.025]), + np.array([0.235, 0.910, 0.945, 0.778, 0.191, 0.235, 0.089]), + np.array([0.870, 0.950, 0.949, 0.696, 0.191, 0.073, 0.007]), + np.array([0.000, 0.557, 0.953, 0.942, 0.426, 0.148, 0.018]) +] + +# Postprocess +def symmetrize_blendshapes( + bs_params: np.ndarray, + mode: str = "average", + symmetric_pairs: list = ARKitLeftRightPair +) -> np.ndarray: + """ + Apply symmetrization to ARKit blendshape parameters (batched version) + + Args: + bs_params: numpy array of shape (N, 52), batch of ARKit parameters + mode: symmetrization mode ["average", "max", "min", "left_dominant", "right_dominant"] + symmetric_pairs: list of left-right parameter pairs + + Returns: + Symmetrized parameters with same shape (N, 52) + """ + + name_to_idx = {name: i for i, name in enumerate(ARKitBlendShape)} + + # Input validation + if bs_params.ndim != 2 or bs_params.shape[1] != 52: + raise ValueError("Input must be of shape (N, 52)") + + symmetric_bs = bs_params.copy() # Shape (N, 52) + + # Precompute valid index pairs + valid_pairs = [] + for left, right in symmetric_pairs: + left_idx = name_to_idx.get(left) + right_idx = name_to_idx.get(right) + if None not in (left_idx, right_idx): + valid_pairs.append((left_idx, right_idx)) + + # Vectorized processing + for l_idx, r_idx in valid_pairs: + left_col = symmetric_bs[:, l_idx] + right_col = symmetric_bs[:, r_idx] + + if mode == "average": + new_vals = (left_col + right_col) / 2 + elif mode == "max": + new_vals = np.maximum(left_col, right_col) + elif mode == "min": + new_vals = np.minimum(left_col, right_col) + elif mode == "left_dominant": + new_vals = left_col + elif mode == "right_dominant": + new_vals = right_col + else: + raise ValueError(f"Invalid mode: {mode}") + + # Update both columns simultaneously + symmetric_bs[:, l_idx] = new_vals + symmetric_bs[:, r_idx] = new_vals + + return symmetric_bs + + +def apply_random_eye_blinks( + input: np.ndarray, + blink_scale: tuple = (0.8, 1.0), + blink_interval: tuple = (60, 120), + blink_duration: int = 7 +) -> np.ndarray: + """ + Apply randomized eye blinks to blendshape parameters + + Args: + output: Input array of shape (N, 52) containing blendshape parameters + blink_scale: Tuple (min, max) for random blink intensity scaling + blink_interval: Tuple (min, max) for random blink spacing in frames + blink_duration: Number of frames for blink animation (fixed) + + Returns: + None (modifies output array in-place) + """ + # Define eye blink patterns (normalized 0-1) + + # Initialize parameters + n_frames = input.shape[0] + input[:,8:10] = np.zeros((n_frames,2)) + current_frame = 0 + + # Main blink application loop + while current_frame < n_frames - blink_duration: + # Randomize blink parameters + scale = np.random.uniform(*blink_scale) + pattern = BLINK_PATTERNS[np.random.randint(0, 4)] + + # Apply blink animation + blink_values = pattern * scale + input[current_frame:current_frame + blink_duration, 8] = blink_values + input[current_frame:current_frame + blink_duration, 9] = blink_values + + # Advance to next blink position + current_frame += blink_duration + np.random.randint(*blink_interval) + + return input + + +def apply_random_eye_blinks_context( + animation_params: np.ndarray, + processed_frames: int = 0, + intensity_range: tuple = (0.8, 1.0) +) -> np.ndarray: + """Applies random eye blink patterns to facial animation parameters. + + Args: + animation_params: Input facial animation parameters array with shape [num_frames, num_features]. + Columns 8 and 9 typically represent left/right eye blink parameters. + processed_frames: Number of already processed frames that shouldn't be modified + intensity_range: Tuple defining (min, max) scaling for blink intensity + + Returns: + Modified animation parameters array with random eye blinks added to unprocessed frames + """ + remaining_frames = animation_params.shape[0] - processed_frames + + # Only apply blinks if there's enough remaining frames (blink pattern requires 7 frames) + if remaining_frames <= 7: + return animation_params + + # Configure blink timing parameters + min_blink_interval = 40 # Minimum frames between blinks + max_blink_interval = 100 # Maximum frames between blinks + + # Find last blink in previously processed frames (column 8 > 0.5 indicates blink) + previous_blink_indices = np.where(animation_params[:processed_frames, 8] > 0.5)[0] + last_processed_blink = previous_blink_indices[-1] - 7 if previous_blink_indices.size > 0 else processed_frames + + # Calculate first new blink position + blink_interval = np.random.randint(min_blink_interval, max_blink_interval) + first_blink_start = max(0, blink_interval - last_processed_blink) + + # Apply first blink if there's enough space + if first_blink_start <= (remaining_frames - 7): + # Randomly select blink pattern and intensity + blink_pattern = BLINK_PATTERNS[np.random.randint(0, 4)] + intensity = np.random.uniform(*intensity_range) + + # Calculate blink frame range + blink_start = processed_frames + first_blink_start + blink_end = blink_start + 7 + + # Apply pattern to both eyes + animation_params[blink_start:blink_end, 8] = blink_pattern * intensity + animation_params[blink_start:blink_end, 9] = blink_pattern * intensity + + # Check space for additional blink + remaining_after_blink = animation_params.shape[0] - blink_end + if remaining_after_blink > min_blink_interval: + # Calculate second blink position + second_intensity = np.random.uniform(*intensity_range) + second_interval = np.random.randint(min_blink_interval, max_blink_interval) + + if (remaining_after_blink - 7) > second_interval: + second_pattern = BLINK_PATTERNS[np.random.randint(0, 4)] + second_blink_start = blink_end + second_interval + second_blink_end = second_blink_start + 7 + + # Apply second blink + animation_params[second_blink_start:second_blink_end, 8] = second_pattern * second_intensity + animation_params[second_blink_start:second_blink_end, 9] = second_pattern * second_intensity + + return animation_params + + +def export_blendshape_animation( + blendshape_weights: np.ndarray, + output_path: str, + blendshape_names: List[str], + fps: float, + rotation_data: Optional[np.ndarray] = None +) -> None: + """ + Export blendshape animation data to JSON format compatible with ARKit. + + Args: + blendshape_weights: 2D numpy array of shape (N, 52) containing animation frames + output_path: Full path for output JSON file (including .json extension) + blendshape_names: Ordered list of 52 ARKit-standard blendshape names + fps: Frame rate for timing calculations (frames per second) + rotation_data: Optional 3D rotation data array of shape (N, 3) + + Raises: + ValueError: If input dimensions are incompatible + IOError: If file writing fails + """ + # Validate input dimensions + if blendshape_weights.shape[1] != 52: + raise ValueError(f"Expected 52 blendshapes, got {blendshape_weights.shape[1]}") + if len(blendshape_names) != 52: + raise ValueError(f"Requires 52 blendshape names, got {len(blendshape_names)}") + if rotation_data is not None and len(rotation_data) != len(blendshape_weights): + raise ValueError("Rotation data length must match animation frames") + + # Build animation data structure + animation_data = { + "names":blendshape_names, + "metadata": { + "fps": fps, + "frame_count": len(blendshape_weights), + "blendshape_names": blendshape_names + }, + "frames": [] + } + + # Convert numpy array to serializable format + for frame_idx in range(blendshape_weights.shape[0]): + frame_data = { + "weights": blendshape_weights[frame_idx].tolist(), + "time": frame_idx / fps, + "rotation": rotation_data[frame_idx].tolist() if rotation_data else [] + } + animation_data["frames"].append(frame_data) + + # Safeguard against data loss + if not output_path.endswith('.json'): + output_path += '.json' + + # Write to file with error handling + try: + with open(output_path, 'w', encoding='utf-8') as json_file: + json.dump(animation_data, json_file, indent=2, ensure_ascii=False) + except Exception as e: + raise IOError(f"Failed to write animation data: {str(e)}") from e + + +def apply_savitzky_golay_smoothing( + input_data: np.ndarray, + window_length: int = 5, + polyorder: int = 2, + axis: int = 0, + validate: bool = True +) -> Tuple[np.ndarray, Optional[float]]: + """ + Apply Savitzky-Golay filter smoothing along specified axis of input data. + + Args: + input_data: 2D numpy array of shape (n_samples, n_features) + window_length: Length of the filter window (must be odd and > polyorder) + polyorder: Order of the polynomial fit + axis: Axis along which to filter (0: column-wise, 1: row-wise) + validate: Enable input validation checks when True + + Returns: + tuple: (smoothed_data, processing_time) + - smoothed_data: Smoothed output array + - processing_time: Execution time in seconds (None in validation mode) + + Raises: + ValueError: For invalid input dimensions or filter parameters + """ + # Validation mode timing bypass + processing_time = None + + if validate: + # Input integrity checks + if input_data.ndim != 2: + raise ValueError(f"Expected 2D input, got {input_data.ndim}D array") + + if window_length % 2 == 0 or window_length < 3: + raise ValueError("Window length must be odd integer ≥ 3") + + if polyorder >= window_length: + raise ValueError("Polynomial order must be < window length") + + # Store original dtype and convert to float64 for numerical stability + original_dtype = input_data.dtype + working_data = input_data.astype(np.float64) + + # Start performance timer + timer_start = time.perf_counter() + + try: + # Vectorized Savitzky-Golay application + smoothed_data = savgol_filter(working_data, + window_length=window_length, + polyorder=polyorder, + axis=axis, + mode='mirror') + except Exception as e: + raise RuntimeError(f"Filtering failed: {str(e)}") from e + + # Stop timer and calculate duration + processing_time = time.perf_counter() - timer_start + + # Restore original data type with overflow protection + return ( + np.clip(smoothed_data, + 0.0, + 1.0 + ).astype(original_dtype), + processing_time + ) + + +def _blend_region_start( + array: np.ndarray, + region: np.ndarray, + processed_boundary: int, + blend_frames: int +) -> None: + """Applies linear blend between last active frame and silent region start.""" + blend_length = min(blend_frames, region[0] - processed_boundary) + if blend_length <= 0: + return + + pre_frame = array[region[0] - 1] + for i in range(blend_length): + weight = (i + 1) / (blend_length + 1) + array[region[0] + i] = pre_frame * (1 - weight) + array[region[0] + i] * weight + +def _blend_region_end( + array: np.ndarray, + region: np.ndarray, + blend_frames: int +) -> None: + """Applies linear blend between silent region end and next active frame.""" + blend_length = min(blend_frames, array.shape[0] - region[-1] - 1) + if blend_length <= 0: + return + + post_frame = array[region[-1] + 1] + for i in range(blend_length): + weight = (i + 1) / (blend_length + 1) + array[region[-1] - i] = post_frame * (1 - weight) + array[region[-1] - i] * weight + +def find_low_value_regions( + signal: np.ndarray, + threshold: float, + min_region_length: int = 5 +) -> list: + """Identifies contiguous regions in a signal where values fall below a threshold. + + Args: + signal: Input 1D array of numerical values + threshold: Value threshold for identifying low regions + min_region_length: Minimum consecutive samples required to qualify as a region + + Returns: + List of numpy arrays, each containing indices for a qualifying low-value region + """ + low_value_indices = np.where(signal < threshold)[0] + contiguous_regions = [] + current_region_length = 0 + region_start_idx = 0 + + for i in range(1, len(low_value_indices)): + # Check if current index continues a consecutive sequence + if low_value_indices[i] != low_value_indices[i - 1] + 1: + # Finalize previous region if it meets length requirement + if current_region_length >= min_region_length: + contiguous_regions.append(low_value_indices[region_start_idx:i]) + # Reset tracking for new potential region + region_start_idx = i + current_region_length = 0 + current_region_length += 1 + + # Add the final region if it qualifies + if current_region_length >= min_region_length: + contiguous_regions.append(low_value_indices[region_start_idx:]) + + return contiguous_regions + + +def smooth_mouth_movements( + blend_shapes: np.ndarray, + processed_frames: int, + volume: np.ndarray = None, + silence_threshold: float = 0.001, + min_silence_duration: int = 7, + blend_window: int = 3 +) -> np.ndarray: + """Reduces jaw movement artifacts during silent periods in audio-driven animation. + + Args: + blend_shapes: Array of facial blend shape weights [num_frames, num_blendshapes] + processed_frames: Number of already processed frames that shouldn't be modified + volume: Audio volume array used to detect silent periods + silence_threshold: Volume threshold for considering a frame silent + min_silence_duration: Minimum consecutive silent frames to qualify for processing + blend_window: Number of frames to smooth at region boundaries + + Returns: + Modified blend shape array with reduced mouth movements during silence + """ + if volume is None: + return blend_shapes + + # Detect silence periods using volume data + silent_regions = find_low_value_regions( + volume, + threshold=silence_threshold, + min_region_length=min_silence_duration + ) + + for region_indices in silent_regions: + # Reduce mouth blend shapes in silent region + mouth_blend_indices = [ARKitBlendShape.index(name) for name in MOUTH_BLENDSHAPES] + for region_indice in region_indices.tolist(): + blend_shapes[region_indice, mouth_blend_indices] *= 0.1 + + try: + # Smooth transition into silent region + _blend_region_start( + blend_shapes, + region_indices, + processed_frames, + blend_window + ) + + # Smooth transition out of silent region + _blend_region_end( + blend_shapes, + region_indices, + blend_window + ) + except IndexError as e: + warnings.warn(f"Edge blending skipped at region {region_indices}: {str(e)}") + + return blend_shapes + + +def apply_frame_blending( + blend_shapes: np.ndarray, + processed_frames: int, + initial_blend_window: int = 3, + subsequent_blend_window: int = 5 +) -> np.ndarray: + """Smooths transitions between processed and unprocessed animation frames using linear blending. + + Args: + blend_shapes: Array of facial blend shape weights [num_frames, num_blendshapes] + processed_frames: Number of already processed frames (0 means no previous processing) + initial_blend_window: Max frames to blend at sequence start + subsequent_blend_window: Max frames to blend between processed and new frames + + Returns: + Modified blend shape array with smoothed transitions + """ + if processed_frames > 0: + # Blend transition between existing and new animation + _blend_animation_segment( + blend_shapes, + transition_start=processed_frames, + blend_window=subsequent_blend_window, + reference_frame=blend_shapes[processed_frames - 1] + ) + else: + # Smooth initial frames from neutral expression (zeros) + _blend_animation_segment( + blend_shapes, + transition_start=0, + blend_window=initial_blend_window, + reference_frame=np.zeros_like(blend_shapes[0]) + ) + return blend_shapes + + +def _blend_animation_segment( + array: np.ndarray, + transition_start: int, + blend_window: int, + reference_frame: np.ndarray +) -> None: + """Applies linear interpolation between reference frame and target frames. + + Args: + array: Blend shape array to modify + transition_start: Starting index for blending + blend_window: Maximum number of frames to blend + reference_frame: The reference frame to blend from + """ + actual_blend_length = min(blend_window, array.shape[0] - transition_start) + + for frame_offset in range(actual_blend_length): + current_idx = transition_start + frame_offset + blend_weight = (frame_offset + 1) / (actual_blend_length + 1) + + # Linear interpolation: ref_frame * (1 - weight) + current_frame * weight + array[current_idx] = (reference_frame * (1 - blend_weight) + + array[current_idx] * blend_weight) + + +BROW1 = np.array([[0.05597309, 0.05727929, 0.07995935, 0. , 0. ], + [0.00757574, 0.00936678, 0.12242376, 0. , 0. ], + [0. , 0. , 0.14943372, 0.04535687, 0.04264118], + [0. , 0. , 0.18015374, 0.09019445, 0.08736137], + [0. , 0. , 0.20549579, 0.12802747, 0.12450772], + [0. , 0. , 0.21098022, 0.1369939 , 0.13343132], + [0. , 0. , 0.20904602, 0.13903855, 0.13562402], + [0. , 0. , 0.20365039, 0.13977394, 0.13653506], + [0. , 0. , 0.19714841, 0.14096624, 0.13805152], + [0. , 0. , 0.20325482, 0.17303431, 0.17028868], + [0. , 0. , 0.21990852, 0.20164253, 0.19818163], + [0. , 0. , 0.23858181, 0.21908803, 0.21540019], + [0. , 0. , 0.2567876 , 0.23762083, 0.23396946], + [0. , 0. , 0.34093422, 0.27898848, 0.27651772], + [0. , 0. , 0.45288125, 0.35008961, 0.34887788], + [0. , 0. , 0.48076251, 0.36878952, 0.36778417], + [0. , 0. , 0.47798249, 0.36362219, 0.36145973], + [0. , 0. , 0.46186113, 0.33865979, 0.33597934], + [0. , 0. , 0.45264384, 0.33152157, 0.32891783], + [0. , 0. , 0.40986338, 0.29646468, 0.2945672 ], + [0. , 0. , 0.35628179, 0.23356403, 0.23155804], + [0. , 0. , 0.30870566, 0.1780673 , 0.17637439], + [0. , 0. , 0.25293985, 0.10710219, 0.10622486], + [0. , 0. , 0.18743332, 0.03252602, 0.03244236], + [0.02340254, 0.02364671, 0.15736724, 0. , 0. ]]) + +BROW2 = np.array([ + [0. , 0. , 0.09799323, 0.05944436, 0.05002545], + [0. , 0. , 0.09780276, 0.07674237, 0.01636653], + [0. , 0. , 0.11136199, 0.1027964 , 0.04249811], + [0. , 0. , 0.26883412, 0.15861984, 0.15832305], + [0. , 0. , 0.42191629, 0.27038204, 0.27007768], + [0. , 0. , 0.3404977 , 0.21633868, 0.21597538], + [0. , 0. , 0.27301185, 0.17176409, 0.17134669], + [0. , 0. , 0.25960442, 0.15670464, 0.15622253], + [0. , 0. , 0.22877269, 0.11805892, 0.11754539], + [0. , 0. , 0.1451605 , 0.06389034, 0.0636282 ]]) + +BROW3 = np.array([ + [0. , 0. , 0.124 , 0.0295, 0.0295], + [0. , 0. , 0.267 , 0.184 , 0.184 ], + [0. , 0. , 0.359 , 0.2765, 0.2765], + [0. , 0. , 0.3945, 0.3125, 0.3125], + [0. , 0. , 0.4125, 0.331 , 0.331 ], + [0. , 0. , 0.4235, 0.3445, 0.3445], + [0. , 0. , 0.4085, 0.3305, 0.3305], + [0. , 0. , 0.3695, 0.294 , 0.294 ], + [0. , 0. , 0.2835, 0.213 , 0.213 ], + [0. , 0. , 0.1795, 0.1005, 0.1005], + [0. , 0. , 0.108 , 0.014 , 0.014 ]]) + + +import numpy as np +from scipy.ndimage import label + + +def apply_random_brow_movement(input_exp, volume): + FRAME_SEGMENT = 150 + HOLD_THRESHOLD = 10 + VOLUME_THRESHOLD = 0.08 + MIN_REGION_LENGTH = 6 + STRENGTH_RANGE = (0.7, 1.3) + + BROW_PEAKS = { + 0: np.argmax(BROW1[:, 2]), + 1: np.argmax(BROW2[:, 2]) + } + + for seg_start in range(0, len(volume), FRAME_SEGMENT): + seg_end = min(seg_start + FRAME_SEGMENT, len(volume)) + seg_volume = volume[seg_start:seg_end] + + candidate_regions = [] + + high_vol_mask = seg_volume > VOLUME_THRESHOLD + labeled_array, num_features = label(high_vol_mask) + + for i in range(1, num_features + 1): + region = (labeled_array == i) + region_indices = np.where(region)[0] + if len(region_indices) >= MIN_REGION_LENGTH: + candidate_regions.append(region_indices) + + if candidate_regions: + selected_region = candidate_regions[np.random.choice(len(candidate_regions))] + region_start = selected_region[0] + region_end = selected_region[-1] + region_length = region_end - region_start + 1 + + brow_idx = np.random.randint(0, 2) + base_brow = BROW1 if brow_idx == 0 else BROW2 + peak_idx = BROW_PEAKS[brow_idx] + + if region_length > HOLD_THRESHOLD: + local_max_pos = seg_volume[selected_region].argmax() + global_peak_frame = seg_start + selected_region[local_max_pos] + + rise_anim = base_brow[:peak_idx + 1] + hold_frame = base_brow[peak_idx:peak_idx + 1] + + insert_start = max(global_peak_frame - peak_idx, seg_start) + insert_end = min(global_peak_frame + (region_length - local_max_pos), seg_end) + + strength = np.random.uniform(*STRENGTH_RANGE) + + if insert_start + len(rise_anim) <= seg_end: + input_exp[insert_start:insert_start + len(rise_anim), :5] += rise_anim * strength + hold_duration = insert_end - (insert_start + len(rise_anim)) + if hold_duration > 0: + input_exp[insert_start + len(rise_anim):insert_end, :5] += np.tile(hold_frame * strength, + (hold_duration, 1)) + else: + anim_length = base_brow.shape[0] + insert_pos = seg_start + region_start + (region_length - anim_length) // 2 + insert_pos = max(seg_start, min(insert_pos, seg_end - anim_length)) + + if insert_pos + anim_length <= seg_end: + strength = np.random.uniform(*STRENGTH_RANGE) + input_exp[insert_pos:insert_pos + anim_length, :5] += base_brow * strength + + return np.clip(input_exp, 0, 1) \ No newline at end of file diff --git a/audio2exp-service/LAM_Audio2Expression/requirements.txt b/audio2exp-service/LAM_Audio2Expression/requirements.txt new file mode 100644 index 0000000..5e29d79 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/requirements.txt @@ -0,0 +1,11 @@ +#spleeter==2.4.0 +opencv_python_headless==4.11.0.86 +gradio==5.25.2 +omegaconf==2.3.0 +addict==2.4.0 +yapf==0.40.1 +librosa==0.11.0 +transformers==4.36.2 +termcolor==3.0.1 +numpy==1.26.3 +patool \ No newline at end of file diff --git a/audio2exp-service/LAM_Audio2Expression/scripts/install/install_cu118.sh b/audio2exp-service/LAM_Audio2Expression/scripts/install/install_cu118.sh new file mode 100644 index 0000000..c3cbc44 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/scripts/install/install_cu118.sh @@ -0,0 +1,9 @@ +# install torch 2.1.2 +# or conda install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=11.8 -c pytorch -c nvidia +pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu118 + +# install dependencies +pip install -r requirements.txt + +# install H5-render +pip install wheels/gradio_gaussian_render-0.0.3-py3-none-any.whl \ No newline at end of file diff --git a/audio2exp-service/LAM_Audio2Expression/scripts/install/install_cu121.sh b/audio2exp-service/LAM_Audio2Expression/scripts/install/install_cu121.sh new file mode 100644 index 0000000..66a0f2c --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/scripts/install/install_cu121.sh @@ -0,0 +1,9 @@ +# install torch 2.1.2 +# or conda install pytorch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 pytorch-cuda=12.1 -c pytorch -c nvidia +pip install torch==2.1.2 torchvision==0.16.2 torchaudio==2.1.2 --index-url https://download.pytorch.org/whl/cu121 + +# install dependencies +pip install -r requirements.txt + +# install H5-render +pip install wheels/gradio_gaussian_render-0.0.3-py3-none-any.whl \ No newline at end of file diff --git a/audio2exp-service/LAM_Audio2Expression/utils/__init__.py b/audio2exp-service/LAM_Audio2Expression/utils/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/audio2exp-service/LAM_Audio2Expression/utils/cache.py b/audio2exp-service/LAM_Audio2Expression/utils/cache.py new file mode 100644 index 0000000..ac8bc33 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/utils/cache.py @@ -0,0 +1,53 @@ +""" +The code is base on https://github.com/Pointcept/Pointcept +""" + +import os +import SharedArray + +try: + from multiprocessing.shared_memory import ShareableList +except ImportError: + import warnings + + warnings.warn("Please update python version >= 3.8 to enable shared_memory") +import numpy as np + + +def shared_array(name, var=None): + if var is not None: + # check exist + if os.path.exists(f"/dev/shm/{name}"): + return SharedArray.attach(f"shm://{name}") + # create shared_array + data = SharedArray.create(f"shm://{name}", var.shape, dtype=var.dtype) + data[...] = var[...] + data.flags.writeable = False + else: + data = SharedArray.attach(f"shm://{name}").copy() + return data + + +def shared_dict(name, var=None): + name = str(name) + assert "." not in name # '.' is used as sep flag + data = {} + if var is not None: + assert isinstance(var, dict) + keys = var.keys() + # current version only cache np.array + keys_valid = [] + for key in keys: + if isinstance(var[key], np.ndarray): + keys_valid.append(key) + keys = keys_valid + + ShareableList(sequence=keys, name=name + ".keys") + for key in keys: + if isinstance(var[key], np.ndarray): + data[key] = shared_array(name=f"{name}.{key}", var=var[key]) + else: + keys = list(ShareableList(name=name + ".keys")) + for key in keys: + data[key] = shared_array(name=f"{name}.{key}") + return data diff --git a/audio2exp-service/LAM_Audio2Expression/utils/comm.py b/audio2exp-service/LAM_Audio2Expression/utils/comm.py new file mode 100644 index 0000000..23bec8e --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/utils/comm.py @@ -0,0 +1,192 @@ +""" +The code is base on https://github.com/Pointcept/Pointcept +""" + +import functools +import numpy as np +import torch +import torch.distributed as dist + +_LOCAL_PROCESS_GROUP = None +""" +A torch process group which only includes processes that on the same machine as the current process. +This variable is set when processes are spawned by `launch()` in "engine/launch.py". +""" + + +def get_world_size() -> int: + if not dist.is_available(): + return 1 + if not dist.is_initialized(): + return 1 + return dist.get_world_size() + + +def get_rank() -> int: + if not dist.is_available(): + return 0 + if not dist.is_initialized(): + return 0 + return dist.get_rank() + + +def get_local_rank() -> int: + """ + Returns: + The rank of the current process within the local (per-machine) process group. + """ + if not dist.is_available(): + return 0 + if not dist.is_initialized(): + return 0 + assert ( + _LOCAL_PROCESS_GROUP is not None + ), "Local process group is not created! Please use launch() to spawn processes!" + return dist.get_rank(group=_LOCAL_PROCESS_GROUP) + + +def get_local_size() -> int: + """ + Returns: + The size of the per-machine process group, + i.e. the number of processes per machine. + """ + if not dist.is_available(): + return 1 + if not dist.is_initialized(): + return 1 + return dist.get_world_size(group=_LOCAL_PROCESS_GROUP) + + +def is_main_process() -> bool: + return get_rank() == 0 + + +def synchronize(): + """ + Helper function to synchronize (barrier) among all processes when + using distributed training + """ + if not dist.is_available(): + return + if not dist.is_initialized(): + return + world_size = dist.get_world_size() + if world_size == 1: + return + if dist.get_backend() == dist.Backend.NCCL: + # This argument is needed to avoid warnings. + # It's valid only for NCCL backend. + dist.barrier(device_ids=[torch.cuda.current_device()]) + else: + dist.barrier() + + +@functools.lru_cache() +def _get_global_gloo_group(): + """ + Return a process group based on gloo backend, containing all the ranks + The result is cached. + """ + if dist.get_backend() == "nccl": + return dist.new_group(backend="gloo") + else: + return dist.group.WORLD + + +def all_gather(data, group=None): + """ + Run all_gather on arbitrary picklable data (not necessarily tensors). + Args: + data: any picklable object + group: a torch process group. By default, will use a group which + contains all ranks on gloo backend. + Returns: + list[data]: list of data gathered from each rank + """ + if get_world_size() == 1: + return [data] + if group is None: + group = ( + _get_global_gloo_group() + ) # use CPU group by default, to reduce GPU RAM usage. + world_size = dist.get_world_size(group) + if world_size == 1: + return [data] + + output = [None for _ in range(world_size)] + dist.all_gather_object(output, data, group=group) + return output + + +def gather(data, dst=0, group=None): + """ + Run gather on arbitrary picklable data (not necessarily tensors). + Args: + data: any picklable object + dst (int): destination rank + group: a torch process group. By default, will use a group which + contains all ranks on gloo backend. + Returns: + list[data]: on dst, a list of data gathered from each rank. Otherwise, + an empty list. + """ + if get_world_size() == 1: + return [data] + if group is None: + group = _get_global_gloo_group() + world_size = dist.get_world_size(group=group) + if world_size == 1: + return [data] + rank = dist.get_rank(group=group) + + if rank == dst: + output = [None for _ in range(world_size)] + dist.gather_object(data, output, dst=dst, group=group) + return output + else: + dist.gather_object(data, None, dst=dst, group=group) + return [] + + +def shared_random_seed(): + """ + Returns: + int: a random number that is the same across all workers. + If workers need a shared RNG, they can use this shared seed to + create one. + All workers must call this function, otherwise it will deadlock. + """ + ints = np.random.randint(2**31) + all_ints = all_gather(ints) + return all_ints[0] + + +def reduce_dict(input_dict, average=True): + """ + Reduce the values in the dictionary from all processes so that process with rank + 0 has the reduced results. + Args: + input_dict (dict): inputs to be reduced. All the values must be scalar CUDA Tensor. + average (bool): whether to do average or sum + Returns: + a dict with the same keys as input_dict, after reduction. + """ + world_size = get_world_size() + if world_size < 2: + return input_dict + with torch.no_grad(): + names = [] + values = [] + # sort the keys so that they are consistent across processes + for k in sorted(input_dict.keys()): + names.append(k) + values.append(input_dict[k]) + values = torch.stack(values, dim=0) + dist.reduce(values, dst=0) + if dist.get_rank() == 0 and average: + # only main process gets accumulated, so only divide by + # world_size in this case + values /= world_size + reduced_dict = {k: v for k, v in zip(names, values)} + return reduced_dict diff --git a/audio2exp-service/LAM_Audio2Expression/utils/config.py b/audio2exp-service/LAM_Audio2Expression/utils/config.py new file mode 100644 index 0000000..3782825 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/utils/config.py @@ -0,0 +1,696 @@ +""" +The code is base on https://github.com/Pointcept/Pointcept +""" +import ast +import copy +import os +import os.path as osp +import platform +import shutil +import sys +import tempfile +import uuid +import warnings +from argparse import Action, ArgumentParser +from collections import abc +from importlib import import_module + +from addict import Dict +from yapf.yapflib.yapf_api import FormatCode + +from .misc import import_modules_from_strings +from .path import check_file_exist + +if platform.system() == "Windows": + import regex as re +else: + import re + +BASE_KEY = "_base_" +DELETE_KEY = "_delete_" +DEPRECATION_KEY = "_deprecation_" +RESERVED_KEYS = ["filename", "text", "pretty_text"] + + +class ConfigDict(Dict): + def __missing__(self, name): + raise KeyError(name) + + def __getattr__(self, name): + try: + value = super(ConfigDict, self).__getattr__(name) + except KeyError: + ex = AttributeError( + f"'{self.__class__.__name__}' object has no " f"attribute '{name}'" + ) + except Exception as e: + ex = e + else: + return value + raise ex + + +def add_args(parser, cfg, prefix=""): + for k, v in cfg.items(): + if isinstance(v, str): + parser.add_argument("--" + prefix + k) + elif isinstance(v, int): + parser.add_argument("--" + prefix + k, type=int) + elif isinstance(v, float): + parser.add_argument("--" + prefix + k, type=float) + elif isinstance(v, bool): + parser.add_argument("--" + prefix + k, action="store_true") + elif isinstance(v, dict): + add_args(parser, v, prefix + k + ".") + elif isinstance(v, abc.Iterable): + parser.add_argument("--" + prefix + k, type=type(v[0]), nargs="+") + else: + print(f"cannot parse key {prefix + k} of type {type(v)}") + return parser + + +class Config: + """A facility for config and config files. + + It supports common file formats as configs: python/json/yaml. The interface + is the same as a dict object and also allows access config values as + attributes. + + Example: + >>> cfg = Config(dict(a=1, b=dict(b1=[0, 1]))) + >>> cfg.a + 1 + >>> cfg.b + {'b1': [0, 1]} + >>> cfg.b.b1 + [0, 1] + >>> cfg = Config.fromfile('tests/data/config/a.py') + >>> cfg.filename + "/home/kchen/projects/mmcv/tests/data/config/a.py" + >>> cfg.item4 + 'test' + >>> cfg + "Config [path: /home/kchen/projects/mmcv/tests/data/config/a.py]: " + "{'item1': [1, 2], 'item2': {'a': 0}, 'item3': True, 'item4': 'test'}" + """ + + @staticmethod + def _validate_py_syntax(filename): + with open(filename, "r", encoding="utf-8") as f: + # Setting encoding explicitly to resolve coding issue on windows + content = f.read() + try: + ast.parse(content) + except SyntaxError as e: + raise SyntaxError( + "There are syntax errors in config " f"file {filename}: {e}" + ) + + @staticmethod + def _substitute_predefined_vars(filename, temp_config_name): + file_dirname = osp.dirname(filename) + file_basename = osp.basename(filename) + file_basename_no_extension = osp.splitext(file_basename)[0] + file_extname = osp.splitext(filename)[1] + support_templates = dict( + fileDirname=file_dirname, + fileBasename=file_basename, + fileBasenameNoExtension=file_basename_no_extension, + fileExtname=file_extname, + ) + with open(filename, "r", encoding="utf-8") as f: + # Setting encoding explicitly to resolve coding issue on windows + config_file = f.read() + for key, value in support_templates.items(): + regexp = r"\{\{\s*" + str(key) + r"\s*\}\}" + value = value.replace("\\", "/") + config_file = re.sub(regexp, value, config_file) + with open(temp_config_name, "w", encoding="utf-8") as tmp_config_file: + tmp_config_file.write(config_file) + + @staticmethod + def _pre_substitute_base_vars(filename, temp_config_name): + """Substitute base variable placehoders to string, so that parsing + would work.""" + with open(filename, "r", encoding="utf-8") as f: + # Setting encoding explicitly to resolve coding issue on windows + config_file = f.read() + base_var_dict = {} + regexp = r"\{\{\s*" + BASE_KEY + r"\.([\w\.]+)\s*\}\}" + base_vars = set(re.findall(regexp, config_file)) + for base_var in base_vars: + randstr = f"_{base_var}_{uuid.uuid4().hex.lower()[:6]}" + base_var_dict[randstr] = base_var + regexp = r"\{\{\s*" + BASE_KEY + r"\." + base_var + r"\s*\}\}" + config_file = re.sub(regexp, f'"{randstr}"', config_file) + with open(temp_config_name, "w", encoding="utf-8") as tmp_config_file: + tmp_config_file.write(config_file) + return base_var_dict + + @staticmethod + def _substitute_base_vars(cfg, base_var_dict, base_cfg): + """Substitute variable strings to their actual values.""" + cfg = copy.deepcopy(cfg) + + if isinstance(cfg, dict): + for k, v in cfg.items(): + if isinstance(v, str) and v in base_var_dict: + new_v = base_cfg + for new_k in base_var_dict[v].split("."): + new_v = new_v[new_k] + cfg[k] = new_v + elif isinstance(v, (list, tuple, dict)): + cfg[k] = Config._substitute_base_vars(v, base_var_dict, base_cfg) + elif isinstance(cfg, tuple): + cfg = tuple( + Config._substitute_base_vars(c, base_var_dict, base_cfg) for c in cfg + ) + elif isinstance(cfg, list): + cfg = [ + Config._substitute_base_vars(c, base_var_dict, base_cfg) for c in cfg + ] + elif isinstance(cfg, str) and cfg in base_var_dict: + new_v = base_cfg + for new_k in base_var_dict[cfg].split("."): + new_v = new_v[new_k] + cfg = new_v + + return cfg + + @staticmethod + def _file2dict(filename, use_predefined_variables=True): + filename = osp.abspath(osp.expanduser(filename)) + check_file_exist(filename) + fileExtname = osp.splitext(filename)[1] + if fileExtname not in [".py", ".json", ".yaml", ".yml"]: + raise IOError("Only py/yml/yaml/json type are supported now!") + + with tempfile.TemporaryDirectory() as temp_config_dir: + temp_config_file = tempfile.NamedTemporaryFile( + dir=temp_config_dir, suffix=fileExtname + ) + if platform.system() == "Windows": + temp_config_file.close() + temp_config_name = osp.basename(temp_config_file.name) + # Substitute predefined variables + if use_predefined_variables: + Config._substitute_predefined_vars(filename, temp_config_file.name) + else: + shutil.copyfile(filename, temp_config_file.name) + # Substitute base variables from placeholders to strings + base_var_dict = Config._pre_substitute_base_vars( + temp_config_file.name, temp_config_file.name + ) + + if filename.endswith(".py"): + temp_module_name = osp.splitext(temp_config_name)[0] + sys.path.insert(0, temp_config_dir) + Config._validate_py_syntax(filename) + mod = import_module(temp_module_name) + sys.path.pop(0) + cfg_dict = { + name: value + for name, value in mod.__dict__.items() + if not name.startswith("__") + } + # delete imported module + del sys.modules[temp_module_name] + elif filename.endswith((".yml", ".yaml", ".json")): + raise NotImplementedError + # close temp file + temp_config_file.close() + + # check deprecation information + if DEPRECATION_KEY in cfg_dict: + deprecation_info = cfg_dict.pop(DEPRECATION_KEY) + warning_msg = ( + f"The config file {filename} will be deprecated " "in the future." + ) + if "expected" in deprecation_info: + warning_msg += f' Please use {deprecation_info["expected"]} ' "instead." + if "reference" in deprecation_info: + warning_msg += ( + " More information can be found at " + f'{deprecation_info["reference"]}' + ) + warnings.warn(warning_msg) + + cfg_text = filename + "\n" + with open(filename, "r", encoding="utf-8") as f: + # Setting encoding explicitly to resolve coding issue on windows + cfg_text += f.read() + + if BASE_KEY in cfg_dict: + cfg_dir = osp.dirname(filename) + base_filename = cfg_dict.pop(BASE_KEY) + base_filename = ( + base_filename if isinstance(base_filename, list) else [base_filename] + ) + + cfg_dict_list = list() + cfg_text_list = list() + for f in base_filename: + _cfg_dict, _cfg_text = Config._file2dict(osp.join(cfg_dir, f)) + cfg_dict_list.append(_cfg_dict) + cfg_text_list.append(_cfg_text) + + base_cfg_dict = dict() + for c in cfg_dict_list: + duplicate_keys = base_cfg_dict.keys() & c.keys() + if len(duplicate_keys) > 0: + raise KeyError( + "Duplicate key is not allowed among bases. " + f"Duplicate keys: {duplicate_keys}" + ) + base_cfg_dict.update(c) + + # Substitute base variables from strings to their actual values + cfg_dict = Config._substitute_base_vars( + cfg_dict, base_var_dict, base_cfg_dict + ) + + base_cfg_dict = Config._merge_a_into_b(cfg_dict, base_cfg_dict) + cfg_dict = base_cfg_dict + + # merge cfg_text + cfg_text_list.append(cfg_text) + cfg_text = "\n".join(cfg_text_list) + + return cfg_dict, cfg_text + + @staticmethod + def _merge_a_into_b(a, b, allow_list_keys=False): + """merge dict ``a`` into dict ``b`` (non-inplace). + + Values in ``a`` will overwrite ``b``. ``b`` is copied first to avoid + in-place modifications. + + Args: + a (dict): The source dict to be merged into ``b``. + b (dict): The origin dict to be fetch keys from ``a``. + allow_list_keys (bool): If True, int string keys (e.g. '0', '1') + are allowed in source ``a`` and will replace the element of the + corresponding index in b if b is a list. Default: False. + + Returns: + dict: The modified dict of ``b`` using ``a``. + + Examples: + # Normally merge a into b. + >>> Config._merge_a_into_b( + ... dict(obj=dict(a=2)), dict(obj=dict(a=1))) + {'obj': {'a': 2}} + + # Delete b first and merge a into b. + >>> Config._merge_a_into_b( + ... dict(obj=dict(_delete_=True, a=2)), dict(obj=dict(a=1))) + {'obj': {'a': 2}} + + # b is a list + >>> Config._merge_a_into_b( + ... {'0': dict(a=2)}, [dict(a=1), dict(b=2)], True) + [{'a': 2}, {'b': 2}] + """ + b = b.copy() + for k, v in a.items(): + if allow_list_keys and k.isdigit() and isinstance(b, list): + k = int(k) + if len(b) <= k: + raise KeyError(f"Index {k} exceeds the length of list {b}") + b[k] = Config._merge_a_into_b(v, b[k], allow_list_keys) + elif isinstance(v, dict) and k in b and not v.pop(DELETE_KEY, False): + allowed_types = (dict, list) if allow_list_keys else dict + if not isinstance(b[k], allowed_types): + raise TypeError( + f"{k}={v} in child config cannot inherit from base " + f"because {k} is a dict in the child config but is of " + f"type {type(b[k])} in base config. You may set " + f"`{DELETE_KEY}=True` to ignore the base config" + ) + b[k] = Config._merge_a_into_b(v, b[k], allow_list_keys) + else: + b[k] = v + return b + + @staticmethod + def fromfile(filename, use_predefined_variables=True, import_custom_modules=True): + cfg_dict, cfg_text = Config._file2dict(filename, use_predefined_variables) + if import_custom_modules and cfg_dict.get("custom_imports", None): + import_modules_from_strings(**cfg_dict["custom_imports"]) + return Config(cfg_dict, cfg_text=cfg_text, filename=filename) + + @staticmethod + def fromstring(cfg_str, file_format): + """Generate config from config str. + + Args: + cfg_str (str): Config str. + file_format (str): Config file format corresponding to the + config str. Only py/yml/yaml/json type are supported now! + + Returns: + obj:`Config`: Config obj. + """ + if file_format not in [".py", ".json", ".yaml", ".yml"]: + raise IOError("Only py/yml/yaml/json type are supported now!") + if file_format != ".py" and "dict(" in cfg_str: + # check if users specify a wrong suffix for python + warnings.warn('Please check "file_format", the file format may be .py') + with tempfile.NamedTemporaryFile( + "w", encoding="utf-8", suffix=file_format, delete=False + ) as temp_file: + temp_file.write(cfg_str) + # on windows, previous implementation cause error + # see PR 1077 for details + cfg = Config.fromfile(temp_file.name) + os.remove(temp_file.name) + return cfg + + @staticmethod + def auto_argparser(description=None): + """Generate argparser from config file automatically (experimental)""" + partial_parser = ArgumentParser(description=description) + partial_parser.add_argument("config", help="config file path") + cfg_file = partial_parser.parse_known_args()[0].config + cfg = Config.fromfile(cfg_file) + parser = ArgumentParser(description=description) + parser.add_argument("config", help="config file path") + add_args(parser, cfg) + return parser, cfg + + def __init__(self, cfg_dict=None, cfg_text=None, filename=None): + if cfg_dict is None: + cfg_dict = dict() + elif not isinstance(cfg_dict, dict): + raise TypeError("cfg_dict must be a dict, but " f"got {type(cfg_dict)}") + for key in cfg_dict: + if key in RESERVED_KEYS: + raise KeyError(f"{key} is reserved for config file") + + super(Config, self).__setattr__("_cfg_dict", ConfigDict(cfg_dict)) + super(Config, self).__setattr__("_filename", filename) + if cfg_text: + text = cfg_text + elif filename: + with open(filename, "r") as f: + text = f.read() + else: + text = "" + super(Config, self).__setattr__("_text", text) + + @property + def filename(self): + return self._filename + + @property + def text(self): + return self._text + + @property + def pretty_text(self): + indent = 4 + + def _indent(s_, num_spaces): + s = s_.split("\n") + if len(s) == 1: + return s_ + first = s.pop(0) + s = [(num_spaces * " ") + line for line in s] + s = "\n".join(s) + s = first + "\n" + s + return s + + def _format_basic_types(k, v, use_mapping=False): + if isinstance(v, str): + v_str = f"'{v}'" + else: + v_str = str(v) + + if use_mapping: + k_str = f"'{k}'" if isinstance(k, str) else str(k) + attr_str = f"{k_str}: {v_str}" + else: + attr_str = f"{str(k)}={v_str}" + attr_str = _indent(attr_str, indent) + + return attr_str + + def _format_list(k, v, use_mapping=False): + # check if all items in the list are dict + if all(isinstance(_, dict) for _ in v): + v_str = "[\n" + v_str += "\n".join( + f"dict({_indent(_format_dict(v_), indent)})," for v_ in v + ).rstrip(",") + if use_mapping: + k_str = f"'{k}'" if isinstance(k, str) else str(k) + attr_str = f"{k_str}: {v_str}" + else: + attr_str = f"{str(k)}={v_str}" + attr_str = _indent(attr_str, indent) + "]" + else: + attr_str = _format_basic_types(k, v, use_mapping) + return attr_str + + def _contain_invalid_identifier(dict_str): + contain_invalid_identifier = False + for key_name in dict_str: + contain_invalid_identifier |= not str(key_name).isidentifier() + return contain_invalid_identifier + + def _format_dict(input_dict, outest_level=False): + r = "" + s = [] + + use_mapping = _contain_invalid_identifier(input_dict) + if use_mapping: + r += "{" + for idx, (k, v) in enumerate(input_dict.items()): + is_last = idx >= len(input_dict) - 1 + end = "" if outest_level or is_last else "," + if isinstance(v, dict): + v_str = "\n" + _format_dict(v) + if use_mapping: + k_str = f"'{k}'" if isinstance(k, str) else str(k) + attr_str = f"{k_str}: dict({v_str}" + else: + attr_str = f"{str(k)}=dict({v_str}" + attr_str = _indent(attr_str, indent) + ")" + end + elif isinstance(v, list): + attr_str = _format_list(k, v, use_mapping) + end + else: + attr_str = _format_basic_types(k, v, use_mapping) + end + + s.append(attr_str) + r += "\n".join(s) + if use_mapping: + r += "}" + return r + + cfg_dict = self._cfg_dict.to_dict() + text = _format_dict(cfg_dict, outest_level=True) + # copied from setup.cfg + yapf_style = dict( + based_on_style="pep8", + blank_line_before_nested_class_or_def=True, + split_before_expression_after_opening_paren=True, + ) + text, _ = FormatCode(text, style_config=yapf_style) + + return text + + def __repr__(self): + return f"Config (path: {self.filename}): {self._cfg_dict.