diff --git a/docker-compose.yml b/docker-compose.yml
index 234190f..0b799ea 100644
--- a/docker-compose.yml
+++ b/docker-compose.yml
@@ -2,7 +2,6 @@ version: "3.10"
 
 services:
   db:
-    container_name: chroma_db
     image: chromadb/chroma:latest
     volumes:
       - chroma-data:/chroma/chroma
@@ -11,18 +10,16 @@ services:
       - "8000:8000"
  
   cache:
-    container_name: request_cache
     image: memcached:latest
     ports:
       - "11211:11211"
     restart: always
 
-  inference:
+  encode:
     build:
       context: ./inference
       dockerfile: Dockerfile
-    container_name: inference
-    command: uvicorn clip:app --host "0.0.0.0" --port 8040
+    command: uvicorn main:app --host "0.0.0.0" --port 8040 --log-config=log_conf.yaml
     restart: unless-stopped
     volumes:
       - inference-model-data:/app/model_data
@@ -31,18 +28,31 @@ services:
     ports:
       - "8040:8040"
 
+  search:
+    build:
+      context: ./inference
+      dockerfile: Dockerfile
+    command: uvicorn main:app --host "0.0.0.0" --port 8050 --log-config=log_conf.yaml
+    restart: unless-stopped
+    volumes:
+      - inference-model-data:/app/model_data
+    env_file:
+      - inference/.env.dist
+    ports:
+      - "8050:8050"
+
   main:
     build:
       context: ./main
       dockerfile: Dockerfile
-    container_name: main_gateway
     command: uvicorn main:app --host "0.0.0.0" --port 80
     restart: unless-stopped
     volumes:
       - main-model-data:/app/model_data
     depends_on:
       - db
-      - inference
+      - encode
+      - search
       - cache
     env_file:
       - main/.env.dist
diff --git a/inference/.env.dist b/inference/.env.dist
index 8b47efb..76b4fdc 100644
--- a/inference/.env.dist
+++ b/inference/.env.dist
@@ -1 +1,2 @@
 CLIP_MODEL=laion/CLIP-ViT-g-14-laion2B-s12B-b42K
+TRANSLATION_MODEL=Helsinki-NLP/opus-mt-ru-en
diff --git a/inference/Dockerfile b/inference/Dockerfile
index cd80f92..3496c73 100644
--- a/inference/Dockerfile
+++ b/inference/Dockerfile
@@ -5,11 +5,9 @@ ENV PYTHONUNBUFFERED 1
 
 WORKDIR /app
 
-COPY requirements.txt /app/
-
 RUN apt-get update && apt-get install ffmpeg -y
+
+COPY requirements.txt /app/
 RUN python -m pip install --upgrade pip && pip install -r requirements.txt
-COPY ./ /app/
 