__repr__()}" + + def __len__(self): + return len(self._cfg_dict) + + def __getattr__(self, name): + return getattr(self._cfg_dict, name) + + def __getitem__(self, name): + return self._cfg_dict.__getitem__(name) + + def __setattr__(self, name, value): + if isinstance(value, dict): + value = ConfigDict(value) + self._cfg_dict.__setattr__(name, value) + + def __setitem__(self, name, value): + if isinstance(value, dict): + value = ConfigDict(value) + self._cfg_dict.__setitem__(name, value) + + def __iter__(self): + return iter(self._cfg_dict) + + def __getstate__(self): + return (self._cfg_dict, self._filename, self._text) + + def __setstate__(self, state): + _cfg_dict, _filename, _text = state + super(Config, self).__setattr__("_cfg_dict", _cfg_dict) + super(Config, self).__setattr__("_filename", _filename) + super(Config, self).__setattr__("_text", _text) + + def dump(self, file=None): + cfg_dict = super(Config, self).__getattribute__("_cfg_dict").to_dict() + if self.filename.endswith(".py"): + if file is None: + return self.pretty_text + else: + with open(file, "w", encoding="utf-8") as f: + f.write(self.pretty_text) + else: + import mmcv + + if file is None: + file_format = self.filename.split(".")[-1] + return mmcv.dump(cfg_dict, file_format=file_format) + else: + mmcv.dump(cfg_dict, file) + + def merge_from_dict(self, options, allow_list_keys=True): + """Merge list into cfg_dict. + + Merge the dict parsed by MultipleKVAction into this cfg. + + Examples: + >>> options = {'models.backbone.depth': 50, + ... 'models.backbone.with_cp':True} + >>> cfg = Config(dict(models=dict(backbone=dict(type='ResNet')))) + >>> cfg.merge_from_dict(options) + >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict') + >>> assert cfg_dict == dict( + ... models=dict(backbone=dict(depth=50, with_cp=True))) + + # Merge list element + >>> cfg = Config(dict(pipeline=[ + ... dict(type='LoadImage'), dict(type='LoadAnnotations')])) + >>> options = dict(pipeline={'0': dict(type='SelfLoadImage')}) + >>> cfg.merge_from_dict(options, allow_list_keys=True) + >>> cfg_dict = super(Config, self).__getattribute__('_cfg_dict') + >>> assert cfg_dict == dict(pipeline=[ + ... dict(type='SelfLoadImage'), dict(type='LoadAnnotations')]) + + Args: + options (dict): dict of configs to merge from. + allow_list_keys (bool): If True, int string keys (e.g. '0', '1') + are allowed in ``options`` and will replace the element of the + corresponding index in the config if the config is a list. + Default: True. + """ + option_cfg_dict = {} + for full_key, v in options.items(): + d = option_cfg_dict + key_list = full_key.split(".") + for subkey in key_list[:-1]: + d.setdefault(subkey, ConfigDict()) + d = d[subkey] + subkey = key_list[-1] + d[subkey] = v + + cfg_dict = super(Config, self).__getattribute__("_cfg_dict") + super(Config, self).__setattr__( + "_cfg_dict", + Config._merge_a_into_b( + option_cfg_dict, cfg_dict, allow_list_keys=allow_list_keys + ), + ) + + +class DictAction(Action): + """ + argparse action to split an argument into KEY=VALUE form + on the first = and append to a dictionary. List options can + be passed as comma separated values, i.e 'KEY=V1,V2,V3', or with explicit + brackets, i.e. 'KEY=[V1,V2,V3]'. It also support nested brackets to build + list/tuple values. e.g. 'KEY=[(V1,V2),(V3,V4)]' + """ + + @staticmethod + def _parse_int_float_bool(val): + try: + return int(val) + except ValueError: + pass + try: + return float(val) + except ValueError: + pass + if val.lower() in ["true", "false"]: + return True if val.lower() == "true" else False + return val + + @staticmethod + def _parse_iterable(val): + """Parse iterable values in the string. + + All elements inside '()' or '[]' are treated as iterable values. + + Args: + val (str): Value string. + + Returns: + list | tuple: The expanded list or tuple from the string. + + Examples: + >>> DictAction._parse_iterable('1,2,3') + [1, 2, 3] + >>> DictAction._parse_iterable('[a, b, c]') + ['a', 'b', 'c'] + >>> DictAction._parse_iterable('[(1, 2, 3), [a, b], c]') + [(1, 2, 3), ['a', 'b'], 'c'] + """ + + def find_next_comma(string): + """Find the position of next comma in the string. + + If no ',' is found in the string, return the string length. All + chars inside '()' and '[]' are treated as one element and thus ',' + inside these brackets are ignored. + """ + assert (string.count("(") == string.count(")")) and ( + string.count("[") == string.count("]") + ), f"Imbalanced brackets exist in {string}" + end = len(string) + for idx, char in enumerate(string): + pre = string[:idx] + # The string before this ',' is balanced + if ( + (char == ",") + and (pre.count("(") == pre.count(")")) + and (pre.count("[") == pre.count("]")) + ): + end = idx + break + return end + + # Strip ' and " characters and replace whitespace. + val = val.strip("'\"").replace(" ", "") + is_tuple = False + if val.startswith("(") and val.endswith(")"): + is_tuple = True + val = val[1:-1] + elif val.startswith("[") and val.endswith("]"): + val = val[1:-1] + elif "," not in val: + # val is a single value + return DictAction._parse_int_float_bool(val) + + values = [] + while len(val) > 0: + comma_idx = find_next_comma(val) + element = DictAction._parse_iterable(val[:comma_idx]) + values.append(element) + val = val[comma_idx + 1 :] + if is_tuple: + values = tuple(values) + return values + + def __call__(self, parser, namespace, values, option_string=None): + options = {} + for kv in values: + key, val = kv.split("=", maxsplit=1) + options[key] = self._parse_iterable(val) + setattr(namespace, self.dest, options) diff --git a/audio2exp-service/LAM_Audio2Expression/utils/env.py b/audio2exp-service/LAM_Audio2Expression/utils/env.py new file mode 100644 index 0000000..802ed90 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/utils/env.py @@ -0,0 +1,33 @@ +""" +The code is base on https://github.com/Pointcept/Pointcept +""" + +import os +import random +import numpy as np +import torch +import torch.backends.cudnn as cudnn + +from datetime import datetime + + +def get_random_seed(): + seed = ( + os.getpid() + + int(datetime.now().strftime("%S%f")) + + int.from_bytes(os.urandom(2), "big") + ) + return seed + + +def set_seed(seed=None): + if seed is None: + seed = get_random_seed() + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) + cudnn.benchmark = False + cudnn.deterministic = True + os.environ["PYTHONHASHSEED"] = str(seed) diff --git a/audio2exp-service/LAM_Audio2Expression/utils/events.py b/audio2exp-service/LAM_Audio2Expression/utils/events.py new file mode 100644 index 0000000..90412dd --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/utils/events.py @@ -0,0 +1,585 @@ +""" +The code is base on https://github.com/Pointcept/Pointcept +""" + + +import datetime +import json +import logging +import os +import time +import torch +import numpy as np + +from typing import List, Optional, Tuple +from collections import defaultdict +from contextlib import contextmanager + +__all__ = [ + "get_event_storage", + "JSONWriter", + "TensorboardXWriter", + "CommonMetricPrinter", + "EventStorage", +] + +_CURRENT_STORAGE_STACK = [] + + +def get_event_storage(): + """ + Returns: + The :class:`EventStorage` object that's currently being used. + Throws an error if no :class:`EventStorage` is currently enabled. + """ + assert len( + _CURRENT_STORAGE_STACK + ), "get_event_storage() has to be called inside a 'with EventStorage(...)' context!" + return _CURRENT_STORAGE_STACK[-1] + + +class EventWriter: + """ + Base class for writers that obtain events from :class:`EventStorage` and process them. + """ + + def write(self): + raise NotImplementedError + + def close(self): + pass + + +class JSONWriter(EventWriter): + """ + Write scalars to a json file. + It saves scalars as one json per line (instead of a big json) for easy parsing. + Examples parsing such a json file: + :: + $ cat metrics.json | jq -s '.[0:2]' + [ + { + "data_time": 0.008433341979980469, + "iteration": 19, + "loss": 1.9228371381759644, + "loss_box_reg": 0.050025828182697296, + "loss_classifier": 0.5316952466964722, + "loss_mask": 0.7236229181289673, + "loss_rpn_box": 0.0856662318110466, + "loss_rpn_cls": 0.48198649287223816, + "lr": 0.007173333333333333, + "time": 0.25401854515075684 + }, + { + "data_time": 0.007216215133666992, + "iteration": 39, + "loss": 1.282649278640747, + "loss_box_reg": 0.06222952902317047, + "loss_classifier": 0.30682939291000366, + "loss_mask": 0.6970193982124329, + "loss_rpn_box": 0.038663312792778015, + "loss_rpn_cls": 0.1471673548221588, + "lr": 0.007706666666666667, + "time": 0.2490077018737793 + } + ] + $ cat metrics.json | jq '.loss_mask' + 0.7126231789588928 + 0.689423680305481 + 0.6776131987571716 + ... + """ + + def __init__(self, json_file, window_size=20): + """ + Args: + json_file (str): path to the json file. New data will be appended if the file exists. + window_size (int): the window size of median smoothing for the scalars whose + `smoothing_hint` are True. + """ + self._file_handle = open(json_file, "a") + self._window_size = window_size + self._last_write = -1 + + def write(self): + storage = get_event_storage() + to_save = defaultdict(dict) + + for k, (v, iter) in storage.latest_with_smoothing_hint( + self._window_size + ).items(): + # keep scalars that have not been written + if iter <= self._last_write: + continue + to_save[iter][k] = v + if len(to_save): + all_iters = sorted(to_save.keys()) + self._last_write = max(all_iters) + + for itr, scalars_per_iter in to_save.items(): + scalars_per_iter["iteration"] = itr + self._file_handle.write(json.dumps(scalars_per_iter, sort_keys=True) + "\n") + self._file_handle.flush() + try: + os.fsync(self._file_handle.fileno()) + except AttributeError: + pass + + def close(self): + self._file_handle.close() + + +class TensorboardXWriter(EventWriter): + """ + Write all scalars to a tensorboard file. + """ + + def __init__(self, log_dir: str, window_size: int = 20, **kwargs): + """ + Args: + log_dir (str): the directory to save the output events + window_size (int): the scalars will be median-smoothed by this window size + kwargs: other arguments passed to `torch.utils.tensorboard.SummaryWriter(...)` + """ + self._window_size = window_size + from torch.utils.tensorboard import SummaryWriter + + self._writer = SummaryWriter(log_dir, **kwargs) + self._last_write = -1 + + def write(self): + storage = get_event_storage() + new_last_write = self._last_write + for k, (v, iter) in storage.latest_with_smoothing_hint( + self._window_size + ).items(): + if iter > self._last_write: + self._writer.add_scalar(k, v, iter) + new_last_write = max(new_last_write, iter) + self._last_write = new_last_write + + # storage.put_{image,histogram} is only meant to be used by + # tensorboard writer. So we access its internal fields directly from here. + if len(storage._vis_data) >= 1: + for img_name, img, step_num in storage._vis_data: + self._writer.add_image(img_name, img, step_num) + # Storage stores all image data and rely on this writer to clear them. + # As a result it assumes only one writer will use its image data. + # An alternative design is to let storage store limited recent + # data (e.g. only the most recent image) that all writers can access. + # In that case a writer may not see all image data if its period is long. + storage.clear_images() + + if len(storage._histograms) >= 1: + for params in storage._histograms: + self._writer.add_histogram_raw(**params) + storage.clear_histograms() + + def close(self): + if hasattr(self, "_writer"): # doesn't exist when the code fails at import + self._writer.close() + + +class CommonMetricPrinter(EventWriter): + """ + Print **common** metrics to the terminal, including + iteration time, ETA, memory, all losses, and the learning rate. + It also applies smoothing using a window of 20 elements. + It's meant to print common metrics in common ways. + To print something in more customized ways, please implement a similar printer by yourself. + """ + + def __init__(self, max_iter: Optional[int] = None, window_size: int = 20): + """ + Args: + max_iter: the maximum number of iterations to train. + Used to compute ETA. If not given, ETA will not be printed. + window_size (int): the losses will be median-smoothed by this window size + """ + self.logger = logging.getLogger(__name__) + self._max_iter = max_iter + self._window_size = window_size + self._last_write = ( + None # (step, time) of last call to write(). Used to compute ETA + ) + + def _get_eta(self, storage) -> Optional[str]: + if self._max_iter is None: + return "" + iteration = storage.iter + try: + eta_seconds = storage.history("time").median(1000) * ( + self._max_iter - iteration - 1 + ) + storage.put_scalar("eta_seconds", eta_seconds, smoothing_hint=False) + return str(datetime.timedelta(seconds=int(eta_seconds))) + except KeyError: + # estimate eta on our own - more noisy + eta_string = None + if self._last_write is not None: + estimate_iter_time = (time.perf_counter() - self._last_write[1]) / ( + iteration - self._last_write[0] + ) + eta_seconds = estimate_iter_time * (self._max_iter - iteration - 1) + eta_string = str(datetime.timedelta(seconds=int(eta_seconds))) + self._last_write = (iteration, time.perf_counter()) + return eta_string + + def write(self): + storage = get_event_storage() + iteration = storage.iter + if iteration == self._max_iter: + # This hook only reports training progress (loss, ETA, etc) but not other data, + # therefore do not write anything after training succeeds, even if this method + # is called. + return + + try: + data_time = storage.history("data_time").avg(20) + except KeyError: + # they may not exist in the first few iterations (due to warmup) + # or when SimpleTrainer is not used + data_time = None + try: + iter_time = storage.history("time").global_avg() + except KeyError: + iter_time = None + try: + lr = "{:.5g}".format(storage.history("lr").latest()) + except KeyError: + lr = "N/A" + + eta_string = self._get_eta(storage) + + if torch.cuda.is_available(): + max_mem_mb = torch.cuda.max_memory_allocated() / 1024.0 / 1024.0 + else: + max_mem_mb = None + + # NOTE: max_mem is parsed by grep in "dev/parse_results.sh" + self.logger.info( + " {eta}iter: {iter} {losses} {time}{data_time}lr: {lr} {memory}".format( + eta=f"eta: {eta_string} " if eta_string else "", + iter=iteration, + losses=" ".join( + [ + "{}: {:.4g}".format(k, v.median(self._window_size)) + for k, v in storage.histories().items() + if "loss" in k + ] + ), + time="time: {:.4f} ".format(iter_time) + if iter_time is not None + else "", + data_time="data_time: {:.4f} ".format(data_time) + if data_time is not None + else "", + lr=lr, + memory="max_mem: {:.0f}M".format(max_mem_mb) + if max_mem_mb is not None + else "", + ) + ) + + +class EventStorage: + """ + The user-facing class that provides metric storage functionalities. + In the future we may add support for storing / logging other types of data if needed. + """ + + def __init__(self, start_iter=0): + """ + Args: + start_iter (int): the iteration number to start with + """ + self._history = defaultdict(AverageMeter) + self._smoothing_hints = {} + self._latest_scalars = {} + self._iter = start_iter + self._current_prefix = "" + self._vis_data = [] + self._histograms = [] + + # def put_image(self, img_name, img_tensor): + # """ + # Add an `img_tensor` associated with `img_name`, to be shown on + # tensorboard. + # Args: + # img_name (str): The name of the image to put into tensorboard. + # img_tensor (torch.Tensor or numpy.array): An `uint8` or `float` + # Tensor of shape `[channel, height, width]` where `channel` is + # 3. The image format should be RGB. The elements in img_tensor + # can either have values in [0, 1] (float32) or [0, 255] (uint8). + # The `img_tensor` will be visualized in tensorboard. + # """ + # self._vis_data.append((img_name, img_tensor, self._iter)) + + def put_scalar(self, name, value, n=1, smoothing_hint=False): + """ + Add a scalar `value` to the `HistoryBuffer` associated with `name`. + Args: + smoothing_hint (bool): a 'hint' on whether this scalar is noisy and should be + smoothed when logged. The hint will be accessible through + :meth:`EventStorage.smoothing_hints`. A writer may ignore the hint + and apply custom smoothing rule. + It defaults to True because most scalars we save need to be smoothed to + provide any useful signal. + """ + name = self._current_prefix + name + history = self._history[name] + history.update(value, n) + self._latest_scalars[name] = (value, self._iter) + + existing_hint = self._smoothing_hints.get(name) + if existing_hint is not None: + assert ( + existing_hint == smoothing_hint + ), "Scalar {} was put with a different smoothing_hint!".format(name) + else: + self._smoothing_hints[name] = smoothing_hint + + # def put_scalars(self, *, smoothing_hint=True, **kwargs): + # """ + # Put multiple scalars from keyword arguments. + # Examples: + # storage.put_scalars(loss=my_loss, accuracy=my_accuracy, smoothing_hint=True) + # """ + # for k, v in kwargs.items(): + # self.put_scalar(k, v, smoothing_hint=smoothing_hint) + # + # def put_histogram(self, hist_name, hist_tensor, bins=1000): + # """ + # Create a histogram from a tensor. + # Args: + # hist_name (str): The name of the histogram to put into tensorboard. + # hist_tensor (torch.Tensor): A Tensor of arbitrary shape to be converted + # into a histogram. + # bins (int): Number of histogram bins. + # """ + # ht_min, ht_max = hist_tensor.min().item(), hist_tensor.max().item() + # + # # Create a histogram with PyTorch + # hist_counts = torch.histc(hist_tensor, bins=bins) + # hist_edges = torch.linspace(start=ht_min, end=ht_max, steps=bins + 1, dtype=torch.float32) + # + # # Parameter for the add_histogram_raw function of SummaryWriter + # hist_params = dict( + # tag=hist_name, + # min=ht_min, + # max=ht_max, + # num=len(hist_tensor), + # sum=float(hist_tensor.sum()), + # sum_squares=float(torch.sum(hist_tensor**2)), + # bucket_limits=hist_edges[1:].tolist(), + # bucket_counts=hist_counts.tolist(), + # global_step=self._iter, + # ) + # self._histograms.append(hist_params) + + def history(self, name): + """ + Returns: + AverageMeter: the history for name + """ + ret = self._history.get(name, None) + if ret is None: + raise KeyError("No history metric available for {}!".format(name)) + return ret + + def histories(self): + """ + Returns: + dict[name -> HistoryBuffer]: the HistoryBuffer for all scalars + """ + return self._history + + def latest(self): + """ + Returns: + dict[str -> (float, int)]: mapping from the name of each scalar to the most + recent value and the iteration number its added. + """ + return self._latest_scalars + + def latest_with_smoothing_hint(self, window_size=20): + """ + Similar to :meth:`latest`, but the returned values + are either the un-smoothed original latest value, + or a median of the given window_size, + depend on whether the smoothing_hint is True. + This provides a default behavior that other writers can use. + """ + result = {} + for k, (v, itr) in self._latest_scalars.items(): + result[k] = ( + self._history[k].median(window_size) if self._smoothing_hints[k] else v, + itr, + ) + return result + + def smoothing_hints(self): + """ + Returns: + dict[name -> bool]: the user-provided hint on whether the scalar + is noisy and needs smoothing. + """ + return self._smoothing_hints + + def step(self): + """ + User should either: (1) Call this function to increment storage.iter when needed. Or + (2) Set `storage.iter` to the correct iteration number before each iteration. + The storage will then be able to associate the new data with an iteration number. + """ + self._iter += 1 + + @property + def iter(self): + """ + Returns: + int: The current iteration number. When used together with a trainer, + this is ensured to be the same as trainer.iter. + """ + return self._iter + + @iter.setter + def iter(self, val): + self._iter = int(val) + + @property + def iteration(self): + # for backward compatibility + return self._iter + + def __enter__(self): + _CURRENT_STORAGE_STACK.append(self) + return self + + def __exit__(self, exc_type, exc_val, exc_tb): + assert _CURRENT_STORAGE_STACK[-1] == self + _CURRENT_STORAGE_STACK.pop() + + @contextmanager + def name_scope(self, name): + """ + Yields: + A context within which all the events added to this storage + will be prefixed by the name scope. + """ + old_prefix = self._current_prefix + self._current_prefix = name.rstrip("/") + "/" + yield + self._current_prefix = old_prefix + + def clear_images(self): + """ + Delete all the stored images for visualization. This should be called + after images are written to tensorboard. + """ + self._vis_data = [] + + def clear_histograms(self): + """ + Delete all the stored histograms for visualization. + This should be called after histograms are written to tensorboard. + """ + self._histograms = [] + + def reset_history(self, name): + ret = self._history.get(name, None) + if ret is None: + raise KeyError("No history metric available for {}!".format(name)) + ret.reset() + + def reset_histories(self): + for name in self._history.keys(): + self._history[name].reset() + + +class AverageMeter: + """Computes and stores the average and current value""" + + def __init__(self): + self.val = 0 + self.avg = 0 + self.total = 0 + self.count = 0 + + def reset(self): + self.val = 0 + self.avg = 0 + self.total = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.total += val * n + self.count += n + self.avg = self.total / self.count + + +class HistoryBuffer: + """ + Track a series of scalar values and provide access to smoothed values over a + window or the global average of the series. + """ + + def __init__(self, max_length: int = 1000000) -> None: + """ + Args: + max_length: maximal number of values that can be stored in the + buffer. When the capacity of the buffer is exhausted, old + values will be removed. + """ + self._max_length: int = max_length + self._data: List[Tuple[float, float]] = [] # (value, iteration) pairs + self._count: int = 0 + self._global_avg: float = 0 + + def update(self, value: float, iteration: Optional[float] = None) -> None: + """ + Add a new scalar value produced at certain iteration. If the length + of the buffer exceeds self._max_length, the oldest element will be + removed from the buffer. + """ + if iteration is None: + iteration = self._count + if len(self._data) == self._max_length: + self._data.pop(0) + self._data.append((value, iteration)) + + self._count += 1 + self._global_avg += (value - self._global_avg) / self._count + + def latest(self) -> float: + """ + Return the latest scalar value added to the buffer. + """ + return self._data[-1][0] + + def median(self, window_size: int) -> float: + """ + Return the median of the latest `window_size` values in the buffer. + """ + return np.median([x[0] for x in self._data[-window_size:]]) + + def avg(self, window_size: int) -> float: + """ + Return the mean of the latest `window_size` values in the buffer. + """ + return np.mean([x[0] for x in self._data[-window_size:]]) + + def global_avg(self) -> float: + """ + Return the mean of all the elements in the buffer. Note that this + includes those getting removed due to limited buffer storage. + """ + return self._global_avg + + def values(self) -> List[Tuple[float, float]]: + """ + Returns: + list[(number, iteration)]: content of the current buffer. + """ + return self._data diff --git a/audio2exp-service/LAM_Audio2Expression/utils/logger.py b/audio2exp-service/LAM_Audio2Expression/utils/logger.py new file mode 100644 index 0000000..6e30c5d --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/utils/logger.py @@ -0,0 +1,167 @@ +""" +The code is base on https://github.com/Pointcept/Pointcept +""" + +import logging +import torch +import torch.distributed as dist + +from termcolor import colored + +logger_initialized = {} +root_status = 0 + + +class _ColorfulFormatter(logging.Formatter): + def __init__(self, *args, **kwargs): + self._root_name = kwargs.pop("root_name") + "." + super(_ColorfulFormatter, self).__init__(*args, **kwargs) + + def formatMessage(self, record): + log = super(_ColorfulFormatter, self).formatMessage(record) + if record.levelno == logging.WARNING: + prefix = colored("WARNING", "red", attrs=["blink"]) + elif record.levelno == logging.ERROR or record.levelno == logging.CRITICAL: + prefix = colored("ERROR", "red", attrs=["blink", "underline"]) + else: + return log + return prefix + " " + log + + +def get_logger(name, log_file=None, log_level=logging.INFO, file_mode="a", color=False): + """Initialize and get a logger by name. + + If the logger has not been initialized, this method will initialize the + logger by adding one or two handlers, otherwise the initialized logger will + be directly returned. During initialization, a StreamHandler will always be + added. If `log_file` is specified and the process rank is 0, a FileHandler + will also be added. + + Args: + name (str): Logger name. + log_file (str | None): The log filename. If specified, a FileHandler + will be added to the logger. + log_level (int): The logger level. Note that only the process of + rank 0 is affected, and other processes will set the level to + "Error" thus be silent most of the time. + file_mode (str): The file mode used in opening log file. + Defaults to 'a'. + color (bool): Colorful log output. Defaults to True + + Returns: + logging.Logger: The expected logger. + """ + logger = logging.getLogger(name) + + if name in logger_initialized: + return logger + # handle hierarchical names + # e.g., logger "a" is initialized, then logger "a.b" will skip the + # initialization since it is a child of "a". + for logger_name in logger_initialized: + if name.startswith(logger_name): + return logger + + logger.propagate = False + + stream_handler = logging.StreamHandler() + handlers = [stream_handler] + + if dist.is_available() and dist.is_initialized(): + rank = dist.get_rank() + else: + rank = 0 + + # only rank 0 will add a FileHandler + if rank == 0 and log_file is not None: + # Here, the default behaviour of the official logger is 'a'. Thus, we + # provide an interface to change the file mode to the default + # behaviour. + file_handler = logging.FileHandler(log_file, file_mode) + handlers.append(file_handler) + + plain_formatter = logging.Formatter( + "[%(asctime)s %(levelname)s %(filename)s line %(lineno)d %(process)d] %(message)s" + ) + if color: + formatter = _ColorfulFormatter( + colored("[%(asctime)s %(name)s]: ", "green") + "%(message)s", + datefmt="%m/%d %H:%M:%S", + root_name=name, + ) + else: + formatter = plain_formatter + for handler in handlers: + handler.setFormatter(formatter) + handler.setLevel(log_level) + logger.addHandler(handler) + + if rank == 0: + logger.setLevel(log_level) + else: + logger.setLevel(logging.ERROR) + + logger_initialized[name] = True + + return logger + + +def print_log(msg, logger=None, level=logging.INFO): + """Print a log message. + + Args: + msg (str): The message to be logged. + logger (logging.Logger | str | None): The logger to be used. + Some special loggers are: + - "silent": no message will be printed. + - other str: the logger obtained with `get_root_logger(logger)`. + - None: The `print()` method will be used to print log messages. + level (int): Logging level. Only available when `logger` is a Logger + object or "root". + """ + if logger is None: + print(msg) + elif isinstance(logger, logging.Logger): + logger.log(level, msg) + elif logger == "silent": + pass + elif isinstance(logger, str): + _logger = get_logger(logger) + _logger.log(level, msg) + else: + raise TypeError( + "logger should be either a logging.Logger object, str, " + f'"silent" or None, but got {type(logger)}' + ) + + +def get_root_logger(log_file=None, log_level=logging.INFO, file_mode="a"): + """Get the root logger. + + The logger will be initialized if it has not been initialized. By default a + StreamHandler will be added. If `log_file` is specified, a FileHandler will + also be added. The name of the root logger is the top-level package name. + + Args: + log_file (str | None): The log filename. If specified, a FileHandler + will be added to the root logger. + log_level (int): The root logger level. Note that only the process of + rank 0 is affected, while other processes will set the level to + "Error" and be silent most of the time. + file_mode (str): File Mode of logger. (w or a) + + Returns: + logging.Logger: The root logger. + """ + logger = get_logger( + name="pointcept", log_file=log_file, log_level=log_level, file_mode=file_mode + ) + return logger + + +def _log_api_usage(identifier: str): + """ + Internal function used to log the usage of different detectron2 components + inside facebook's infra. + """ + torch._C._log_api_usage_once("pointcept." + identifier) diff --git a/audio2exp-service/LAM_Audio2Expression/utils/misc.py b/audio2exp-service/LAM_Audio2Expression/utils/misc.py new file mode 100644 index 0000000..dbd257e --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/utils/misc.py @@ -0,0 +1,156 @@ +""" +The code is base on https://github.com/Pointcept/Pointcept +""" + +import os +import warnings +from collections import abc +import numpy as np +import torch +from importlib import import_module + + +class AverageMeter(object): + """Computes and stores the average and current value""" + + def __init__(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def reset(self): + self.val = 0 + self.avg = 0 + self.sum = 0 + self.count = 0 + + def update(self, val, n=1): + self.val = val + self.sum += val * n + self.count += n + self.avg = self.sum / self.count + + +def intersection_and_union(output, target, K, ignore_index=-1): + # 'K' classes, output and target sizes are N or N * L or N * H * W, each value in range 0 to K - 1. + assert output.ndim in [1, 2, 3] + assert output.shape == target.shape + output = output.reshape(output.size).copy() + target = target.reshape(target.size) + output[np.where(target == ignore_index)[0]] = ignore_index + intersection = output[np.where(output == target)[0]] + area_intersection, _ = np.histogram(intersection, bins=np.arange(K + 1)) + area_output, _ = np.histogram(output, bins=np.arange(K + 1)) + area_target, _ = np.histogram(target, bins=np.arange(K + 1)) + area_union = area_output + area_target - area_intersection + return area_intersection, area_union, area_target + + +def intersection_and_union_gpu(output, target, k, ignore_index=-1): + # 'K' classes, output and target sizes are N or N * L or N * H * W, each value in range 0 to K - 1. + assert output.dim() in [1, 2, 3] + assert output.shape == target.shape + output = output.view(-1) + target = target.view(-1) + output[target == ignore_index] = ignore_index + intersection = output[output == target] + area_intersection = torch.histc(intersection, bins=k, min=0, max=k - 1) + area_output = torch.histc(output, bins=k, min=0, max=k - 1) + area_target = torch.histc(target, bins=k, min=0, max=k - 1) + area_union = area_output + area_target - area_intersection + return area_intersection, area_union, area_target + + +def make_dirs(dir_name): + if not os.path.exists(dir_name): + os.makedirs(dir_name, exist_ok=True) + + +def find_free_port(): + import socket + + sock = socket.socket(socket.AF_INET, socket.SOCK_STREAM) + # Binding to port 0 will cause the OS to find an available port for us + sock.bind(("", 0)) + port = sock.getsockname()[1] + sock.close() + # NOTE: there is still a chance the port could be taken by other processes. + return port + + +def is_seq_of(seq, expected_type, seq_type=None): + """Check whether it is a sequence of some type. + + Args: + seq (Sequence): The sequence to be checked. + expected_type (type): Expected type of sequence items. + seq_type (type, optional): Expected sequence type. + + Returns: + bool: Whether the sequence is valid. + """ + if seq_type is None: + exp_seq_type = abc.Sequence + else: + assert isinstance(seq_type, type) + exp_seq_type = seq_type + if not isinstance(seq, exp_seq_type): + return False + for item in seq: + if not isinstance(item, expected_type): + return False + return True + + +def is_str(x): + """Whether the input is an string instance. + + Note: This method is deprecated since python 2 is no longer supported. + """ + return isinstance(x, str) + + +def import_modules_from_strings(imports, allow_failed_imports=False): + """Import modules from the given list of strings. + + Args: + imports (list | str | None): The given module names to be imported. + allow_failed_imports (bool): If True, the failed imports will return + None. Otherwise, an ImportError is raise. Default: False. + + Returns: + list[module] | module | None: The imported modules. + + Examples: + >>> osp, sys = import_modules_from_strings( + ... ['os.path', 'sys']) + >>> import os.path as osp_ + >>> import sys as sys_ + >>> assert osp == osp_ + >>> assert sys == sys_ + """ + if not imports: + return + single_import = False + if isinstance(imports, str): + single_import = True + imports = [imports] + if not isinstance(imports, list): + raise TypeError(f"custom_imports must be a list but got type {type(imports)}") + imported = [] + for imp in imports: + if not isinstance(imp, str): + raise TypeError(f"{imp} is of type {type(imp)} and cannot be imported.") + try: + imported_tmp = import_module(imp) + except ImportError: + if allow_failed_imports: + warnings.warn(f"{imp} failed to import and is ignored.", UserWarning) + imported_tmp = None + else: + raise ImportError + imported.append(imported_tmp) + if single_import: + imported = imported[0] + return imported diff --git a/audio2exp-service/LAM_Audio2Expression/utils/optimizer.py b/audio2exp-service/LAM_Audio2Expression/utils/optimizer.py new file mode 100644 index 0000000..2eb70a3 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/utils/optimizer.py @@ -0,0 +1,52 @@ +""" +The code is base on https://github.com/Pointcept/Pointcept +""" + +import torch +from utils.logger import get_root_logger +from utils.registry import Registry + +OPTIMIZERS = Registry("optimizers") + + +OPTIMIZERS.register_module(module=torch.optim.SGD, name="SGD") +OPTIMIZERS.register_module(module=torch.optim.Adam, name="Adam") +OPTIMIZERS.register_module(module=torch.optim.AdamW, name="AdamW") + + +def build_optimizer(cfg, model, param_dicts=None): + if param_dicts is None: + cfg.params = model.parameters() + else: + cfg.params = [dict(names=[], params=[], lr=cfg.lr)] + for i in range(len(param_dicts)): + param_group = dict(names=[], params=[]) + if "lr" in param_dicts[i].keys(): + param_group["lr"] = param_dicts[i].lr + if "momentum" in param_dicts[i].keys(): + param_group["momentum"] = param_dicts[i].momentum + if "weight_decay" in param_dicts[i].keys(): + param_group["weight_decay"] = param_dicts[i].weight_decay + cfg.params.append(param_group) + + for n, p in model.named_parameters(): + flag = False + for i in range(len(param_dicts)): + if param_dicts[i].keyword in n: + cfg.params[i + 1]["names"].append(n) + cfg.params[i + 1]["params"].append(p) + flag = True + break + if not flag: + cfg.params[0]["names"].append(n) + cfg.params[0]["params"].append(p) + + logger = get_root_logger() + for i in range(len(cfg.params)): + param_names = cfg.params[i].pop("names") + message = "" + for key in cfg.params[i].keys(): + if key != "params": + message += f" {key}: {cfg.params[i][key]};" + logger.info(f"Params Group {i+1} -{message} Params: {param_names}.") + return OPTIMIZERS.build(cfg=cfg) diff --git a/audio2exp-service/LAM_Audio2Expression/utils/path.py b/audio2exp-service/LAM_Audio2Expression/utils/path.py new file mode 100644 index 0000000..5d1da76 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/utils/path.py @@ -0,0 +1,105 @@ +""" +The code is base on https://github.com/Pointcept/Pointcept +""" +import os +import os.path as osp +from pathlib import Path + +from .misc import is_str + + +def is_filepath(x): + return is_str(x) or isinstance(x, Path) + + +def fopen(filepath, *args, **kwargs): + if is_str(filepath): + return open(filepath, *args, **kwargs) + elif isinstance(filepath, Path): + return filepath.open(*args, **kwargs) + raise ValueError("`filepath` should be a string or a Path") + + +def check_file_exist(filename, msg_tmpl='file "{}" does not exist'): + if not osp.isfile(filename): + raise FileNotFoundError(msg_tmpl.format(filename)) + + +def mkdir_or_exist(dir_name, mode=0o777): + if dir_name == "": + return + dir_name = osp.expanduser(dir_name) + os.makedirs(dir_name, mode=mode, exist_ok=True) + + +def symlink(src, dst, overwrite=True, **kwargs): + if os.path.lexists(dst) and overwrite: + os.remove(dst) + os.symlink(src, dst, **kwargs) + + +def scandir(dir_path, suffix=None, recursive=False, case_sensitive=True): + """Scan a directory to find the interested files. + + Args: + dir_path (str | obj:`Path`): Path of the directory. + suffix (str | tuple(str), optional): File suffix that we are + interested in. Default: None. + recursive (bool, optional): If set to True, recursively scan the + directory. Default: False. + case_sensitive (bool, optional) : If set to False, ignore the case of + suffix. Default: True. + + Returns: + A generator for all the interested files with relative paths. + """ + if isinstance(dir_path, (str, Path)): + dir_path = str(dir_path) + else: + raise TypeError('"dir_path" must be a string or Path object') + + if (suffix is not None) and not isinstance(suffix, (str, tuple)): + raise TypeError('"suffix" must be a string or tuple of strings') + + if suffix is not None and not case_sensitive: + suffix = ( + suffix.lower() + if isinstance(suffix, str) + else tuple(item.lower() for item in suffix) + ) + + root = dir_path + + def _scandir(dir_path, suffix, recursive, case_sensitive): + for entry in os.scandir(dir_path): + if not entry.name.startswith(".") and entry.is_file(): + rel_path = osp.relpath(entry.path, root) + _rel_path = rel_path if case_sensitive else rel_path.lower() + if suffix is None or _rel_path.endswith(suffix): + yield rel_path + elif recursive and os.path.isdir(entry.path): + # scan recursively if entry.path is a directory + yield from _scandir(entry.path, suffix, recursive, case_sensitive) + + return _scandir(dir_path, suffix, recursive, case_sensitive) + + +def find_vcs_root(path, markers=(".git",)): + """Finds the root directory (including itself) of specified markers. + + Args: + path (str): Path of directory or file. + markers (list[str], optional): List of file or directory names. + + Returns: + The directory contained one of the markers or None if not found. + """ + if osp.isfile(path): + path = osp.dirname(path) + + prev, cur = None, osp.abspath(osp.expanduser(path)) + while cur != prev: + if any(osp.exists(osp.join(cur, marker)) for marker in markers): + return cur + prev, cur = cur, osp.split(cur)[0] + return None diff --git a/audio2exp-service/LAM_Audio2Expression/utils/registry.py b/audio2exp-service/LAM_Audio2Expression/utils/registry.py new file mode 100644 index 0000000..bd0e55c --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/utils/registry.py @@ -0,0 +1,318 @@ +""" +The code is base on https://github.com/Pointcept/Pointcept +""" +import inspect +import warnings +from functools import partial + +from .misc import is_seq_of + + +def build_from_cfg(cfg, registry, default_args=None): + """Build a module from configs dict. + + Args: + cfg (dict): Config dict. It should at least contain the key "type". + registry (:obj:`Registry`): The registry to search the type from. + default_args (dict, optional): Default initialization arguments. + + Returns: + object: The constructed object. + """ + if not isinstance(cfg, dict): + raise TypeError(f"cfg must be a dict, but got {type(cfg)}") + if "type" not in cfg: + if default_args is None or "type" not in default_args: + raise KeyError( + '`cfg` or `default_args` must contain the key "type", ' + f"but got {cfg}\n{default_args}" + ) + if not isinstance(registry, Registry): + raise TypeError( + "registry must be an mmcv.Registry object, " f"but got {type(registry)}" + ) + if not (isinstance(default_args, dict) or default_args is None): + raise TypeError( + "default_args must be a dict or None, " f"but got {type(default_args)}" + ) + + args = cfg.copy() + + if default_args is not None: + for name, value in default_args.items(): + args.setdefault(name, value) + + obj_type = args.pop("type") + if isinstance(obj_type, str): + obj_cls = registry.get(obj_type) + if obj_cls is None: + raise KeyError(f"{obj_type} is not in the {registry.name} registry") + elif inspect.isclass(obj_type): + obj_cls = obj_type + else: + raise TypeError(f"type must be a str or valid type, but got {type(obj_type)}") + try: + return obj_cls(**args) + except Exception as e: + # Normal TypeError does not print class name. + raise type(e)(f"{obj_cls.__name__}: {e}") + + +class Registry: + """A registry to map strings to classes. + + Registered object could be built from registry. + Example: + >>> MODELS = Registry('models') + >>> @MODELS.register_module() + >>> class ResNet: + >>> pass + >>> resnet = MODELS.build(dict(type='ResNet')) + + Please refer to + https://mmcv.readthedocs.io/en/latest/understand_mmcv/registry.html for + advanced usage. + + Args: + name (str): Registry name. + build_func(func, optional): Build function to construct instance from + Registry, func:`build_from_cfg` is used if neither ``parent`` or + ``build_func`` is specified. If ``parent`` is specified and + ``build_func`` is not given, ``build_func`` will be inherited + from ``parent``. Default: None. + parent (Registry, optional): Parent registry. The class registered in + children registry could be built from parent. Default: None. + scope (str, optional): The scope of registry. It is the key to search + for children registry. If not specified, scope will be the name of + the package where class is defined, e.g. mmdet, mmcls, mmseg. + Default: None. + """ + + def __init__(self, name, build_func=None, parent=None, scope=None): + self._name = name + self._module_dict = dict() + self._children = dict() + self._scope = self.infer_scope() if scope is None else scope + + # self.build_func will be set with the following priority: + # 1. build_func + # 2. parent.build_func + # 3. build_from_cfg + if build_func is None: + if parent is not None: + self.build_func = parent.build_func + else: + self.build_func = build_from_cfg + else: + self.build_func = build_func + if parent is not None: + assert isinstance(parent, Registry) + parent._add_children(self) + self.parent = parent + else: + self.parent = None + + def __len__(self): + return len(self._module_dict) + + def __contains__(self, key): + return self.get(key) is not None + + def __repr__(self): + format_str = ( + self.__class__.__name__ + f"(name={self._name}, " + f"items={self._module_dict})" + ) + return format_str + + @staticmethod + def infer_scope(): + """Infer the scope of registry. + + The name of the package where registry is defined will be returned. + + Example: + # in mmdet/models/backbone/resnet.py + >>> MODELS = Registry('models') + >>> @MODELS.register_module() + >>> class ResNet: + >>> pass + The scope of ``ResNet`` will be ``mmdet``. + + + Returns: + scope (str): The inferred scope name. + """ + # inspect.stack() trace where this function is called, the index-2 + # indicates the frame where `infer_scope()` is called + filename = inspect.getmodule(inspect.stack()[2][0]).__name__ + split_filename = filename.split(".") + return split_filename[0] + + @staticmethod + def split_scope_key(key): + """Split scope and key. + + The first scope will be split from key. + + Examples: + >>> Registry.split_scope_key('mmdet.ResNet') + 'mmdet', 'ResNet' + >>> Registry.split_scope_key('ResNet') + None, 'ResNet' + + Return: + scope (str, None): The first scope. + key (str): The remaining key. + """ + split_index = key.find(".") + if split_index != -1: + return key[:split_index], key[split_index + 1 :] + else: + return None, key + + @property + def name(self): + return self._name + + @property + def scope(self): + return self._scope + + @property + def module_dict(self): + return self._module_dict + + @property + def children(self): + return self._children + + def get(self, key): + """Get the registry record. + + Args: + key (str): The class name in string format. + + Returns: + class: The corresponding class. + """ + scope, real_key = self.split_scope_key(key) + if scope is None or scope == self._scope: + # get from self + if real_key in self._module_dict: + return self._module_dict[real_key] + else: + # get from self._children + if scope in self._children: + return self._children[scope].get(real_key) + else: + # goto root + parent = self.parent + while parent.parent is not None: + parent = parent.parent + return parent.get(key) + + def build(self, *args, **kwargs): + return self.build_func(*args, **kwargs, registry=self) + + def _add_children(self, registry): + """Add children for a registry. + + The ``registry`` will be added as children based on its scope. + The parent registry could build objects from children registry. + + Example: + >>> models = Registry('models') + >>> mmdet_models = Registry('models', parent=models) + >>> @mmdet_models.register_module() + >>> class ResNet: + >>> pass + >>> resnet = models.build(dict(type='mmdet.ResNet')) + """ + + assert isinstance(registry, Registry) + assert registry.scope is not None + assert ( + registry.scope not in self.children + ), f"scope {registry.scope} exists in {self.name} registry" + self.children[registry.scope] = registry + + def _register_module(self, module_class, module_name=None, force=False): + if not inspect.isclass(module_class): + raise TypeError("module must be a class, " f"but got {type(module_class)}") + + if module_name is None: + module_name = module_class.__name__ + if isinstance(module_name, str): + module_name = [module_name] + for name in module_name: + if not force and name in self._module_dict: + raise KeyError(f"{name} is already registered " f"in {self.name}") + self._module_dict[name] = module_class + + def deprecated_register_module(self, cls=None, force=False): + warnings.warn( + "The old API of register_module(module, force=False) " + "is deprecated and will be removed, please use the new API " + "register_module(name=None, force=False, module=None) instead." + ) + if cls is None: + return partial(self.deprecated_register_module, force=force) + self._register_module(cls, force=force) + return cls + + def register_module(self, name=None, force=False, module=None): + """Register a module. + + A record will be added to `self._module_dict`, whose key is the class + name or the specified name, and value is the class itself. + It can be used as a decorator or a normal function. + + Example: + >>> backbones = Registry('backbone') + >>> @backbones.register_module() + >>> class ResNet: + >>> pass + + >>> backbones = Registry('backbone') + >>> @backbones.register_module(name='mnet') + >>> class MobileNet: + >>> pass + + >>> backbones = Registry('backbone') + >>> class ResNet: + >>> pass + >>> backbones.register_module(ResNet) + + Args: + name (str | None): The module name to be registered. If not + specified, the class name will be used. + force (bool, optional): Whether to override an existing class with + the same name. Default: False. + module (type): Module class to be registered. + """ + if not isinstance(force, bool): + raise TypeError(f"force must be a boolean, but got {type(force)}") + # NOTE: This is a walkaround to be compatible with the old api, + # while it may introduce unexpected bugs. + if isinstance(name, type): + return self.deprecated_register_module(name, force=force) + + # raise the error ahead of time + if not (name is None or isinstance(name, str) or is_seq_of(name, str)): + raise TypeError( + "name must be either of None, an instance of str or a sequence" + f" of str, but got {type(name)}" + ) + + # use it as a normal method: x.register_module(module=SomeClass) + if module is not None: + self._register_module(module_class=module, module_name=name, force=force) + return module + + # use it as a decorator: @x.register_module() + def _register(cls): + self._register_module(module_class=cls, module_name=name, force=force) + return cls + + return _register diff --git a/audio2exp-service/LAM_Audio2Expression/utils/scheduler.py b/audio2exp-service/LAM_Audio2Expression/utils/scheduler.py new file mode 100644 index 0000000..bb31459 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/utils/scheduler.py @@ -0,0 +1,144 @@ +""" +The code is base on https://github.com/Pointcept/Pointcept +""" + +import torch.optim.lr_scheduler as lr_scheduler +from .registry import Registry + +SCHEDULERS = Registry("schedulers") + + +@SCHEDULERS.register_module() +class MultiStepLR(lr_scheduler.MultiStepLR): + def __init__( + self, + optimizer, + milestones, + total_steps, + gamma=0.1, + last_epoch=-1, + verbose=False, + ): + super().__init__( + optimizer=optimizer, + milestones=[rate * total_steps for rate in milestones], + gamma=gamma, + last_epoch=last_epoch, + verbose=verbose, + ) + + +@SCHEDULERS.register_module() +class MultiStepWithWarmupLR(lr_scheduler.LambdaLR): + def __init__( + self, + optimizer, + milestones, + total_steps, + gamma=0.1, + warmup_rate=0.05, + warmup_scale=1e-6, + last_epoch=-1, + verbose=False, + ): + milestones = [rate * total_steps for rate in milestones] + + def multi_step_with_warmup(s): + factor = 1.0 + for i in range(len(milestones)): + if s < milestones[i]: + break + factor *= gamma + + if s <= warmup_rate * total_steps: + warmup_coefficient = 1 - (1 - s / warmup_rate / total_steps) * ( + 1 - warmup_scale + ) + else: + warmup_coefficient = 1.0 + return warmup_coefficient * factor + + super().__init__( + optimizer=optimizer, + lr_lambda=multi_step_with_warmup, + last_epoch=last_epoch, + verbose=verbose, + ) + + +@SCHEDULERS.register_module() +class PolyLR(lr_scheduler.LambdaLR): + def __init__(self, optimizer, total_steps, power=0.9, last_epoch=-1, verbose=False): + super().__init__( + optimizer=optimizer, + lr_lambda=lambda s: (1 - s / (total_steps + 1)) ** power, + last_epoch=last_epoch, + verbose=verbose, + ) + + +@SCHEDULERS.register_module() +class ExpLR(lr_scheduler.LambdaLR): + def __init__(self, optimizer, total_steps, gamma=0.9, last_epoch=-1, verbose=False): + super().__init__( + optimizer=optimizer, + lr_lambda=lambda s: gamma ** (s / total_steps), + last_epoch=last_epoch, + verbose=verbose, + ) + + +@SCHEDULERS.register_module() +class CosineAnnealingLR(lr_scheduler.CosineAnnealingLR): + def __init__(self, optimizer, total_steps, eta_min=0, last_epoch=-1, verbose=False): + super().__init__( + optimizer=optimizer, + T_max=total_steps, + eta_min=eta_min, + last_epoch=last_epoch, + verbose=verbose, + ) + + +@SCHEDULERS.register_module() +class OneCycleLR(lr_scheduler.OneCycleLR): + r""" + torch.optim.lr_scheduler.OneCycleLR, Block total_steps + """ + + def __init__( + self, + optimizer, + max_lr, + total_steps=None, + pct_start=0.3, + anneal_strategy="cos", + cycle_momentum=True, + base_momentum=0.85, + max_momentum=0.95, + div_factor=25.0, + final_div_factor=1e4, + three_phase=False, + last_epoch=-1, + verbose=False, + ): + super().__init__( + optimizer=optimizer, + max_lr=max_lr, + total_steps=total_steps, + pct_start=pct_start, + anneal_strategy=anneal_strategy, + cycle_momentum=cycle_momentum, + base_momentum=base_momentum, + max_momentum=max_momentum, + div_factor=div_factor, + final_div_factor=final_div_factor, + three_phase=three_phase, + last_epoch=last_epoch, + verbose=verbose, + ) + + +def build_scheduler(cfg, optimizer): + cfg.optimizer = optimizer + return SCHEDULERS.build(cfg=cfg) diff --git a/audio2exp-service/LAM_Audio2Expression/utils/timer.py b/audio2exp-service/LAM_Audio2Expression/utils/timer.py new file mode 100644 index 0000000..7b7e9cb --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/utils/timer.py @@ -0,0 +1,71 @@ +""" +The code is base on https://github.com/Pointcept/Pointcept +""" + +from time import perf_counter +from typing import Optional + + +class Timer: + """ + A timer which computes the time elapsed since the start/reset of the timer. + """ + + def __init__(self) -> None: + self.reset() + + def reset(self) -> None: + """ + Reset the timer. + """ + self._start = perf_counter() + self._paused: Optional[float] = None + self._total_paused = 0 + self._count_start = 1 + + def pause(self) -> None: + """ + Pause the timer. + """ + if self._paused is not None: + raise ValueError("Trying to pause a Timer that is already paused!") + self._paused = perf_counter() + + def is_paused(self) -> bool: + """ + Returns: + bool: whether the timer is currently paused + """ + return self._paused is not None + + def resume(self) -> None: + """ + Resume the timer. + """ + if self._paused is None: + raise ValueError("Trying to resume a Timer that is not paused!") + # pyre-fixme[58]: `-` is not supported for operand types `float` and + # `Optional[float]`. + self._total_paused += perf_counter() - self._paused + self._paused = None + self._count_start += 1 + + def seconds(self) -> float: + """ + Returns: + (float): the total number of seconds since the start/reset of the + timer, excluding the time when the timer is paused. + """ + if self._paused is not None: + end_time: float = self._paused # type: ignore + else: + end_time = perf_counter() + return end_time - self._start - self._total_paused + + def avg_seconds(self) -> float: + """ + Returns: + (float): the average number of seconds between every start/reset and + pause. + """ + return self.seconds() / self._count_start diff --git a/audio2exp-service/LAM_Audio2Expression/utils/visualization.py b/audio2exp-service/LAM_Audio2Expression/utils/visualization.py new file mode 100644 index 0000000..053cb64 --- /dev/null +++ b/audio2exp-service/LAM_Audio2Expression/utils/visualization.py @@ -0,0 +1,86 @@ +""" +The code is base on https://github.com/Pointcept/Pointcept +""" + +import os +import open3d as o3d +import numpy as np +import torch + + +def to_numpy(x): + if isinstance(x, torch.Tensor): + x = x.clone().detach().cpu().numpy() + assert isinstance(x, np.ndarray) + return x + + +def save_point_cloud(coord, color=None, file_path="pc.ply", logger=None): + os.makedirs(os.path.dirname(file_path), exist_ok=True) + coord = to_numpy(coord) + if color is not None: + color = to_numpy(color) + pcd = o3d.geometry.PointCloud() + pcd.points = o3d.utility.Vector3dVector(coord) + pcd.colors = o3d.utility.Vector3dVector( + np.ones_like(coord) if color is None else color + ) + o3d.io.write_point_cloud(file_path, pcd) + if logger is not None: + logger.info(f"Save Point Cloud to: {file_path}") + + +def save_bounding_boxes( + bboxes_corners, color=(1.0, 0.0, 0.0), file_path="bbox.ply", logger=None +): + bboxes_corners = to_numpy(bboxes_corners) + # point list + points = bboxes_corners.reshape(-1, 3) + # line list + box_lines = np.array( + [ + [0, 1], + [1, 2], + [2, 3], + [3, 0], + [4, 5], + [5, 6], + [6, 7], + [7, 0], + [0, 4], + [1, 5], + [2, 6], + [3, 7], + ] + ) + lines = [] + for i, _ in enumerate(bboxes_corners): + lines.append(box_lines + i * 8) + lines = np.concatenate(lines) + # color list + color = np.array([color for _ in range(len(lines))]) + # generate line set + line_set = o3d.geometry.LineSet() + line_set.points = o3d.utility.Vector3dVector(points) + line_set.lines = o3d.utility.Vector2iVector(lines) + line_set.colors = o3d.utility.Vector3dVector(color) + o3d.io.write_line_set(file_path, line_set) + + if logger is not None: + logger.info(f"Save Boxes to: {file_path}") + + +def save_lines( + points, lines, color=(1.0, 0.0, 0.0), file_path="lines.ply", logger=None +): + points = to_numpy(points) + lines = to_numpy(lines) + colors = np.array([color for _ in range(len(lines))]) + line_set = o3d.geometry.LineSet() + line_set.points = o3d.utility.Vector3dVector(points) + line_set.lines = o3d.utility.Vector2iVector(lines) + line_set.colors = o3d.utility.Vector3dVector(colors) + o3d.io.write_line_set(file_path, line_set) + + if logger is not None: + logger.info(f"Save Lines to: {file_path}") diff --git a/audio2exp-service/LAM_Audio2Expression/wheels/gradio_gaussian_render-0.0.3-py3-none-any.whl b/audio2exp-service/LAM_Audio2Expression/wheels/gradio_gaussian_render-0.0.3-py3-none-any.whl new file mode 100644 index 0000000..739925e Binary files /dev/null and b/audio2exp-service/LAM_Audio2Expression/wheels/gradio_gaussian_render-0.0.3-py3-none-any.whl differ diff --git a/audio2exp-service/README.md b/audio2exp-service/README.md new file mode 100644 index 0000000..fea64eb --- /dev/null +++ b/audio2exp-service/README.md @@ -0,0 +1,201 @@ +# Audio2Expression Service + +gourmet-sp の TTS 音声から表情データを生成するマイクロサービス。 + +## アーキテクチャ + +``` +┌─ gourmet-sp (既存) ─────────────────────────────┐ +│ ユーザー → LLM → GCP TTS → 音声(base64) │ +└────────────────────────┬────────────────────────┘ + │ POST /api/audio2expression + ▼ +┌─ Audio2Expression Service (このサービス) ───────┐ +│ 音声 → Audio2Expression → 表情データ (52ch) │ +└────────────────────────┬────────────────────────┘ + │ WebSocket + ▼ +┌─ ブラウザ ──────────────────────────────────────┐ +│ LAMAvatar (WebGL) ← 表情データで口パク │ +└─────────────────────────────────────────────────┘ +``` + +## セットアップ + +### 1. 依存関係のインストール + +```bash +cd audio2exp-service +pip install -r requirements.txt +``` + +### 2. モデルのダウンロード（オプション） + +Audio2Expression モデルを使用する場合: + +```bash +# HuggingFaceからダウンロード +huggingface-cli download 3DAIGC/LAM_audio2exp --local-dir ./models +``` + +モデルがない場合は**モックモード**で動作します（音声振幅に基づく簡易的な口パク）。 + +### 3. サービス起動 + +```bash +# 基本起動（モックモード） +python app.py + +# モデル指定 +python app.py --model-path ./models/pretrained_models/lam_audio2exp_streaming.tar + +# ポート指定 +python app.py --port 8283 +``` + +## API + +### REST API + +#### POST /api/audio2expression + +TTS音声を表情データに変換。 + +**Request:** +```json +{ + "audio_base64": "base64エンコードされた音声 (PCM 16-bit, 16kHz)", + "session_id": "セッションID", + "is_final": false +} +``` + +**Response:** +```json +{ + "session_id": "セッションID", + "channels": ["browDownLeft", "browDownRight", ...], + "weights": [[0.0, 0.1, ...]], + "timestamp": 1234567890.123 +} +``` + +### WebSocket + +#### WS /ws/{session_id} + +リアルタイム表情データストリーミング。 + +**接続:** +```javascript +const ws = new WebSocket('ws://localhost:8283/ws/my-session'); +``` + +**受信データ:** +```json +{ + "type": "expression", + "session_id": "my-session", + "channels": ["browDownLeft", ...], + "weights": [[0.0, 0.1, ...]], + "is_final": false, + "timestamp": 1234567890.123 +} +``` + +## gourmet-sp との連携 + +### バックエンド側（最小変更） + +TTS音声取得後に、このサービスにも送信: + +```python +# 既存のTTS処理後に追加 +async def send_to_audio2expression(audio_base64: str, session_id: str): + async with aiohttp.ClientSession() as session: + await session.post( + "http://localhost:8283/api/audio2expression", + json={ + "audio_base64": audio_base64, + "session_id": session_id, + "is_final": False + } + ) +``` + +### フロントエンド側（LAMAvatar） + +WebSocket接続: + +```javascript +const controller = window.lamAvatarController; +await controller.connectWebSocket('ws://localhost:8283/ws/' + sessionId); +``` + +## GCP Cloud Run デプロイ + +### PowerShell スクリプトでデプロイ（推奨） + +```powershell +cd audio2exp-service +./deploy.ps1 +``` + +### 手動デプロイ + +```bash +cd audio2exp-service + +# ビルド +gcloud builds submit --tag gcr.io/hp-support-477512/audio2exp-service --project hp-support-477512 + +# デプロイ +gcloud run deploy audio2exp-service \ + --image gcr.io/hp-support-477512/audio2exp-service \ + --platform managed \ + --region us-central1 \ + --allow-unauthenticated \ + --memory 1Gi \ + --cpu 1 \ + --timeout 300 \ + --project hp-support-477512 +``` + +### デプロイ後の確認 + +```bash +# サービスURLを取得 +gcloud run services describe audio2exp-service \ + --region us-central1 \ + --format 'value(status.url)' \ + --project hp-support-477512 + +# ヘルスチェック +curl https://audio2exp-service-xxxxx-uc.a.run.app/health +``` + +## gourmet-support との連携設定 + +### 1. gourmet-support の deploy.ps1 に環境変数を追加 + +```powershell +# deploy.ps1 の環境変数部分に追加 +$AUDIO2EXP_SERVICE_URL = "https://audio2exp-service-xxxxx-uc.a.run.app" + +# --set-env-vars に追加 +--set-env-vars "...,AUDIO2EXP_SERVICE_URL=$AUDIO2EXP_SERVICE_URL" +``` + +### 2. バックエンドコードの追加 + +`integration/gourmet_support_patch.py` を参照して、 +TTS処理後に audio2exp-service へ音声を転送するコードを追加。 + +## 動作モード + +| モード | 条件 | 精度 | +|--------|------|------| +| 推論モード | Audio2Expressionモデルあり | 高精度 | +| モックモード | モデルなし | 音声振幅ベース（簡易） | + +モックモードでも口パクの動作確認は可能です。 diff --git a/audio2exp-service/app.py b/audio2exp-service/app.py new file mode 100644 index 0000000..3ad9aaf --- /dev/null +++ b/audio2exp-service/app.py @@ -0,0 +1,427 @@ +""" +Audio2Expression Service for LAM Lip Sync - Cloud Run Optimized +Bypass DDP initialization and use /tmp for all file writes. +""" + +import asyncio +import base64 +import json +import os +import struct +import sys +import time +import logging +import traceback +from contextlib import asynccontextmanager +from typing import Dict, List +import numpy as np +from fastapi import FastAPI, WebSocket, WebSocketDisconnect, HTTPException +from fastapi.middleware.cors import CORSMiddleware +from pydantic import BaseModel +import uvicorn +import torch + +# --- 1. Logger setup --- +# Cloud Run restricts filesystem writes. Use stdout only. +logging.basicConfig(level=logging.INFO, stream=sys.stdout) +logger = logging.getLogger("Audio2Expression") + +# --- Path configuration --- +SCRIPT_DIR = os.path.dirname(os.path.abspath(__file__)) + +# Cloud Run: GCS FUSE mount path (primary), Docker-baked models (fallback) +MOUNT_PATH = os.environ.get("MODEL_MOUNT_PATH", "/mnt/models") +MODEL_SUBDIR = os.environ.get("MODEL_SUBDIR", "audio2exp") + +# LAM module path resolution +LAM_A2E_CANDIDATES = [ + os.environ.get("LAM_A2E_PATH"), + os.path.join(SCRIPT_DIR, "LAM_Audio2Expression"), +] +LAM_A2E_PATH = None +for candidate in LAM_A2E_CANDIDATES: + if candidate and os.path.exists(candidate): + LAM_A2E_PATH = os.path.abspath(candidate) + break + +if LAM_A2E_PATH: + sys.path.insert(0, LAM_A2E_PATH) + logger.info(f"Added LAM_Audio2Expression to path: {LAM_A2E_PATH}") +else: + logger.error("LAM_Audio2Expression not found!") + +# --- ARKit 52 channels --- +ARKIT_CHANNELS = [ + "browDownLeft", "browDownRight", "browInnerUp", "browOuterUpLeft", "browOuterUpRight", + "cheekPuff", "cheekSquintLeft", "cheekSquintRight", + "eyeBlinkLeft", "eyeBlinkRight", "eyeLookDownLeft", "eyeLookDownRight", + "eyeLookInLeft", "eyeLookInRight", "eyeLookOutLeft", "eyeLookOutRight", + "eyeLookUpLeft", "eyeLookUpRight", "eyeSquintLeft", "eyeSquintRight", + "eyeWideLeft", "eyeWideRight", + "jawForward", "jawLeft", "jawOpen", "jawRight", + "mouthClose", "mouthDimpleLeft", "mouthDimpleRight", "mouthFrownLeft", "mouthFrownRight", + "mouthFunnel", "mouthLeft", "mouthLowerDownLeft", "mouthLowerDownRight", + "mouthPressLeft", "mouthPressRight", "mouthPucker", "mouthRight", + "mouthRollLower", "mouthRollUpper", "mouthShrugLower", "mouthShrugUpper", + "mouthSmileLeft", "mouthSmileRight", "mouthStretchLeft", "mouthStretchRight", + "mouthUpperUpLeft", "mouthUpperUpRight", + "noseSneerLeft", "noseSneerRight", + "tongueOut" +] + + +# --- JBIN bundle generator --- +def create_jbin_bundle(expression: np.ndarray, audio: np.ndarray, + expression_sample_rate: int = 30, + audio_sample_rate: int = 24000, + batch_id: int = 0, + start_of_batch: bool = False, + end_of_batch: bool = False) -> bytes: + if audio.dtype == np.float32: + audio_int16 = (audio * 32767).astype(np.int16) + else: + audio_int16 = audio.astype(np.int16) + + expression_f32 = np.ascontiguousarray(expression.astype(np.float32)) + expression_bytes = expression_f32.tobytes() + audio_bytes = audio_int16.tobytes() + + descriptor = { + "data_records": { + "arkit_face": { + "data_type": "float32", + "data_offset": 0, + "shape": list(expression_f32.shape), + "channel_names": ARKIT_CHANNELS, + "sample_rate": expression_sample_rate, + "data_id": 0, + "timeline_axis": 0, + "channel_axis": 1 + }, + "avatar_audio": { + "data_type": "int16", + "data_offset": len(expression_bytes), + "shape": [1, len(audio_int16)], + "sample_rate": audio_sample_rate, + "data_id": 1, + "timeline_axis": 1 + } + }, + "metadata": {}, + "events": [], + "batch_id": batch_id, + "start_of_batch": start_of_batch, + "end_of_batch": end_of_batch + } + json_bytes = json.dumps(descriptor).encode('utf-8') + header = (b'JBIN' + + struct.pack(' str: + """Resolve model file: FUSE mount first, then Docker-baked fallback.""" + candidates = [] + fuse_dir = os.path.join(MOUNT_PATH, MODEL_SUBDIR) + if subdir: + candidates.append(os.path.join(fuse_dir, subdir)) + candidates.append(os.path.join(SCRIPT_DIR, "models", subdir)) + else: + candidates.append(os.path.join(fuse_dir, filename)) + candidates.append(os.path.join(SCRIPT_DIR, "models", filename)) + + for path in candidates: + if os.path.exists(path): + logger.info(f"Found: {path}") + return path + logger.error(f"NOT FOUND: {filename} (searched {candidates})") + return None + + def initialize(self): + if self.initialized: + return + if not LAM_A2E_PATH: + logger.error("Cannot initialize: LAM_A2E_PATH not found") + return + + try: + logger.info("Initializing Audio2Expression Engine...") + + from engines.defaults import default_config_parser + from engines.infer import INFER + + # --- CRITICAL: Force DDP environment for single-process --- + os.environ["WORLD_SIZE"] = "1" + os.environ["RANK"] = "0" + os.environ["MASTER_ADDR"] = "localhost" + os.environ["MASTER_PORT"] = "12345" + + # Resolve model paths via FUSE mount (primary) or Docker-baked (fallback) + lam_weight_path = self._resolve_model_path("lam_audio2exp_streaming.pth") + wav2vec_path = self._resolve_model_path(None, subdir="wav2vec2-base-960h") + + if not lam_weight_path: + logger.error("LAM model weight (.pth) not found. Aborting.") + return + if not wav2vec_path: + logger.error("wav2vec2 model not found. Aborting.") + return + + # wav2vec config: use config.json from the model directory itself + wav2vec_config = os.path.join(wav2vec_path, "config.json") + if not os.path.exists(wav2vec_config): + logger.error(f"wav2vec2 config.json not found at: {wav2vec_config}") + return + logger.info(f"wav2vec2 config: {wav2vec_config}") + + config_file = os.path.join(LAM_A2E_PATH, "configs", + "lam_audio2exp_config_streaming.py") + + # --- CRITICAL: Config override to bypass DDP --- + # save_path -> /tmp (only writable dir on Cloud Run) + # This allows default_config_parser's os.makedirs() and cfg.dump() to succeed. + save_path = "/tmp/audio2exp_logs" + os.makedirs(save_path, exist_ok=True) + os.makedirs(os.path.join(save_path, "model"), exist_ok=True) + + cfg_options = { + "weight": lam_weight_path, + "save_path": save_path, + "model": { + "backbone": { + "wav2vec2_config_path": wav2vec_config, + "pretrained_encoder_path": wav2vec_path + } + }, + "num_worker": 0, + "batch_size": 1, + } + + logger.info(f"Loading config: {config_file}") + logger.info(f"Model weight: {lam_weight_path}") + logger.info(f"wav2vec2 path: {wav2vec_path}") + cfg = default_config_parser(config_file, cfg_options) + + # --- CRITICAL: Skip default_setup() entirely --- + # default_setup() calls comm.get_world_size(), batch_size asserts, + # and num_worker calculations that are unnecessary for inference. + # Instead, set the minimal required fields manually. + cfg.device = torch.device('cpu') + cfg.num_worker = 0 + cfg.num_worker_per_gpu = 0 + cfg.batch_size_per_gpu = 1 + cfg.batch_size_val_per_gpu = 1 + cfg.batch_size_test_per_gpu = 1 + + logger.info("Building INFER model (skipping default_setup)...") + self.infer = INFER.build(dict(type=cfg.infer.type, cfg=cfg)) + + # Force CPU + eval mode + self.infer.model.to(torch.device('cpu')) + self.infer.model.eval() + + # Warmup inference + logger.info("Running warmup inference...") + dummy_audio = np.zeros(self.input_sample_rate, dtype=np.float32) + self.infer.infer_streaming_audio( + audio=dummy_audio, ssr=self.input_sample_rate, context=None + ) + + self.initialized = True + logger.info("Model initialized successfully!") + + except Exception as e: + logger.critical(f"Initialization FAILED: {e}") + traceback.print_exc() + self.initialized = False + + def process_full_audio(self, audio: np.ndarray, + sample_rate: int = 24000) -> np.ndarray: + if not self.initialized: + logger.warning("Model not initialized, returning mock expression.") + return self._mock_expression(audio, sample_rate=sample_rate) + + chunk_samples = sample_rate + all_expressions = [] + context = None + + try: + for start in range(0, len(audio), chunk_samples): + end = min(start + chunk_samples, len(audio)) + chunk = audio[start:end] + if len(chunk) < sample_rate // 10: + continue + + result, context = self.infer.infer_streaming_audio( + audio=chunk, ssr=sample_rate, context=context + ) + expr = result.get("expression") + if expr is not None: + all_expressions.append(expr.astype(np.float32)) + + if not all_expressions: + return np.zeros((1, 52), dtype=np.float32) + + return np.concatenate(all_expressions, axis=0) + + except Exception as e: + logger.error(f"Inference error: {e}") + return self._mock_expression(audio, sample_rate=sample_rate) + + def _mock_expression(self, audio: np.ndarray, + sample_rate: int = 24000) -> np.ndarray: + frame_rate = 30 + samples_per_frame = sample_rate // frame_rate + num_frames = max(1, len(audio) // samples_per_frame) + return np.zeros((num_frames, 52), dtype=np.float32) + + +# --- FastAPI Setup --- +engine = Audio2ExpressionEngine() +active_connections: Dict[str, WebSocket] = {} +session_batch_ids: Dict[str, int] = {} +session_chunk_counts: Dict[str, int] = {} + + +class AudioRequest(BaseModel): + audio_base64: str + session_id: str + is_start: bool = False + is_final: bool = False + audio_format: str = "pcm" + sample_rate: int = 24000 + + +class ExpressionResponse(BaseModel): + session_id: str + names: List[str] + frames: List[dict] + frame_rate: int = 30 + timestamp: float + batch_id: int = 0 + + +@asynccontextmanager +async def lifespan(app: FastAPI): + # Synchronous model load at startup + engine.initialize() + yield + + +app = FastAPI(title="Gourmet AI Concierge LipSync", lifespan=lifespan) +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], allow_methods=["*"], allow_headers=["*"] +) + + +@app.get("/health") +async def health_check(): + return { + "status": "ok", + "model_initialized": engine.initialized, + "mode": "inference" if engine.initialized else "mock", + "mount_check": os.path.exists(os.path.join(MOUNT_PATH, MODEL_SUBDIR)) + } + + +@app.post("/api/audio2expression", response_model=ExpressionResponse) +async def process_audio_endpoint(request: AudioRequest): + try: + audio_bytes = base64.b64decode(request.audio_base64) + + if request.audio_format == "mp3": + try: + from pydub import AudioSegment + import io + seg = AudioSegment.from_mp3(io.BytesIO(audio_bytes)).set_channels(1) + audio_int16 = np.array(seg.get_array_of_samples(), dtype=np.int16) + actual_sr = seg.frame_rate + except ImportError: + audio_int16 = np.frombuffer(audio_bytes, dtype=np.int16) + actual_sr = request.sample_rate + else: + audio_int16 = np.frombuffer(audio_bytes, dtype=np.int16) + actual_sr = request.sample_rate + + audio_float = audio_int16.astype(np.float32) / 32768.0 + + sid = request.session_id + if request.is_start or sid not in session_batch_ids: + session_batch_ids[sid] = session_batch_ids.get(sid, 0) + 1 + session_chunk_counts[sid] = 0 + + batch_id = session_batch_ids[sid] + session_chunk_counts[sid] = session_chunk_counts.get(sid, 0) + 1 + is_start_chunk = (session_chunk_counts[sid] == 1) + + expression = engine.process_full_audio(audio_float, sample_rate=actual_sr) + + if expression is None: + raise HTTPException(status_code=500, detail="Inference failed") + + # Send JBIN via WebSocket if connected + if sid in active_connections: + await send_bundled_to_ws( + active_connections[sid], expression, audio_int16, sid, + batch_id=batch_id, + start_of_batch=is_start_chunk, + end_of_batch=request.is_final, + audio_sample_rate=actual_sr + ) + + frames = [{"weights": row} for row in expression.tolist()] + return ExpressionResponse( + session_id=sid, names=ARKIT_CHANNELS, frames=frames, + frame_rate=30, timestamp=time.time(), batch_id=batch_id + ) + + except HTTPException: + raise + except Exception as e: + logger.error(f"API Error: {e}") + traceback.print_exc() + raise HTTPException(status_code=500, detail=str(e)) + + +async def send_bundled_to_ws(ws: WebSocket, expression: np.ndarray, + audio: np.ndarray, session_id: str, + batch_id: int, start_of_batch: bool, + end_of_batch: bool, audio_sample_rate: int): + try: + jbin_data = create_jbin_bundle( + expression, audio, 30, audio_sample_rate, + batch_id, start_of_batch, end_of_batch + ) + await ws.send_bytes(jbin_data) + except Exception as e: + logger.error(f"WS Send Error [{session_id}]: {e}") + + +@app.websocket("/ws/{session_id}") +async def websocket_endpoint(websocket: WebSocket, session_id: str): + await websocket.accept() + active_connections[session_id] = websocket + try: + while True: + data = await websocket.receive_text() + msg = json.loads(data) + if msg.get("type") == "ping": + await websocket.send_json({"type": "pong"}) + except WebSocketDisconnect: + pass + finally: + active_connections.pop(session_id, None) + + +if __name__ == "__main__": + uvicorn.run(app, host="0.0.0.0", port=int(os.environ.get("PORT", 8080))) diff --git a/audio2exp-service/cloudbuild.yaml b/audio2exp-service/cloudbuild.yaml new file mode 100644 index 0000000..8398cfc --- /dev/null +++ b/audio2exp-service/cloudbuild.yaml @@ -0,0 +1,80 @@ +# Cloud Build configuration for audio2exp-service +# Models are baked into Docker image (fallback) AND served via GCS FUSE mount (primary) + +options: + machineType: 'E2_HIGHCPU_8' + diskSizeGb: 100 + +steps: + # Step 1: Download models from GCS (baked into image as fallback) + - name: 'gcr.io/google.com/cloudsdktool/cloud-sdk' + entrypoint: 'bash' + args: + - '-c' + - | + mkdir -p models + echo "Downloading LAM model..." + gsutil cp gs://hp-support-477512-models/audio2exp/lam_audio2exp_streaming.pth models/ + echo "Downloading wav2vec2 model..." + gsutil -m cp -r gs://hp-support-477512-models/audio2exp/wav2vec2-base-960h models/ + echo "Models downloaded:" + ls -la models/ + ls -la models/wav2vec2-base-960h/ || true + + # Step 2: Build Docker image (models are copied into image) + - name: 'gcr.io/cloud-builders/docker' + args: + - 'build' + - '--no-cache' + - '-t' + - 'gcr.io/$PROJECT_ID/audio2exp-service:$BUILD_ID' + - '-t' + - 'gcr.io/$PROJECT_ID/audio2exp-service:latest' + - '.' + + # Step 3: Push to Container Registry + - name: 'gcr.io/cloud-builders/docker' + args: + - 'push' + - '--all-tags' + - 'gcr.io/$PROJECT_ID/audio2exp-service' + + # Step 4: Deploy to Cloud Run with GCS FUSE volume mount + - name: 'gcr.io/google.com/cloudsdktool/cloud-sdk' + entrypoint: gcloud + args: + - 'run' + - 'deploy' + - 'audio2exp-service' + - '--image' + - 'gcr.io/$PROJECT_ID/audio2exp-service:$BUILD_ID' + - '--region' + - 'us-central1' + - '--platform' + - 'managed' + - '--allow-unauthenticated' + - '--execution-environment' + - 'gen2' + - '--memory' + - '8Gi' + - '--cpu' + - '4' + - '--cpu-boost' + - '--timeout' + - '300' + - '--concurrency' + - '10' + - '--min-instances' + - '0' + - '--max-instances' + - '4' + - '--add-volume' + - 'name=models,type=cloud-storage,bucket=hp-support-477512-models' + - '--add-volume-mount' + - 'volume=models,mount-path=/mnt/models' + +images: + - 'gcr.io/$PROJECT_ID/audio2exp-service:$BUILD_ID' + - 'gcr.io/$PROJECT_ID/audio2exp-service:latest' + +timeout: '3600s' diff --git a/audio2exp-service/cpu_support.patch b/audio2exp-service/cpu_support.patch new file mode 100644 index 0000000..854c442 --- /dev/null +++ b/audio2exp-service/cpu_support.patch @@ -0,0 +1,69 @@ +diff --git a/engines/infer.py b/engines/infer.py +index ffd6cfe..a5eac9f 100644 +--- a/engines/infer.py ++++ b/engines/infer.py +@@ -41,14 +41,24 @@ from models.utils import smooth_mouth_movements, apply_frame_blending, apply_sav + + INFER = Registry("infer") + ++# Device detection for CPU/GPU support ++def get_device(): ++ """Get the best available device (CUDA or CPU)""" ++ if torch.cuda.is_available(): ++ return torch.device('cuda') ++ else: ++ return torch.device('cpu') ++ + class InferBase: + def __init__(self, cfg, model=None, verbose=False) -> None: + torch.multiprocessing.set_sharing_strategy("file_system") ++ self.device = get_device() + self.logger = get_root_logger( + log_file=os.path.join(cfg.save_path, "infer.log"), + file_mode="a" if cfg.resume else "w", + ) + self.logger.info("=> Loading config ...") ++ self.logger.info(f"=> Using device: {self.device}") + self.cfg = cfg + self.verbose = verbose + if self.verbose: +@@ -65,13 +75,13 @@ class InferBase: + n_parameters = sum(p.numel() for p in model.parameters() if p.requires_grad) + self.logger.info(f"Num params: {n_parameters}") + model = create_ddp_model( +- model.cuda(), ++ model.to(self.device), + broadcast_buffers=False, + find_unused_parameters=self.cfg.find_unused_parameters, + ) + if os.path.isfile(self.cfg.weight): + self.logger.info(f"Loading weight at: {self.cfg.weight}") +- checkpoint = torch.load(self.cfg.weight) ++ checkpoint = torch.load(self.cfg.weight, map_location=self.device) + weight = OrderedDict() + for key, value in checkpoint["state_dict"].items(): + if key.startswith("module."): +@@ -117,9 +127,9 @@ class Audio2ExpressionInfer(InferBase): + with torch.no_grad(): + input_dict = {} + input_dict['id_idx'] = F.one_hot(torch.tensor(self.cfg.id_idx), +- self.cfg.model.backbone.num_identity_classes).cuda(non_blocking=True)[None,...] ++ self.cfg.model.backbone.num_identity_classes).to(self.device)[None,...] + speech_array, ssr = librosa.load(self.cfg.audio_input, sr=16000) +- input_dict['input_audio_array'] = torch.FloatTensor(speech_array).cuda(non_blocking=True)[None,...] ++ input_dict['input_audio_array'] = torch.FloatTensor(speech_array).to(self.device)[None,...] + + end = time.time() + output_dict = self.model(input_dict) +@@ -198,9 +208,9 @@ class Audio2ExpressionInfer(InferBase): + try: + input_dict = {} + input_dict['id_idx'] = F.one_hot(torch.tensor(self.cfg.id_idx), +- self.cfg.model.backbone.num_identity_classes).cuda(non_blocking=True)[ ++ self.cfg.model.backbone.num_identity_classes).to(self.device)[ + None, ...] +- input_dict['input_audio_array'] = torch.FloatTensor(input_audio).cuda(non_blocking=True)[None, ...] ++ input_dict['input_audio_array'] = torch.FloatTensor(input_audio).to(self.device)[None, ...] + output_dict = self.model(input_dict) + out_exp = output_dict['pred_exp'].squeeze().cpu().numpy()[start_frame:, :] + except: diff --git a/audio2exp-service/deploy.ps1 b/audio2exp-service/deploy.ps1 new file mode 100644 index 0000000..c62da94 --- /dev/null +++ b/audio2exp-service/deploy.ps1 @@ -0,0 +1,69 @@ +# Audio2Expression Service デプロイスクリプト (PowerShell) + +# 設定 +$PROJECT_ID = "hp-support-477512" +$SERVICE_NAME = "audio2exp-service" +$REGION = "us-central1" +$IMAGE_NAME = "gcr.io/$PROJECT_ID/$SERVICE_NAME" + +Write-Host "====================================" -ForegroundColor Cyan +Write-Host "Audio2Expression Service デプロイ" -ForegroundColor Cyan +Write-Host "====================================" -ForegroundColor Cyan +Write-Host "" + +# デバッグ: 変数確認 +Write-Host "PROJECT_ID: $PROJECT_ID" -ForegroundColor Gray +Write-Host "IMAGE_NAME: $IMAGE_NAME" -ForegroundColor Gray +Write-Host "" + +# 1. イメージビルド +Write-Host "[1/3] Dockerイメージをビルド中..." -ForegroundColor Yellow +gcloud builds submit --tag "$IMAGE_NAME" --project "$PROJECT_ID" + +if ($LASTEXITCODE -ne 0) { + Write-Host "ビルドに失敗しました" -ForegroundColor Red + exit 1 +} +Write-Host "ビルド完了" -ForegroundColor Green +Write-Host "" + +# 2. Cloud Runにデプロイ +Write-Host "[2/3] Cloud Runにデプロイ中..." -ForegroundColor Yellow +gcloud run deploy "$SERVICE_NAME" ` + --image "$IMAGE_NAME" ` + --platform managed ` + --region "$REGION" ` + --allow-unauthenticated ` + --memory 1Gi ` + --cpu 1 ` + --timeout 300 ` + --max-instances 10 ` + --project "$PROJECT_ID" + +if ($LASTEXITCODE -ne 0) { + Write-Host "デプロイに失敗しました" -ForegroundColor Red + exit 1 +} +Write-Host "デプロイ完了" -ForegroundColor Green +Write-Host "" + +# 3. URLを取得 +Write-Host "[3/3] サービスURLを取得中..." -ForegroundColor Yellow +$SERVICE_URL = gcloud run services describe "$SERVICE_NAME" ` + --region "$REGION" ` + --format 'value(status.url)' ` + --project "$PROJECT_ID" + +Write-Host "" +Write-Host "====================================" -ForegroundColor Cyan +Write-Host "デプロイが完了しました!" -ForegroundColor Green +Write-Host "====================================" -ForegroundColor Cyan +Write-Host "" +Write-Host "サービスURL: $SERVICE_URL" -ForegroundColor Yellow +Write-Host "" +Write-Host "次のステップ:" -ForegroundColor Cyan +Write-Host "1. gourmet-support に環境変数を追加:" +Write-Host " AUDIO2EXP_SERVICE_URL=$SERVICE_URL" -ForegroundColor Yellow +Write-Host "" +Write-Host "2. gourmet-support の deploy.ps1 を修正して再デプロイ" +Write-Host "" diff --git a/audio2exp-service/fix_gcs_model.sh b/audio2exp-service/fix_gcs_model.sh new file mode 100755 index 0000000..cd55e6a --- /dev/null +++ b/audio2exp-service/fix_gcs_model.sh @@ -0,0 +1,54 @@ +#!/bin/bash +# Fix the corrupted model file in GCS +# The issue: GCS has a 356MB file but the correct file is 390MB + +set -e + +# Correct model file path +LOCAL_MODEL="/home/user/LAM_gpro/OpenAvatarChat/models/LAM_audio2exp/pretrained_models/lam_audio2exp_streaming.tar" + +# GCS destination +GCS_BUCKET="gs://hp-support-477512-models/audio2exp" + +echo "=== Fixing GCS Model File ===" +echo "" + +# Check local file +echo "[1/4] Checking local model file..." +if [ ! -f "$LOCAL_MODEL" ]; then + echo "ERROR: Local model file not found: $LOCAL_MODEL" + exit 1 +fi + +LOCAL_SIZE=$(stat -c%s "$LOCAL_MODEL" 2>/dev/null || stat -f%z "$LOCAL_MODEL") +echo "Local file size: $LOCAL_SIZE bytes ($(numfmt --to=iec $LOCAL_SIZE 2>/dev/null || echo "$LOCAL_SIZE"))" +echo "Local file hash: $(md5sum "$LOCAL_MODEL" | cut -d' ' -f1)" + +# Verify local file works with PyTorch +echo "" +echo "[2/4] Verifying local file works with PyTorch..." +python3 -c " +import torch +checkpoint = torch.load('$LOCAL_MODEL', map_location='cpu', weights_only=False) +print(f'SUCCESS: Loaded checkpoint with {len(checkpoint[\"state_dict\"])} parameters') +" || { echo "ERROR: Local file is invalid"; exit 1; } + +# Upload to GCS +echo "" +echo "[3/4] Uploading correct model to GCS..." +echo "Destination: $GCS_BUCKET/lam_audio2exp_streaming.tar" +gsutil cp "$LOCAL_MODEL" "$GCS_BUCKET/lam_audio2exp_streaming.tar" + +# Verify upload +echo "" +echo "[4/4] Verifying GCS file..." +gsutil ls -la "$GCS_BUCKET/lam_audio2exp_streaming.tar" + +echo "" +echo "=== DONE ===" +echo "The correct model file has been uploaded to GCS." +echo "Now redeploy the Cloud Run service to use the fixed model." +echo "" +echo "To redeploy, run:" +echo " cd /home/user/LAM_gpro/audio2exp-service" +echo " gcloud builds submit --config=cloudbuild.yaml" diff --git a/audio2exp-service/integration/app_customer_support_modified.py b/audio2exp-service/integration/app_customer_support_modified.py new file mode 100644 index 0000000..ccf9bed --- /dev/null +++ b/audio2exp-service/integration/app_customer_support_modified.py @@ -0,0 +1,884 @@ +# -*- coding: utf-8 -*- +""" +汎用カスタマーサポートシステム (Gemini API版) - 改善版 +モジュール分割版(3ファイル構成) + +分割構成: +- api_integrations.py: 外部API連携 +- support_core.py: ビジネスロジック・コアクラス +- app_customer_support.py: Webアプリケーション層(本ファイル) +""" +import os +import re +import json +import time +import base64 +import logging +import threading +import queue +import requests +from datetime import datetime +from flask import Flask, request, jsonify, render_template +from flask_cors import CORS +from flask_socketio import SocketIO, emit +from google import genai +from google.genai import types +from google.cloud import texttospeech +from google.cloud import speech + +# 新しいモジュールからインポート +from api_integrations import ( + enrich_shops_with_photos, + extract_area_from_text, + GOOGLE_PLACES_API_KEY +) +from support_core import ( + load_system_prompts, + INITIAL_GREETINGS, + SYSTEM_PROMPTS, + SupportSession, + SupportAssistant +) + +# ロギング設定 +logging.basicConfig( + level=logging.INFO, + format='%(asctime)s [%(levelname)s] %(message)s' +) +logger = logging.getLogger(__name__) + +# 長期記憶モジュールをインポート +try: + from long_term_memory import LongTermMemory, PreferenceExtractor, extract_name_from_text + LONG_TERM_MEMORY_ENABLED = True +except Exception as e: + logger.warning(f"[LTM] 長期記憶モジュールのインポート失敗: {e}") + LONG_TERM_MEMORY_ENABLED = False + +# ======================================== +# Audio2Expression Service 設定 +# ======================================== +AUDIO2EXP_SERVICE_URL = os.getenv("AUDIO2EXP_SERVICE_URL", "") +if AUDIO2EXP_SERVICE_URL: + logger.info(f"[Audio2Exp] サービスURL設定済み: {AUDIO2EXP_SERVICE_URL}") +else: + logger.info("[Audio2Exp] サービスURL未設定（リップシンク無効）") + +app = Flask(__name__) +app.config["JSON_AS_ASCII"] = False # UTF-8エンコーディングを有効化 + +# ======================================== +# CORS & SocketIO 設定 (Claudeアドバイス適用版) +# ======================================== + +# 許可するオリジン(末尾のスラッシュなし) +allowed_origins = [ + "https://gourmet-sp-two.vercel.app", + "https://gourmet-sp.vercel.app", + "http://localhost:4321" +] + +# SocketIO初期化 (cors_allowed_originsを明示的に指定) +socketio = SocketIO( + app, + cors_allowed_origins=allowed_origins, + async_mode='threading', + logger=False, + engineio_logger=False +) + +# Flask-CORS初期化 (supports_credentials=True) +CORS(app, resources={ + r"/*": { + "origins": allowed_origins, + "methods": ["GET", "POST", "OPTIONS"], + "allow_headers": ["Content-Type", "Authorization"], + "supports_credentials": True + } +}) + +# 【重要】全レスポンスに強制的にCORSヘッダーを注入するフック +@app.after_request +def after_request(response): + origin = request.headers.get('Origin') + if origin in allowed_origins: + response.headers['Access-Control-Allow-Origin'] = origin + response.headers['Access-Control-Allow-Credentials'] = 'true' + response.headers['Access-Control-Allow-Methods'] = 'GET, POST, OPTIONS' + response.headers['Access-Control-Allow-Headers'] = 'Content-Type, Authorization' + # UTF-8エンコーディングを明示 + if response.content_type and 'application/json' in response.content_type: + response.headers['Content-Type'] = 'application/json; charset=utf-8' + return response + +# Google Cloud TTS/STT初期化 +tts_client = texttospeech.TextToSpeechClient() +stt_client = speech.SpeechClient() + +# プロンプト読み込み +SYSTEM_PROMPTS = load_system_prompts() + + +# ======================================== +# Audio2Expression: 表情フレーム取得関数 +# ======================================== +def get_expression_frames(audio_base64: str, session_id: str, audio_format: str = 'mp3'): + """ + Audio2Expression サービスに音声を送信して表情フレームを取得 + MP3をそのまま送信（audio2exp-serviceがpydubで変換対応済み） + + Returns: dict with {names, frames, frame_rate} or None + """ + if not AUDIO2EXP_SERVICE_URL or not session_id: + return None + + try: + response = requests.post( + f"{AUDIO2EXP_SERVICE_URL}/api/audio2expression", + json={ + "audio_base64": audio_base64, + "session_id": session_id, + "is_start": True, + "is_final": True, + "audio_format": audio_format + }, + timeout=10 + ) + if response.status_code == 200: + result = response.json() + frame_count = len(result.get('frames', [])) + logger.info(f"[Audio2Exp] 表情生成成功: {frame_count}フレーム, session={session_id}") + return result + else: + logger.warning(f"[Audio2Exp] 送信失敗: status={response.status_code}") + return None + except Exception as e: + logger.warning(f"[Audio2Exp] 送信エラー: {e}") + return None + + +@app.route('/') +def index(): + """フロントエンド表示""" + return render_template('support.html') + + +@app.route('/api/session/start', methods=['POST', 'OPTIONS']) +def start_session(): + """ + セッション開始 - モード対応 + + 【重要】改善されたフロー: + 1. セッション初期化(モード・言語設定) + 2. アシスタント作成(最新の状態で) + 3. 初回メッセージ生成 + 4. 履歴に追加 + """ + if request.method == 'OPTIONS': + return '', 204 + + try: + data = request.json or {} + user_info = data.get('user_info', {}) + language = data.get('language', 'ja') + mode = data.get('mode', 'chat') + + # 1. セッション初期化 + session = SupportSession() + session.initialize(user_info, language=language, mode=mode) + logger.info(f"[Start Session] 新規セッション作成: {session.session_id}") + + # 2. アシスタント作成(最新の状態で) + assistant = SupportAssistant(session, SYSTEM_PROMPTS) + + # 3. 初回メッセージ生成 + initial_message = assistant.get_initial_message() + + # 4. 履歴に追加（roleは'model') + session.add_message('model', initial_message, 'chat') + + logger.info(f"[API] セッション開始: {session.session_id}, 言語: {language}, モード: {mode}") + + # レスポンス作成 + response_data = { + 'session_id': session.session_id, + 'initial_message': initial_message + } + + # コンシェルジュモードのみ、名前情報を返す + if mode == 'concierge': + session_data = session.get_data() + profile = session_data.get('long_term_profile') if session_data else None + if profile: + response_data['user_profile'] = { + 'preferred_name': profile.get('preferred_name'), + 'name_honorific': profile.get('name_honorific') + } + logger.info(f"[API] user_profile を返却: {response_data['user_profile']}") + + return jsonify(response_data) + + except Exception as e: + logger.error(f"[API] セッション開始エラー: {e}") + return jsonify({'error': str(e)}), 500 + + +@app.route('/api/chat', methods=['POST', 'OPTIONS']) +def chat(): + """ + チャット処理 - 改善版 + + 【重要】改善されたフロー(順序を厳守): + 1. 状態確定 (State First): モード・言語を更新 + 2. ユーザー入力を記録: メッセージを履歴に追加 + 3. 知能生成 (Assistant作成): 最新の状態でアシスタントを作成 + 4. 推論開始: Gemini APIを呼び出し + 5. アシスタント応答を記録: 履歴に追加 + """ + if request.method == 'OPTIONS': + return '', 204 + + try: + data = request.json + session_id = data.get('session_id') + user_message = data.get('message') + stage = data.get('stage', 'conversation') + language = data.get('language', 'ja') + mode = data.get('mode', 'chat') + + if not session_id or not user_message: + return jsonify({'error': 'session_idとmessageが必要です'}), 400 + + session = SupportSession(session_id) + session_data = session.get_data() + + if not session_data: + return jsonify({'error': 'セッションが見つかりません'}), 404 + + logger.info(f"[Chat] セッション: {session_id}, モード: {mode}, 言語: {language}") + + # 1. 状態確定 (State First) + session.update_language(language) + session.update_mode(mode) + + # 2. ユーザー入力を記録 + session.add_message('user', user_message, 'chat') + + # 3. 