-EXPOSE 8040
-CMD uvicorn clip:app --port 8040
+COPY ./ /app/
diff --git a/inference/clip.py b/inference/clip.py
index f361947..439ca56 100644
--- a/inference/clip.py
+++ b/inference/clip.py
@@ -1,62 +1,59 @@
-import torch
-from typing import Optional
-from fastapi import FastAPI, HTTPException
-from fastapi.responses import JSONResponse
+from dataclasses import dataclass
+from logging import Logger
+from typing import Callable, Literal
+
 from PIL import Image
-from pydantic import BaseModel
-from deps import Model, Processor, lifespan
-from frame_video import create_key_frames_for_video
-
-app = FastAPI(lifespan=lifespan)
-
-class EncodeRequest(BaseModel):
-    link: Optional[str] = None
-    description: Optional[str] = None
-
-@app.get("/")
-async def root():
-    return JSONResponse(content={"ok": True})
-
-@app.post("/encode")
-async def encode(request: EncodeRequest, processor: Processor, model: Model):
-    if not any((request.description, request.link)):
-        raise HTTPException(
-            status_code=400, detail="Please provide either 'description' as string or 'link' as video URL, or both."
-        )
-    
-    text_features, image_features = None, None
-    
-    if request.description:    
-        text_inputs = processor(text=[request.description], return_tensors="pt", padding=True)
+import torch
+from transformers import CLIPModel, CLIPProcessor
+
+from frame_video import VideoFrame, create_key_frames_for_video
+
+
+@dataclass
+class CLIP:
+    processor: CLIPProcessor
+    model: CLIPModel
+    logger: Logger
+
+    _create_key_frames_for_video: Callable[[str], list[VideoFrame]] = create_key_frames_for_video
+
+    def __call__(self, encode_source: str, encode_type: Literal["text"] | Literal["video"]) -> list[float]:
+        if encode_type == "text":
+            self.logger.info("Processing text input: %s, input length: %s", encode_source, len(encode_source))
+            return self._encode_text(encode_source)
+
+        if encode_type == "video":
+            self.logger.info("Processing video input: %s", encode_source)
+            return self._encode_video(encode_source)
+
+    def _encode_text(self, description: str) -> list[float]:
+        description = description[:65]  # meet the processor max length
+        text_inputs = self.processor(text=[description], return_tensors="pt", padding=True)
         with torch.no_grad():
-            text_features = model.get_text_features(**text_inputs)
+            text_features = self.model.get_text_features(**text_inputs)
             text_features /= text_features.norm(dim=-1, keepdim=True)
-    
-    if request.link:
-        images = create_key_frames_for_video(request.link)
+
+        result = text_features.tolist()[0]
+        self.logger.info("Processed result vector - %s", result)
+        return result
+
+    def _encode_video(self, link: str) -> list[float]:
+        images = self._create_key_frames_for_video(link)
         image_inputs = []
         for image in images:
             image = Image.open(image.file)
-            image_input = processor(images=image, return_tensors="pt")
+            image_input = self.processor(images=image, return_tensors="pt")
             image_inputs.append(image_input)
         with torch.no_grad():
-            image_features = model.get_image_features(**image_inputs[0])
+            image_features = self.model.get_image_features(**image_inputs[0])
             for image_input in image_inputs[1:]:
-                image_feature = model.get_image_features(**image_input)
+                image_feature = self.model.get_image_features(**image_input)
                 image_features = torch.cat((image_features, image_feature), dim=0)
 
             features = torch.mean(image_features, dim=0)
             features /= features.norm(dim=-1, keepdim=True)
 
-    if request.description and request.link:
-        text_weight = 1.0
-        video_weight = 2.0  # Giving more importance to video
-        # Merged weighted vectors of text and video didn't work so well, leave off for now
-        unified_features = (text_features * text_weight + image_features * video_weight) / (text_weight + video_weight)
-        return {"features": image_features.tolist()[0]}
-
-    elif request.description:
-        return {"features": text_features.tolist()[0]}
+        result = features.tolist()
+        self.logger.info("Processed result vector - %s", result)
+        return result
 
-    elif request.link:
-        return {"features": image_features.tolist()[0]}
diff --git a/inference/deps.py b/inference/deps.py
index 3f77a14..dc76a0b 100644
--- a/inference/deps.py
+++ b/inference/deps.py
@@ -1,4 +1,5 @@
 from contextlib import asynccontextmanager
+import logging
 from typing import Annotated
 
 from fastapi import Depends, FastAPI, Request
@@ -9,10 +10,13 @@
 
 @asynccontextmanager
 async def lifespan(app: FastAPI):
+    logger = logging.getLogger(__name__)
+    logger.info("Setting up CLIP model...")
     app.state.clip_model = CLIPModel.from_pretrained(
         Settings.clip_model,
         cache_dir="./model_cache"
     )
+    logger.info("Setting up CLIP processor...")
     app.state.processor = CLIPProcessor.from_pretrained(
         Settings.clip_model,
         cache_dir="./model_cache"
diff --git a/inference/frame_video.py b/inference/frame_video.py
index 374cc20..d5a089d 100644
--- a/inference/frame_video.py
+++ b/inference/frame_video.py
@@ -4,7 +4,7 @@
 from io import BytesIO
 from dataclasses import dataclass
 import requests
-from scenedetect import detect, ContentDetector
+from scenedetect import detect, ContentDetector, AdaptiveDetector
 