知能生成 (Assistant作成) + assistant = SupportAssistant(session, SYSTEM_PROMPTS) + + # 4. 推論開始 + result = assistant.process_user_message(user_message, stage) + + # 5. アシスタント応答を記録 + session.add_message('model', result['response'], 'chat') + + if result['summary']: + session.add_message('model', result['summary'], 'summary') + + # ショップデータ処理 + shops = result.get('shops') or [] # None対策 + response_text = result['response'] + is_followup = result.get('is_followup', False) + + # 多言語メッセージ辞書 + shop_messages = { + 'ja': { + 'intro': lambda count: f"ご希望に合うお店を{count}件ご紹介します。\n\n", + 'not_found': "申し訳ございません。条件に合うお店が見つかりませんでした。別の条件でお探しいただけますか?" + }, + 'en': { + 'intro': lambda count: f"Here are {count} restaurant recommendations for you.\n\n", + 'not_found': "Sorry, we couldn't find any restaurants matching your criteria. Would you like to search with different conditions?" + }, + 'zh': { + 'intro': lambda count: f"为您推荐{count}家餐厅。\n\n", + 'not_found': "很抱歉,没有找到符合条件的餐厅。要用其他条件搜索吗?" + }, + 'ko': { + 'intro': lambda count: f"고객님께 {count}개의 식당을 추천합니다.\n\n", + 'not_found': "죄송합니다. 조건에 맞는 식당을 찾을 수 없었습니다. 다른 조건으로 찾으시겠습니까?" + } + } + + current_messages = shop_messages.get(language, shop_messages['ja']) + + if shops and not is_followup: + original_count = len(shops) + area = extract_area_from_text(user_message, language) + logger.info(f"[Chat] 抽出エリア: '{area}' from '{user_message}'") + + # Places APIで写真を取得 + shops = enrich_shops_with_photos(shops, area, language) or [] + + if shops: + shop_list = [] + for i, shop in enumerate(shops, 1): + name = shop.get('name', '') + shop_area = shop.get('area', '') + description = shop.get('description', '') + if shop_area: + shop_list.append(f"{i}. **{name}**({shop_area}): {description}") + else: + shop_list.append(f"{i}. **{name}**: {description}") + + response_text = current_messages['intro'](len(shops)) + "\n\n".join(shop_list) + logger.info(f"[Chat] {len(shops)}件のショップデータを返却(元: {original_count}件, 言語: {language})") + else: + response_text = current_messages['not_found'] + logger.warning(f"[Chat] 全店舗が除外されました(元: {original_count}件)") + + elif is_followup: + logger.info(f"[Chat] 深掘り質問への回答: {response_text[:100]}...") + + # ======================================== + # 長期記憶: LLMからのaction処理（新設計版） + # ======================================== + if LONG_TERM_MEMORY_ENABLED: + try: + # user_id をセッションデータから取得 + user_id = session_data.get('user_id') + + # ======================================== + # LLMからのaction指示を処理 + # ======================================== + # 初回訪問時の名前登録も、名前変更も、すべてLLMのactionで統一 + action = result.get('action') + if action and action.get('type') == 'update_user_profile': + updates = action.get('updates', {}) + if updates and user_id: + ltm = LongTermMemory() + # user_id をキーにしてプロファイルを更新（UPSERT動作） + success = ltm.update_profile(user_id, updates) + if success: + logger.info(f"[LTM] LLMからの指示でプロファイル更新成功: updates={updates}, user_id={user_id}") + else: + logger.error(f"[LTM] LLMからの指示でプロファイル更新失敗: updates={updates}, user_id={user_id}") + elif not user_id: + logger.warning(f"[LTM] user_id が空のためプロファイル更新をスキップ: action={action}") + + # ======================================== + # ショップカード提示時にサマリーを保存（マージ） + # ======================================== + if shops and not is_followup and user_id and mode == 'concierge': + try: + # 提案した店舗名を取得 + shop_names = [s.get('name', '不明') for s in shops] + timestamp = datetime.now().strftime('%Y-%m-%d %H:%M') + + # サマリーを生成 + shop_summary = f"[{timestamp}] 検索条件: {user_message[:100]}\n提案店舗: {', '.join(shop_names)}" + + ltm = LongTermMemory() + if ltm.append_conversation_summary(user_id, shop_summary): + logger.info(f"[LTM] ショップ提案サマリー保存成功: {len(shops)}件") + else: + logger.warning(f"[LTM] ショップ提案サマリー保存失敗") + except Exception as e: + logger.error(f"[LTM] ショップサマリー保存エラー: {e}") + + except Exception as e: + logger.error(f"[LTM] 処理エラー: {e}") + + # 【デバッグ】最終的なshopsの内容を確認 + logger.info(f"[Chat] 最終shops配列: {len(shops)}件") + if shops: + logger.info(f"[Chat] shops[0] keys: {list(shops[0].keys())}") + return jsonify({ + 'response': response_text, + 'summary': result['summary'], + 'shops': shops, + 'should_confirm': result['should_confirm'], + 'is_followup': is_followup + }) + + except Exception as e: + logger.error(f"[API] チャットエラー: {e}") + return jsonify({'error': str(e)}), 500 + + +@app.route('/api/finalize', methods=['POST', 'OPTIONS']) +def finalize_session(): + """セッション完了""" + if request.method == 'OPTIONS': + return '', 204 + + try: + data = request.json + session_id = data.get('session_id') + + if not session_id: + return jsonify({'error': 'session_idが必要です'}), 400 + + session = SupportSession(session_id) + session_data = session.get_data() + + if not session_data: + return jsonify({'error': 'セッションが見つかりません'}), 404 + + assistant = SupportAssistant(session, SYSTEM_PROMPTS) + final_summary = assistant.generate_final_summary() + + # ======================================== + # 長期記憶: セッション終了時にサマリーを追記（マージ） + # ======================================== + if LONG_TERM_MEMORY_ENABLED and session_data.get('mode') == 'concierge': + user_id = session_data.get('user_id') + if user_id and final_summary: + try: + ltm = LongTermMemory() + # 既存サマリーにマージ（過去セッションの記録を保持） + ltm.append_conversation_summary(user_id, final_summary) + logger.info(f"[LTM] セッション終了サマリー追記成功: user_id={user_id}") + except Exception as e: + logger.error(f"[LTM] サマリー保存エラー: {e}") + + logger.info(f"[LTM] セッション終了: {session_id}") + + return jsonify({ + 'summary': final_summary, + 'session_id': session_id + }) + + except Exception as e: + logger.error(f"[API] 完了処理エラー: {e}") + return jsonify({'error': str(e)}), 500 + + +@app.route('/api/cancel', methods=['POST', 'OPTIONS']) +def cancel_processing(): + """処理中止""" + if request.method == 'OPTIONS': + return '', 204 + + try: + data = request.json + session_id = data.get('session_id') + + if not session_id: + return jsonify({'error': 'session_idが必要です'}), 400 + + logger.info(f"[API] 処理中止リクエスト: {session_id}") + + # セッションのステータスを更新 + session = SupportSession(session_id) + session_data = session.get_data() + + if session_data: + session.update_status('cancelled') + + return jsonify({ + 'success': True, + 'message': '処理を中止しました' + }) + + except Exception as e: + logger.error(f"[API] 中止処理エラー: {e}") + return jsonify({'error': str(e)}), 500 + + +@app.route('/api/tts/synthesize', methods=['POST', 'OPTIONS']) +def synthesize_speech(): + """ + 音声合成 - Audio2Expression対応版 + + session_id が指定された場合、Audio2Expressionサービスにも音声を送信 + """ + if request.method == 'OPTIONS': + return '', 204 + + try: + data = request.json + text = data.get('text', '') + language_code = data.get('language_code', 'ja-JP') + voice_name = data.get('voice_name', 'ja-JP-Chirp3-HD-Leda') + speaking_rate = data.get('speaking_rate', 1.0) + pitch = data.get('pitch', 0.0) + session_id = data.get('session_id', '') # ★追加: リップシンク用セッションID + + if not text: + return jsonify({'success': False, 'error': 'テキストが必要です'}), 400 + + MAX_CHARS = 1000 + if len(text) > MAX_CHARS: + logger.warning(f"[TTS] テキストが長すぎるため切り詰めます: {len(text)} → {MAX_CHARS} 文字") + text = text[:MAX_CHARS] + '...' + + logger.info(f"[TTS] 合成開始: {len(text)} 文字, session_id={session_id}") + + synthesis_input = texttospeech.SynthesisInput(text=text) + + try: + voice = texttospeech.VoiceSelectionParams( + language_code=language_code, + name=voice_name + ) + except Exception as voice_error: + logger.warning(f"[TTS] 指定音声が無効、デフォルトに変更: {voice_error}") + voice = texttospeech.VoiceSelectionParams( + language_code=language_code, + name='ja-JP-Neural2-B' + ) + + # ★ MP3形式（フロントエンド再生用） + audio_config_mp3 = texttospeech.AudioConfig( + audio_encoding=texttospeech.AudioEncoding.MP3, + speaking_rate=speaking_rate, + pitch=pitch + ) + + response_mp3 = tts_client.synthesize_speech( + input=synthesis_input, + voice=voice, + audio_config=audio_config_mp3 + ) + + audio_base64 = base64.b64encode(response_mp3.audio_content).decode('utf-8') + logger.info(f"[TTS] MP3合成成功: {len(audio_base64)} bytes (base64)") + + # ======================================== + # ★ 同期Expression生成: TTS応答にexpression同梱（遅延ゼロのリップシンク） + # min-instances=1でコールドスタート排除済み + # ======================================== + expression_data = None + if AUDIO2EXP_SERVICE_URL and session_id: + try: + exp_start = time.time() + expression_data = get_expression_frames(audio_base64, session_id, 'mp3') + exp_elapsed = time.time() - exp_start + frame_count = len(expression_data.get('frames', [])) if expression_data else 0 + logger.info(f"[Audio2Exp] 同期生成完了: {exp_elapsed:.2f}秒, {frame_count}フレーム") + except Exception as e: + logger.warning(f"[Audio2Exp] 同期生成エラー: {e}") + + result = { + 'success': True, + 'audio': audio_base64 + } + if expression_data: + result['expression'] = expression_data + + return jsonify(result) + + except Exception as e: + logger.error(f"[TTS] エラー: {e}", exc_info=True) + return jsonify({ + 'success': False, + 'error': str(e) + }), 500 + + +@app.route('/api/stt/transcribe', methods=['POST', 'OPTIONS']) +def transcribe_audio(): + """音声認識""" + if request.method == 'OPTIONS': + return '', 204 + + try: + data = request.json + audio_base64 = data.get('audio', '') + language_code = data.get('language_code', 'ja-JP') + + if not audio_base64: + return jsonify({'success': False, 'error': '音声データが必要です'}), 400 + + logger.info(f"[STT] 認識開始: {len(audio_base64)} bytes (base64)") + + audio_content = base64.b64decode(audio_base64) + audio = speech.RecognitionAudio(content=audio_content) + + config = speech.RecognitionConfig( + encoding=speech.RecognitionConfig.AudioEncoding.WEBM_OPUS, + sample_rate_hertz=48000, + language_code=language_code, + enable_automatic_punctuation=True, + model='default' + ) + + response = stt_client.recognize(config=config, audio=audio) + + transcript = '' + if response.results: + transcript = response.results[0].alternatives[0].transcript + confidence = response.results[0].alternatives[0].confidence + logger.info(f"[STT] 認識成功: '{transcript}' (信頼度: {confidence:.2f})") + else: + logger.warning("[STT] 音声が認識されませんでした") + + return jsonify({ + 'success': True, + 'transcript': transcript + }) + + except Exception as e: + logger.error(f"[STT] エラー: {e}", exc_info=True) + return jsonify({ + 'success': False, + 'error': str(e) + }), 500 + + +@app.route('/api/stt/stream', methods=['POST', 'OPTIONS']) +def transcribe_audio_streaming(): + """音声認識 (Streaming)""" + if request.method == 'OPTIONS': + return '', 204 + + try: + data = request.json + audio_base64 = data.get('audio', '') + language_code = data.get('language_code', 'ja-JP') + + if not audio_base64: + return jsonify({'success': False, 'error': '音声データが必要です'}), 400 + + logger.info(f"[STT Streaming] 認識開始: {len(audio_base64)} bytes (base64)") + + audio_content = base64.b64decode(audio_base64) + + recognition_config = speech.RecognitionConfig( + encoding=speech.RecognitionConfig.AudioEncoding.WEBM_OPUS, + sample_rate_hertz=48000, + language_code=language_code, + enable_automatic_punctuation=True, + model='default' + ) + + streaming_config = speech.StreamingRecognitionConfig( + config=recognition_config, + interim_results=False, + single_utterance=True + ) + + CHUNK_SIZE = 1024 * 16 + + def audio_generator(): + for i in range(0, len(audio_content), CHUNK_SIZE): + chunk = audio_content[i:i + CHUNK_SIZE] + yield speech.StreamingRecognizeRequest(audio_content=chunk) + + responses = stt_client.streaming_recognize(streaming_config, audio_generator()) + + transcript = '' + confidence = 0.0 + + for response in responses: + if not response.results: + continue + + for result in response.results: + if result.is_final and result.alternatives: + transcript = result.alternatives[0].transcript + confidence = result.alternatives[0].confidence + logger.info(f"[STT Streaming] 認識成功: '{transcript}' (信頼度: {confidence:.2f})") + break + + if transcript: + break + + if not transcript: + logger.warning("[STT Streaming] 音声が認識されませんでした") + + return jsonify({ + 'success': True, + 'transcript': transcript, + 'confidence': confidence + }) + + except Exception as e: + logger.error(f"[STT Streaming] エラー: {e}", exc_info=True) + return jsonify({ + 'success': False, + 'error': str(e) + }), 500 + + +@app.route('/api/session/', methods=['GET', 'OPTIONS']) +def get_session(session_id): + """セッション情報取得""" + if request.method == 'OPTIONS': + return '', 204 + + try: + session = SupportSession(session_id) + data = session.get_data() + + if not data: + return jsonify({'error': 'セッションが見つかりません'}), 404 + + return jsonify(data) + + except Exception as e: + logger.error(f"[API] セッション取得エラー: {e}") + return jsonify({'error': str(e)}), 500 + + +@app.route('/health', methods=['GET', 'OPTIONS']) +def health_check(): + """ヘルスチェック""" + if request.method == 'OPTIONS': + return '', 204 + + return jsonify({ + 'status': 'healthy', + 'timestamp': datetime.now().isoformat(), + 'services': { + 'gemini': 'ok', + 'ram_session': 'ok', + 'tts': 'ok', + 'stt': 'ok', + 'places_api': 'ok' if GOOGLE_PLACES_API_KEY else 'not configured', + 'audio2exp': 'ok' if AUDIO2EXP_SERVICE_URL else 'not configured' + } + }) + + +# ======================================== +# WebSocket Streaming STT +# ======================================== + +active_streams = {} + +@socketio.on('connect') +def handle_connect(): + logger.info(f"[WebSocket STT] クライアント接続: {request.sid}") + emit('connected', {'status': 'ready'}) + +@socketio.on('disconnect') +def handle_disconnect(): + logger.info(f"[WebSocket STT] クライアント切断: {request.sid}") + if request.sid in active_streams: + stream_data = active_streams[request.sid] + if 'stop_event' in stream_data: + stream_data['stop_event'].set() + del active_streams[request.sid] + +@socketio.on('start_stream') +def handle_start_stream(data): + language_code = data.get('language_code', 'ja-JP') + sample_rate = data.get('sample_rate', 16000) # フロントエンドから受け取る + client_sid = request.sid + logger.info(f"[WebSocket STT] ストリーム開始: {client_sid}, 言語: {language_code}, サンプルレート: {sample_rate}Hz") + + recognition_config = speech.RecognitionConfig( + encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16, + sample_rate_hertz=sample_rate, # 動的に設定 + language_code=language_code, + enable_automatic_punctuation=True, + model='latest_long' # より高精度なモデルに変更 + ) + + streaming_config = speech.StreamingRecognitionConfig( + config=recognition_config, + interim_results=True, + single_utterance=False + ) + + audio_queue = queue.Queue() + stop_event = threading.Event() + + active_streams[client_sid] = { + 'audio_queue': audio_queue, + 'stop_event': stop_event, + 'streaming_config': streaming_config + } + + def audio_generator(): + while not stop_event.is_set(): + try: + chunk = audio_queue.get(timeout=0.5) + if chunk is None: + break + yield speech.StreamingRecognizeRequest(audio_content=chunk) + except queue.Empty: + continue + + def recognition_thread(): + try: + logger.info(f"[WebSocket STT] 認識スレッド開始: {client_sid}") + responses = stt_client.streaming_recognize(streaming_config, audio_generator()) + + for response in responses: + if stop_event.is_set(): + break + + if not response.results: + continue + + result = response.results[0] + + if result.alternatives: + transcript = result.alternatives[0].transcript + confidence = result.alternatives[0].confidence if result.is_final else 0.0 + + socketio.emit('transcript', { + 'text': transcript, + 'is_final': result.is_final, + 'confidence': confidence + }, room=client_sid) + + if result.is_final: + logger.info(f"[WebSocket STT] 最終認識: '{transcript}' (信頼度: {confidence:.2f})") + else: + logger.debug(f"[WebSocket STT] 途中認識: '{transcript}'") + + except Exception as e: + logger.error(f"[WebSocket STT] 認識エラー: {e}", exc_info=True) + socketio.emit('error', {'message': str(e)}, room=client_sid) + + thread = threading.Thread(target=recognition_thread, daemon=True) + thread.start() + + emit('stream_started', {'status': 'streaming'}) + +@socketio.on('audio_chunk') +def handle_audio_chunk(data): + if request.sid not in active_streams: + logger.warning(f"[WebSocket STT] 未初期化のストリーム: {request.sid}") + return + + try: + chunk_base64 = data.get('chunk', '') + if not chunk_base64: + return + + # ★★★ sample_rateを取得(16kHzで受信) ★★★ + sample_rate = data.get('sample_rate', 16000) + + # ★★★ 統計情報を取得してログ出力(必ず出力) ★★★ + stats = data.get('stats') + logger.info(f"[audio_chunk受信] sample_rate: {sample_rate}Hz, stats: {stats}") + + if stats: + logger.info(f"[AudioWorklet統計] サンプルレート: {sample_rate}Hz, " + f"サンプル総数: {stats.get('totalSamples')}, " + f"送信チャンク数: {stats.get('chunksSent')}, " + f"空入力回数: {stats.get('emptyInputCount')}, " + f"process呼び出し回数: {stats.get('processCalls')}, " + f"オーバーフロー回数: {stats.get('overflowCount', 0)}") # ★ オーバーフロー追加 + + audio_chunk = base64.b64decode(chunk_base64) + + # ★★★ 16kHzそのままGoogle STTに送る ★★★ + stream_data = active_streams[request.sid] + stream_data['audio_queue'].put(audio_chunk) + + except Exception as e: + logger.error(f"[WebSocket STT] チャンク処理エラー: {e}", exc_info=True) + +@socketio.on('stop_stream') +def handle_stop_stream(): + logger.info(f"[WebSocket STT] ストリーム停止: {request.sid}") + + if request.sid in active_streams: + stream_data = active_streams[request.sid] + stream_data['audio_queue'].put(None) + stream_data['stop_event'].set() + del active_streams[request.sid] + + emit('stream_stopped', {'status': 'stopped'}) + + +if __name__ == '__main__': + port = int(os.getenv('PORT', 8080)) + socketio.run(app, host='0.0.0.0', port=port, debug=False) diff --git a/audio2exp-service/integration/gourmet_support_integration.py b/audio2exp-service/integration/gourmet_support_integration.py new file mode 100644 index 0000000..58b9a6c --- /dev/null +++ b/audio2exp-service/integration/gourmet_support_integration.py @@ -0,0 +1,129 @@ +""" +gourmet-support 連携用コード + +このファイルを gourmet-support バックエンドに追加し、 +TTS処理後に audio2exp-service へ音声を転送します。 + +使用方法: + 1. このファイルを gourmet-support/app/ にコピー + 2. TTS処理部分で send_audio_to_expression() を呼び出し +""" + +import asyncio +import aiohttp +import os +from typing import Optional + +# Audio2Expression Service URL (環境変数で設定) +AUDIO2EXP_SERVICE_URL = os.getenv( + "AUDIO2EXP_SERVICE_URL", + "https://audio2exp-service-xxxxx-an.a.run.app" # Cloud Run URL +) + + +async def send_audio_to_expression( + audio_base64: str, + session_id: str, + is_final: bool = False +) -> Optional[dict]: + """ + TTS音声を Audio2Expression サービスに送信 + + Args: + audio_base64: Base64エンコードされた音声データ (PCM 16-bit, サンプルレート任意) + session_id: セッションID (WebSocket接続と紐付け) + is_final: 音声ストリームの最終チャンクかどうか + + Returns: + 表情データ (成功時) または None (失敗時) + + Usage: + # TTS処理後 + tts_audio_base64 = synthesize_tts(text) + await send_audio_to_expression(tts_audio_base64, session_id) + """ + if not AUDIO2EXP_SERVICE_URL: + return None + + try: + async with aiohttp.ClientSession() as session: + async with session.post( + f"{AUDIO2EXP_SERVICE_URL}/api/audio2expression", + json={ + "audio_base64": audio_base64, + "session_id": session_id, + "is_final": is_final + }, + timeout=aiohttp.ClientTimeout(total=10) + ) as response: + if response.status == 200: + return await response.json() + else: + print(f"[Audio2Exp] Error: {response.status}") + return None + except Exception as e: + print(f"[Audio2Exp] Request failed: {e}") + return None + + +def send_audio_to_expression_sync( + audio_base64: str, + session_id: str, + is_final: bool = False +) -> Optional[dict]: + """ + 同期版: TTS音声を Audio2Expression サービスに送信 + """ + import requests + + if not AUDIO2EXP_SERVICE_URL: + return None + + try: + response = requests.post( + f"{AUDIO2EXP_SERVICE_URL}/api/audio2expression", + json={ + "audio_base64": audio_base64, + "session_id": session_id, + "is_final": is_final + }, + timeout=10 + ) + if response.status_code == 200: + return response.json() + else: + print(f"[Audio2Exp] Error: {response.status_code}") + return None + except Exception as e: + print(f"[Audio2Exp] Request failed: {e}") + return None + + +# ============================================ +# 既存の TTS 処理への組み込み例 +# ============================================ +# +# Before (既存コード): +# ```python +# @app.post("/api/tts/synthesize") +# async def synthesize_tts(request: TTSRequest): +# audio_base64 = await gcp_tts_synthesize(request.text) +# return {"success": True, "audio": audio_base64} +# ``` +# +# After (変更後): +# ```python +# from gourmet_support_integration import send_audio_to_expression +# +# @app.post("/api/tts/synthesize") +# async def synthesize_tts(request: TTSRequest): +# audio_base64 = await gcp_tts_synthesize(request.text) +# +# # Audio2Expression に送信 (非同期、レスポンスを待たない) +# if request.session_id: +# asyncio.create_task( +# send_audio_to_expression(audio_base64, request.session_id) +# ) +# +# return {"success": True, "audio": audio_base64} +# ``` diff --git a/audio2exp-service/integration/gourmet_support_patch.py b/audio2exp-service/integration/gourmet_support_patch.py new file mode 100644 index 0000000..ef60569 --- /dev/null +++ b/audio2exp-service/integration/gourmet_support_patch.py @@ -0,0 +1,198 @@ +""" +gourmet-support TTS エンドポイント修正パッチ + +app_customer_support.py の synthesize_speech() 関数を以下のように修正してください。 + +【変更点】 +1. session_id パラメータを追加 +2. LINEAR16形式でも音声生成（Audio2Expression用） +3. audio2exp-service への非同期送信を追加 +""" + +# ============================================ +# 追加するインポート (ファイル先頭に追加) +# ============================================ +""" +import asyncio +import aiohttp +import os + +AUDIO2EXP_SERVICE_URL = os.getenv("AUDIO2EXP_SERVICE_URL", "") +""" + +# ============================================ +# 追加する関数 (ファイル内に追加) +# ============================================ +""" +async def send_to_audio2exp_async(audio_base64_pcm: str, session_id: str): + '''Audio2Expression サービスに非同期で音声を送信''' + if not AUDIO2EXP_SERVICE_URL or not session_id: + return + + try: + async with aiohttp.ClientSession() as session: + await session.post( + f"{AUDIO2EXP_SERVICE_URL}/api/audio2expression", + json={ + "audio_base64": audio_base64_pcm, + "session_id": session_id, + "is_final": True + }, + timeout=aiohttp.ClientTimeout(total=10) + ) + logger.info(f"[Audio2Exp] 送信成功: session={session_id}") + except Exception as e: + logger.warning(f"[Audio2Exp] 送信失敗: {e}") + + +def send_to_audio2exp(audio_base64_pcm: str, session_id: str): + '''Audio2Expression サービスに音声を送信（同期ラッパー）''' + if not AUDIO2EXP_SERVICE_URL or not session_id: + return + + try: + # 新しいイベントループで非同期実行 + loop = asyncio.new_event_loop() + asyncio.set_event_loop(loop) + loop.run_until_complete(send_to_audio2exp_async(audio_base64_pcm, session_id)) + loop.close() + except Exception as e: + logger.warning(f"[Audio2Exp] 送信失敗: {e}") +""" + +# ============================================ +# synthesize_speech() 関数の修正版 +# ============================================ +MODIFIED_SYNTHESIZE_SPEECH = ''' +@app.route('/api/tts/synthesize', methods=['POST', 'OPTIONS']) +def synthesize_speech(): + """音声合成 - Audio2Expression対応版""" + if request.method == 'OPTIONS': + return '', 204 + + try: + data = request.json + text = data.get('text', '') + language_code = data.get('language_code', 'ja-JP') + voice_name = data.get('voice_name', 'ja-JP-Chirp3-HD-Leda') + speaking_rate = data.get('speaking_rate', 1.0) + pitch = data.get('pitch', 0.0) + session_id = data.get('session_id', '') # ★追加: セッションID + + if not text: + return jsonify({'success': False, 'error': 'テキストが必要です'}), 400 + + MAX_CHARS = 1000 + if len(text) > MAX_CHARS: + logger.warning(f"[TTS] テキストが長すぎるため切り詰めます: {len(text)} → {MAX_CHARS} 文字") + text = text[:MAX_CHARS] + '...' + + logger.info(f"[TTS] 合成開始: {len(text)} 文字") + + synthesis_input = texttospeech.SynthesisInput(text=text) + + try: + voice = texttospeech.VoiceSelectionParams( + language_code=language_code, + name=voice_name + ) + except Exception as voice_error: + logger.warning(f"[TTS] 指定音声が無効、デフォルトに変更: {voice_error}") + voice = texttospeech.VoiceSelectionParams( + language_code=language_code, + name='ja-JP-Neural2-B' + ) + + # ★ MP3形式（フロントエンド再生用） + audio_config_mp3 = texttospeech.AudioConfig( + audio_encoding=texttospeech.AudioEncoding.MP3, + speaking_rate=speaking_rate, + pitch=pitch + ) + + response_mp3 = tts_client.synthesize_speech( + input=synthesis_input, + voice=voice, + audio_config=audio_config_mp3 + ) + + audio_base64 = base64.b64encode(response_mp3.audio_content).decode('utf-8') + logger.info(f"[TTS] MP3合成成功: {len(audio_base64)} bytes (base64)") + + # ★ Audio2Expression用にLINEAR16形式も生成して送信 + if AUDIO2EXP_SERVICE_URL and session_id: + try: + audio_config_pcm = texttospeech.AudioConfig( + audio_encoding=texttospeech.AudioEncoding.LINEAR16, + sample_rate_hertz=16000, + speaking_rate=speaking_rate, + pitch=pitch + ) + + response_pcm = tts_client.synthesize_speech( + input=synthesis_input, + voice=voice, + audio_config=audio_config_pcm + ) + + audio_base64_pcm = base64.b64encode(response_pcm.audio_content).decode('utf-8') + + # 非同期で送信（レスポンスを待たない） + import threading + thread = threading.Thread( + target=send_to_audio2exp, + args=(audio_base64_pcm, session_id) + ) + thread.start() + + logger.info(f"[TTS] Audio2Exp送信開始: session={session_id}") + except Exception as e: + logger.warning(f"[TTS] Audio2Exp送信準備エラー: {e}") + + return jsonify({ + 'success': True, + 'audio': audio_base64 + }) + + except Exception as e: + logger.error(f"[TTS] エラー: {e}", exc_info=True) + return jsonify({'success': False, 'error': str(e)}), 500 +''' + +# ============================================ +# フロントエンド側の変更 (core-controller.ts) +# ============================================ +FRONTEND_CHANGE = ''' +// TTS呼び出し時に session_id を追加 + +// Before: +const response = await fetch(`${this.apiBase}/api/tts/synthesize`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + text: cleanText, + language_code: langConfig.tts, + voice_name: langConfig.voice + }) +}); + +// After: +const response = await fetch(`${this.apiBase}/api/tts/synthesize`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + text: cleanText, + language_code: langConfig.tts, + voice_name: langConfig.voice, + session_id: this.sessionId // ★追加 + }) +}); +''' + +if __name__ == "__main__": + print("=== gourmet-support TTS 修正パッチ ===") + print("\n1. インポート追加") + print("2. send_to_audio2exp 関数追加") + print("3. synthesize_speech() 関数を修正版に置換") + print("4. フロントエンド (core-controller.ts) に session_id 追加") + print("\n詳細はこのファイルを参照してください。") diff --git a/audio2exp-service/requirements.txt b/audio2exp-service/requirements.txt new file mode 100644 index 0000000..18dad79 --- /dev/null +++ b/audio2exp-service/requirements.txt @@ -0,0 +1,18 @@ +fastapi>=0.100.0 +uvicorn>=0.23.0 +websockets>=11.0 +numpy<2 +pydantic>=2.0.0 + +# Audio processing +pydub>=0.25.0 + +# For Audio2Expression (CPU mode) +torch>=2.0.0 +torchaudio>=2.0.0 +transformers==4.36.2 +librosa>=0.10.0 +omegaconf>=2.3.0 +addict>=2.4.0 +yapf +termcolor diff --git a/audio2exp-service/run_local.sh b/audio2exp-service/run_local.sh new file mode 100755 index 0000000..75e80b6 --- /dev/null +++ b/audio2exp-service/run_local.sh @@ -0,0 +1,43 @@ +#!/bin/bash +# Local run script for audio2exp-service with real LAM Audio2Expression model +# This script sets up the correct paths for the OpenAvatarChat models + +SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)" +PROJECT_ROOT="$(dirname "$SCRIPT_DIR")" + +# Set environment variables for model paths +export LAM_A2E_PATH="$PROJECT_ROOT/OpenAvatarChat/src/handlers/avatar/lam/LAM_Audio2Expression" +export LAM_WEIGHT_PATH="$PROJECT_ROOT/OpenAvatarChat/models/LAM_audio2exp/pretrained_models/lam_audio2exp_streaming.tar" +export WAV2VEC_PATH="$PROJECT_ROOT/OpenAvatarChat/models/wav2vec2-base-960h" + +echo "========================================" +echo "Audio2Expression Service - Local Mode" +echo "========================================" +echo "LAM_A2E_PATH: $LAM_A2E_PATH" +echo "LAM_WEIGHT_PATH: $LAM_WEIGHT_PATH" +echo "WAV2VEC_PATH: $WAV2VEC_PATH" +echo "========================================" + +# Check if paths exist +if [ ! -d "$LAM_A2E_PATH" ]; then + echo "ERROR: LAM_Audio2Expression not found at $LAM_A2E_PATH" + echo "Run: cd $PROJECT_ROOT/OpenAvatarChat && git submodule update --init src/handlers/avatar/lam/LAM_Audio2Expression" + exit 1 +fi + +if [ ! -f "$LAM_WEIGHT_PATH" ]; then + echo "ERROR: Model weights not found at $LAM_WEIGHT_PATH" + echo "Run: wget https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/aigc3d/data/LAM/LAM_audio2exp_streaming.tar -P $PROJECT_ROOT/OpenAvatarChat/models/LAM_audio2exp/" + echo " tar -xzvf $PROJECT_ROOT/OpenAvatarChat/models/LAM_audio2exp/LAM_audio2exp_streaming.tar -C $PROJECT_ROOT/OpenAvatarChat/models/LAM_audio2exp" + exit 1 +fi + +if [ ! -d "$WAV2VEC_PATH" ]; then + echo "ERROR: wav2vec2 model not found at $WAV2VEC_PATH" + echo "Run: git clone --depth 1 https://www.modelscope.cn/AI-ModelScope/wav2vec2-base-960h.git $WAV2VEC_PATH" + exit 1 +fi + +# Run the service +cd "$SCRIPT_DIR" +python app.py --host 0.0.0.0 --port 8283 diff --git a/audio2exp-service/start.sh b/audio2exp-service/start.sh new file mode 100644 index 0000000..183ad83 --- /dev/null +++ b/audio2exp-service/start.sh @@ -0,0 +1,8 @@ +#!/bin/bash +set -e + +echo "[Startup] Starting Audio2Expression service..." +echo "[Startup] Checking FUSE mount contents:" +ls -l /mnt/models/audio2exp/ || echo "[Startup] WARNING: FUSE mount not available" + +exec uvicorn app:app --host 0.0.0.0 --port ${PORT:-8080} --workers 1 diff --git a/audio2exp-service/test_client.py b/audio2exp-service/test_client.py new file mode 100644 index 0000000..b29b79e --- /dev/null +++ b/audio2exp-service/test_client.py @@ -0,0 +1,163 @@ +""" +Test client for Audio2Expression Service + +Usage: + # Test with audio file + python test_client.py --audio test.wav + + # Test with generated sine wave + python test_client.py --generate + + # Test WebSocket connection + python test_client.py --websocket --session test-session +""" + +import argparse +import asyncio +import base64 +import json +import numpy as np +import requests +import websockets + + +def generate_test_audio(duration: float = 1.0, sample_rate: int = 16000) -> bytes: + """Generate test audio (sine wave with varying amplitude)""" + t = np.linspace(0, duration, int(sample_rate * duration)) + # Varying amplitude to simulate speech + amplitude = 0.5 * (1 + np.sin(2 * np.pi * 2 * t)) # 2Hz modulation + audio = (amplitude * np.sin(2 * np.pi * 440 * t) * 32767).astype(np.int16) + return audio.tobytes() + + +def load_audio_file(path: str) -> bytes: + """Load audio file and convert to PCM 16-bit""" + try: + import librosa + audio, sr = librosa.load(path, sr=16000) + audio_int16 = (audio * 32767).astype(np.int16) + return audio_int16.tobytes() + except ImportError: + print("librosa not installed, using wave module") + import wave + with wave.open(path, 'rb') as wf: + return wf.readframes(wf.getnframes()) + + +def test_rest_api(host: str, audio_bytes: bytes, session_id: str): + """Test REST API endpoint""" + print(f"\n=== Testing REST API: {host}/api/audio2expression ===") + + audio_base64 = base64.b64encode(audio_bytes).decode('utf-8') + + response = requests.post( + f"{host}/api/audio2expression", + json={ + "audio_base64": audio_base64, + "session_id": session_id, + "is_final": True + } + ) + + if response.status_code == 200: + data = response.json() + print(f"✅ Success!") + print(f" Session ID: {data['session_id']}") + print(f" Channels: {len(data['channels'])} (52 expected)") + print(f" Frames: {len(data['weights'])}") + if data['weights']: + # Show some key channels + channels = data['channels'] + weights = data['weights'][0] + jaw_open_idx = channels.index('jawOpen') if 'jawOpen' in channels else -1 + if jaw_open_idx >= 0: + print(f" jawOpen: {weights[jaw_open_idx]:.4f}") + else: + print(f"❌ Error: {response.status_code}") + print(f" {response.text}") + + +async def test_websocket(host: str, session_id: str, audio_bytes: bytes): + """Test WebSocket endpoint""" + ws_url = host.replace("http://", "ws://").replace("https://", "wss://") + ws_url = f"{ws_url}/ws/{session_id}" + + print(f"\n=== Testing WebSocket: {ws_url} ===") + + try: + async with websockets.connect(ws_url) as ws: + print("✅ Connected!") + + # Send audio data + audio_base64 = base64.b64encode(audio_bytes).decode('utf-8') + await ws.send(json.dumps({ + "type": "audio", + "audio": audio_base64, + "is_final": True + })) + print(" Sent audio data") + + # Receive expression data + try: + response = await asyncio.wait_for(ws.recv(), timeout=5.0) + data = json.loads(response) + print(f" Received: {data['type']}") + if data['type'] == 'expression': + print(f" Channels: {len(data['channels'])}") + print(f" Frames: {len(data['weights'])}") + except asyncio.TimeoutError: + print(" ⚠️ No response (timeout)") + + except Exception as e: + print(f"❌ Connection failed: {e}") + + +def test_health(host: str): + """Test health endpoint""" + print(f"\n=== Testing Health: {host}/health ===") + try: + response = requests.get(f"{host}/health") + data = response.json() + print(f"✅ Status: {data['status']}") + print(f" Mode: {data['mode']}") + print(f" Initialized: {data['initialized']}") + except Exception as e: + print(f"❌ Error: {e}") + + +def main(): + parser = argparse.ArgumentParser(description="Test Audio2Expression Service") + parser.add_argument("--host", default="http://localhost:8283", help="Service URL") + parser.add_argument("--audio", help="Path to audio file (WAV)") + parser.add_argument("--generate", action="store_true", help="Generate test audio") + parser.add_argument("--websocket", action="store_true", help="Test WebSocket") + parser.add_argument("--session", default="test-session", help="Session ID") + parser.add_argument("--health", action="store_true", help="Test health endpoint only") + args = parser.parse_args() + + # Health check + test_health(args.host) + + if args.health: + return + + # Prepare audio + if args.audio: + print(f"\nLoading audio from: {args.audio}") + audio_bytes = load_audio_file(args.audio) + else: + print("\nGenerating test audio (1 second sine wave)") + audio_bytes = generate_test_audio(1.0) + + print(f"Audio size: {len(audio_bytes)} bytes") + + # Test REST API + test_rest_api(args.host, audio_bytes, args.session) + + # Test WebSocket + if args.websocket: + asyncio.run(test_websocket(args.host, args.session, audio_bytes)) + + +if __name__ == "__main__": + main() diff --git a/gourmet-sp/README.md b/gourmet-sp/README.md new file mode 100644 index 0000000..dcc0e23 --- /dev/null +++ b/gourmet-sp/README.md @@ -0,0 +1,236 @@ +# Gourmet Support AI - LAM 3D Avatar Integration + +このディレクトリは、グルメサポートAIのコンシェルジュモードに LAM (Large Avatar Model) 3Dアバターを統合するためのテスト環境です。 + +## セットアップ手順 + +### 1. ローカル環境にコピー + +このディレクトリの `src/` と `public/` を、ローカルの gourmet-sp プロジェクトにコピーしてください。 + +```bash +# ローカルのgourmet-spディレクトリで実行 +cp -r /path/to/LAM_gpro/gourmet-sp/src ./ +cp -r /path/to/LAM_gpro/gourmet-sp/public ./ +``` + +### 2. NPMパッケージのインストール + +LAM WebGL レンダラーをインストール： + +```bash +npm install gaussian-splat-renderer-for-lam +``` + +### 3. アバターファイルの配置 + +LAMで生成した3Dアバター（.zipファイル）を配置： + +```bash +mkdir -p public/avatar +cp /path/to/your-avatar.zip public/avatar/concierge.zip +``` + +### 4. 開発サーバーの起動 + +```bash +npm run dev +# http://localhost:4321/concierge でアクセス +``` + +## コンポーネント構成 + +``` +src/ +├── components/ +│ ├── Concierge.astro # メインコンシェルジュUI（LAM統合済み） +│ └── LAMAvatar.astro # LAM 3Dアバターコンポーネント +└── pages/ + └── concierge.astro # コンシェルジュページ +``` + +## LAMAvatar コンポーネントの使い方 + +```astro +--- +import LAMAvatar from '../components/LAMAvatar.astro'; +--- + + +``` + +### Props + +| Prop | Type | Default | Description | +|------|------|---------|-------------| +| `avatarPath` | string | `/avatar/concierge.zip` | アバター.zipファイルのパス | +| `width` | string | `100%` | コンテナの幅 | +| `height` | string | `100%` | コンテナの高さ | +| `wsUrl` | string | `''` | OpenAvatarChat WebSocket URL | +| `autoConnect` | boolean | `false` | 自動WebSocket接続 | + +### JavaScript API + +```javascript +// グローバルにアクセス可能 +const controller = window.lamAvatarController; + +// 状態を設定（Idle, Listening, Thinking, Responding） +controller.setChatState('Responding'); + +// 表情データを設定（Audio2Expressionの出力） +controller.setExpressionData({ + 'jawOpen': 0.5, + 'mouthSmile_L': 0.3, + 'mouthSmile_R': 0.3, + // ... 他のARKitブレンドシェイプ +}); + +// Audio2Expressionフレームから更新 +controller.updateFromAudio2Expression({ + names: ['jawOpen', 'mouthSmile_L', ...], + weights: [0.5, 0.3, ...] +}); +``` + +## Concierge コンポーネントの設定 + +```astro +--- +import ConciergeComponent from '../components/Concierge.astro'; +--- + + + + + + +``` + +## 3Dアバターの生成方法 + +1. **コンシェルジュ画像を用意** + - 正面向きの顔写真 + - 高解像度推奨（512x512以上） + +2. **LAMで3Dアバターを生成**（GPU環境が必要） + ```bash + cd /path/to/LAM_gpro + python app_lam.py + # Gradio UIで画像をアップロード + # ZIPファイルをエクスポート + ``` + +3. **生成されたZIPを配置** + ```bash + cp generated_avatar.zip public/avatar/concierge.zip + ``` + +## OpenAvatarChat WebSocket 連携（リップシンク） + +OpenAvatarChatバックエンドとWebSocketで接続して、リアルタイムリップシンクを実現します。 + +### 接続方法 + +```astro + + +``` + +```javascript +// または、JavaScriptから手動接続 +const controller = window.lamAvatarController; + +// WebSocket接続 +await controller.connectWebSocket('wss://your-server:8282/ws'); + +// 接続状態の確認 +console.log('Connected:', controller.isWebSocketConnected()); + +// 切断 +controller.disconnectWebSocket(); +``` + +### イベントリスナー + +```javascript +// 接続状態の変更を監視 +document.getElementById('lamAvatarContainer').addEventListener('lamConnectionChange', (e) => { + console.log('WebSocket connected:', e.detail.connected); +}); + +// チャット状態の変更を監視 +document.getElementById('lamAvatarContainer').addEventListener('lamStateChange', (e) => { + console.log('Chat state:', e.detail.state); +}); +``` + +### データフロー + +1. **OpenAvatarChat バックエンド** がAudio2Expressionで音声を解析 +2. **JBIN形式** でARKit表情データ（52チャンネル）をWebSocket送信 +3. **LAMWebSocketManager** がバイナリをパースして表情データに変換 +4. **GaussianSplatRenderer** がリアルタイムでアバターを更新 + +### ファイル構成 + +``` +src/scripts/lam/ +└── lam-websocket-manager.ts # JBIN パーサー & WebSocket管理 +``` + +## Audio2Expression との連携（手動モード） + +WebSocketを使わずに、手動で表情データを設定する場合： + +```javascript +// バックエンドからの表情データを受信 +socket.on('expression_frame', (frame) => { + window.lamAvatarController.updateFromAudio2Expression(frame); +}); +``` + +## トラブルシューティング + +### NPMパッケージがインストールできない + +```bash +# Node.js 18以上が必要 +node --version + +# キャッシュクリア +npm cache clean --force +npm install gaussian-splat-renderer-for-lam +``` + +### 3Dアバターが表示されない + +1. ブラウザがWebGL 2.0をサポートしているか確認 +2. アバター.zipファイルのパスが正しいか確認 +3. コンソールエラーを確認 + +### フォールバック画像が表示される + +NPMパッケージがインストールされていないか、WebGLが利用できない場合、自動的に2D画像にフォールバックします。 + +## 関連リポジトリ + +- [LAM (Large Avatar Model)](https://github.com/aigc3d/LAM) - 3Dアバター生成 +- [LAM_WebRender](https://github.com/aigc3d/LAM_WebRender) - WebGLレンダラー +- [LAM_Audio2Expression](https://github.com/aigc3d/LAM_Audio2Expression) - 音声→表情変換 +- [OpenAvatarChat](https://github.com/HumanAIGC-Engineering/OpenAvatarChat) - 統合SDK diff --git a/gourmet-sp/public/TripAdvisor-logo.png b/gourmet-sp/public/TripAdvisor-logo.png new file mode 100644 index 0000000..29b4799 Binary files /dev/null and b/gourmet-sp/public/TripAdvisor-logo.png differ diff --git a/gourmet-sp/public/audio-processor.js b/gourmet-sp/public/audio-processor.js new file mode 100644 index 0000000..5265b5c --- /dev/null +++ b/gourmet-sp/public/audio-processor.js @@ -0,0 +1,55 @@ +/** + * AudioWorklet Processor for Real-time PCM Extraction + * iPhone完全最適化版 + */ + +class AudioProcessor extends AudioWorkletProcessor { + constructor() { + super(); + // ★★★ さらにバッファを小さく（遅延最小化） ★★★ + this.bufferSize = 1024; // 2048 → 1024（約0.064秒） + this.buffer = new Int16Array(this.bufferSize); + this.bufferIndex = 0; + this.sampleCount = 0; + } + + process(inputs, outputs, parameters) { + const input = inputs[0]; + + // ★★★ 入力がない場合もカウント（デバッグ用） ★★★ + if (!input || input.length === 0) { + return true; + } + + const channelData = input[0]; + if (!channelData || channelData.length === 0) { + return true; + } + + // Float32Array を Int16Array に変換 + for (let i = 0; i < channelData.length; i++) { + this.sampleCount++; + + // Float32 (-1.0 ~ 1.0) を Int16 (-32768 ~ 32767) に変換 + const s = Math.max(-1, Math.min(1, channelData[i])); + const int16Value = Math.round(s < 0 ? s * 0x8000 : s * 0x7FFF); + + // バッファに書き込み + this.buffer[this.bufferIndex++] = int16Value; + + // バッファサイズに達したら送信 + if (this.bufferIndex >= this.bufferSize) { + // ★★★ コピーではなく新しいバッファを作成 ★★★ + const chunk = new Int16Array(this.buffer); + this.port.postMessage({ audioChunk: chunk }); + + // バッファリセット + this.bufferIndex = 0; + } + } + + return true; + } +} + +registerProcessor('audio-processor', AudioProcessor); diff --git a/gourmet-sp/public/avatar/concierge.zip b/gourmet-sp/public/avatar/concierge.zip new file mode 100644 index 0000000..2ab7869 Binary files /dev/null and b/gourmet-sp/public/avatar/concierge.zip differ diff --git a/gourmet-sp/public/avatar/p2-1.zip b/gourmet-sp/public/avatar/p2-1.zip new file mode 100644 index 0000000..2ab7869 Binary files /dev/null and b/gourmet-sp/public/avatar/p2-1.zip differ diff --git a/gourmet-sp/public/avatar/test_expression_1s.json b/gourmet-sp/public/avatar/test_expression_1s.json new file mode 100644 index 0000000..d1b4a89 --- /dev/null +++ b/gourmet-sp/public/avatar/test_expression_1s.json @@ -0,0 +1,61359 @@ +{ + "names": [ + "browDownLeft", + "browDownRight", + "browInnerUp", + "browOuterUpLeft", + "browOuterUpRight", + "cheekPuff", + "cheekSquintLeft", + "cheekSquintRight", + "eyeBlinkLeft", + "eyeBlinkRight", + "eyeLookDownLeft", + "eyeLookDownRight", + "eyeLookInLeft", + "eyeLookInRight", + "eyeLookOutLeft", + "eyeLookOutRight", + "eyeLookUpLeft", + "eyeLookUpRight", + "eyeSquintLeft", + "eyeSquintRight", + "eyeWideLeft", + "eyeWideRight", + "jawForward", + "jawLeft", + "jawOpen", + "jawRight", + "mouthClose", + "mouthDimpleLeft", + 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b/gourmet-sp/public/images/avatar-anime.png new file mode 100644 index 0000000..7839996 Binary files /dev/null and b/gourmet-sp/public/images/avatar-anime.png differ diff --git a/gourmet-sp/public/instagram-logo.png b/gourmet-sp/public/instagram-logo.png new file mode 100644 index 0000000..9eb2e8a Binary files /dev/null and b/gourmet-sp/public/instagram-logo.png differ diff --git a/gourmet-sp/public/ios-install-demo.mp4 b/gourmet-sp/public/ios-install-demo.mp4 new file mode 100644 index 0000000..c416fbe Binary files /dev/null and b/gourmet-sp/public/ios-install-demo.mp4 differ diff --git a/gourmet-sp/public/manifest.webmanifest b/gourmet-sp/public/manifest.webmanifest new file mode 100644 index 0000000..1ca5b7c --- /dev/null +++ b/gourmet-sp/public/manifest.webmanifest @@ -0,0 +1,23 @@ +{ + "name": "グルメサポートAI", + "short_name": "グルメAI", + "description": "AIがあなたのお店探しをサポート", + "start_url": "/", + "display": "standalone", + "background_color": "#667eea", + "theme_color": "#667eea", + "orientation": "portrait", + "icons": [ + { + "src": "/pwa-152x152.png", + "sizes": "152x152", + "type": "image/png" + }, + { + "src": "/pwa-192x192.png", + "sizes": "192x192", + "type": "image/png", + "purpose": "any maskable" + } + ] +} diff --git a/gourmet-sp/public/mic-off.svg b/gourmet-sp/public/mic-off.svg new file mode 100644 index 0000000..4e967c1 --- /dev/null +++ b/gourmet-sp/public/mic-off.svg @@ -0,0 +1,14 @@ + + + + + + + + + + + + + + diff --git a/gourmet-sp/public/mic-on.svg b/gourmet-sp/public/mic-on.svg new file mode 100644 index 0000000..5ddb700 --- /dev/null +++ b/gourmet-sp/public/mic-on.svg @@ -0,0 +1,29 @@ + + + + + + + + + + + + + + + + + + + + + + + + + + + + + diff --git a/gourmet-sp/public/pwa-152x152.png b/gourmet-sp/public/pwa-152x152.png new file mode 100644 index 0000000..9aee1de Binary files /dev/null and b/gourmet-sp/public/pwa-152x152.png differ diff --git a/gourmet-sp/public/pwa-192x192.png b/gourmet-sp/public/pwa-192x192.png new file mode 100644 index 0000000..7388ad1 Binary files /dev/null and b/gourmet-sp/public/pwa-192x192.png differ diff --git a/gourmet-sp/public/splash.mp4 b/gourmet-sp/public/splash.mp4 new file mode 100644 index 0000000..9c2cebc Binary files /dev/null and b/gourmet-sp/public/splash.mp4 differ diff --git a/gourmet-sp/public/wait.mp4 b/gourmet-sp/public/wait.mp4 new file mode 100644 index 0000000..e8ca1fb Binary files /dev/null and b/gourmet-sp/public/wait.mp4 differ diff --git a/gourmet-sp/src/assets/astro.svg b/gourmet-sp/src/assets/astro.svg new file mode 100644 index 0000000..8cf8fb0 --- /dev/null +++ b/gourmet-sp/src/assets/astro.svg @@ -0,0 +1 @@ + diff --git a/gourmet-sp/src/assets/background.svg b/gourmet-sp/src/assets/background.svg new file mode 100644 index 0000000..4b2be0a --- /dev/null +++ b/gourmet-sp/src/assets/background.svg @@ -0,0 +1 @@ + diff --git a/gourmet-sp/src/components/Concierge.astro b/gourmet-sp/src/components/Concierge.astro new file mode 100644 index 0000000..3e3ed0a --- /dev/null +++ b/gourmet-sp/src/components/Concierge.astro @@ -0,0 +1,318 @@ +--- +// Concierge.astro - コンシェルジュモードUI (チャットモードと同様の機能) +// LAM 3D Avatar Integration enabled +import ReservationModal from './ReservationModal.astro'; +import LAMAvatar from './LAMAvatar.astro'; + +export interface Props { + apiBaseUrl?: string; + useLAMAvatar?: boolean; // Enable LAM 3D Avatar + avatarPath?: string; // Path to LAM avatar .zip file +} + +const { + apiBaseUrl = '', + useLAMAvatar = true, // Default to LAM 3D Avatar + avatarPath = '/avatar/concierge.zip' +} = Astro.props; +--- + +
+ +
+ +

Loading...

+
+ + + +
+
+ +
+ +
+ +
+
+ +
+ {useLAMAvatar ? ( + + + ) : ( + +
+ AI Avatar +
+ )} +
+ +
🎤 Ready
+ +
+ +
+
+ + +
+
+ +
+ + + + +
+
+ + + + + + + + diff --git a/gourmet-sp/src/components/GourmetChat.astro b/gourmet-sp/src/components/GourmetChat.astro new file mode 100644 index 0000000..2bcf239 --- /dev/null +++ b/gourmet-sp/src/components/GourmetChat.astro @@ -0,0 +1,356 @@ +--- +// GourmetChat.astro - チャットモード(モード切替機能付き) +export interface Props { + apiBaseUrl?: string; +} + +const { apiBaseUrl = '' } = Astro.props; +--- + +
+
+ +

Loading...

+
+ + + + +
+
+ +
+ +
+ +
+
+ +
🎤 Ready
+
+ +
+
+ + +
+
+ +
+ + + + + + + +
+
+ + + + + + diff --git a/gourmet-sp/src/components/InstallPrompt.astro b/gourmet-sp/src/components/InstallPrompt.astro new file mode 100644 index 0000000..6211b99 --- /dev/null +++ b/gourmet-sp/src/components/InstallPrompt.astro @@ -0,0 +1,267 @@ + + + + + + + diff --git a/gourmet-sp/src/components/LAMAvatar.astro b/gourmet-sp/src/components/LAMAvatar.astro new file mode 100644 index 0000000..30bdcec --- /dev/null +++ b/gourmet-sp/src/components/LAMAvatar.astro @@ -0,0 +1,582 @@ +--- +// LAMAvatar.astro - LAM 3D Avatar Component using gaussian-splat-renderer-for-lam +// This component renders a 3D avatar using WebGL Gaussian Splatting +// With OpenAvatarChat WebSocket integration for real-time lip sync + +export interface Props { + avatarPath?: string; // Path to avatar .zip file + width?: string; + height?: string; + wsUrl?: string; // WebSocket URL for OpenAvatarChat backend + autoConnect?: boolean; // Auto-connect to WebSocket on load +} + +const { + avatarPath = '/avatar/concierge.zip', + width = '100%', + height = '100%', + wsUrl = '', + autoConnect = false +} = Astro.props; +--- + +
+
+
+ +

Loading 3D Avatar...

+
+
+ AI Avatar +
+
+ + + + diff --git a/gourmet-sp/src/components/ProposalCard.astro b/gourmet-sp/src/components/ProposalCard.astro new file mode 100644 index 0000000..cd3e7e9 --- /dev/null +++ b/gourmet-sp/src/components/ProposalCard.astro @@ -0,0 +1,514 @@ +--- +// ProposalCard.astro - レストラン提案カード +export interface Props { + proposal: { + name: string; + rating: number; + reviewCount: number; + heroImage: string; + official_website?: string; + maps_url?: string; + tabelog_url?: string; + category: string; + priceRange: string; + location: string; + description: string; + highlights: string[]; + tips: string; + }; +} + +const { proposal } = Astro.props; +--- + +
+
+ {`${proposal.name}のイメージ`} +
+ {proposal.category} +
+
+ + + + {proposal.location} +
+
+ +
+
+

{proposal.name}

+ +
+
+
+ {[...Array(5)].map((_, i) => ( + + + + ))} +
+ {proposal.rating} + ({proposal.reviewCount}件) +
+ +
+ + + + {proposal.priceRange} +
+
+
+ +
+

{proposal.description}

+
+ +
+

✨ おすすめポイント

+
    + {proposal.highlights.map((highlight) => ( +
  • + + + + + + {highlight} +
    • + ))} +
+
+ +
+
+ + + +
+
+

来店のポイント

+

{proposal.tips}

+
+
+ +
+

詳しい情報を見る

+ + {proposal.official_website && ( + +
+ + + +
+
+
ホットペッパー
+
予約・クーポン
+
+ + + + + )} + + {proposal.maps_url && ( + +
+ + + +
+
+
Google Maps
+
場所・ルート検索
+
+ + + + + )} + + {proposal.tabelog_url && ( + +
+ + + +
+
+
食べログ
+
口コミ・写真
+
+ + + + + )} +
+
+
+ + diff --git a/gourmet-sp/src/components/ReservationModal.astro b/gourmet-sp/src/components/ReservationModal.astro new file mode 100644 index 0000000..884fae8 --- /dev/null +++ b/gourmet-sp/src/components/ReservationModal.astro @@ -0,0 +1,1728 @@ +--- +// ReservationModal.astro - 予約依頼モーダル +export interface Props { + apiBaseUrl?: string; +} + +const { apiBaseUrl = '' } = Astro.props; +--- + +
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📞 予約依頼

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🏆 お店を選択

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優先順にクリックしてください（最大3件）

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+ ℹ ①→②→③の順で電話します。予約成立時点で終了します。 +

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📋 予約内容

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👤 予約者情報

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※ 店舗への連絡先として伝えます

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+ + + + diff --git a/gourmet-sp/src/components/ShopCardList.astro b/gourmet-sp/src/components/ShopCardList.astro new file mode 100644 index 0000000..ef1ddea --- /dev/null +++ b/gourmet-sp/src/components/ShopCardList.astro @@ -0,0 +1,1106 @@ +--- +// ShopCardList.astro - ProposalCard形式のショップカードリスト +export interface Props { + apiBaseUrl?: string; +} + +const { apiBaseUrl = '' } = Astro.props; +--- + +
+
+ + + + diff --git a/gourmet-sp/src/constants/i18n.ts b/gourmet-sp/src/constants/i18n.ts new file mode 100644 index 0000000..cbe10c3 --- /dev/null +++ b/gourmet-sp/src/constants/i18n.ts @@ -0,0 +1,434 @@ +// src/constants/i18n.ts + +export const i18n = { + ja: { + // --- UIテキスト --- + pageTitle: 'グルメサポートAI', + pageTitleConcierge: 'AIコンシェルジュ', + pageSubtitle: 'AIがあなたにぴったりのお店をご提案します', + shopListTitle: 'おすすめのお店', + shopListEmpty: 'チャットで検索すると、ここにお店が表示されます', + footerMessage: '素敵なグルメ体験をお楽しみください', + initialGreeting: 'こんにちは!グルメサポートAIです。\n\n本日はどのようなお店をお探ししましょうか?', + initialGreetingConcierge: '初めまして、AIコンシェルジュです。\n宜しければ、あなたを何とお呼びすればいいか、教えて頂けますか？', + voiceStatusStopped: '🎤 音声認識: 停止中', + voiceStatusListening: '🎤 話してください...', + voiceStatusRecording: '🎤 録音中...', + voiceStatusWaiting: '🎤 認識待機中...', + voiceStatusRecognizing: '🔊 音声認識中...', + voiceStatusSynthesizing: '🔊 音声合成中...', + voiceStatusSpeaking: '🔊 音声再生中...', + voiceStatusComplete: '✅ 認識完了', + inputPlaceholder: 'メッセージを入力...', + btnVoiceInput: '音声入力', + btnTTSOn: '音声読み上げON', + btnTTSOff: '音声読み上げOFF', + btnSend: '送信', + btnReservation: '📞 予約依頼する', + clickPrompt: '音声を再生するには、画面をクリックしてください', + recordingTimeLimit: '録音時間が上限（55秒）に達したため自動停止しました', + micAccessError: 'マイクアクセスエラー:', + voiceNotRecognized: '音声が認識されませんでした', + sttError: '音声認識エラー:', + initError: '初期化に失敗しました。ページを再読み込みしてください。', + searchError: 'お店を検索してからご利用ください。', + loadingMessage: '提案するお店の情報を探しています。少々お待ちください...', + summaryTitle: '📋 質問要約書', + summaryFooter: '担当スタッフが内容を確認し、追ってご連絡いたします。', + confirmReset: '初期画面に戻りますか?\n\nチャット履歴とショップリストが削除されます', + resetSuccess: '✨ 初期画面に戻りました', + + // --- 会話・即答テキスト --- + ackConfirm: '確認しますので、少々お待ちください。', + ackSearch: 'お調べします。', + ackUnderstood: 'かしこまりました。', + ackYes: 'はい、承知しました。', + fallbackResponse: (text: string) => `"${text}"とのこと。お調べしますので、少々お待ちください。`, + additionalResponse: '只今、お店の情報を確認中です。もう少々お待ちください。', + ttsIntro: 'お待たせしました。', + waitMessage: 'AIがお店を検索しています...', + + // 日時指定があった場合の警告メッセージ + dateWarningMsg: 'お店の空席状況は、後ほど私がお店に直接電話して確認しますので、まずはお店のご希望をお聞かせ下さい。', + // 短すぎる発言への警告 + shortMsgWarning: 'すみません、もう少し詳しくご希望を教えていただけますか？', + + // --- ロジック用パターン（正規表現） --- + patterns: { + // 日時キーワード（明日、◯時など） + dateCheck: /(明日|明後日|来週|来月|今夜|今日|(\d+)月|(\d+)日|(\d+)時|(\d+):(\d+)|スケジュール|空いてる|空き)/, + + // フィラー（文頭の言い淀み除去用） + fillers: /^(えーと|あの|んー|うーん|えっと|まあ|ていうか|なんか|じゃあ|それじゃあ|そしたら|そうしたら|では|なら|だったら|とりあえず|まずは)[、,.\s]*/, + + // スマート即答振り分け用キーワード + ackQuestions: /ございますか|でしょうか|いかがですか|ありますか/, + ackLocation: /どこ|場所|エリア|地域|駅/, + ackSearch: /探して|探し|教えて|おすすめ|紹介/ + }, + + // --- 予約モーダル --- + reservationModalTitle: '予約依頼', + reservationShopSelect: 'お店を選択', + reservationSelectionGuide: '優先順にクリックしてください（最大3件）', + reservationResetBtn: 'やり直し', + reservationPriorityNote: '①→②→③の順で電話します。予約成立時点で終了します。', + reservationContentTitle: '予約内容', + reservationGuestCount: '人数', + reservationGuestOption: (n: number) => n === 10 ? '10名以上' : `${n}名`, + reservationDate: '希望日', + reservationTime: '開始時間', + reservationSelectPlaceholder: '選択してください', + reservationTimeFlexibility: '時間の許容範囲', + reservationTimeExact: '開始時間優先', + reservationTimePlus30: '+30分まで', + reservationTimePlus60: '+60分まで', + reservationTimePlus90: '+90分まで', + reservationSeatPreference: '席の希望', + reservationSeatTable: 'テーブル席', + reservationSeatCounter: 'カウンター席', + reservationSeatPrivate: '個室', + reservationOtherRequests: 'その他の希望', + reservationOtherRequestsPlaceholder: '誕生日ケーキ、アレルギー対応、禁煙席など', + reservationPerShopBtn: 'お店毎に予約内容を変える', + reservationBackToCommon: '共通設定に戻す', + reservationReserverInfo: '予約者情報', + reservationReserverName: 'お名前', + reservationReserverNamePlaceholder: '予約者のお名前', + reservationReserverPhone: '携帯番号', + reservationReserverPhoneHint: '※ 店舗への連絡先として伝えます', + reservationCancel: 'キャンセル', + reservationSubmit: '予約依頼を開始する', + reservationSelectionRemaining: (n: number) => `あと${n}件選択できます`, + reservationSelectionComplete: '選択完了（変更するには店舗をクリック）', + reservationSelectShopsFirst: '先にお店を選択してください', + reservationPhoneLabel: '電話番号', + reservationPhoneHint: '※ Places APIから取得。修正可能', + reservationPerShopPlaceholder: 'このお店への特別なリクエスト', + reservationAlertTitle: '予約依頼を受け付けました。', + reservationAlertReserver: '予約者', + reservationAlertContact: '連絡先', + reservationAlertShops: '選択されたお店', + reservationAlertNoPhone: '電話番号なし', + reservationAlertDevNote: '（この機能は現在開発中です）', + reservationVoiceRecording: '録音中...', + reservationVoiceWaiting: '認識待機中...', + reservationVoiceSpeaking: '話してください...', + reservationVoiceError: 'マイクアクセスエラー', + reservationVoiceRecognizing: '音声認識中...', + reservationVoiceComplete: '入力完了', + reservationVoiceNotRecognized: '音声が認識されませんでした' + }, + en: { + pageTitle: 'Gourmet Support AI', + pageTitleConcierge: 'AI Concierge', + pageSubtitle: 'AI will suggest the perfect restaurant for you', + shopListTitle: 'Recommended Restaurants', + shopListEmpty: 'Search in the chat to see restaurants here', + footerMessage: 'Enjoy your wonderful dining experience', + initialGreeting: 'Hello! I\'m the Gourmet Support AI.\n\nWhat kind of restaurant are you looking for today? I can help you find restaurants anywhere in the world.', + initialGreetingConcierge: 'Nice to meet you! I am your AI Concierge.\nMay I ask what I should call you?', + voiceStatusStopped: '🎤 Voice Recognition: Stopped', + voiceStatusListening: '🎤 Please speak...', + voiceStatusRecording: '🎤 Recording...', + voiceStatusWaiting: '🎤 Waiting for recognition...', + voiceStatusRecognizing: '🔊 Recognizing voice...', + voiceStatusSynthesizing: '🔊 Synthesizing voice...', + voiceStatusSpeaking: '🔊 Playing audio...', + voiceStatusComplete: '✅ Recognition complete', + inputPlaceholder: 'Enter message...', + btnVoiceInput: 'Voice input', + btnTTSOn: 'Voice reading ON', + btnTTSOff: 'Voice reading OFF', + btnSend: 'Send', + btnReservation: '📞 Request reservation', + clickPrompt: 'Click the screen to play audio', + recordingTimeLimit: 'Recording stopped automatically (55s limit reached)', + micAccessError: 'Microphone access error:', + voiceNotRecognized: 'Voice not recognized', + confirmReset: 'Reset to initial screen?\n\nChat history and shop list will be cleared', + resetSuccess: '✨ Reset to initial screen', + sttError: 'Voice recognition error:', + initError: 'Initialization failed. Please reload the page.', + searchError: 'Please search for restaurants first.', + loadingMessage: 'Searching for restaurant recommendations. Please wait...', + summaryTitle: '📋 Inquiry Summary', + summaryFooter: 'Our staff will review your inquiry and contact you shortly.', + ackConfirm: 'Let me check. Please wait a moment.', + ackSearch: 'Let me look that up.', + ackUnderstood: 'Understood.', + ackYes: 'Yes, I understand.', + fallbackResponse: (text: string) => `You said "${text}". Let me search for that. Please wait.`, + additionalResponse: 'Currently searching for restaurant information. Please wait a moment.', + ttsIntro: 'Thank you for waiting.', + waitMessage: 'AI is searching for restaurants...', + dateWarningMsg: 'I will check seat availability later by phone. First, please tell me your restaurant preferences.', + shortMsgWarning: 'Could you please provide more details?', + patterns: { + dateCheck: /(tomorrow|tonight|next week|next month|today|(\d+)(am|pm)|schedule|available|free)/i, + fillers: /^(um|uh|well|so|like|actually|basically|anyway|you know|then)[,.\s]*/i, + ackQuestions: /(do you have|is there|can you)/i, + ackLocation: /(where|location|area|station)/i, + ackSearch: /(find|search|recommend|tell me)/i + }, + // Reservation Modal + reservationModalTitle: 'Reservation Request', + reservationShopSelect: 'Select Restaurants', + reservationSelectionGuide: 'Click in priority order (up to 3)', + reservationResetBtn: 'Reset', + reservationPriorityNote: 'We will call in order ①→②→③. Process ends when reservation is confirmed.', + reservationContentTitle: 'Reservation Details', + reservationGuestCount: 'Party Size', + reservationGuestOption: (n: number) => n === 10 ? '10+ people' : `${n} ${n === 1 ? 'person' : 'people'}`, + reservationDate: 'Preferred Date', + reservationTime: 'Start Time', + reservationSelectPlaceholder: 'Please select', + reservationTimeFlexibility: 'Time Flexibility', + reservationTimeExact: 'Exact time preferred', + reservationTimePlus30: 'Up to +30 min', + reservationTimePlus60: 'Up to +60 min', + reservationTimePlus90: 'Up to +90 min', + reservationSeatPreference: 'Seating Preference', + reservationSeatTable: 'Table', + reservationSeatCounter: 'Counter', + reservationSeatPrivate: 'Private room', + reservationOtherRequests: 'Special Requests', + reservationOtherRequestsPlaceholder: 'Birthday cake, allergy accommodations, non-smoking, etc.', + reservationPerShopBtn: 'Set different details per restaurant', + reservationBackToCommon: 'Back to common settings', + reservationReserverInfo: 'Your Information', + reservationReserverName: 'Name', + reservationReserverNamePlaceholder: 'Your name', + reservationReserverPhone: 'Phone Number', + reservationReserverPhoneHint: '※ Will be provided to the restaurant', + reservationCancel: 'Cancel', + reservationSubmit: 'Submit Reservation Request', + reservationSelectionRemaining: (n: number) => `${n} more can be selected`, + reservationSelectionComplete: 'Selection complete (click to change)', + reservationSelectShopsFirst: 'Please select restaurants first', + reservationPhoneLabel: 'Phone Number', + reservationPhoneHint: '※ Retrieved from Places API. Editable', + reservationPerShopPlaceholder: 'Special requests for this restaurant', + reservationAlertTitle: 'Reservation request received.', + reservationAlertReserver: 'Name', + reservationAlertContact: 'Contact', + reservationAlertShops: 'Selected Restaurants', + reservationAlertNoPhone: 'No phone', + reservationAlertDevNote: '(This feature is currently under development)', + reservationVoiceRecording: 'Recording...', + reservationVoiceWaiting: 'Waiting for recognition...', + reservationVoiceSpeaking: 'Please speak...', + reservationVoiceError: 'Microphone access error', + reservationVoiceRecognizing: 'Recognizing voice...', + reservationVoiceComplete: 'Input complete', + reservationVoiceNotRecognized: 'Voice not recognized' + }, + zh: { + pageTitle: '美食支持AI', + pageTitleConcierge: 'AI礼宾服务', + pageSubtitle: 'AI为您推荐完美的餐厅', + shopListTitle: '推荐餐厅', + shopListEmpty: '在聊天中搜索后，餐厅将显示在这里', + footerMessage: '祝您享受美好的美食体验', + initialGreeting: '您好！我是美食支持AI。\n\n今天您想找什么样的餐厅呢？我可以帮您搜索全球各地的餐厅。', + initialGreetingConcierge: '您好！我是AI礼宾员。\n请问我应该怎么称呼您？', + voiceStatusStopped: '🎤 语音识别: 已停止', + voiceStatusListening: '🎤 请说话...', + voiceStatusRecording: '🎤 录音中...', + voiceStatusWaiting: '🎤 等待识别...', + voiceStatusRecognizing: '🔊 识别语音中...', + voiceStatusSynthesizing: '🔊 语音合成中...', + voiceStatusSpeaking: '🔊 播放音频中...', + voiceStatusComplete: '✅ 识别完成', + inputPlaceholder: '输入消息...', + btnVoiceInput: '语音输入', + btnTTSOn: '语音朗读开启', + btnTTSOff: '语音朗读关闭', + btnSend: '发送', + confirmReset: '返回初始画面?\n\n聊天记录和店铺列表将被清除', + resetSuccess: '✨ 已返回初始画面', + btnReservation: '📞 申请预约', + clickPrompt: '点击屏幕播放音频', + recordingTimeLimit: '录音已自动停止（达到55秒上限）', + micAccessError: '麦克风访问错误:', + voiceNotRecognized: '未识别到语音', + sttError: '语音识别错误:', + initError: '初始化失败。请重新加载页面。', + searchError: '请先搜索餐厅。', + loadingMessage: '正在搜索推荐餐厅。请稍候...', + summaryTitle: '📋 咨询摘要', + summaryFooter: '我们的工作人员将审核您的咨询并尽快联系您。', + ackConfirm: '我确认一下。请稍等。', + ackSearch: '我查一下。', + ackUnderstood: '明白了。', + ackYes: '好的，我知道了。', + fallbackResponse: (text: string) => `您说"${text}"。我搜索一下。请稍等。`, + additionalResponse: '正在确认餐厅信息。请稍候。', + ttsIntro: '让您久等了。', + waitMessage: 'AI正在搜索餐厅...', + dateWarningMsg: '我会稍后打电话确认空位。请先告诉我您对餐厅的要求。', + shortMsgWarning: '不好意思，请详细说明您的要求。', + patterns: { + dateCheck: /(明天|下周|今天|空位|预订)/, + fillers: /^(这个|那个|嗯|然后)[，,.\s]*/, + ackQuestions: /吗/, + ackLocation: /(哪里|地点|区域)/, + ackSearch: /(找|推荐|告诉)/ + }, + // 预约模态框 + reservationModalTitle: '预约申请', + reservationShopSelect: '选择餐厅', + reservationSelectionGuide: '按优先顺序点击（最多3家）', + reservationResetBtn: '重置', + reservationPriorityNote: '将按①→②→③的顺序致电。预约成功后结束。', + reservationContentTitle: '预约详情', + reservationGuestCount: '人数', + reservationGuestOption: (n: number) => n === 10 ? '10人以上' : `${n}人`, + reservationDate: '希望日期', + reservationTime: '开始时间', + reservationSelectPlaceholder: '请选择', + reservationTimeFlexibility: '时间弹性', + reservationTimeExact: '优先开始时间', + reservationTimePlus30: '最多+30分钟', + reservationTimePlus60: '最多+60分钟', + reservationTimePlus90: '最多+90分钟', + reservationSeatPreference: '座位偏好', + reservationSeatTable: '桌席', + reservationSeatCounter: '吧台席', + reservationSeatPrivate: '包间', + reservationOtherRequests: '其他要求', + reservationOtherRequestsPlaceholder: '生日蛋糕、过敏对应、禁烟座位等', + reservationPerShopBtn: '为每家餐厅设置不同详情', + reservationBackToCommon: '返回通用设置', + reservationReserverInfo: '预约人信息', + reservationReserverName: '姓名', + reservationReserverNamePlaceholder: '预约人姓名', + reservationReserverPhone: '手机号码', + reservationReserverPhoneHint: '※ 将告知餐厅作为联系方式', + reservationCancel: '取消', + reservationSubmit: '提交预约申请', + reservationSelectionRemaining: (n: number) => `还可选择${n}家`, + reservationSelectionComplete: '选择完成（点击可更改）', + reservationSelectShopsFirst: '请先选择餐厅', + reservationPhoneLabel: '电话号码', + reservationPhoneHint: '※ 从Places API获取。可编辑', + reservationPerShopPlaceholder: '对此餐厅的特殊要求', + reservationAlertTitle: '已接受预约申请。', + reservationAlertReserver: '预约人', + reservationAlertContact: '联系方式', + reservationAlertShops: '选择的餐厅', + reservationAlertNoPhone: '无电话', + reservationAlertDevNote: '（此功能目前正在开发中）', + reservationVoiceRecording: '录音中...', + reservationVoiceWaiting: '等待识别...', + reservationVoiceSpeaking: '请说话...', + reservationVoiceError: '麦克风访问错误', + reservationVoiceRecognizing: '识别语音中...', + reservationVoiceComplete: '输入完成', + reservationVoiceNotRecognized: '未识别到语音' + }, + ko: { + pageTitle: '미식 지원 AI', + pageTitleConcierge: 'AI 컨시어지', + pageSubtitle: 'AI가 완벽한 레스토랑을 추천해 드립니다', + shopListTitle: '추천 레스토랑', + shopListEmpty: '채팅에서 검색하면 여기에 레스토랑이 표시됩니다', + footerMessage: '멋진 미식 경험을 즐기세요', + initialGreeting: '안녕하세요! 미식 지원 AI입니다.\n\n오늘은 어떤 음식점을 찾으시나요? 전 세계 어디든 음식점을 검색해 드릴 수 있습니다.', + initialGreetingConcierge: '처음 뵙겠습니다! AI 컨시어지입니다.\n어떻게 불러드리면 될까요?', + voiceStatusStopped: '🎤 음성 인식: 정지됨', + voiceStatusListening: '🎤 말씀해 주세요...', + voiceStatusRecording: '🎤 녹음 중...', + voiceStatusWaiting: '🎤 인식 대기 중...', + voiceStatusRecognizing: '🔊 음성 인식 중...', + voiceStatusSynthesizing: '🔊 음성 합성 중...', + voiceStatusSpeaking: '🔊 오디오 재생 중...', + voiceStatusComplete: '✅ 인식 완료', + inputPlaceholder: '메시지 입력...', + btnVoiceInput: '음성 입력', + btnTTSOn: '음성 읽기 켜짐', + btnTTSOff: '음성 읽기 꺼짐', + btnSend: '전송', + confirmReset: '초기 화면으로 돌아가시겠습니까?\n\n채팅 기록과 매장 목록이 삭제됩니다', + resetSuccess: '✨ 초기 화면으로 돌아갔습니다', + btnReservation: '📞 예약 신청', + clickPrompt: '오디오를 재생하려면 화면을 클릭하세요', + recordingTimeLimit: '녹음이 자동으로 중지되었습니다 (55초 제한 도달)', + micAccessError: '마이크 액세스 오류:', + voiceNotRecognized: '음성이 인식되지 않았습니다', + sttError: '음성 인식 오류:', + initError: '초기화 실패. 페이지를 새로고침하세요.', + searchError: '먼저 레스토랑을 검색하세요.', + loadingMessage: '추천 레스토랑을 검색 중입니다. 잠시만 기다려주세요...', + summaryTitle: '📋 문의 요약', + summaryFooter: '담당자가 문의 내용을 검토하고 곧 연락드리겠습니다.', + ackConfirm: '확인하겠습니다. 잠시만 기다려주세요.', + ackSearch: '찾아보겠습니다.', + ackUnderstood: '알겠습니다.', + ackYes: '네, 알겠습니다.', + fallbackResponse: (text: string) => `"${text}"라고 말씀하셨네요. 검색해 보겠습니다. 잠시만 기다려주세요.`, + additionalResponse: '지금 레스토랑 정보를 확인 중입니다. 잠시만 기다려주세요.', + ttsIntro: '기다려 주셔서 감사합니다.', + waitMessage: 'AI가 레스토랑을 검색하고 있습니다...', + dateWarningMsg: '빈 자리는 나중에 전화로 확인할게요. 먼저 어떤 식당을 원하시는지 알려주세요.', + shortMsgWarning: '죄송합니다. 좀 더 자세히 말씀해 주시겠습니까?', + patterns: { + dateCheck: /(내일|다음주|오늘|자리|예약)/, + fillers: /^(저|그|음|그러면)[，,.\s]*/, + ackQuestions: /(나요|가요|있나요)/, + ackLocation: /(어디|장소|지역)/, + ackSearch: /(찾아|추천|알려)/ + }, + // 예약 모달 + reservationModalTitle: '예약 신청', + reservationShopSelect: '레스토랑 선택', + reservationSelectionGuide: '우선순위대로 클릭하세요 (최대 3곳)', + reservationResetBtn: '다시 하기', + reservationPriorityNote: '①→②→③ 순서로 전화합니다. 예약 성립 시 종료합니다.', + reservationContentTitle: '예약 내용', + reservationGuestCount: '인원', + reservationGuestOption: (n: number) => n === 10 ? '10명 이상' : `${n}명`, + reservationDate: '희망 날짜', + reservationTime: '시작 시간', + reservationSelectPlaceholder: '선택하세요', + reservationTimeFlexibility: '시간 허용 범위', + reservationTimeExact: '시작 시간 우선', + reservationTimePlus30: '+30분까지', + reservationTimePlus60: '+60분까지', + reservationTimePlus90: '+90분까지', + reservationSeatPreference: '좌석 희망', + reservationSeatTable: '테이블석', + reservationSeatCounter: '카운터석', + reservationSeatPrivate: '룸', + reservationOtherRequests: '기타 요청', + reservationOtherRequestsPlaceholder: '생일 케이크, 알레르기 대응, 금연석 등', + reservationPerShopBtn: '레스토랑별로 예약 내용 다르게 설정', + reservationBackToCommon: '공통 설정으로 돌아가기', + reservationReserverInfo: '예약자 정보', + reservationReserverName: '성함', + reservationReserverNamePlaceholder: '예약자 성함', + reservationReserverPhone: '전화번호', + reservationReserverPhoneHint: '※ 레스토랑에 연락처로 전달됩니다', + reservationCancel: '취소', + reservationSubmit: '예약 신청 시작', + reservationSelectionRemaining: (n: number) => `${n}곳 더 선택 가능`, + reservationSelectionComplete: '선택 완료 (변경하려면 클릭)', + reservationSelectShopsFirst: '먼저 레스토랑을 선택하세요', + reservationPhoneLabel: '전화번호', + reservationPhoneHint: '※ Places API에서 가져옴. 수정 가능', + reservationPerShopPlaceholder: '이 레스토랑에 대한 특별 요청', + reservationAlertTitle: '예약 신청을 접수했습니다.', + reservationAlertReserver: '예약자', + reservationAlertContact: '연락처', + reservationAlertShops: '선택한 레스토랑', + reservationAlertNoPhone: '전화번호 없음', + reservationAlertDevNote: '(이 기능은 현재 개발 중입니다)', + reservationVoiceRecording: '녹음 중...', + reservationVoiceWaiting: '인식 대기 중...', + reservationVoiceSpeaking: '말씀해 주세요...', + reservationVoiceError: '마이크 액세스 오류', + reservationVoiceRecognizing: '음성 인식 중...', + reservationVoiceComplete: '입력 완료', + reservationVoiceNotRecognized: '음성이 인식되지 않았습니다' + } +}; diff --git a/gourmet-sp/src/env.d.ts b/gourmet-sp/src/env.d.ts new file mode 100644 index 0000000..9bc5cb4 --- /dev/null +++ b/gourmet-sp/src/env.d.ts @@ -0,0 +1 @@ +/// \ No newline at end of file diff --git a/gourmet-sp/src/layouts/Layout.astro b/gourmet-sp/src/layouts/Layout.astro new file mode 100644 index 0000000..4cea277 --- /dev/null +++ b/gourmet-sp/src/layouts/Layout.astro @@ -0,0 +1,39 @@ +--- +// src/layouts/Layout.astro +import InstallPrompt from '../components/InstallPrompt.astro'; +--- + + + + + + + + + + + + + + Gourmet SP + + + + + + + + + + + \ No newline at end of file diff --git a/gourmet-sp/src/pages/404.astro b/gourmet-sp/src/pages/404.astro new file mode 100644 index 0000000..27e82e3 --- /dev/null +++ b/gourmet-sp/src/pages/404.astro @@ -0,0 +1,18 @@ +--- +import Layout from '../layouts/Layout.astro'; +// Layoutがない場合は ... で囲んでください +--- + + + + + 404 Not Found + + +
+