 @dataclass
 class VideoFrame:
@@ -14,6 +14,7 @@ class VideoFrame:
 def create_key_frames_for_video(
         video_link: str, 
         frame_change_threshold: float = 7.5,
+        min_scene_len: int = 10,
         num_of_thumbnails: int = 10
     ) -> list[VideoFrame]:
     frames: list[VideoFrame] = []
@@ -21,12 +22,16 @@ def create_key_frames_for_video(
     with tempfile.NamedTemporaryFile(delete=False, suffix='.mp4') as tmp_file:
         tmp_file.write(video_data.getvalue())
         video_path = tmp_file.name
-    scenes = detect(video_path, ContentDetector(threshold=frame_change_threshold))
+    scenes = detect(
+        video_path=video_path, 
+        detector=ContentDetector(threshold=frame_change_threshold, min_scene_len=min_scene_len)
+    )
 
-    # Gradually reduce number of key frames with a sliding window
+    # Gradually reduce number of key frames with a increasingly smaller steps
     while len(scenes) > num_of_thumbnails:
-        scenes.pop()
-        scenes.pop(0)
+        step = len(scenes) / (num_of_thumbnails - 1)
+        to_remove_indices = [int(round(i * step)) for i in range(num_of_thumbnails)]
+        scenes = [scenes[i] for i in range(len(scenes)) if i not in to_remove_indices] 
     for i, scene in enumerate(scenes):
         scene_start, _ = scene
         frame_data = create_frame_in_ram(video_path, scene_start.get_timecode())
@@ -39,6 +44,7 @@ def create_key_frames_for_video(
         return create_key_frames_for_video(
             video_link=video_link,
             frame_change_threshold=frame_change_threshold - 2.5,
+            min_scene_len=min_scene_len - 2 if min_scene_len > 2 else min_scene_len,
             num_of_thumbnails=num_of_thumbnails
         )
     return frames
diff --git a/inference/log_conf.yaml b/inference/log_conf.yaml
new file mode 100644
index 0000000..32e7e50
--- /dev/null
+++ b/inference/log_conf.yaml
@@ -0,0 +1,34 @@
+version: 1
+disable_existing_loggers: False
+formatters:
+  default:
+    # "()": uvicorn.logging.DefaultFormatter
+    format: '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+  access:
+    # "()": uvicorn.logging.AccessFormatter
+    format: '%(asctime)s - %(name)s - %(levelname)s - %(message)s'
+handlers:
+  default:
+    formatter: default
+    class: logging.StreamHandler
+    stream: ext://sys.stderr
+  access:
+    formatter: access
+    class: logging.StreamHandler
+    stream: ext://sys.stdout
+loggers:
+  uvicorn.error:
+    level: INFO
+    handlers:
+      - default
+    propagate: no
+  uvicorn.access:
+    level: INFO
+    handlers:
+      - access
+    propagate: no
+root:
+  level: INFO
+  handlers:
+    - default
+  propagate: no
diff --git a/inference/main.py b/inference/main.py
new file mode 100644
index 0000000..bffefea
--- /dev/null
+++ b/inference/main.py
@@ -0,0 +1,41 @@
+import logging
+from fastapi import FastAPI
+from fastapi.responses import JSONResponse
+
+from deps import Model, Processor, lifespan
+from clip import CLIP
+from models import EncodeRequest, EncodeSearchRequest
+
+app = FastAPI(lifespan=lifespan)
+logger = logging.getLogger(__name__)
+
+@app.get("/")
+async def root():
+    return JSONResponse(content={"ok": True})
+
+@app.post("/encode")
+async def encode(
+        request: EncodeRequest,
+        processor: Processor,
+        model: Model,
+):
+    logger.info("Initializing CLIP module...")
+    clip = CLIP(processor=processor, model=model, logger=logger)
+    logger.info("CLIP module successfully initialized")
+
+    video_features = clip(request.link, encode_type="video")
+    return {
+        "video": video_features,
+    }
+
+@app.post("/encode-search")
+async def encode_search(
+        request: EncodeSearchRequest, processor: Processor, model: Model
+):
+    logger.info("Initializing CLIP module...")
+    clip = CLIP(processor=processor, model=model, logger=logger)
+    logger.info("CLIP module successfully initialized")
+
+    features = clip(request.query, encode_type="text")
+
+    return {"features": features}
diff --git a/inference/models.py b/inference/models.py
new file mode 100644
index 0000000..61888e4
--- /dev/null
+++ b/inference/models.py
@@ -0,0 +1,10 @@
+from pydantic import BaseModel
+
+
+class EncodeRequest(BaseModel):
+    link: str
+    description: str | None = None
+
+
+class EncodeSearchRequest(BaseModel):
+    query: str
diff --git a/inference/requirements.txt b/inference/requirements.txt
index 5291272..14962b3 100644
--- a/inference/requirements.txt
+++ b/inference/requirements.txt
@@ -9,3 +9,5 @@ pillow==10.3.0
 scenedetect==0.6.3
 opencv-python==4.10.0.82
 environs==11.0.0
+PyYAML>=6.0
+sentencepiece==0.2.0
diff --git a/inference/settings.py b/inference/settings.py
index 82c3549..55ade0a 100644
--- a/inference/settings.py
+++ b/inference/settings.py
@@ -5,4 +5,5 @@
 