404

+

ページが見つかりません

+ トップへ戻る +
+ + diff --git a/gourmet-sp/src/pages/chat.astro b/gourmet-sp/src/pages/chat.astro new file mode 100644 index 0000000..f9cd30c --- /dev/null +++ b/gourmet-sp/src/pages/chat.astro @@ -0,0 +1,46 @@ +--- +// chat.astro - チャットのみのページ（テスト用） +import GourmetChat from '../components/GourmetChat.astro'; + +// APIベースURL +const apiBaseUrl = import.meta.env.PUBLIC_API_URL || ''; +--- + + + + + + + グルメサポートAI - チャット + + + + + + +
+ +
+ + diff --git a/gourmet-sp/src/pages/concierge.astro b/gourmet-sp/src/pages/concierge.astro new file mode 100644 index 0000000..7ec98f0 --- /dev/null +++ b/gourmet-sp/src/pages/concierge.astro @@ -0,0 +1,558 @@ +--- +// src/pages/concierge.astro +import ConciergeComponent from '../components/Concierge.astro'; +import ShopCardList from '../components/ShopCardList.astro'; + +const apiBaseUrl = import.meta.env.PUBLIC_API_URL || ''; +--- + + + + + + + Concierge Mode - AI Gourmet Chat + + + + + + + + + + + + + + + + + + +
+
+

+ + AI コンシェルジュ +

+

おもてなしの心でお店探しをサポート

+
+ +
+
+ +
+ +
+
+

🍽 おすすめのお店

+

チャットで検索すると、ここにお店が表示されます

+
+ +
+
+ +
+ 素敵なグルメ体験をお楽しみください ✨ +
+
+ + + + diff --git a/gourmet-sp/src/pages/index.astro b/gourmet-sp/src/pages/index.astro new file mode 100644 index 0000000..06ded57 --- /dev/null +++ b/gourmet-sp/src/pages/index.astro @@ -0,0 +1,571 @@ + +--- +// index.astro - グルメサポートメインページ +import GourmetChat from '../components/GourmetChat.astro'; +import ShopCardList from '../components/ShopCardList.astro'; +import ReservationModal from '../components/ReservationModal.astro'; + +// APIベースURL（Cloud Runのエンドポイント） +// 本番環境では環境変数から取得 +const apiBaseUrl = import.meta.env.PUBLIC_API_URL || ''; +--- + + + + + + + グルメサポートAI - お店探しをお手伝い + + + + + + + + + + + + + + + + + + + +
+
+