 
 class Settings:
-    clip_model: str = env.str("CLIP_MODEL")
+    clip_model: str = env.str("CLIP_MODEL", default="laion/CLIP-ViT-g-14-laion2B-s12B-b42K")
+    translation_model: str = env.str("TRANSLATION_MODEL", default="Helsinki-NLP/opus-mt-ru-en")
diff --git a/main/.env.dist b/main/.env.dist
index 19f980c..a81ca7a 100644
--- a/main/.env.dist
+++ b/main/.env.dist
@@ -1,3 +1,4 @@
-CLIP_URL=http://inference:8040/encode
-DB_HOST=chroma_db
+ENCODE_CLIP_URL=http://encode:8040/
+SEARCH_CLIP_URL=http://search:8050/
+DB_HOST=db
 DB_PORT=8000
diff --git a/main/chroma.py b/main/chroma.py
index 1a18fe7..9a86893 100644
--- a/main/chroma.py
+++ b/main/chroma.py
@@ -1,3 +1,4 @@
+from uuid import uuid4
 import chromadb
 from chromadb.server import Settings as ChromaSettings
 from models import Feature
@@ -25,8 +26,10 @@ def __init__(
 
     def add_feature(self, feature: Feature) -> None:
         self.collection.add(
-            ids=[feature.link],
+            ids=[str(uuid4())],
             embeddings=[feature.features],
+            documents=[feature.link],
+            metadatas=[{"feature_type": feature.feature_type}]
         )
     
     def search_relevant_videos(self, search_feature: Feature, top_k: int = 100) -> list[str]:
@@ -34,13 +37,13 @@ def search_relevant_videos(self, search_feature: Feature, top_k: int = 100) -> l
             query_embeddings=search_feature.features,
             n_results=top_k
         )
-        return results['ids'][0]
+        return results['documents'][0]
 
     def add_text_search_suggestion(self, suggestion_query: str) -> None:
         subsearches = suggestion_query.split()
         self.desc_collection.add(
             documents=[suggestion_query] + subsearches,
-            ids=[str(hash(query)) for query in [suggestion_query] + subsearches]
+            ids=[str(uuid4()) for _ in [suggestion_query] + subsearches]
         )
 