+ + グルメサポートAI +

+

AIがあなたにぴったりのお店をご提案します

+
+ +
+
+ +
+ +
+
+

🍽 おすすめのお店

+

チャットで検索すると、ここにお店が表示されます

+
+ +
+
+ +
+ 素敵なグルメ体験をお楽しみください ✨ +
+
+ + + + + + diff --git a/gourmet-sp/src/scripts/chat/audio-manager.ts b/gourmet-sp/src/scripts/chat/audio-manager.ts new file mode 100644 index 0000000..3f7caf7 --- /dev/null +++ b/gourmet-sp/src/scripts/chat/audio-manager.ts @@ -0,0 +1,733 @@ +// src/scripts/chat/audio-manager.ts +// ★根本修正: サーバー準備完了を待ってから音声送信開始2 + +const b64chars = 'ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz0123456789+/'; +function fastArrayBufferToBase64(buffer: ArrayBuffer) { + let binary = ''; + const bytes = new Uint8Array(buffer); + const len = bytes.byteLength; + for (let i = 0; i < len; i += 3) { + const c1 = bytes[i]; + const c2 = bytes[i + 1]; + const c3 = bytes[i + 2]; + const enc1 = c1 >> 2; + const enc2 = ((c1 & 3) << 4) | (c2 >> 4); + const enc3 = ((c2 & 15) << 2) | (c3 >> 6); + const enc4 = c3 & 63; + binary += b64chars[enc1] + b64chars[enc2]; + if (Number.isNaN(c2)) { binary += '=='; } + else if (Number.isNaN(c3)) { binary += b64chars[enc3] + '='; } + else { binary += b64chars[enc3] + b64chars[enc4]; } + } + return binary; +} + +export class AudioManager { + private audioContext: AudioContext | null = null; + private globalAudioContext: AudioContext | null = null; + private audioWorkletNode: AudioWorkletNode | null = null; + private mediaStream: MediaStream | null = null; + private analyser: AnalyserNode | null = null; + + private mediaRecorder: MediaRecorder | null = null; + private audioChunks: Blob[] = []; + + private vadCheckInterval: number | null = null; + private silenceTimer: number | null = null; + private hasSpoken = false; + private recordingStartTime = 0; + private recordingTimer: number | null = null; + + // ★追加: 音声送信を遅延開始するためのフラグ + private canSendAudio = false; + private audioBuffer: Array<{chunk: ArrayBuffer, sampleRate: number}> = []; + + private readonly SILENCE_THRESHOLD = 35; + private SILENCE_DURATION: number; + private readonly MIN_RECORDING_TIME = 3000; + private readonly MAX_RECORDING_TIME = 60000; + + private consecutiveSilenceCount = 0; + private readonly REQUIRED_SILENCE_CHECKS = 5; + + private isIOS = /iPhone|iPad|iPod/i.test(navigator.userAgent); + + constructor(silenceDuration: number = 3500) { + this.SILENCE_DURATION = silenceDuration; + } + + public unlockAudioParams(elementToUnlock: HTMLAudioElement) { + if (this.globalAudioContext && this.globalAudioContext.state === 'suspended') { + this.globalAudioContext.resume(); + } + if (this.audioContext && this.audioContext.state === 'suspended') { + this.audioContext.resume(); + } + + // ★iOS対策: HTMLAudioElementも明示的にアンロック + if (elementToUnlock) { + elementToUnlock.muted = true; + elementToUnlock.play().then(() => { + elementToUnlock.pause(); + elementToUnlock.currentTime = 0; + elementToUnlock.muted = false; + }).catch(() => { + // エラーは無視（既にアンロック済みの場合） + }); + } + } + + public fullResetAudioResources() { + this.stopStreaming(); + + if (this.globalAudioContext && this.globalAudioContext.state !== 'closed') { + this.globalAudioContext.close(); + this.globalAudioContext = null; + } + if (this.audioContext && this.audioContext.state !== 'closed') { + this.audioContext.close(); + this.audioContext = null; + } + if (this.mediaStream) { + this.mediaStream.getTracks().forEach(track => track.stop()); + this.mediaStream = null; + } + } + + private async getUserMediaSafe(constraints: MediaStreamConstraints): Promise { + if (navigator.mediaDevices && navigator.mediaDevices.getUserMedia) { + return navigator.mediaDevices.getUserMedia(constraints); + } + // @ts-ignore + const legacyGetUserMedia = navigator.getUserMedia || navigator.webkitGetUserMedia || navigator.mozGetUserMedia || navigator.msGetUserMedia; + if (legacyGetUserMedia) { + return new Promise((resolve, reject) => { + legacyGetUserMedia.call(navigator, constraints, resolve, reject); + }); + } + throw new Error('マイク機能が見つかりません。HTTPS(鍵マーク)のURLでアクセスしているか確認してください。'); + } + + public async startStreaming( + socket: any, + languageCode: string, + onStopCallback: () => void, + onSpeechStart?: () => void + ) { + if (this.isIOS) { + await this.startStreaming_iOS(socket, languageCode, onStopCallback); + } else { + await this.startStreaming_Default(socket, languageCode, onStopCallback, onSpeechStart); + } + } + + public stopStreaming() { + if (this.isIOS) { + this.stopStreaming_iOS(); + } else { + this.stopStreaming_Default(); + } + if (this.mediaRecorder && this.mediaRecorder.state !== 'inactive') { + this.mediaRecorder.stop(); + } + this.mediaRecorder = null; + + // ★バッファとフラグをリセット + this.canSendAudio = false; + this.audioBuffer = []; + } + + // --- iOS用実装 --- + private async startStreaming_iOS(socket: any, languageCode: string, onStopCallback: () => void) { + try { + // ★初期化 + this.canSendAudio = false; + this.audioBuffer = []; + + if (this.recordingTimer) { clearTimeout(this.recordingTimer); this.recordingTimer = null; } + + if (this.audioWorkletNode) { + this.audioWorkletNode.port.onmessage = null; + this.audioWorkletNode.disconnect(); + this.audioWorkletNode = null; + } + + if (!this.globalAudioContext) { + // @ts-ignore + const AudioContextClass = window.AudioContext || window.webkitAudioContext; + this.globalAudioContext = new AudioContextClass({ + latencyHint: 'interactive', + sampleRate: 48000 + }); + } + + if (this.globalAudioContext.state === 'suspended') { + await this.globalAudioContext.resume(); + } + + const audioConstraints = { + channelCount: 1, + echoCancellation: true, + noiseSuppression: true, + autoGainControl: true, + sampleRate: 48000 + }; + + let needNewStream = false; + + if (this.mediaStream) { + const tracks = this.mediaStream.getAudioTracks(); + if (tracks.length === 0 || + tracks[0].readyState !== 'live' || + !tracks[0].enabled || + tracks[0].muted) { + needNewStream = true; + } + } else { + needNewStream = true; + } + + if (needNewStream) { + if (this.mediaStream) { + this.mediaStream.getTracks().forEach(track => track.stop()); + this.mediaStream = null; + } + this.mediaStream = await this.getUserMediaSafe({ audio: audioConstraints }); + } + + const targetSampleRate = 16000; + const nativeSampleRate = this.globalAudioContext.sampleRate; + const downsampleRatio = nativeSampleRate / targetSampleRate; + + const source = this.globalAudioContext.createMediaStreamSource(this.mediaStream); + const processorName = 'audio-processor-ios-' + Date.now(); + + const audioProcessorCode = ` + class AudioProcessor extends AudioWorkletProcessor { + constructor() { + super(); + this.bufferSize = 8192; + this.buffer = new Int16Array(this.bufferSize); + this.writeIndex = 0; + this.ratio = ${downsampleRatio}; + this.inputSampleCount = 0; + this.lastFlushTime = Date.now(); + } + process(inputs, outputs, parameters) { + const input = inputs[0]; + if (!input || input.length === 0) return true; + const channelData = input[0]; + if (!channelData || channelData.length === 0) return true; + for (let i = 0; i < channelData.length; i++) { + this.inputSampleCount++; + if (this.inputSampleCount >= this.ratio) { + this.inputSampleCount -= this.ratio; + if (this.writeIndex < this.bufferSize) { + const s = Math.max(-1, Math.min(1, channelData[i])); + const int16Value = s < 0 ? s * 0x8000 : s * 0x7FFF; + this.buffer[this.writeIndex++] = int16Value; + } + if (this.writeIndex >= this.bufferSize || + (this.writeIndex > 0 && Date.now() - this.lastFlushTime > 500)) { + this.flush(); + } + } + } + return true; + } + flush() { + if (this.writeIndex === 0) return; + const chunk = this.buffer.slice(0, this.writeIndex); + this.port.postMessage({ audioChunk: chunk }, [chunk.buffer]); + this.writeIndex = 0; + this.lastFlushTime = Date.now(); + } + } + registerProcessor('${processorName}', AudioProcessor); + `; + + const blob = new Blob([audioProcessorCode], { type: 'application/javascript' }); + const processorUrl = URL.createObjectURL(blob); + await this.globalAudioContext.audioWorklet.addModule(processorUrl); + URL.revokeObjectURL(processorUrl); + + // ★STEP1: AudioWorkletNode生成後、初期化完了を待つ + this.audioWorkletNode = new AudioWorkletNode(this.globalAudioContext, processorName); + await new Promise(resolve => setTimeout(resolve, 50)); + + // ★STEP2: onmessageハンドラー設定(バッファリング付き) + this.audioWorkletNode.port.onmessage = (event) => { + const { audioChunk } = event.data; + if (!socket || !socket.connected) return; + + try { + const base64 = fastArrayBufferToBase64(audioChunk.buffer); + + // ★送信許可が出ていない場合はバッファに保存 + if (!this.canSendAudio) { + this.audioBuffer.push({ chunk: audioChunk.buffer, sampleRate: 16000 }); + // バッファが大きくなりすぎないよう制限(最大3秒分 = 48チャンク) + if (this.audioBuffer.length > 48) { + this.audioBuffer.shift(); + } + return; + } + + // ★送信許可が出たら即座に送信 + socket.emit('audio_chunk', { chunk: base64, sample_rate: 16000 }); + } catch (e) { } + }; + + // ★STEP3: 音声グラフ接続 + source.connect(this.audioWorkletNode); + this.audioWorkletNode.connect(this.globalAudioContext.destination); + + // ★STEP4: Socket通知(サーバー準備開始) + if (socket && socket.connected) { + socket.emit('stop_stream'); + await new Promise(resolve => setTimeout(resolve, 100)); + } + + // ★STEP5: start_stream送信して、サーバー準備完了を待つ + const streamReadyPromise = new Promise((resolve) => { + const timeout = setTimeout(() => resolve(), 500); + socket.once('stream_ready', () => { + clearTimeout(timeout); + resolve(); + }); + }); + + socket.emit('start_stream', { + language_code: languageCode, + sample_rate: 16000 + }); + + // ★STEP6: サーバー準備完了を待機(最大500ms) + await streamReadyPromise; + + // ★STEP7: 送信許可フラグを立てる + this.canSendAudio = true; + + // ★STEP8: バッファに溜まった音声を一気に送信 + if (this.audioBuffer.length > 0) { + for (const buffered of this.audioBuffer) { + try { + const base64 = fastArrayBufferToBase64(buffered.chunk); + socket.emit('audio_chunk', { chunk: base64, sample_rate: buffered.sampleRate }); + } catch (e) { } + } + this.audioBuffer = []; + } + + this.recordingTimer = window.setTimeout(() => { + this.stopStreaming_iOS(); + onStopCallback(); + }, this.MAX_RECORDING_TIME); + + } catch (error) { + this.canSendAudio = false; + this.audioBuffer = []; + if (this.audioWorkletNode) { + this.audioWorkletNode.port.onmessage = null; + this.audioWorkletNode.disconnect(); + this.audioWorkletNode = null; + } + throw error; + } + } + + private stopStreaming_iOS() { + this.canSendAudio = false; + this.audioBuffer = []; + + if (this.recordingTimer) { clearTimeout(this.recordingTimer); this.recordingTimer = null; } + + if (this.audioWorkletNode) { + try { + this.audioWorkletNode.port.onmessage = null; + this.audioWorkletNode.disconnect(); + } catch (e) { } + this.audioWorkletNode = null; + } + + if (this.mediaStream) { + const tracks = this.mediaStream.getAudioTracks(); + if (tracks.length === 0 || tracks[0].readyState === 'ended') { + this.mediaStream.getTracks().forEach(track => track.stop()); + this.mediaStream = null; + } + } + } + + // --- PC / Android用実装(修正版) --- + private async startStreaming_Default( + socket: any, + languageCode: string, + onStopCallback: () => void, + onSpeechStart?: () => void + ) { + try { + // ★初期化 + this.canSendAudio = false; + this.audioBuffer = []; + + if (this.recordingTimer) { clearTimeout(this.recordingTimer); this.recordingTimer = null; } + + if (this.audioWorkletNode) { + this.audioWorkletNode.port.onmessage = null; + this.audioWorkletNode.disconnect(); + this.audioWorkletNode = null; + } + + if (!this.audioContext) { + // @ts-ignore + const AudioContextClass = window.AudioContext || window.webkitAudioContext; + this.audioContext = new AudioContextClass({ + latencyHint: 'interactive', + sampleRate: 48000 + }); + } + + if (this.audioContext!.state === 'suspended') { + await this.audioContext!.resume(); + } + + if (this.mediaStream) { + this.mediaStream.getTracks().forEach(track => track.stop()); + this.mediaStream = null; + } + + const audioConstraints = { + channelCount: 1, + echoCancellation: true, + noiseSuppression: true, + autoGainControl: true + }; + + this.mediaStream = await this.getUserMediaSafe({ audio: audioConstraints }); + + const targetSampleRate = 16000; + const nativeSampleRate = this.audioContext!.sampleRate; + const downsampleRatio = nativeSampleRate / targetSampleRate; + + const source = this.audioContext!.createMediaStreamSource(this.mediaStream); + + const audioProcessorCode = ` + class AudioProcessor extends AudioWorkletProcessor { + constructor() { + super(); + this.bufferSize = 16000; + this.buffer = new Int16Array(this.bufferSize); + this.writeIndex = 0; + this.ratio = ${downsampleRatio}; + this.inputSampleCount = 0; + this.flushThreshold = 8000; + } + process(inputs, outputs, parameters) { + const input = inputs[0]; + if (!input || input.length === 0) return true; + const channelData = input[0]; + if (!channelData || channelData.length === 0) return true; + for (let i = 0; i < channelData.length; i++) { + this.inputSampleCount++; + if (this.inputSampleCount >= this.ratio) { + this.inputSampleCount -= this.ratio; + if (this.writeIndex < this.bufferSize) { + const s = Math.max(-1, Math.min(1, channelData[i])); + const int16Value = s < 0 ? s * 0x8000 : s * 0x7FFF; + this.buffer[this.writeIndex++] = int16Value; + } + if (this.writeIndex >= this.bufferSize) { + this.flush(); + } + } + } + return true; + } + flush() { + if (this.writeIndex === 0) return; + const chunk = this.buffer.slice(0, this.writeIndex); + this.port.postMessage({ audioChunk: chunk }, [chunk.buffer]); + this.writeIndex = 0; + } + } + registerProcessor('audio-processor', AudioProcessor); + `; + + try { + const blob = new Blob([audioProcessorCode], { type: 'application/javascript' }); + const processorUrl = URL.createObjectURL(blob); + await this.audioContext!.audioWorklet.addModule(processorUrl); + URL.revokeObjectURL(processorUrl); + } catch (workletError) { + throw new Error(`音声処理初期化エラー: ${(workletError as Error).message}`); + } + + // ★STEP1: AudioWorkletNode生成後、初期化完了を待つ + this.audioWorkletNode = new AudioWorkletNode(this.audioContext!, 'audio-processor'); + await new Promise(resolve => setTimeout(resolve, 50)); + + // ★STEP2: onmessageハンドラー設定(バッファリング付き) + this.audioWorkletNode.port.onmessage = (event) => { + const { audioChunk } = event.data; + if (!socket || !socket.connected) return; + + try { + // ★送信許可が出ていない場合はバッファに保存 + if (!this.canSendAudio) { + this.audioBuffer.push({ chunk: audioChunk, sampleRate: 16000 }); + if (this.audioBuffer.length > 48) { + this.audioBuffer.shift(); + } + return; + } + + // ★送信許可が出たら即座に送信 + const blob = new Blob([audioChunk], { type: 'application/octet-stream' }); + const reader = new FileReader(); + reader.onload = () => { + const result = reader.result as string; + const base64 = result.split(',')[1]; + socket.emit('audio_chunk', { chunk: base64, sample_rate: 16000 }); + }; + reader.readAsDataURL(blob); + } catch (e) { } + }; + + // ★STEP3: 音声グラフ接続 + source.connect(this.audioWorkletNode); + this.audioWorkletNode.connect(this.audioContext!.destination); + + // ★待機: AudioWorkletが音声処理を開始するまで + await new Promise(resolve => setTimeout(resolve, 200)); + + // ★STEP4: Socket通知(サーバー準備開始) + if (socket && socket.connected) { + socket.emit('stop_stream'); + await new Promise(resolve => setTimeout(resolve, 100)); + } + + // ★STEP5: start_stream送信して、サーバー準備完了を待つ + const streamReadyPromise = new Promise((resolve) => { + const timeout = setTimeout(() => resolve(), 700); + socket.once('stream_ready', () => { + clearTimeout(timeout); + resolve(); + }); + }); + + socket.emit('start_stream', { + language_code: languageCode, + sample_rate: 16000 + }); + + // ★STEP6: サーバー準備完了を待機(最大700ms) + await streamReadyPromise; + + // ★追加待機: バッファに音声を蓄積 + await new Promise(resolve => setTimeout(resolve, 200)); + + // ★STEP7: 送信許可フラグを立てる + this.canSendAudio = true; + + // ★STEP8: バッファに溜まった音声を一気に送信（順序保証） + if (this.audioBuffer.length > 0) { + for (const buffered of this.audioBuffer) { + try { + const blob = new Blob([buffered.chunk], { type: 'application/octet-stream' }); + const base64 = await new Promise((resolve, reject) => { + const reader = new FileReader(); + reader.onload = () => { + const result = reader.result as string; + resolve(result.split(',')[1]); + }; + reader.onerror = reject; + reader.readAsDataURL(blob); + }); + socket.emit('audio_chunk', { chunk: base64, sample_rate: buffered.sampleRate }); + } catch (e) { } + } + this.audioBuffer = []; + } + + // VAD設定 + this.analyser = this.audioContext!.createAnalyser(); + this.analyser.fftSize = 512; + source.connect(this.analyser); + const dataArray = new Uint8Array(this.analyser.frequencyBinCount); + this.hasSpoken = false; + this.recordingStartTime = Date.now(); + this.consecutiveSilenceCount = 0; + + this.vadCheckInterval = window.setInterval(() => { + if (!this.analyser) return; + if (Date.now() - this.recordingStartTime < this.MIN_RECORDING_TIME) return; + this.analyser.getByteFrequencyData(dataArray); + const average = dataArray.reduce((a, b) => a + b, 0) / dataArray.length; + + if (average > this.SILENCE_THRESHOLD) { + this.hasSpoken = true; + this.consecutiveSilenceCount = 0; + if (this.silenceTimer) { + clearTimeout(this.silenceTimer); + this.silenceTimer = null; + } + if (onSpeechStart) onSpeechStart(); + } else if (this.hasSpoken) { + this.consecutiveSilenceCount++; + if (this.consecutiveSilenceCount >= this.REQUIRED_SILENCE_CHECKS && !this.silenceTimer) { + this.silenceTimer = window.setTimeout(() => { + this.stopStreaming_Default(); + onStopCallback(); + }, this.SILENCE_DURATION); + } + } + }, 100); + + this.recordingTimer = window.setTimeout(() => { + this.stopStreaming_Default(); + onStopCallback(); + }, this.MAX_RECORDING_TIME); + + } catch (error) { + this.canSendAudio = false; + this.audioBuffer = []; + if (this.mediaStream) { + this.mediaStream.getTracks().forEach(track => track.stop()); + this.mediaStream = null; + } + throw error; + } + } + + private stopVAD_Default() { + if (this.vadCheckInterval) { clearInterval(this.vadCheckInterval); this.vadCheckInterval = null; } + if (this.silenceTimer) { clearTimeout(this.silenceTimer); this.silenceTimer = null; } + if (this.analyser) { this.analyser = null; } + this.consecutiveSilenceCount = 0; + if (this.audioContext && this.audioContext.state !== 'closed') { + this.audioContext.close(); + this.audioContext = null; + } + } + + private stopStreaming_Default() { + this.stopVAD_Default(); + this.canSendAudio = false; + this.audioBuffer = []; + + if (this.recordingTimer) { clearTimeout(this.recordingTimer); this.recordingTimer = null; } + + if (this.audioWorkletNode) { + this.audioWorkletNode.port.onmessage = null; + this.audioWorkletNode.disconnect(); + this.audioWorkletNode = null; + } + if (this.mediaStream) { + this.mediaStream.getTracks().forEach(track => track.stop()); + this.mediaStream = null; + } + this.hasSpoken = false; + this.consecutiveSilenceCount = 0; + } + + // --- レガシー録音 --- + public async startLegacyRecording( + onStopCallback: (audioBlob: Blob) => void, + onSpeechStart?: () => void + ) { + try { + if (this.recordingTimer) { clearTimeout(this.recordingTimer); this.recordingTimer = null; } + + const stream = await this.getUserMediaSafe({ + audio: { + channelCount: 1, + sampleRate: 16000, + echoCancellation: true, + noiseSuppression: true + } + }); + this.mediaStream = stream; + + // @ts-ignore + this.mediaRecorder = new MediaRecorder(stream, { mimeType: 'audio/webm;codecs=opus' }); + this.audioChunks = []; + this.hasSpoken = false; + this.recordingStartTime = Date.now(); + this.consecutiveSilenceCount = 0; + + // @ts-ignore + const AudioContextClass = window.AudioContext || window.webkitAudioContext; + // @ts-ignore + this.audioContext = new AudioContextClass(); + + const source = this.audioContext!.createMediaStreamSource(stream); + this.analyser = this.audioContext!.createAnalyser(); + this.analyser.fftSize = 512; + source.connect(this.analyser); + const dataArray = new Uint8Array(this.analyser.frequencyBinCount); + + this.vadCheckInterval = window.setInterval(() => { + if (!this.analyser) return; + if (Date.now() - this.recordingStartTime < this.MIN_RECORDING_TIME) return; + + this.analyser.getByteFrequencyData(dataArray); + const average = dataArray.reduce((a, b) => a + b, 0) / dataArray.length; + + if (average > this.SILENCE_THRESHOLD) { + this.hasSpoken = true; + this.consecutiveSilenceCount = 0; + if (this.silenceTimer) { + clearTimeout(this.silenceTimer); + this.silenceTimer = null; + } + if (onSpeechStart) onSpeechStart(); + } else if (this.hasSpoken) { + this.consecutiveSilenceCount++; + if (this.consecutiveSilenceCount >= this.REQUIRED_SILENCE_CHECKS && !this.silenceTimer) { + this.silenceTimer = window.setTimeout(() => { + if (this.mediaRecorder && this.mediaRecorder.state === 'recording') { + this.mediaRecorder.stop(); + } + }, this.SILENCE_DURATION); + } + } + }, 100); + + // @ts-ignore + this.mediaRecorder.ondataavailable = (event) => { + if (event.data.size > 0) this.audioChunks.push(event.data); + }; + + // @ts-ignore + this.mediaRecorder.onstop = async () => { + this.stopVAD_Default(); + stream.getTracks().forEach(track => track.stop()); + if (this.recordingTimer) clearTimeout(this.recordingTimer); + + if (this.audioChunks.length > 0) { + const audioBlob = new Blob(this.audioChunks, { type: 'audio/webm' }); + onStopCallback(audioBlob); + } + }; + + // @ts-ignore + this.mediaRecorder.start(); + + this.recordingTimer = window.setTimeout(() => { + if (this.mediaRecorder && this.mediaRecorder.state === 'recording') { + this.mediaRecorder.stop(); + } + }, this.MAX_RECORDING_TIME); + + } catch (error) { + throw error; + } + } + + public async playTTS(_audioBase64: string): Promise { + return Promise.resolve(); + } + + public stopTTS() {} +} diff --git a/gourmet-sp/src/scripts/chat/chat-controller.ts b/gourmet-sp/src/scripts/chat/chat-controller.ts new file mode 100644 index 0000000..b4ace25 --- /dev/null +++ b/gourmet-sp/src/scripts/chat/chat-controller.ts @@ -0,0 +1,45 @@ +// src/scripts/chat/chat-controller.ts +import { CoreController } from './core-controller'; +import { AudioManager } from './audio-manager'; + +export class ChatController extends CoreController { + + constructor(container: HTMLElement, apiBase: string) { + super(container, apiBase); + this.audioManager = new AudioManager(4500); + // チャットモードに設定 + this.currentMode = 'chat'; + this.init(); + } + + // 初期化プロセスをオーバーライド + protected async init() { + // 親クラスの初期化を実行 + await super.init(); + + // チャットモード固有の要素とイベントを追加 + const query = (sel: string) => this.container.querySelector(sel) as HTMLElement; + this.els.modeSwitch = query('#modeSwitch') as HTMLInputElement; + + // モードスイッチの初期状態を設定(チャットモード = unchecked) + if (this.els.modeSwitch) { + this.els.modeSwitch.checked = false; + + // モードスイッチのイベントリスナー追加 + this.els.modeSwitch.addEventListener('change', () => { + this.toggleMode(); + }); + } + } + + // モード切り替え処理 - ページ遷移 + private toggleMode() { + const isChecked = this.els.modeSwitch?.checked; + if (isChecked) { + // コンシェルジュモードへページ遷移 + console.log('[ChatController] Switching to Concierge mode...'); + window.location.href = '/concierge'; + } + // チャットモードは既に現在のページなので何もしない + } +} diff --git a/gourmet-sp/src/scripts/chat/concierge-controller.ts b/gourmet-sp/src/scripts/chat/concierge-controller.ts new file mode 100644 index 0000000..7efde16 --- /dev/null +++ b/gourmet-sp/src/scripts/chat/concierge-controller.ts @@ -0,0 +1,831 @@ + + +// src/scripts/chat/concierge-controller.ts +import { CoreController } from './core-controller'; +import { AudioManager } from './audio-manager'; + +declare const io: any; + +export class ConciergeController extends CoreController { + // Audio2Expression はバックエンドTTSエンドポイント経由で統合済み + private pendingAckPromise: Promise | null = null; + + constructor(container: HTMLElement, apiBase: string) { + super(container, apiBase); + + // ★コンシェルジュモード用のAudioManagerを6.5秒設定で再初期化２ + this.audioManager = new AudioManager(8000); + + // コンシェルジュモードに設定 + this.currentMode = 'concierge'; + this.init(); + } + + // 初期化プロセスをオーバーライド + protected async init() { + // 親クラスの初期化を実行 + await super.init(); + + // コンシェルジュ固有の要素とイベントを追加 + const query = (sel: string) => this.container.querySelector(sel) as HTMLElement; + this.els.avatarContainer = query('.avatar-container'); + this.els.avatarImage = query('#avatarImage') as HTMLImageElement; + this.els.modeSwitch = query('#modeSwitch') as HTMLInputElement; + + // モードスイッチのイベントリスナー追加 + if (this.els.modeSwitch) { + this.els.modeSwitch.addEventListener('change', () => { + this.toggleMode(); + }); + } + + // ★ LAMAvatar との統合: 外部TTSプレーヤーをリンク + // LAMAvatar が後から初期化される可能性があるため、即時 + 遅延でリンク + const linkTtsPlayer = () => { + const lam = (window as any).lamAvatarController; + if (lam && typeof lam.setExternalTtsPlayer === 'function') { + lam.setExternalTtsPlayer(this.ttsPlayer); + console.log('[Concierge] Linked external TTS player with LAMAvatar'); + return true; + } + return false; + }; + if (!linkTtsPlayer()) { + setTimeout(() => linkTtsPlayer(), 2000); + } + } + + // ======================================== + // 🎯 セッション初期化をオーバーライド(挨拶文を変更) + // ======================================== + protected async initializeSession() { + try { + if (this.sessionId) { + try { + await fetch(`${this.apiBase}/api/session/end`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ session_id: this.sessionId }) + }); + } catch (e) {} + } + + // ★ user_id を取得（親クラスのメソッドを使用） + const userId = this.getUserId(); + + const res = await fetch(`${this.apiBase}/api/session/start`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + user_info: { user_id: userId }, + language: this.currentLanguage, + mode: 'concierge' + }) + }); + const data = await res.json(); + this.sessionId = data.session_id; + + // リップシンク: バックエンドTTSエンドポイント経由で表情データ取得（追加接続不要） + + // ✅ バックエンドからの初回メッセージを使用（長期記憶対応） + const greetingText = data.initial_message || this.t('initialGreetingConcierge'); + this.addMessage('assistant', greetingText, null, true); + + const ackTexts = [ + this.t('ackConfirm'), this.t('ackSearch'), this.t('ackUnderstood'), + this.t('ackYes'), this.t('ttsIntro') + ]; + const langConfig = this.LANGUAGE_CODE_MAP[this.currentLanguage]; + + const ackPromises = ackTexts.map(async (text) => { + try { + const ackResponse = await fetch(`${this.apiBase}/api/tts/synthesize`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + text: text, language_code: langConfig.tts, voice_name: langConfig.voice, + session_id: this.sessionId + }) + }); + const ackData = await ackResponse.json(); + if (ackData.success && ackData.audio) { + this.preGeneratedAcks.set(text, ackData.audio); + } + } catch (_e) { } + }); + + await Promise.all([ + this.speakTextGCP(greetingText), + ...ackPromises + ]); + + this.els.userInput.disabled = false; + this.els.sendBtn.disabled = false; + this.els.micBtn.disabled = false; + this.els.speakerBtn.disabled = false; + this.els.speakerBtn.classList.remove('disabled'); + this.els.reservationBtn.classList.remove('visible'); + + } catch (e) { + console.error('[Session] Initialization error:', e); + } + } + + // ======================================== + // 🔧 Socket.IOの初期化をオーバーライド + // ======================================== + protected initSocket() { + // @ts-ignore + this.socket = io(this.apiBase || window.location.origin); + + this.socket.on('connect', () => { }); + + // ✅ コンシェルジュ版のhandleStreamingSTTCompleteを呼ぶように再登録 + this.socket.on('transcript', (data: any) => { + const { text, is_final } = data; + if (this.isAISpeaking) return; + if (is_final) { + this.handleStreamingSTTComplete(text); // ← オーバーライド版が呼ばれる + this.currentAISpeech = ""; + } else { + this.els.userInput.value = text; + } + }); + + this.socket.on('error', (data: any) => { + this.addMessage('system', `${this.t('sttError')} ${data.message}`); + if (this.isRecording) this.stopStreamingSTT(); + }); + } + + // コンシェルジュモード固有: アバターアニメーション制御 + 公式リップシンク + protected async speakTextGCP(text: string, stopPrevious: boolean = true, autoRestartMic: boolean = false, skipAudio: boolean = false) { + if (skipAudio || !this.isTTSEnabled || !text) return Promise.resolve(); + + if (stopPrevious) { + this.ttsPlayer.pause(); + } + + // アバターアニメーションを開始 + if (this.els.avatarContainer) { + this.els.avatarContainer.classList.add('speaking'); + } + + // ★ 公式同期: TTS音声をaudio2exp-serviceに送信して表情を生成 + const cleanText = this.stripMarkdown(text); + try { + this.isAISpeaking = true; + if (this.isRecording && (this.isIOS || this.isAndroid)) { + this.stopStreamingSTT(); + } + + this.els.voiceStatus.innerHTML = this.t('voiceStatusSynthesizing'); + this.els.voiceStatus.className = 'voice-status speaking'; + const langConfig = this.LANGUAGE_CODE_MAP[this.currentLanguage]; + + // TTS音声を取得 + const response = await fetch(`${this.apiBase}/api/tts/synthesize`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + text: cleanText, language_code: langConfig.tts, voice_name: langConfig.voice, + session_id: this.sessionId + }) + }); + const data = await response.json(); + + if (data.success && data.audio) { + // ★ TTS応答に同梱されたExpressionを即バッファ投入（遅延ゼロ） + if (data.expression) this.applyExpressionFromTts(data.expression); + this.ttsPlayer.src = `data:audio/mp3;base64,${data.audio}`; + const playPromise = new Promise((resolve) => { + this.ttsPlayer.onended = async () => { + this.els.voiceStatus.innerHTML = this.t('voiceStatusStopped'); + this.els.voiceStatus.className = 'voice-status stopped'; + this.isAISpeaking = false; + this.stopAvatarAnimation(); + if (autoRestartMic) { + if (!this.isRecording) { + try { await this.toggleRecording(); } catch (_error) { this.showMicPrompt(); } + } + } + resolve(); + }; + this.ttsPlayer.onerror = () => { + this.isAISpeaking = false; + this.stopAvatarAnimation(); + resolve(); + }; + }); + + if (this.isUserInteracted) { + this.lastAISpeech = this.normalizeText(cleanText); + await this.ttsPlayer.play(); + await playPromise; + } else { + this.showClickPrompt(); + this.els.voiceStatus.innerHTML = this.t('voiceStatusStopped'); + this.els.voiceStatus.className = 'voice-status stopped'; + this.isAISpeaking = false; + this.stopAvatarAnimation(); + } + } else { + this.isAISpeaking = false; + this.stopAvatarAnimation(); + } + } catch (_error) { + this.els.voiceStatus.innerHTML = this.t('voiceStatusStopped'); + this.els.voiceStatus.className = 'voice-status stopped'; + this.isAISpeaking = false; + this.stopAvatarAnimation(); + } + } + + /** + * TTS応答に同梱されたExpressionデータをバッファに即投入（遅延ゼロ） + * 同期方式: バックエンドがTTS+audio2expを同期実行し、結果を同梱して返す + */ + private applyExpressionFromTts(expression: any): void { + const lamController = (window as any).lamAvatarController; + if (!lamController) return; + + // 新セグメント開始時は必ずバッファクリア（前セグメントのフレーム混入防止） + if (typeof lamController.clearFrameBuffer === 'function') { + lamController.clearFrameBuffer(); + } + + if (expression?.names && expression?.frames?.length > 0) { + const frames = expression.frames.map((f: { weights: number[] }) => { + const frame: { [key: string]: number } = {}; + expression.names.forEach((name: string, i: number) => { frame[name] = f.weights[i]; }); + return frame; + }); + lamController.queueExpressionFrames(frames, expression.frame_rate || 30); + console.log(`[Concierge] Expression sync: ${frames.length} frames queued`); + } + } + + // アバターアニメーション停止 + private stopAvatarAnimation() { + if (this.els.avatarContainer) { + this.els.avatarContainer.classList.remove('speaking'); + } + // ※ LAMAvatar の状態は ttsPlayer イベント（ended/pause）で管理 + } + + + // ======================================== + // 🎯 UI言語更新をオーバーライド(挨拶文をコンシェルジュ用に) + // ======================================== + protected updateUILanguage() { + // ✅ バックエンドからの長期記憶対応済み挨拶を保持 + const initialMessage = this.els.chatArea.querySelector('.message.assistant[data-initial="true"] .message-text'); + const savedGreeting = initialMessage?.textContent; + + // 親クラスのupdateUILanguageを実行（UIラベル等を更新） + super.updateUILanguage(); + + // ✅ 長期記憶対応済み挨拶を復元（親が上書きしたものを戻す） + if (initialMessage && savedGreeting) { + initialMessage.textContent = savedGreeting; + } + + // ✅ ページタイトルをコンシェルジュ用に設定 + const pageTitle = document.getElementById('pageTitle'); + if (pageTitle) { + pageTitle.innerHTML = ` ${this.t('pageTitleConcierge')}`; + } + } + + // モード切り替え処理 - ページ遷移 + private toggleMode() { + const isChecked = this.els.modeSwitch?.checked; + if (!isChecked) { + // チャットモードへページ遷移 + console.log('[ConciergeController] Switching to Chat mode...'); + window.location.href = '/'; + } + // コンシェルジュモードは既に現在のページなので何もしない + } + + // すべての活動を停止(アバターアニメーションも含む) + protected stopAllActivities() { + super.stopAllActivities(); + this.stopAvatarAnimation(); + } + + // ======================================== + // 🎯 並行処理フロー: 応答を分割してTTS処理 + // ======================================== + + /** + * センテンス単位でテキストを分割 + * 日本語: 。で分割 + * 英語・韓国語: . で分割 + * 中国語: 。で分割 + */ + private splitIntoSentences(text: string, language: string): string[] { + let separator: RegExp; + + if (language === 'ja' || language === 'zh') { + // 日本語・中国語: 。で分割 + separator = /。/; + } else { + // 英語・韓国語: . で分割 + separator = /\.\s+/; + } + + const sentences = text.split(separator).filter(s => s.trim().length > 0); + + // 分割したセンテンスに句点を戻す + return sentences.map((s, idx) => { + if (idx < sentences.length - 1 || text.endsWith('。') || text.endsWith('. ')) { + return language === 'ja' || language === 'zh' ? s + '。' : s + '. '; + } + return s; + }); + } + + /** + * 応答を分割して並行処理でTTS生成・再生 + * チャットモードのお店紹介フローを参考に実装 + */ + private async speakResponseInChunks(response: string, isTextInput: boolean = false) { + // テキスト入力またはTTS無効の場合は従来通り + if (isTextInput || !this.isTTSEnabled) { + return this.speakTextGCP(response, true, false, isTextInput); + } + + try { + // ★ ack再生中ならttsPlayer解放を待つ（並行処理の同期ポイント） + if (this.pendingAckPromise) { + await this.pendingAckPromise; + this.pendingAckPromise = null; + } + this.stopCurrentAudio(); // ttsPlayer確実解放 + + this.isAISpeaking = true; + if (this.isRecording) { + this.stopStreamingSTT(); + } + + // センテンス分割 + const sentences = this.splitIntoSentences(response, this.currentLanguage); + + // 1センテンスしかない場合は従来通り + if (sentences.length <= 1) { + await this.speakTextGCP(response, true, false, isTextInput); + this.isAISpeaking = false; + return; + } + + // 最初のセンテンスと残りのセンテンスに分割 + const firstSentence = sentences[0]; + const remainingSentences = sentences.slice(1).join(''); + + const langConfig = this.LANGUAGE_CODE_MAP[this.currentLanguage]; + + // ★並行処理: TTS生成と表情生成を同時に実行して遅延を最小化 + if (this.isUserInteracted) { + const cleanFirst = this.stripMarkdown(firstSentence); + const cleanRemaining = remainingSentences.trim().length > 0 + ? this.stripMarkdown(remainingSentences) : null; + + // ★ 4つのAPIコールを可能な限り並行で開始 + // 1. 最初のセンテンスTTS + const firstTtsPromise = fetch(`${this.apiBase}/api/tts/synthesize`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + text: cleanFirst, language_code: langConfig.tts, + voice_name: langConfig.voice, session_id: this.sessionId + }) + }).then(r => r.json()); + + // 2. 残りのセンテンスTTS（あれば） + const remainingTtsPromise = cleanRemaining + ? fetch(`${this.apiBase}/api/tts/synthesize`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + text: cleanRemaining, language_code: langConfig.tts, + voice_name: langConfig.voice, session_id: this.sessionId + }) + }).then(r => r.json()) + : null; + + // ★ 最初のTTSが返ったら即再生（Expression同梱済み） + const firstTtsResult = await firstTtsPromise; + if (firstTtsResult.success && firstTtsResult.audio) { + // ★ TTS応答に同梱されたExpressionを即バッファ投入（遅延ゼロ） + if (firstTtsResult.expression) this.applyExpressionFromTts(firstTtsResult.expression); + + this.lastAISpeech = this.normalizeText(cleanFirst); + this.stopCurrentAudio(); + this.ttsPlayer.src = `data:audio/mp3;base64,${firstTtsResult.audio}`; + + // 残りのTTS結果を先に取得（TTS応答にExpression同梱済み） + let remainingTtsResult: any = null; + if (remainingTtsPromise) { + remainingTtsResult = await remainingTtsPromise; + } + + // 最初のセンテンス再生 + await new Promise((resolve) => { + this.ttsPlayer.onended = () => { + this.els.voiceStatus.innerHTML = this.t('voiceStatusStopped'); + this.els.voiceStatus.className = 'voice-status stopped'; + resolve(); + }; + this.els.voiceStatus.innerHTML = this.t('voiceStatusSpeaking'); + this.els.voiceStatus.className = 'voice-status speaking'; + this.ttsPlayer.play(); + }); + + // ★ 残りのセンテンスを続けて再生（Expression同梱済み） + if (remainingTtsResult?.success && remainingTtsResult?.audio) { + this.lastAISpeech = this.normalizeText(cleanRemaining || ''); + + // ★ TTS応答に同梱されたExpressionを即バッファ投入 + if (remainingTtsResult.expression) this.applyExpressionFromTts(remainingTtsResult.expression); + + this.stopCurrentAudio(); + this.ttsPlayer.src = `data:audio/mp3;base64,${remainingTtsResult.audio}`; + + await new Promise((resolve) => { + this.ttsPlayer.onended = () => { + this.els.voiceStatus.innerHTML = this.t('voiceStatusStopped'); + this.els.voiceStatus.className = 'voice-status stopped'; + resolve(); + }; + this.els.voiceStatus.innerHTML = this.t('voiceStatusSpeaking'); + this.els.voiceStatus.className = 'voice-status speaking'; + this.ttsPlayer.play(); + }); + } + } + } + + this.isAISpeaking = false; + } catch (error) { + console.error('[TTS並行処理エラー]', error); + this.isAISpeaking = false; + // エラー時はフォールバック + await this.speakTextGCP(response, true, false, isTextInput); + } + } + + // ======================================== + // 🎯 コンシェルジュモード専用: 音声入力完了時の即答処理 + // ======================================== + protected async handleStreamingSTTComplete(transcript: string) { + this.stopStreamingSTT(); + + if ('mediaSession' in navigator) { + try { navigator.mediaSession.playbackState = 'playing'; } catch (e) {} + } + + this.els.voiceStatus.innerHTML = this.t('voiceStatusComplete'); + this.els.voiceStatus.className = 'voice-status'; + + // オウム返し判定(エコーバック防止) + const normTranscript = this.normalizeText(transcript); + if (this.isSemanticEcho(normTranscript, this.lastAISpeech)) { + this.els.voiceStatus.innerHTML = this.t('voiceStatusStopped'); + this.els.voiceStatus.className = 'voice-status stopped'; + this.lastAISpeech = ''; + return; + } + + this.els.userInput.value = transcript; + this.addMessage('user', transcript); + + // 短すぎる入力チェック + const textLength = transcript.trim().replace(/\s+/g, '').length; + if (textLength < 2) { + const msg = this.t('shortMsgWarning'); + this.addMessage('assistant', msg); + if (this.isTTSEnabled && this.isUserInteracted) { + await this.speakTextGCP(msg, true); + } else { + await new Promise(r => setTimeout(r, 2000)); + } + this.els.userInput.value = ''; + this.els.voiceStatus.innerHTML = this.t('voiceStatusStopped'); + this.els.voiceStatus.className = 'voice-status stopped'; + return; + } + + // ✅ 修正: 即答を「はい」だけに簡略化 + const ackText = this.t('ackYes'); // 「はい」のみ + const preGeneratedAudio = this.preGeneratedAcks.get(ackText); + + // 即答を再生（ttsPlayerで） + if (preGeneratedAudio && this.isTTSEnabled && this.isUserInteracted) { + this.pendingAckPromise = new Promise((resolve) => { + this.lastAISpeech = this.normalizeText(ackText); + this.ttsPlayer.src = `data:audio/mp3;base64,${preGeneratedAudio}`; + let resolved = false; + const done = () => { if (!resolved) { resolved = true; resolve(); } }; + this.ttsPlayer.onended = done; + this.ttsPlayer.onpause = done; // ★ pause時もresolve（src変更やstop時のデッドロック防止） + this.ttsPlayer.play().catch(_e => done()); + }); + } else if (this.isTTSEnabled) { + this.pendingAckPromise = this.speakTextGCP(ackText, false); + } + + this.addMessage('assistant', ackText); + + // ★ 並行処理: ack再生完了を待たず、即LLMリクエスト開始（~700ms短縮） + // pendingAckPromiseはsendMessage内でTTS再生前にawaitされる + if (this.els.userInput.value.trim()) { + this.isFromVoiceInput = true; + this.sendMessage(); + } + + this.els.voiceStatus.innerHTML = this.t('voiceStatusStopped'); + this.els.voiceStatus.className = 'voice-status stopped'; + } + + // ======================================== + // 🎯 コンシェルジュモード専用: メッセージ送信処理 + // ======================================== + protected async sendMessage() { + let firstAckPromise: Promise | null = null; + // ★ voice入力時はunlockAudioParamsスキップ（ack再生中のttsPlayerを中断させない） + if (!this.pendingAckPromise) { + this.unlockAudioParams(); + } + const message = this.els.userInput.value.trim(); + if (!message || this.isProcessing) return; + + const currentSessionId = this.sessionId; + const isTextInput = !this.isFromVoiceInput; + + this.isProcessing = true; + this.els.sendBtn.disabled = true; + this.els.micBtn.disabled = true; + this.els.userInput.disabled = true; + + // ✅ テキスト入力時も「はい」だけに簡略化 + if (!this.isFromVoiceInput) { + this.addMessage('user', message); + const textLength = message.trim().replace(/\s+/g, '').length; + if (textLength < 2) { + const msg = this.t('shortMsgWarning'); + this.addMessage('assistant', msg); + if (this.isTTSEnabled && this.isUserInteracted) await this.speakTextGCP(msg, true); + this.resetInputState(); + return; + } + + this.els.userInput.value = ''; + + // ✅ 修正: 即答を「はい」だけに + const ackText = this.t('ackYes'); + this.currentAISpeech = ackText; + this.addMessage('assistant', ackText); + + if (this.isTTSEnabled && !isTextInput) { + try { + const preGeneratedAudio = this.preGeneratedAcks.get(ackText); + if (preGeneratedAudio && this.isUserInteracted) { + firstAckPromise = new Promise((resolve) => { + this.lastAISpeech = this.normalizeText(ackText); + this.ttsPlayer.src = `data:audio/mp3;base64,${preGeneratedAudio}`; + this.ttsPlayer.onended = () => resolve(); + this.ttsPlayer.play().catch(_e => resolve()); + }); + } else { + firstAckPromise = this.speakTextGCP(ackText, false); + } + } catch (_e) {} + } + if (firstAckPromise) await firstAckPromise; + + // ✅ 修正: オウム返しパターンを削除 + // (generateFallbackResponse, additionalResponse の呼び出しを削除) + } + + this.isFromVoiceInput = false; + + // ✅ 待機アニメーションは6.5秒後に表示(LLM送信直前にタイマースタート) + if (this.waitOverlayTimer) clearTimeout(this.waitOverlayTimer); + let responseReceived = false; + + // タイマーセットをtry直前に移動(即答処理の後) + this.waitOverlayTimer = window.setTimeout(() => { + if (!responseReceived) { + this.showWaitOverlay(); + } + }, 6500); + + try { + const response = await fetch(`${this.apiBase}/api/chat`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + session_id: currentSessionId, + message: message, + stage: this.currentStage, + language: this.currentLanguage, + mode: this.currentMode + }) + }); + const data = await response.json(); + + // ✅ レスポンス到着フラグを立てる + responseReceived = true; + + if (this.sessionId !== currentSessionId) return; + + // ✅ タイマーをクリアしてアニメーションを非表示 + if (this.waitOverlayTimer) { + clearTimeout(this.waitOverlayTimer); + this.waitOverlayTimer = null; + } + this.hideWaitOverlay(); + this.currentAISpeech = data.response; + this.addMessage('assistant', data.response, data.summary); + + if (!isTextInput && this.isTTSEnabled) { + this.stopCurrentAudio(); + } + + if (data.shops && data.shops.length > 0) { + this.currentShops = data.shops; + this.els.reservationBtn.classList.add('visible'); + this.els.userInput.value = ''; + document.dispatchEvent(new CustomEvent('displayShops', { + detail: { shops: data.shops, language: this.currentLanguage } + })); + + const section = document.getElementById('shopListSection'); + if (section) section.classList.add('has-shops'); + if (window.innerWidth < 1024) { + setTimeout(() => { + const shopSection = document.getElementById('shopListSection'); + if (shopSection) shopSection.scrollIntoView({ behavior: 'smooth', block: 'start' }); + }, 300); + } + + (async () => { + try { + // ★ ack再生中ならttsPlayer解放を待つ（並行処理の同期ポイント） + if (this.pendingAckPromise) { + await this.pendingAckPromise; + this.pendingAckPromise = null; + } + this.stopCurrentAudio(); // ttsPlayer確実解放 + + this.isAISpeaking = true; + if (this.isRecording) { this.stopStreamingSTT(); } + + await this.speakTextGCP(this.t('ttsIntro'), true, false, isTextInput); + + const lines = data.response.split('\n\n'); + let introText = ""; + let shopLines = lines; + if (lines[0].includes('ご希望に合うお店') && lines[0].includes('ご紹介します')) { + introText = lines[0]; + shopLines = lines.slice(1); + } + + let introPart2Promise: Promise | null = null; + if (introText && this.isTTSEnabled && this.isUserInteracted && !isTextInput) { + const preGeneratedIntro = this.preGeneratedAcks.get(introText); + if (preGeneratedIntro) { + introPart2Promise = new Promise((resolve) => { + this.lastAISpeech = this.normalizeText(introText); + this.ttsPlayer.src = `data:audio/mp3;base64,${preGeneratedIntro}`; + this.ttsPlayer.onended = () => resolve(); + this.ttsPlayer.play(); + }); + } else { + introPart2Promise = this.speakTextGCP(introText, false, false, isTextInput); + } + } + + let firstShopTtsPromise: Promise | null = null; + let remainingShopTtsPromise: Promise | null = null; + const shopLangConfig = this.LANGUAGE_CODE_MAP[this.currentLanguage]; + + if (shopLines.length > 0 && this.isTTSEnabled && this.isUserInteracted && !isTextInput) { + const firstShop = shopLines[0]; + const restShops = shopLines.slice(1).join('\n\n'); + + // ★ 1行目先行: 最初のショップと残りのTTSを並行開始 + firstShopTtsPromise = fetch(`${this.apiBase}/api/tts/synthesize`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + text: this.stripMarkdown(firstShop), language_code: shopLangConfig.tts, + voice_name: shopLangConfig.voice, session_id: this.sessionId + }) + }).then(r => r.json()); + + if (restShops) { + remainingShopTtsPromise = fetch(`${this.apiBase}/api/tts/synthesize`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + text: this.stripMarkdown(restShops), language_code: shopLangConfig.tts, + voice_name: shopLangConfig.voice, session_id: this.sessionId + }) + }).then(r => r.json()); + } + } + + if (introPart2Promise) await introPart2Promise; + + if (firstShopTtsPromise) { + const firstResult = await firstShopTtsPromise; + if (firstResult?.success && firstResult?.audio) { + const firstShopText = this.stripMarkdown(shopLines[0]); + this.lastAISpeech = this.normalizeText(firstShopText); + + // ★ TTS応答に同梱されたExpressionを即バッファ投入 + if (firstResult.expression) this.applyExpressionFromTts(firstResult.expression); + + if (!isTextInput && this.isTTSEnabled) { + this.stopCurrentAudio(); + } + + this.ttsPlayer.src = `data:audio/mp3;base64,${firstResult.audio}`; + + // 残りのTTS結果を先に取得（Expression同梱済み） + let remainingResult: any = null; + if (remainingShopTtsPromise) { + remainingResult = await remainingShopTtsPromise; + } + + await new Promise((resolve) => { + this.ttsPlayer.onended = () => { + this.els.voiceStatus.innerHTML = this.t('voiceStatusStopped'); + this.els.voiceStatus.className = 'voice-status stopped'; + resolve(); + }; + this.els.voiceStatus.innerHTML = this.t('voiceStatusSpeaking'); + this.els.voiceStatus.className = 'voice-status speaking'; + this.ttsPlayer.play(); + }); + + if (remainingResult?.success && remainingResult?.audio) { + const restShopsText = this.stripMarkdown(shopLines.slice(1).join('\n\n')); + this.lastAISpeech = this.normalizeText(restShopsText); + + // ★ TTS応答に同梱されたExpressionを即バッファ投入 + if (remainingResult.expression) this.applyExpressionFromTts(remainingResult.expression); + + if (!isTextInput && this.isTTSEnabled) { + this.stopCurrentAudio(); + } + + this.ttsPlayer.src = `data:audio/mp3;base64,${remainingResult.audio}`; + await new Promise((resolve) => { + this.ttsPlayer.onended = () => { + this.els.voiceStatus.innerHTML = this.t('voiceStatusStopped'); + this.els.voiceStatus.className = 'voice-status stopped'; + resolve(); + }; + this.els.voiceStatus.innerHTML = this.t('voiceStatusSpeaking'); + this.els.voiceStatus.className = 'voice-status speaking'; + this.ttsPlayer.play(); + }); + } + } + } + this.isAISpeaking = false; + } catch (_e) { this.isAISpeaking = false; } + })(); + } else { + if (data.response) { + const extractedShops = this.extractShopsFromResponse(data.response); + if (extractedShops.length > 0) { + this.currentShops = extractedShops; + this.els.reservationBtn.classList.add('visible'); + document.dispatchEvent(new CustomEvent('displayShops', { + detail: { shops: extractedShops, language: this.currentLanguage } + })); + const section = document.getElementById('shopListSection'); + if (section) section.classList.add('has-shops'); + // ★並行処理フローを適用 + this.speakResponseInChunks(data.response, isTextInput); + } else { + // ★並行処理フローを適用 + this.speakResponseInChunks(data.response, isTextInput); + } + } + } + } catch (error) { + console.error('送信エラー:', error); + this.hideWaitOverlay(); + this.showError('メッセージの送信に失敗しました。'); + } finally { + this.resetInputState(); + this.els.userInput.blur(); + } + } + +} diff --git a/gourmet-sp/src/scripts/chat/core-controller.ts b/gourmet-sp/src/scripts/chat/core-controller.ts new file mode 100644 index 0000000..25f656f --- /dev/null +++ b/gourmet-sp/src/scripts/chat/core-controller.ts @@ -0,0 +1,1040 @@ + +// src/scripts/chat/core-controller.ts +import { i18n } from '../../constants/i18n'; +import { AudioManager } from './audio-manager'; + +declare const io: any; + +export class CoreController { + protected container: HTMLElement; + protected apiBase: string; + protected audioManager: AudioManager; + protected socket: any = null; + + protected currentLanguage: 'ja' | 'en' | 'zh' | 'ko' = 'ja'; + protected sessionId: string | null = null; + protected isProcessing = false; + protected currentStage = 'conversation'; + protected isRecording = false; + protected waitOverlayTimer: number | null = null; + protected isTTSEnabled = true; + protected isUserInteracted = false; + protected currentShops: any[] = []; + protected isFromVoiceInput = false; + protected lastAISpeech = ''; + protected preGeneratedAcks: Map = new Map(); + protected isAISpeaking = false; + protected currentAISpeech = ""; + protected currentMode: 'chat' | 'concierge' = 'chat'; + + // ★追加: バックグラウンド状態の追跡 + protected isInBackground = false; + protected backgroundStartTime = 0; + protected readonly BACKGROUND_RESET_THRESHOLD = 120000; // 120秒 + + protected isIOS = /iPhone|iPad|iPod/i.test(navigator.userAgent); + protected isAndroid = /Android/i.test(navigator.userAgent); + + protected els: any = {}; + protected ttsPlayer: HTMLAudioElement; + + protected readonly LANGUAGE_CODE_MAP = { + ja: { tts: 'ja-JP', stt: 'ja-JP', voice: 'ja-JP-Chirp3-HD-Leda' }, + en: { tts: 'en-US', stt: 'en-US', voice: 'en-US-Studio-O' }, + zh: { tts: 'cmn-CN', stt: 'cmn-CN', voice: 'cmn-CN-Wavenet-A' }, + ko: { tts: 'ko-KR', stt: 'ko-KR', voice: 'ko-KR-Wavenet-A' } + }; + + constructor(container: HTMLElement, apiBase: string) { + this.container = container; + this.apiBase = apiBase; + this.audioManager = new AudioManager(); + this.ttsPlayer = new Audio(); + + const query = (sel: string) => container.querySelector(sel) as HTMLElement; + this.els = { + chatArea: query('#chatArea'), + userInput: query('#userInput') as HTMLInputElement, + sendBtn: query('#sendBtn'), + micBtn: query('#micBtnFloat'), + speakerBtn: query('#speakerBtnFloat'), + voiceStatus: query('#voiceStatus'), + waitOverlay: query('#waitOverlay'), + waitVideo: query('#waitVideo') as HTMLVideoElement, + splashOverlay: query('#splashOverlay'), + splashVideo: query('#splashVideo') as HTMLVideoElement, + reservationBtn: query('#reservationBtnFloat'), + stopBtn: query('#stopBtn'), + languageSelect: query('#languageSelect') as HTMLSelectElement + }; + } + + protected async init() { + console.log('[Core] Starting initialization...'); + + this.bindEvents(); + this.initSocket(); + + setTimeout(() => { + if (this.els.splashVideo) this.els.splashVideo.loop = false; + if (this.els.splashOverlay) { + this.els.splashOverlay.classList.add('fade-out'); + setTimeout(() => this.els.splashOverlay.classList.add('hidden'), 800); + } + }, 10000); + + await this.initializeSession(); + this.updateUILanguage(); + + setTimeout(() => { + if (this.els.splashOverlay) { + this.els.splashOverlay.classList.add('fade-out'); + setTimeout(() => this.els.splashOverlay.classList.add('hidden'), 800); + } + }, 2000); + + console.log('[Core] Initialization completed'); + } + + protected getUserId(): string { + const STORAGE_KEY = 'gourmet_support_user_id'; + let userId = localStorage.getItem(STORAGE_KEY); + if (!userId) { + userId = 'user_' + Date.now() + '_' + Math.random().toString(36).substr(2, 9); + localStorage.setItem(STORAGE_KEY, userId); + console.log('[Core] 新規 user_id を生成:', userId); + } + return userId; + } + + protected async resetAppContent() { + console.log('[Reset] Starting soft reset...'); + const oldSessionId = this.sessionId; + this.stopAllActivities(); + + if (oldSessionId) { + try { + await fetch(`${this.apiBase}/api/cancel`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ session_id: oldSessionId }) + }); + } catch (e) { console.log('[Reset] Cancel error:', e); } + } + + if (this.els.chatArea) this.els.chatArea.innerHTML = ''; + const shopCardList = document.getElementById('shopCardList'); + if (shopCardList) shopCardList.innerHTML = ''; + const shopListSection = document.getElementById('shopListSection'); + if (shopListSection) shopListSection.classList.remove('has-shops'); + const floatingButtons = document.querySelector('.floating-buttons'); + if (floatingButtons) floatingButtons.classList.remove('shop-card-active'); + + this.els.userInput.value = ''; + this.els.userInput.disabled = true; + this.els.sendBtn.disabled = true; + this.els.micBtn.disabled = true; + this.els.speakerBtn.disabled = true; + this.els.reservationBtn.classList.remove('visible'); + + this.currentShops = []; + this.sessionId = null; + this.lastAISpeech = ''; + this.preGeneratedAcks.clear(); + this.isProcessing = false; + this.isAISpeaking = false; + this.isFromVoiceInput = false; + + await new Promise(resolve => setTimeout(resolve, 300)); + await this.initializeSession(); + + // ★追加: スクロール位置をリセット（ヘッダーが隠れないように） + this.container.scrollIntoView({ behavior: 'smooth', block: 'start' }); + window.scrollTo({ top: 0, behavior: 'smooth' }); + + console.log('[Reset] Completed'); + } + + protected bindEvents() { + this.els.sendBtn?.addEventListener('click', () => this.sendMessage()); + + this.els.micBtn?.addEventListener('click', () => { + this.toggleRecording(); + }); + + this.els.speakerBtn?.addEventListener('click', () => this.toggleTTS()); + this.els.reservationBtn?.addEventListener('click', () => this.openReservationModal()); + this.els.stopBtn?.addEventListener('click', () => this.stopAllActivities()); + + this.els.userInput?.addEventListener('keypress', (e: KeyboardEvent) => { + if (e.key === 'Enter') this.sendMessage(); + }); + + this.els.languageSelect?.addEventListener('change', () => { + this.currentLanguage = this.els.languageSelect.value as any; + this.updateUILanguage(); + }); + + const floatingButtons = this.container.querySelector('.floating-buttons'); + this.els.userInput?.addEventListener('focus', () => { + setTimeout(() => { if (floatingButtons) floatingButtons.classList.add('keyboard-active'); }, 300); + }); + this.els.userInput?.addEventListener('blur', () => { + if (floatingButtons) floatingButtons.classList.remove('keyboard-active'); + }); + + const resetHandler = async () => { await this.resetAppContent(); }; + const resetWrapper = async () => { + await resetHandler(); + document.addEventListener('gourmet-app:reset', resetWrapper, { once: true }); + }; + document.addEventListener('gourmet-app:reset', resetWrapper, { once: true }); + + // ★追加: バックグラウンド復帰時の復旧処理 + document.addEventListener('visibilitychange', async () => { + if (document.hidden) { + this.isInBackground = true; + this.backgroundStartTime = Date.now(); + } else if (this.isInBackground) { + this.isInBackground = false; + const backgroundDuration = Date.now() - this.backgroundStartTime; + console.log(`[Foreground] Resuming from background (${Math.round(backgroundDuration / 1000)}s)`); + + // ★120秒以上バックグラウンドにいた場合はソフトリセット + if (backgroundDuration > this.BACKGROUND_RESET_THRESHOLD) { + console.log('[Foreground] Long background duration - triggering soft reset...'); + await this.resetAppContent(); + return; + } + + // 1. Socket.IO再接続（状態に関わらず試行） + if (this.socket && !this.socket.connected) { + console.log('[Foreground] Reconnecting socket...'); + this.socket.connect(); + } + + // 2. UI状態をリセット（操作可能にする） + this.isProcessing = false; + this.isAISpeaking = false; + this.hideWaitOverlay(); + + // 3. 要素が存在する場合のみ更新 + if (this.els.sendBtn) this.els.sendBtn.disabled = false; + if (this.els.micBtn) this.els.micBtn.disabled = false; + if (this.els.userInput) this.els.userInput.disabled = false; + if (this.els.voiceStatus) { + this.els.voiceStatus.innerHTML = this.t('voiceStatusStopped'); + this.els.voiceStatus.className = 'voice-status stopped'; + } + } + }); + } + + // ★修正: Socket.IO接続設定に再接続オプションを追加（transportsは削除） + protected initSocket() { + // @ts-ignore + this.socket = io(this.apiBase || window.location.origin, { + reconnection: true, + reconnectionDelay: 1000, + reconnectionAttempts: 5, + timeout: 10000 + }); + + this.socket.on('connect', () => { }); + + this.socket.on('transcript', (data: any) => { + const { text, is_final } = data; + if (this.isAISpeaking) return; + if (is_final) { + this.handleStreamingSTTComplete(text); + this.currentAISpeech = ""; + } else { + this.els.userInput.value = text; + } + }); + + this.socket.on('error', (data: any) => { + this.addMessage('system', `${this.t('sttError')} ${data.message}`); + if (this.isRecording) this.stopStreamingSTT(); + }); + } + + protected async initializeSession() { + try { + if (this.sessionId) { + try { + await fetch(`${this.apiBase}/api/session/end`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ session_id: this.sessionId }) + }); + } catch (e) {} + } + + const res = await fetch(`${this.apiBase}/api/session/start`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ user_info: {}, language: this.currentLanguage }) + }); + const data = await res.json(); + this.sessionId = data.session_id; + + this.addMessage('assistant', this.t('initialGreeting'), null, true); + + const ackTexts = [ + this.t('ackConfirm'), this.t('ackSearch'), this.t('ackUnderstood'), + this.t('ackYes'), this.t('ttsIntro') + ]; + const langConfig = this.LANGUAGE_CODE_MAP[this.currentLanguage]; + + const ackPromises = ackTexts.map(async (text) => { + try { + const ackResponse = await fetch(`${this.apiBase}/api/tts/synthesize`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + text: text, language_code: langConfig.tts, voice_name: langConfig.voice + }) + }); + const ackData = await ackResponse.json(); + if (ackData.success && ackData.audio) { + this.preGeneratedAcks.set(text, ackData.audio); + } + } catch (_e) { } + }); + + await Promise.all([ + this.speakTextGCP(this.t('initialGreeting')), + ...ackPromises + ]); + + this.els.userInput.disabled = false; + this.els.sendBtn.disabled = false; + this.els.micBtn.disabled = false; + this.els.speakerBtn.disabled = false; + this.els.speakerBtn.classList.remove('disabled'); + this.els.reservationBtn.classList.remove('visible'); + + } catch (e) { + console.error('[Session] Initialization error:', e); + } + } + + protected async toggleRecording() { + this.enableAudioPlayback(); + this.els.userInput.value = ''; + + if (this.isRecording) { + this.stopStreamingSTT(); + return; + } + + if (this.isProcessing || this.isAISpeaking || !this.ttsPlayer.paused) { + if (this.isProcessing) { + fetch(`${this.apiBase}/api/cancel`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ session_id: this.sessionId }) + }).catch(err => console.error('中止リクエスト失敗:', err)); + } + + this.stopCurrentAudio(); + this.hideWaitOverlay(); + this.isProcessing = false; + this.isAISpeaking = false; + this.resetInputState(); + } + + if (this.socket && this.socket.connected) { + this.isRecording = true; + this.els.micBtn.classList.add('recording'); + this.els.voiceStatus.innerHTML = this.t('voiceStatusListening'); + this.els.voiceStatus.className = 'voice-status listening'; + + try { + const langCode = this.LANGUAGE_CODE_MAP[this.currentLanguage].stt; + await this.audioManager.startStreaming( + this.socket, langCode, + () => { this.stopStreamingSTT(); }, + () => { this.els.voiceStatus.innerHTML = this.t('voiceStatusRecording'); } + ); + } catch (error: any) { + this.stopStreamingSTT(); + if (!error.message?.includes('マイク')) { + this.showError(this.t('micAccessError')); + } + } + } else { + await this.startLegacyRecording(); + } + } + + protected async startLegacyRecording() { + try { + this.isRecording = true; + this.els.micBtn.classList.add('recording'); + this.els.voiceStatus.innerHTML = this.t('voiceStatusListening'); + + await this.audioManager.startLegacyRecording( + async (audioBlob) => { + await this.transcribeAudio(audioBlob); + this.stopStreamingSTT(); + }, + () => { this.els.voiceStatus.innerHTML = this.t('voiceStatusRecording'); } + ); + } catch (error: any) { + this.addMessage('system', `${this.t('micAccessError')} ${error.message}`); + this.stopStreamingSTT(); + } + } + + protected async transcribeAudio(audioBlob: Blob) { + console.log('Legacy audio blob size:', audioBlob.size); + } + + protected stopStreamingSTT() { + this.audioManager.stopStreaming(); + if (this.socket && this.socket.connected) { + this.socket.emit('stop_stream'); + } + this.isRecording = false; + this.els.micBtn.classList.remove('recording'); + this.els.voiceStatus.innerHTML = this.t('voiceStatusStopped'); + this.els.voiceStatus.className = 'voice-status stopped'; + } + + protected async handleStreamingSTTComplete(transcript: string) { + this.stopStreamingSTT(); + + if ('mediaSession' in navigator) { + try { navigator.mediaSession.playbackState = 'playing'; } catch (e) {} + } + + this.els.voiceStatus.innerHTML = this.t('voiceStatusComplete'); + this.els.voiceStatus.className = 'voice-status'; + + const normTranscript = this.normalizeText(transcript); + if (this.isSemanticEcho(normTranscript, this.lastAISpeech)) { + this.els.voiceStatus.innerHTML = this.t('voiceStatusStopped'); + this.els.voiceStatus.className = 'voice-status stopped'; + this.lastAISpeech = ''; + return; + } + + this.els.userInput.value = transcript; + this.addMessage('user', transcript); + + const textLength = transcript.trim().replace(/\s+/g, '').length; + if (textLength < 2) { + const msg = this.t('shortMsgWarning'); + this.addMessage('assistant', msg); + if (this.isTTSEnabled && this.isUserInteracted) { + await this.speakTextGCP(msg, true); + } else { + await new Promise(r => setTimeout(r, 2000)); + } + this.els.userInput.value = ''; + this.els.voiceStatus.innerHTML = this.t('voiceStatusStopped'); + this.els.voiceStatus.className = 'voice-status stopped'; + return; + } + + const ack = this.selectSmartAcknowledgment(transcript); + const preGeneratedAudio = this.preGeneratedAcks.get(ack.text); + + let firstAckPromise: Promise | null = null; + if (preGeneratedAudio && this.isTTSEnabled && this.isUserInteracted) { + firstAckPromise = new Promise((resolve) => { + this.lastAISpeech = this.normalizeText(ack.text); + this.ttsPlayer.src = `data:audio/mp3;base64,${preGeneratedAudio}`; + this.ttsPlayer.onended = () => resolve(); + this.ttsPlayer.play().catch(_e => resolve()); + }); + } else if (this.isTTSEnabled) { + firstAckPromise = this.speakTextGCP(ack.text, false); + } + + this.addMessage('assistant', ack.text); + + (async () => { + try { + if (firstAckPromise) await firstAckPromise; + const cleanText = this.removeFillers(transcript); + const fallbackResponse = this.generateFallbackResponse(cleanText); + + if (this.isTTSEnabled && this.isUserInteracted) await this.speakTextGCP(fallbackResponse, false); + this.addMessage('assistant', fallbackResponse); + + setTimeout(async () => { + const additionalResponse = this.t('additionalResponse'); + if (this.isTTSEnabled && this.isUserInteracted) await this.speakTextGCP(additionalResponse, false); + this.addMessage('assistant', additionalResponse); + }, 3000); + + if (this.els.userInput.value.trim()) { + this.isFromVoiceInput = true; + this.sendMessage(); + } + } catch (_error) { + if (this.els.userInput.value.trim()) { + this.isFromVoiceInput = true; + this.sendMessage(); + } + } + })(); + + this.els.voiceStatus.innerHTML = this.t('voiceStatusStopped'); + this.els.voiceStatus.className = 'voice-status stopped'; + } + +// Part 1からの続き... + + protected async sendMessage() { + let firstAckPromise: Promise | null = null; + this.unlockAudioParams(); + const message = this.els.userInput.value.trim(); + if (!message || this.isProcessing) return; + + const currentSessionId = this.sessionId; + const isTextInput = !this.isFromVoiceInput; + + this.isProcessing = true; + this.els.sendBtn.disabled = true; + this.els.micBtn.disabled = true; + this.els.userInput.disabled = true; + + if (!this.isFromVoiceInput) { + this.addMessage('user', message); + const textLength = message.trim().replace(/\s+/g, '').length; + if (textLength < 2) { + const msg = this.t('shortMsgWarning'); + this.addMessage('assistant', msg); + if (this.isTTSEnabled && this.isUserInteracted) await this.speakTextGCP(msg, true); + this.resetInputState(); + return; + } + + this.els.userInput.value = ''; + + const ack = this.selectSmartAcknowledgment(message); + this.currentAISpeech = ack.text; + this.addMessage('assistant', ack.text); + + if (this.isTTSEnabled && !isTextInput) { + try { + const preGeneratedAudio = this.preGeneratedAcks.get(ack.text); + if (preGeneratedAudio && this.isUserInteracted) { + firstAckPromise = new Promise((resolve) => { + this.lastAISpeech = this.normalizeText(ack.text); + this.ttsPlayer.src = `data:audio/mp3;base64,${preGeneratedAudio}`; + this.ttsPlayer.onended = () => resolve(); + this.ttsPlayer.play().catch(_e => resolve()); + }); + } else { + firstAckPromise = this.speakTextGCP(ack.text, false); + } + } catch (_e) {} + } + if (firstAckPromise) await firstAckPromise; + + const cleanText = this.removeFillers(message); + const fallbackResponse = this.generateFallbackResponse(cleanText); + + if (this.isTTSEnabled && this.isUserInteracted) await this.speakTextGCP(fallbackResponse, false, false, isTextInput); + this.addMessage('assistant', fallbackResponse); + + setTimeout(async () => { + const additionalResponse = this.t('additionalResponse'); + if (this.isTTSEnabled && this.isUserInteracted) await this.speakTextGCP(additionalResponse, false, false, isTextInput); + this.addMessage('assistant', additionalResponse); + }, 3000); + } + + this.isFromVoiceInput = false; + + if (this.waitOverlayTimer) clearTimeout(this.waitOverlayTimer); + this.waitOverlayTimer = window.setTimeout(() => { this.showWaitOverlay(); }, 4000); + + try { + const response = await fetch(`${this.apiBase}/api/chat`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + session_id: currentSessionId, + message: message, + stage: this.currentStage, + language: this.currentLanguage, + mode: this.currentMode + }) + }); + const data = await response.json(); + + if (this.sessionId !== currentSessionId) return; + + this.hideWaitOverlay(); + this.currentAISpeech = data.response; + this.addMessage('assistant', data.response, data.summary); + + if (!isTextInput && this.isTTSEnabled) { + this.stopCurrentAudio(); + } + + if (data.shops && data.shops.length > 0) { + this.currentShops = data.shops; + this.els.reservationBtn.classList.add('visible'); + this.els.userInput.value = ''; + document.dispatchEvent(new CustomEvent('displayShops', { + detail: { shops: data.shops, language: this.currentLanguage } + })); + + const section = document.getElementById('shopListSection'); + if (section) section.classList.add('has-shops'); + if (window.innerWidth < 1024) { + setTimeout(() => { + const shopSection = document.getElementById('shopListSection'); + if (shopSection) shopSection.scrollIntoView({ behavior: 'smooth', block: 'start' }); + }, 300); + } + + (async () => { + try { + this.isAISpeaking = true; + if (this.isRecording) { this.stopStreamingSTT(); } + + await this.speakTextGCP(this.t('ttsIntro'), true, false, isTextInput); + + const lines = data.response.split('\n\n'); + let introText = ""; + let shopLines = lines; + if (lines[0].includes('ご希望に合うお店') && lines[0].includes('ご紹介します')) { + introText = lines[0]; + shopLines = lines.slice(1); + } + + let introPart2Promise: Promise | null = null; + if (introText && this.isTTSEnabled && this.isUserInteracted && !isTextInput) { + const preGeneratedIntro = this.preGeneratedAcks.get(introText); + if (preGeneratedIntro) { + introPart2Promise = new Promise((resolve) => { + this.lastAISpeech = this.normalizeText(introText); + this.ttsPlayer.src = `data:audio/mp3;base64,${preGeneratedIntro}`; + this.ttsPlayer.onended = () => resolve(); + this.ttsPlayer.play(); + }); + } else { + introPart2Promise = this.speakTextGCP(introText, false, false, isTextInput); + } + } + + let firstShopAudioPromise: Promise | null = null; + let remainingAudioPromise: Promise | null = null; + const shopLangConfig = this.LANGUAGE_CODE_MAP[this.currentLanguage]; + + if (shopLines.length > 0 && this.isTTSEnabled && this.isUserInteracted && !isTextInput) { + const firstShop = shopLines[0]; + const restShops = shopLines.slice(1).join('\n\n'); + firstShopAudioPromise = (async () => { + const cleanText = this.stripMarkdown(firstShop); + const response = await fetch(`${this.apiBase}/api/tts/synthesize`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + text: cleanText, language_code: shopLangConfig.tts, voice_name: shopLangConfig.voice + }) + }); + const result = await response.json(); + return result.success ? `data:audio/mp3;base64,${result.audio}` : null; + })(); + + if (restShops) { + remainingAudioPromise = (async () => { + const cleanText = this.stripMarkdown(restShops); + const response = await fetch(`${this.apiBase}/api/tts/synthesize`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + text: cleanText, language_code: shopLangConfig.tts, voice_name: shopLangConfig.voice + }) + }); + const result = await response.json(); + return result.success ? `data:audio/mp3;base64,${result.audio}` : null; + })(); + } + } + + if (introPart2Promise) await introPart2Promise; + + if (firstShopAudioPromise) { + const firstShopAudio = await firstShopAudioPromise; + if (firstShopAudio) { + const firstShopText = this.stripMarkdown(shopLines[0]); + this.lastAISpeech = this.normalizeText(firstShopText); + + if (!isTextInput && this.isTTSEnabled) { + this.stopCurrentAudio(); + } + + this.ttsPlayer.src = firstShopAudio; + await new Promise((resolve) => { + this.ttsPlayer.onended = () => { + this.els.voiceStatus.innerHTML = this.t('voiceStatusStopped'); + this.els.voiceStatus.className = 'voice-status stopped'; + resolve(); + }; + this.els.voiceStatus.innerHTML = this.t('voiceStatusSpeaking'); + this.els.voiceStatus.className = 'voice-status speaking'; + this.ttsPlayer.play(); + }); + + if (remainingAudioPromise) { + const remainingAudio = await remainingAudioPromise; + if (remainingAudio) { + const restShopsText = this.stripMarkdown(shopLines.slice(1).join('\n\n')); + this.lastAISpeech = this.normalizeText(restShopsText); + await new Promise(r => setTimeout(r, 500)); + + if (!isTextInput && this.isTTSEnabled) { + this.stopCurrentAudio(); + } + + this.ttsPlayer.src = remainingAudio; + await new Promise((resolve) => { + this.ttsPlayer.onended = () => { + this.els.voiceStatus.innerHTML = this.t('voiceStatusStopped'); + this.els.voiceStatus.className = 'voice-status stopped'; + resolve(); + }; + this.els.voiceStatus.innerHTML = this.t('voiceStatusSpeaking'); + this.els.voiceStatus.className = 'voice-status speaking'; + this.ttsPlayer.play(); + }); + } + } + } + } + this.isAISpeaking = false; + } catch (_e) { this.isAISpeaking = false; } + })(); + } else { + if (data.response) { + const extractedShops = this.extractShopsFromResponse(data.response); + if (extractedShops.length > 0) { + this.currentShops = extractedShops; + this.els.reservationBtn.classList.add('visible'); + document.dispatchEvent(new CustomEvent('displayShops', { + detail: { shops: extractedShops, language: this.currentLanguage } + })); + const section = document.getElementById('shopListSection'); + if (section) section.classList.add('has-shops'); + this.speakTextGCP(data.response, true, false, isTextInput); + } else { + this.speakTextGCP(data.response, true, false, isTextInput); + } + } + } + } catch (error) { + console.error('送信エラー:', error); + this.hideWaitOverlay(); + this.showError('メッセージの送信に失敗しました。'); + } finally { + this.resetInputState(); + this.els.userInput.blur(); + } + } + + protected async speakTextGCP(text: string, stopPrevious: boolean = true, autoRestartMic: boolean = false, skipAudio: boolean = false) { + if (skipAudio) return Promise.resolve(); + if (!this.isTTSEnabled || !text) return Promise.resolve(); + + if (stopPrevious && this.isTTSEnabled) { + this.ttsPlayer.pause(); + } + + const cleanText = this.stripMarkdown(text); + try { + this.isAISpeaking = true; + if (this.isRecording && (this.isIOS || this.isAndroid)) { + this.stopStreamingSTT(); + } + + this.els.voiceStatus.innerHTML = this.t('voiceStatusSynthesizing'); + this.els.voiceStatus.className = 'voice-status speaking'; + const langConfig = this.LANGUAGE_CODE_MAP[this.currentLanguage]; + + const response = await fetch(`${this.apiBase}/api/tts/synthesize`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ + text: cleanText, language_code: langConfig.tts, voice_name: langConfig.voice + }) + }); + const data = await response.json(); + if (data.success && data.audio) { + this.ttsPlayer.src = `data:audio/mp3;base64,${data.audio}`; + const playPromise = new Promise((resolve) => { + this.ttsPlayer.onended = async () => { + this.els.voiceStatus.innerHTML = this.t('voiceStatusStopped'); + this.els.voiceStatus.className = 'voice-status stopped'; + this.isAISpeaking = false; + if (autoRestartMic) { + if (!this.isRecording) { + try { await this.toggleRecording(); } catch (_error) { this.showMicPrompt(); } + } + } + resolve(); + }; + this.ttsPlayer.onerror = () => { + this.isAISpeaking = false; + resolve(); + }; + }); + + if (this.isUserInteracted) { + this.lastAISpeech = this.normalizeText(cleanText); + await this.ttsPlayer.play(); + await playPromise; + } else { + this.showClickPrompt(); + this.els.voiceStatus.innerHTML = this.t('voiceStatusStopped'); + this.els.voiceStatus.className = 'voice-status stopped'; + this.isAISpeaking = false; + } + } else { + this.isAISpeaking = false; + } + } catch (_error) { + this.els.voiceStatus.innerHTML = this.t('voiceStatusStopped'); + this.els.voiceStatus.className = 'voice-status stopped'; + this.isAISpeaking = false; + } + } + + protected showWaitOverlay() { + this.els.waitOverlay.classList.remove('hidden'); + this.els.waitVideo.currentTime = 0; + this.els.waitVideo.play().catch((e: any) => console.log('Video err', e)); + } + + protected hideWaitOverlay() { + if (this.waitOverlayTimer) { clearTimeout(this.waitOverlayTimer); this.waitOverlayTimer = null; } + this.els.waitOverlay.classList.add('hidden'); + setTimeout(() => this.els.waitVideo.pause(), 500); + } + + protected unlockAudioParams() { + this.audioManager.unlockAudioParams(this.ttsPlayer); + } + + protected enableAudioPlayback() { + if (!this.isUserInteracted) { + this.isUserInteracted = true; + const clickPrompt = this.container.querySelector('.click-prompt'); + if (clickPrompt) clickPrompt.remove(); + this.unlockAudioParams(); + } + } + + protected stopCurrentAudio() { + this.ttsPlayer.pause(); + this.ttsPlayer.currentTime = 0; + } + + protected showClickPrompt() { + const prompt = document.createElement('div'); + prompt.className = 'click-prompt'; + prompt.innerHTML = `