     def get_text_search_suggestions(self, search_query: str, top_k: int = 20) -> list[str]:
diff --git a/main/clip.py b/main/clip.py
index b94c8dd..e5264c9 100644
--- a/main/clip.py
+++ b/main/clip.py
@@ -1,26 +1,37 @@
 import aiohttp
-from models import Video, Feature
+from settings import Settings
+from models import SearchFeature, Video, Feature
 
 class CLIPService:
-    def __init__(self, url: str) -> None:
-        self.clip_url = url
+    def __init__(self) -> None:
+        self.encode_clip_url = Settings.encode_clip_url
+        self.search_clip_url = Settings.search_clip_url
+        self.session_timeout = aiohttp.ClientTimeout(60 * 5)
     
-    async def get_video_embedding(self, request: Video) -> Feature:
-        async with aiohttp.ClientSession().post(
-            url=self.clip_url, 
+    async def get_video_embeddings(self, request: Video) -> list[Feature]:
+        async with aiohttp.ClientSession(timeout=self.session_timeout).post(
+            url=f"{self.encode_clip_url}encode", 
             json=request.model_dump(mode="json")
         ) as resp:
             features = await resp.json()
 
-        return Feature(features=features['features'], link=request.link, description=request.description)
+        return [
+            Feature(
+                features=v,
+                link=request.link,
+                description=request.description,
+                feature_type=k
+            )
+            for k, v in features.items()
+        ]
      
     async def get_text_embedding(
             self, 
-            request: Video, 
+            request: SearchFeature, 
     ) -> Feature:
         async with aiohttp.ClientSession().post(
-            self.clip_url, 
+            f"{self.search_clip_url}encode-search", 
             json=request.model_dump(mode="json")
         ) as resp:
             features = await resp.json()
-        return Feature(features=features['features'])
+        return Feature(features=features['features'], feature_type="description")
diff --git a/main/deps.py b/main/deps.py
index 18e8277..6afa00e 100644
--- a/main/deps.py
+++ b/main/deps.py
@@ -14,9 +14,7 @@
 
 
 def get_clip_service() -> CLIPService:
-    return CLIPService(
-        url=Settings.clip_url
-    )
+    return CLIPService()
 
 def get_chroma_storage() -> ChromaStorage:
     return ChromaStorage()
diff --git a/main/main.py b/main/main.py
index 86903d5..98ba956 100644
--- a/main/main.py
+++ b/main/main.py
@@ -3,18 +3,22 @@
 
 from deps import Opus, Clip, Chroma, Speller, lifespan
 from settings import Settings
-from models import Video, Text, SuggestRequest
+from models import SearchFeature, Video, Text, SuggestRequest
 
 app = FastAPI(lifespan=lifespan)
 
+
 @app.post("/index")
 async def add_video_to_index(request: Video, clip: Clip, chroma: Chroma) -> Video:
     """Добавляет новое видео в хранилище - индекс"""
-    feature = await clip.get_video_embedding(request)
+    features = await clip.get_video_embeddings(request)
     if request.description is not None:
         chroma.add_text_search_suggestion(suggestion_query=request.description)
-    chroma.add_feature(feature=feature)
-    return request.model_dump(mode="dict")
+
+    for feature in features:
+        chroma.add_feature(feature=feature)
+    return request
+
 