🔊

${this.t('clickPrompt')}

🔊

`; + prompt.addEventListener('click', () => this.enableAudioPlayback()); + this.container.style.position = 'relative'; + this.container.appendChild(prompt); + } + + protected showMicPrompt() { + const modal = document.createElement('div'); + modal.id = 'mic-prompt-modal'; + modal.style.cssText = `position: fixed; top: 0; left: 0; right: 0; bottom: 0; background: rgba(0, 0, 0, 0.8); display: flex; align-items: center; justify-content: center; z-index: 10000; animation: fadeIn 0.3s ease;`; + modal.innerHTML = ` +
+
🎤
+
マイクをONにしてください
+
AIの回答が終わりました。
続けて話すにはマイクボタンをタップしてください。
+ +
+ `; + const style = document.createElement('style'); + style.textContent = `@keyframes fadeIn { from { opacity: 0; } to { opacity: 1; } }`; + document.head.appendChild(style); + document.body.appendChild(modal); + + const btn = document.getElementById('mic-prompt-btn'); + btn?.addEventListener('click', async () => { + modal.remove(); + await this.toggleRecording(); + }); + setTimeout(() => { if (document.getElementById('mic-prompt-modal')) { modal.remove(); } }, 3000); + } + + protected stripMarkdown(text: string): string { + return text.replace(/\*\*([^*]+)\*\*/g, '$1').replace(/\*([^*]+)\*/g, '$1').replace(/__([^_]+)__/g, '$1').replace(/_([^_]+)_/g, '$1').replace(/^#+\s*/gm, '').replace(/\[([^\]]+)\]\([^)]+\)/g, '$1').replace(/`([^`]+)`/g, '$1').replace(/^(\d+)\.\s+/gm, '$1番目、').replace(/\s+/g, ' ').trim(); + } + + protected normalizeText(text: string): string { + return text.replace(/\s+/g, '').replace(/[、。！？,.!?]/g, '').toLowerCase(); + } + + protected removeFillers(text: string): string { + // @ts-ignore + const pattern = i18n[this.currentLanguage].patterns.fillers; + return text.replace(pattern, ''); + } + + protected generateFallbackResponse(text: string): string { + return this.t('fallbackResponse', text); + } + + protected selectSmartAcknowledgment(userMessage: string) { + const messageLower = userMessage.trim(); + // @ts-ignore + const p = i18n[this.currentLanguage].patterns; + if (p.ackQuestions.test(messageLower)) return { text: this.t('ackConfirm'), logText: `質問形式` }; + if (p.ackLocation.test(messageLower)) return { text: this.t('ackSearch'), logText: `場所` }; + if (p.ackSearch.test(messageLower)) return { text: this.t('ackUnderstood'), logText: `検索` }; + return { text: this.t('ackYes'), logText: `デフォルト` }; + } + + protected isSemanticEcho(transcript: string, aiText: string): boolean { + if (!aiText || !transcript) return false; + const normTranscript = this.normalizeText(transcript); + const normAI = this.normalizeText(aiText); + if (normAI === normTranscript) return true; + if (normAI.includes(normTranscript) && normTranscript.length > 5) return true; + return false; + } + + protected extractShopsFromResponse(text: string): any[] { + const shops: any[] = []; + const pattern = /(\d+)\.\s*\*\*([^*]+)\*\*[::\s]*([^\n]+)/g; + let match; + while ((match = pattern.exec(text)) !== null) { + const fullName = match[2].trim(); + const description = match[3].trim(); + let name = fullName; + const nameMatch = fullName.match(/^([^(]+)[(]([^)]+)[)]/); + if (nameMatch) name = nameMatch[1].trim(); + const encodedName = encodeURIComponent(name); + shops.push({ name: name, description: description, category: 'イタリアン', hotpepper_url: `https://www.hotpepper.jp/SA11/srchRS/?keyword=${encodedName}`, maps_url: `https://www.google.com/maps/search/${encodedName}`, tabelog_url: `https://tabelog.com/rstLst/?vs=1&sa=&sk=${encodedName}` }); + } + return shops; + } + + protected openReservationModal() { + if (this.currentShops.length === 0) { this.showError(this.t('searchError')); return; } + document.dispatchEvent(new CustomEvent('openReservationModal', { detail: { shops: this.currentShops } })); + } + + protected toggleTTS() { + if (!this.isUserInteracted) { this.enableAudioPlayback(); return; } + this.enableAudioPlayback(); + this.isTTSEnabled = !this.isTTSEnabled; + + this.els.speakerBtn.title = this.isTTSEnabled ? this.t('btnTTSOn') : this.t('btnTTSOff'); + if (this.isTTSEnabled) { + this.els.speakerBtn.classList.remove('disabled'); + } else { + this.els.speakerBtn.classList.add('disabled'); + } + + if (!this.isTTSEnabled) this.stopCurrentAudio(); + } + + protected stopAllActivities() { + if (this.isProcessing) { + fetch(`${this.apiBase}/api/cancel`, { + method: 'POST', + headers: { 'Content-Type': 'application/json' }, + body: JSON.stringify({ session_id: this.sessionId }) + }).catch(err => console.error('中止リクエスト失敗:', err)); + } + + this.audioManager.fullResetAudioResources(); + this.isRecording = false; + this.els.micBtn.classList.remove('recording'); + if (this.socket && this.socket.connected) { this.socket.emit('stop_stream'); } + this.stopCurrentAudio(); + this.hideWaitOverlay(); + this.isProcessing = false; + this.isAISpeaking = false; + this.els.voiceStatus.innerHTML = this.t('voiceStatusStopped'); + this.els.voiceStatus.className = 'voice-status stopped'; + this.els.userInput.value = ''; + + // ★修正: containerにスクロール（chat-header-controlsが隠れないように） + if (window.innerWidth < 1024) { + setTimeout(() => { this.container.scrollIntoView({ behavior: 'smooth', block: 'start' }); }, 100); + } + } + + protected addMessage(role: string, text: string, summary: string | null = null, isInitial: boolean = false) { + const div = document.createElement('div'); + div.className = `message ${role}`; + if (isInitial) div.setAttribute('data-initial', 'true'); + + let contentHtml = `
${text}
`; + div.innerHTML = `
${role === 'assistant' ? '🍽' : '👤'}
${contentHtml}`; + this.els.chatArea.appendChild(div); + this.els.chatArea.scrollTop = this.els.chatArea.scrollHeight; + } + + protected resetInputState() { + this.isProcessing = false; + this.els.sendBtn.disabled = false; + this.els.micBtn.disabled = false; + this.els.userInput.disabled = false; + } + + protected showError(msg: string) { + const div = document.createElement('div'); + div.className = 'error-message'; + div.innerText = msg; + this.els.chatArea.appendChild(div); + this.els.chatArea.scrollTop = this.els.chatArea.scrollHeight; + } + + protected t(key: string, ...args: any[]): string { + // @ts-ignore + const translation = i18n[this.currentLanguage][key]; + if (typeof translation === 'function') return translation(...args); + return translation || key; + } + + protected updateUILanguage() { + console.log('[Core] Updating UI language to:', this.currentLanguage); + + this.els.voiceStatus.innerHTML = this.t('voiceStatusStopped'); + this.els.userInput.placeholder = this.t('inputPlaceholder'); + this.els.micBtn.title = this.t('btnVoiceInput'); + this.els.speakerBtn.title = this.isTTSEnabled ? this.t('btnTTSOn') : this.t('btnTTSOff'); + this.els.sendBtn.textContent = this.t('btnSend'); + this.els.reservationBtn.innerHTML = this.t('btnReservation'); + + const pageTitle = document.getElementById('pageTitle'); + if (pageTitle) pageTitle.innerHTML = ` ${this.t('pageTitle')}`; + const pageSubtitle = document.getElementById('pageSubtitle'); + if (pageSubtitle) pageSubtitle.textContent = this.t('pageSubtitle'); + const shopListTitle = document.getElementById('shopListTitle'); + if (shopListTitle) shopListTitle.innerHTML = `🍽 ${this.t('shopListTitle')}`; + const shopListEmpty = document.getElementById('shopListEmpty'); + if (shopListEmpty) shopListEmpty.textContent = this.t('shopListEmpty'); + const pageFooter = document.getElementById('pageFooter'); + if (pageFooter) pageFooter.innerHTML = `${this.t('footerMessage')} ✨`; + + const initialMessage = this.els.chatArea.querySelector('.message.assistant[data-initial="true"] .message-text'); + if (initialMessage) { + initialMessage.textContent = this.t('initialGreeting'); + } + + const waitText = document.querySelector('.wait-text'); + if (waitText) waitText.textContent = this.t('waitMessage'); + + document.dispatchEvent(new CustomEvent('languageChange', { detail: { language: this.currentLanguage } })); + } +} diff --git a/gourmet-sp/src/scripts/lam/audio-sync-player.ts b/gourmet-sp/src/scripts/lam/audio-sync-player.ts new file mode 100644 index 0000000..1c39e8a --- /dev/null +++ b/gourmet-sp/src/scripts/lam/audio-sync-player.ts @@ -0,0 +1,262 @@ +/** + * AudioSyncPlayer - Audio playback with precise timing for expression sync + * + * Official OpenAvatarChat synchronization approach: + * - Audio and expression data are bundled together from server + * - This player plays audio and tracks playback position + * - Expression frames are indexed based on audio playback time + * + * @module audio-sync-player + */ + +export interface AudioSample { + audioData: Int16Array | Float32Array; + sampleRate: number; + startTime?: number; // Playback start time in seconds + batchId: number; + endOfBatch: boolean; +} + +export interface AudioSyncPlayerOptions { + sampleRate?: number; + onEnded?: (batchId: number) => void; + onStarted?: (batchId: number) => void; +} + +export class AudioSyncPlayer { + private audioContext: AudioContext | null = null; + private gainNode: GainNode | null = null; + private sampleRate: number; + private isMuted: boolean = false; + + // Playback tracking + private _firstStartAbsoluteTime: number | null = null; // When playback started (Date.now()) + private _samplesList: AudioSample[] = []; + private _currentBatchId: number = -1; + private _isPlaying: boolean = false; + + // Callbacks + private onEnded: ((batchId: number) => void) | null = null; + private onStarted: ((batchId: number) => void) | null = null; + + // Queued audio sources + private scheduledSources: AudioBufferSourceNode[] = []; + private nextStartTime: number = 0; + + constructor(options: AudioSyncPlayerOptions = {}) { + this.sampleRate = options.sampleRate || 16000; + this.onEnded = options.onEnded || null; + this.onStarted = options.onStarted || null; + } + + /** + * Initialize audio context (must be called after user interaction) + */ + async initialize(): Promise { + if (this.audioContext) return; + + this.audioContext = new AudioContext({ sampleRate: this.sampleRate }); + this.gainNode = this.audioContext.createGain(); + this.gainNode.connect(this.audioContext.destination); + this.gainNode.gain.value = this.isMuted ? 0 : 1; + + // Resume context if suspended + if (this.audioContext.state === 'suspended') { + await this.audioContext.resume(); + } + } + + /** + * Get the absolute time when playback started + */ + get firstStartAbsoluteTime(): number | null { + return this._firstStartAbsoluteTime; + } + + /** + * Get all samples list with their start times + */ + get samplesList(): AudioSample[] { + return this._samplesList; + } + + /** + * Get current batch ID + */ + get currentBatchId(): number { + return this._currentBatchId; + } + + /** + * Check if currently playing + */ + get isPlaying(): boolean { + return this._isPlaying; + } + + /** + * Feed audio data for playback + */ + async feed(sample: AudioSample): Promise { + if (!this.audioContext || !this.gainNode) { + await this.initialize(); + } + + const ctx = this.audioContext!; + const gain = this.gainNode!; + + // Check if this is a new batch (new speech) + if (sample.batchId !== this._currentBatchId) { + // New batch - reset timing + this._currentBatchId = sample.batchId; + this._firstStartAbsoluteTime = null; + this._samplesList = []; + this.nextStartTime = ctx.currentTime; + + // Cancel any scheduled sources from previous batch + this.cancelScheduledSources(); + } + + // Convert Int16 to Float32 if needed + let audioFloat: Float32Array; + if (sample.audioData instanceof Int16Array) { + audioFloat = new Float32Array(sample.audioData.length); + for (let i = 0; i < sample.audioData.length; i++) { + audioFloat[i] = sample.audioData[i] / 32768.0; + } + } else { + audioFloat = sample.audioData; + } + + // Create audio buffer + const buffer = ctx.createBuffer(1, audioFloat.length, sample.sampleRate); + buffer.copyToChannel(audioFloat, 0); + + // Create source node + const source = ctx.createBufferSource(); + source.buffer = buffer; + source.connect(gain); + + // Calculate start time + const startTime = Math.max(ctx.currentTime, this.nextStartTime); + const duration = audioFloat.length / sample.sampleRate; + + // Record sample info with start time + const sampleInfo: AudioSample = { + ...sample, + startTime: startTime - (this.nextStartTime === ctx.currentTime ? 0 : this.nextStartTime - ctx.currentTime) + }; + this._samplesList.push(sampleInfo); + + // Track first start time + if (this._firstStartAbsoluteTime === null) { + this._firstStartAbsoluteTime = Date.now(); + this._isPlaying = true; + this.onStarted?.(sample.batchId); + console.log(`[AudioSyncPlayer] Started batch ${sample.batchId}`); + } + + // Schedule playback + source.start(startTime); + this.scheduledSources.push(source); + this.nextStartTime = startTime + duration; + + // Handle end of batch + if (sample.endOfBatch) { + source.onended = () => { + this._isPlaying = false; + console.log(`[AudioSyncPlayer] Ended batch ${sample.batchId}`); + this.onEnded?.(sample.batchId); + }; + } + + console.log(`[AudioSyncPlayer] Queued ${duration.toFixed(2)}s audio, batch=${sample.batchId}, end=${sample.endOfBatch}`); + } + + /** + * Cancel all scheduled audio sources + */ + private cancelScheduledSources(): void { + for (const source of this.scheduledSources) { + try { + source.stop(); + source.disconnect(); + } catch (e) { + // Ignore errors from already stopped sources + } + } + this.scheduledSources = []; + } + + /** + * Stop playback and clear queue + */ + stop(): void { + this.cancelScheduledSources(); + this._isPlaying = false; + this._firstStartAbsoluteTime = null; + this._samplesList = []; + this.nextStartTime = this.audioContext?.currentTime || 0; + } + + /** + * Set mute state + */ + setMute(muted: boolean): void { + this.isMuted = muted; + if (this.gainNode) { + this.gainNode.gain.value = muted ? 0 : 1; + } + } + + /** + * Destroy the player + */ + destroy(): void { + this.stop(); + if (this.audioContext) { + this.audioContext.close(); + this.audioContext = null; + } + this.gainNode = null; + } + + /** + * Calculate current playback offset in milliseconds + * Used for expression frame synchronization + */ + getCurrentPlaybackOffset(): number { + if (!this._firstStartAbsoluteTime || !this._isPlaying) { + return -1; + } + return Date.now() - this._firstStartAbsoluteTime; + } + + /** + * Get the sample index for a given offset time + */ + getSampleIndexForOffset(offsetMs: number): { sampleIndex: number; subOffsetMs: number } { + if (this._samplesList.length === 0) { + return { sampleIndex: -1, subOffsetMs: 0 }; + } + + let lastIndex = 0; + let firstSampleStartTime: number | undefined; + + for (let i = 0; i < this._samplesList.length; i++) { + const sample = this._samplesList[i]; + if (firstSampleStartTime === undefined && sample.startTime !== undefined) { + firstSampleStartTime = sample.startTime; + } + if (sample.startTime !== undefined && + (sample.startTime - (firstSampleStartTime || 0)) * 1000 <= offsetMs) { + lastIndex = i; + } + } + + const sample = this._samplesList[lastIndex]; + const subOffsetMs = offsetMs - (sample.startTime || 0) * 1000; + + return { sampleIndex: lastIndex, subOffsetMs }; + } +} diff --git a/gourmet-sp/src/scripts/lam/lam-websocket-manager.ts b/gourmet-sp/src/scripts/lam/lam-websocket-manager.ts new file mode 100644 index 0000000..b6bf446 --- /dev/null +++ b/gourmet-sp/src/scripts/lam/lam-websocket-manager.ts @@ -0,0 +1,531 @@ +/** + * LAM WebSocket Manager + * OpenAvatarChatのバックエンドと通信してリップシンクデータを受信 + * + * Official synchronization approach: + * - Server sends BUNDLED audio+expression in JBIN format + * - Client plays audio and syncs expression based on playback position + */ + +import { AudioSyncPlayer } from './audio-sync-player'; +import type { AudioSample } from './audio-sync-player'; + +// JBIN形式のバイナリデータをパース +export interface MotionDataDescription { + data_records: { + arkit_face?: { + shape: number[]; + data_type: string; + sample_rate: number; + data_offset: number; + channel_names: string[]; + }; + avatar_audio?: { + shape: number[]; + data_type: string; + sample_rate: number; + data_offset: number; + }; + }; + batch_id: number; + batch_name: string; + start_of_batch: boolean; + end_of_batch: boolean; +} + +export interface MotionData { + description: MotionDataDescription; + arkitFace: Float32Array | null; + audio: Int16Array | null; +} + +export interface ExpressionData { + [key: string]: number; +} + +export interface ExpressionFrameData { + frames: ExpressionData[]; // All frames for this audio chunk + frameRate: number; // Frames per second + frameCount: number; // Total number of frames +} + +// Bundled motion data group (official sync approach) +export interface MotionDataGroup { + batchId: number; + arkitFaceArrays: Float32Array[]; // Expression frames for each audio chunk + channelNames: string[]; + sampleRate: number; // Expression frame rate + arkitFaceShape: number; // Number of channels per frame (52) +} + +/** + * JBIN形式のバイナリデータをパース + */ +export function parseMotionData(buffer: ArrayBuffer): MotionData { + const view = new DataView(buffer); + + // マジックナンバー確認 "JBIN" + const fourcc = String.fromCharCode( + view.getUint8(0), + view.getUint8(1), + view.getUint8(2), + view.getUint8(3) + ); + + if (fourcc !== 'JBIN') { + throw new Error(`Invalid JBIN format: ${fourcc}`); + } + + // ヘッダーサイズ読み取り (Little Endian) + const jsonSize = view.getUint32(4, true); + const binSize = view.getUint32(8, true); + + // JSON部分をデコード + const jsonBytes = new Uint8Array(buffer, 12, jsonSize); + const jsonString = new TextDecoder().decode(jsonBytes); + const description: MotionDataDescription = JSON.parse(jsonString); + + // バイナリデータ開始位置 + const binaryOffset = 12 + jsonSize; + + // ARKit顔表情データの抽出 + let arkitFace: Float32Array | null = null; + if (description.data_records.arkit_face) { + const faceRecord = description.data_records.arkit_face; + const faceOffset = binaryOffset + faceRecord.data_offset; + const faceLength = faceRecord.shape.reduce((a, b) => a * b, 1); + arkitFace = new Float32Array(buffer, faceOffset, faceLength); + } + + // オーディオデータの抽出 + let audio: Int16Array | null = null; + if (description.data_records.avatar_audio) { + const audioRecord = description.data_records.avatar_audio; + const audioOffset = binaryOffset + audioRecord.data_offset; + const audioLength = audioRecord.shape.reduce((a, b) => a * b, 1); + audio = new Int16Array(buffer, audioOffset, audioLength); + } + + return { description, arkitFace, audio }; +} + +/** + * ARKit表情データをExpressionDataに変換 + */ +export function convertToExpressionData( + arkitFace: Float32Array, + channelNames: string[] +): ExpressionData { + const expressionData: ExpressionData = {}; + channelNames.forEach((name, index) => { + if (index < arkitFace.length) { + expressionData[name] = arkitFace[index]; + } + }); + return expressionData; +} + +/** + * LAM WebSocket Manager + * Handles bundled audio+expression data with official sync approach + */ +export class LAMWebSocketManager { + private ws: WebSocket | null = null; + private definition: MotionDataDescription | null = null; + private channelNames: string[] = []; + private onExpressionUpdate: ((data: ExpressionData) => void) | null = null; + private onExpressionFrames: ((data: ExpressionFrameData) => void) | null = null; + private onAudioData: ((audio: Int16Array) => void) | null = null; + private onConnectionChange: ((connected: boolean) => void) | null = null; + private onBatchStarted: ((batchId: number) => void) | null = null; + private onBatchEnded: ((batchId: number) => void) | null = null; + private reconnectAttempts = 0; + private maxReconnectAttempts = 5; + private reconnectDelay = 1000; + private pingInterval: ReturnType | null = null; + private currentWsUrl: string = ''; + + // Official sync: AudioSyncPlayer + motion data groups + private audioPlayer: AudioSyncPlayer; + private motionDataGroups: MotionDataGroup[] = []; + private currentBatchId: number = -1; + private arkitFaceShape: number = 52; + private arkitFaceSampleRate: number = 30; + + constructor(options?: { + onExpressionUpdate?: (data: ExpressionData) => void; + onExpressionFrames?: (data: ExpressionFrameData) => void; + onAudioData?: (audio: Int16Array) => void; + onConnectionChange?: (connected: boolean) => void; + onBatchStarted?: (batchId: number) => void; + onBatchEnded?: (batchId: number) => void; + }) { + if (options) { + this.onExpressionUpdate = options.onExpressionUpdate || null; + this.onExpressionFrames = options.onExpressionFrames || null; + this.onAudioData = options.onAudioData || null; + this.onConnectionChange = options.onConnectionChange || null; + this.onBatchStarted = options.onBatchStarted || null; + this.onBatchEnded = options.onBatchEnded || null; + } + + // Initialize AudioSyncPlayer + this.audioPlayer = new AudioSyncPlayer({ + sampleRate: 16000, + onStarted: (batchId) => { + console.log(`[LAM WebSocket] Audio playback started for batch ${batchId}`); + this.onBatchStarted?.(batchId); + }, + onEnded: (batchId) => { + console.log(`[LAM WebSocket] Audio playback ended for batch ${batchId}`); + this.onBatchEnded?.(batchId); + // Clean up old motion data groups + this.motionDataGroups = this.motionDataGroups.filter(g => g.batchId > batchId); + } + }); + } + + /** + * WebSocket接続を開始 + */ + connect(wsUrl: string): Promise { + return new Promise((resolve, reject) => { + try { + this.ws = new WebSocket(wsUrl); + this.ws.binaryType = 'arraybuffer'; + + this.ws.onopen = () => { + console.log('[LAM WebSocket] Connected'); + this.reconnectAttempts = 0; + this.currentWsUrl = wsUrl; + this.onConnectionChange?.(true); + this.startPing(); + resolve(); + }; + + this.ws.onmessage = (event) => { + this.handleMessage(event); + }; + + this.ws.onclose = (event) => { + console.log('[LAM WebSocket] Disconnected', event.code, event.reason); + this.stopPing(); + this.onConnectionChange?.(false); + this.attemptReconnect(this.currentWsUrl); + }; + + this.ws.onerror = (error) => { + console.error('[LAM WebSocket] Error:', error); + reject(error); + }; + } catch (error) { + reject(error); + } + }); + } + + /** + * メッセージ処理 + */ + private handleMessage(event: MessageEvent): void { + if (!(event.data instanceof ArrayBuffer)) { + // JSON形式のメッセージ（レガシー対応） + try { + const msg = JSON.parse(event.data); + + // audio2exp-service からの表情データ（複数フレーム対応）- レガシーJSON形式 + if (msg.type === 'expression' && msg.channels && msg.weights) { + const frameRate = msg.frame_rate || 30; + const frameCount = msg.frame_count || msg.weights.length; + + // 複数フレームがある場合はフレームデータとして送信 + if (msg.weights.length > 1 && this.onExpressionFrames) { + const frames: ExpressionData[] = msg.weights.map((frameWeights: number[]) => { + const frame: ExpressionData = {}; + msg.channels.forEach((name: string, index: number) => { + if (index < frameWeights.length) { + frame[name] = frameWeights[index]; + } + }); + return frame; + }); + + this.onExpressionFrames({ + frames, + frameRate, + frameCount + }); + console.log(`[LAM WebSocket] Expression frames received (legacy): ${frameCount} frames at ${frameRate}fps`); + } else { + // 1フレームの場合は従来通り + const expressionData: ExpressionData = {}; + msg.channels.forEach((name: string, index: number) => { + if (msg.weights[0] && index < msg.weights[0].length) { + expressionData[name] = msg.weights[0][index]; + } + }); + this.onExpressionUpdate?.(expressionData); + } + return; + } + + // pong応答 + if (msg.type === 'pong') { + return; + } + + console.log('[LAM WebSocket] JSON message:', msg); + } catch (e) { + console.warn('[LAM WebSocket] Unknown text message:', event.data); + } + return; + } + + // JBIN形式のバンドルデータを処理（公式同期アプローチ） + try { + const motionData = parseMotionData(event.data); + const desc = motionData.description; + + // チャンネル名を保存 + if (desc.data_records.arkit_face?.channel_names) { + this.channelNames = desc.data_records.arkit_face.channel_names; + this.arkitFaceSampleRate = desc.data_records.arkit_face.sample_rate || 30; + this.arkitFaceShape = desc.data_records.arkit_face.shape?.[1] || 52; + } + + const batchId = desc.batch_id || 0; + + // 新しいバッチの場合はmotion data groupをリセット + if (desc.start_of_batch || batchId !== this.currentBatchId) { + this.currentBatchId = batchId; + // 新しいグループを作成 + this.motionDataGroups = this.motionDataGroups.filter(g => g.batchId !== batchId); + this.motionDataGroups.push({ + batchId, + arkitFaceArrays: [], + channelNames: this.channelNames, + sampleRate: this.arkitFaceSampleRate, + arkitFaceShape: this.arkitFaceShape + }); + } + + // 表情データを保存 + if (motionData.arkitFace) { + const group = this.motionDataGroups.find(g => g.batchId === batchId); + if (group) { + group.arkitFaceArrays.push(motionData.arkitFace); + } + } + + // オーディオデータをプレーヤーに送信 + if (motionData.audio) { + const audioSample: AudioSample = { + audioData: motionData.audio, + sampleRate: desc.data_records.avatar_audio?.sample_rate || 16000, + batchId, + endOfBatch: desc.end_of_batch + }; + this.audioPlayer.feed(audioSample); + + // レガシーコールバックも呼び出し + this.onAudioData?.(motionData.audio); + } + + console.log(`[LAM WebSocket] JBIN bundle received: batch=${batchId}, start=${desc.start_of_batch}, end=${desc.end_of_batch}`); + + } catch (error) { + console.error('[LAM WebSocket] JBIN parse error:', error); + } + } + + /** + * Get current expression frame based on audio playback position + * This is the official OpenAvatarChat synchronization method + */ + getCurrentExpressionFrame(): ExpressionData | null { + const offsetMs = this.audioPlayer.getCurrentPlaybackOffset(); + if (offsetMs < 0) { + return null; + } + + // Find the motion data group for current batch + const group = this.motionDataGroups.find(g => g.batchId === this.audioPlayer.currentBatchId); + if (!group || group.arkitFaceArrays.length === 0) { + return null; + } + + // Get the sample index based on playback position + const { sampleIndex, subOffsetMs } = this.audioPlayer.getSampleIndexForOffset(offsetMs); + if (sampleIndex < 0 || sampleIndex >= group.arkitFaceArrays.length) { + return null; + } + + // Calculate frame index within the sample + const frameOffset = Math.floor((subOffsetMs / 1000) * group.sampleRate); + const arkitFaceArray = group.arkitFaceArrays[sampleIndex]; + + // Extract frame data + const startIdx = frameOffset * group.arkitFaceShape; + const endIdx = startIdx + group.arkitFaceShape; + + if (startIdx >= arkitFaceArray.length) { + // Use last frame if we're past the end + const lastFrameStart = Math.max(0, arkitFaceArray.length - group.arkitFaceShape); + const frameData = arkitFaceArray.slice(lastFrameStart, lastFrameStart + group.arkitFaceShape); + return this.arrayToExpressionData(frameData, group.channelNames); + } + + const frameData = arkitFaceArray.slice(startIdx, endIdx); + return this.arrayToExpressionData(frameData, group.channelNames); + } + + /** + * Convert Float32Array to ExpressionData object + */ + private arrayToExpressionData(frameData: Float32Array, channelNames: string[]): ExpressionData { + const result: ExpressionData = {}; + channelNames.forEach((name, index) => { + if (index < frameData.length) { + result[name] = frameData[index]; + } + }); + return result; + } + + /** + * Check if audio is currently playing + */ + isAudioPlaying(): boolean { + return this.audioPlayer.isPlaying; + } + + /** + * Stop audio playback + */ + stopAudio(): void { + this.audioPlayer.stop(); + } + + /** + * Set audio mute state + */ + setAudioMute(muted: boolean): void { + this.audioPlayer.setMute(muted); + } + + /** + * Initialize audio player (call after user interaction) + */ + async initializeAudio(): Promise { + await this.audioPlayer.initialize(); + } + + /** + * 再接続を試みる + */ + private attemptReconnect(wsUrl: string): void { + if (this.reconnectAttempts >= this.maxReconnectAttempts) { + console.error('[LAM WebSocket] Max reconnect attempts reached'); + return; + } + + this.reconnectAttempts++; + const delay = this.reconnectDelay * Math.pow(2, this.reconnectAttempts - 1); + console.log(`[LAM WebSocket] Reconnecting in ${delay}ms (attempt ${this.reconnectAttempts})`); + + setTimeout(() => { + this.connect(wsUrl).catch(console.error); + }, delay); + } + + /** + * スピーチ終了を通知 + */ + sendEndSpeech(): void { + if (this.ws && this.ws.readyState === WebSocket.OPEN) { + this.ws.send(JSON.stringify({ + header: { name: 'EndSpeech' } + })); + } + } + + /** + * 接続を閉じる + */ + disconnect(): void { + this.stopPing(); + if (this.ws) { + this.ws.close(); + this.ws = null; + } + this.definition = null; + this.channelNames = []; + this.audioPlayer.stop(); + this.motionDataGroups = []; + } + + /** + * Destroy the manager and clean up resources + */ + destroy(): void { + this.disconnect(); + this.audioPlayer.destroy(); + } + + /** + * Ping送信を開始（キープアライブ） + */ + private startPing(): void { + this.stopPing(); + this.pingInterval = setInterval(() => { + if (this.ws && this.ws.readyState === WebSocket.OPEN) { + this.ws.send(JSON.stringify({ type: 'ping' })); + } + }, 5000); // 5秒間隔でping + } + + /** + * Ping送信を停止 + */ + private stopPing(): void { + if (this.pingInterval) { + clearInterval(this.pingInterval); + this.pingInterval = null; + } + } + + /** + * 接続状態を確認 + */ + isConnected(): boolean { + return this.ws !== null && this.ws.readyState === WebSocket.OPEN; + } + + /** + * チャンネル名一覧を取得 + */ + getChannelNames(): string[] { + return this.channelNames; + } +} + +/** + * ARKit 52チャンネル名（標準） + */ +export const ARKIT_CHANNEL_NAMES = [ + 'browDownLeft', 'browDownRight', 'browInnerUp', 'browOuterUpLeft', 'browOuterUpRight', + 'cheekPuff', 'cheekSquintLeft', 'cheekSquintRight', + 'eyeBlinkLeft', 'eyeBlinkRight', 'eyeLookDownLeft', 'eyeLookDownRight', + 'eyeLookInLeft', 'eyeLookInRight', 'eyeLookOutLeft', 'eyeLookOutRight', + 'eyeLookUpLeft', 'eyeLookUpRight', 'eyeSquintLeft', 'eyeSquintRight', + 'eyeWideLeft', 'eyeWideRight', + 'jawForward', 'jawLeft', 'jawOpen', 'jawRight', + 'mouthClose', 'mouthDimpleLeft', 'mouthDimpleRight', 'mouthFrownLeft', 'mouthFrownRight', + 'mouthFunnel', 'mouthLeft', 'mouthLowerDownLeft', 'mouthLowerDownRight', + 'mouthPressLeft', 'mouthPressRight', 'mouthPucker', 'mouthRight', + 'mouthRollLower', 'mouthRollUpper', 'mouthShrugLower', 'mouthShrugUpper', + 'mouthSmileLeft', 'mouthSmileRight', 'mouthStretchLeft', 'mouthStretchRight', + 'mouthUpperUpLeft', 'mouthUpperUpRight', + 'noseSneerLeft', 'noseSneerRight', + 'tongueOut' +]; diff --git a/gourmet-sp/src/styles/global.css b/gourmet-sp/src/styles/global.css new file mode 100644 index 0000000..d36bf51 --- /dev/null +++ b/gourmet-sp/src/styles/global.css @@ -0,0 +1,5 @@ +@import "tailwindcss"; +/* src/styles/global.css */ +@tailwind base; +@tailwind components; +@tailwind utilities; \ No newline at end of file