 @app.get("/search")
 @cache(expire=Settings.cache_lifetime)
@@ -29,8 +33,8 @@ async def search_for_related_videos(
     spelled_search = speller(params.text)
     translated_search = translator(spelled_search)
     search_vector = await clip.get_text_embedding(
-        Video(
-            description=translated_search
+        SearchFeature(
+            query=translated_search
         )
     )
     return {"results": chroma.search_relevant_videos(search_feature=search_vector, top_k=params.return_amount)}
diff --git a/main/models.py b/main/models.py
index 633e847..e25975a 100644
--- a/main/models.py
+++ b/main/models.py
@@ -1,17 +1,20 @@
-from typing import Optional
+from typing import Literal, Optional
 from pydantic import BaseModel
 
 class Video(BaseModel):
     """Represents a Link to Video with text description 
     to be vectorized and added to index"""
-    description: Optional[str] = None
-    link: Optional[str] = None
+    link: str
+    description: str | None = None
 
 class Text(BaseModel):
     """Represents a text query to search related videos"""
     text: str
     return_amount: int = 50
 
+class SearchFeature(BaseModel):
+    query: str
+
 class SuggestRequest(BaseModel):
     """Represents a text query to suggest related completions"""
     text: str
@@ -21,3 +24,4 @@ class Feature(BaseModel):
     link: Optional[str] = None
     description: Optional[str] = None
     features: list[float]
+    feature_type: Literal["description"] | Literal["video"] | Literal["audio"]
diff --git a/main/settings.py b/main/settings.py
index 68e1c3a..09db4bd 100644
--- a/main/settings.py
+++ b/main/settings.py
@@ -7,6 +7,7 @@
 class Settings:
     db_host: str = env.str("DB_HOST", default="chroma_db")
     db_port: int = env.int("DB_PORT", default=8080)
-    clip_url: str = env.str("CLIP_URL", default="http://inference:8040/encode")
+    encode_clip_url: str = env.str("ENCODE_CLIP_URL", default="http://encode:8040/")
+    search_clip_url: str = env.str("SEARCH_CLIP_URL", default="http://search:8050/")
     memcached_host: str = env.str("MEMCACHED_HOST", default="request_cache")
     cache_lifetime: int = env.int("CACHE_LIFETIME", default=3600)
diff --git a/poetry.lock b/poetry.lock
index 825512c..1120b4a 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -226,6 +226,20 @@ tests = ["attrs[tests-no-zope]", "zope-interface"]
 tests-mypy = ["mypy (>=1.6)", "pytest-mypy-plugins"]
 tests-no-zope = ["attrs[tests-mypy]", "cloudpickle", "hypothesis", "pympler", "pytest (>=4.3.0)", "pytest-xdist[psutil]"]
 
+[[package]]
+name = "audioread"
+version = "3.0.1"
+description = "Multi-library, cross-platform audio decoding."
+optional = false
+python-versions = ">=3.6"
+files = [
+    {file = "audioread-3.0.1-py3-none-any.whl", hash = "sha256:4cdce70b8adc0da0a3c9e0d85fb10b3ace30fbdf8d1670fd443929b61d117c33"},
+    {file = "audioread-3.0.1.tar.gz", hash = "sha256:ac5460a5498c48bdf2e8e767402583a4dcd13f4414d286f42ce4379e8b35066d"},
+]
+
+[package.extras]
+test = ["tox"]
+
 [[package]]
 name = "autocorrect"
 version = "2.6.1"
@@ -236,6 +250,64 @@ files = [
     {file = "autocorrect-2.6.1.tar.gz", hash = "sha256:2bc68192dc645b44bece2613caac338e93548c3dac9c563095b27224c7fd4391"},
 ]
 
+[[package]]
+name = "av"
+version = "12.1.0"
+description = "Pythonic bindings for FFmpeg's libraries."
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "av-12.1.0-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:c0df2ad330ccf63ed8192d637306f13123cdf1c06717168d1de8b9a084d62f70"},
+    {file = "av-12.1.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:9e66ad48dc3f618cf4a75cc14dd7e119d1151ff3c13b9b064014c79bad20df85"},
+    {file = "av-12.1.0-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f0e8fbbe3cffd04dcbfaf7f9e0469c8c9d3ae962728487aae0dbbac9ebb62567"},
+    {file = "av-12.1.0-cp310-cp310-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:93c24d21b116e3af45e2f4b3a7ff1c96ae9a266bcde33a689ace0c52888e74d9"},
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 [[package]]
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 version = "3.3.2"
@@ -550,6 +686,56 @@ humanfriendly = ">=9.1"
 [package.extras]
 cron = ["capturer (>=2.4)"]
 
+[[package]]
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+
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+description = "Decorators for Humans"
+optional = false
+python-versions = ">=3.5"
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 [[package]]
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 version = "1.2.14"
@@ -728,6 +914,28 @@ typer = ">=0.12.3"
 [package.extras]
 standard = ["fastapi", "uvicorn[standard] (>=0.15.0)"]
 
+[[package]]
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+version = "1.0.2"
+description = "Faster Whisper transcription with CTranslate2"
+optional = false
+python-versions = ">=3.8"
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+
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+conversion = ["transformers[torch] (>=4.23)"]
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+
 [[package]]
 name = "filelock"
 version = "3.14.0"
@@ -1360,6 +1568,17 @@ MarkupSafe = ">=2.0"
 [package.extras]
 i18n = ["Babel (>=2.7)"]
 
+[[package]]
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+version = "1.4.2"
+description = "Lightweight pipelining with Python functions"
+optional = false
+python-versions = ">=3.8"
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 [[package]]
 name = "jsonpatch"
 version = "1.33"
@@ -1503,6 +1722,86 @@ orjson = ">=3.9.14,<4.0.0"
 pydantic = ">=1,<3"
 requests = ">=2,<3"
 
+[[package]]
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+
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 [[package]]
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 version = "3.0.0"
@@ -1764,6 +2063,71 @@ docs = ["sphinx"]
 gmpy = ["gmpy2 (>=2.1.0a4)"]
 tests = ["pytest (>=4.6)"]
 
+[[package]]
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+
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 version = "1.26.4"
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+xxhash = ["xxhash (>=1.4.3)"]
+
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 version = "3.5.0"
@@ -2713,6 +3132,17 @@ files = [
 [package.dependencies]
 pyasn1 = ">=0.4.6,<0.7.0"
 
+[[package]]
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+version = "2.22"
+description = "C parser in Python"
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 version = "2.7.3"
@@ -3374,6 +3804,155 @@ opencv = ["opencv-python"]
 opencv-headless = ["opencv-python-headless"]
 pyav = ["av"]
 
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+    {file = "sentencepiece-0.2.0.tar.gz", hash = "sha256:a52c19171daaf2e697dc6cbe67684e0fa341b1248966f6aebb541de654d15843"},
+]
+
 [[package]]
 name = "setuptools"
 version = "70.0.0"
@@ -3422,6 +4001,75 @@ files = [
     {file = "sniffio-1.3.1.tar.gz", hash = "sha256:f4324edc670a0f49750a81b895f35c3adb843cca46f0530f79fc1babb23789dc"},
 ]
 
+[[package]]
+name = "soundfile"
+version = "0.12.1"
+description = "An audio library based on libsndfile, CFFI and NumPy"
+optional = false
+python-versions = "*"
+files = [
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+    {file = "soundfile-0.12.1-py2.py3-none-win_amd64.whl", hash = "sha256:0d86924c00b62552b650ddd28af426e3ff2d4dc2e9047dae5b3d8452e0a49a77"},
+    {file = "soundfile-0.12.1.tar.gz", hash = "sha256:e8e1017b2cf1dda767aef19d2fd9ee5ebe07e050d430f77a0a7c66ba08b8cdae"},
+]
+
+[package.dependencies]
+cffi = ">=1.0"
+
+[package.extras]
+numpy = ["numpy"]
+
+[[package]]
+name = "soxr"
+version = "0.3.7"
+description = "High quality, one-dimensional sample-rate conversion library"
+optional = false
+python-versions = ">=3.6"
+files = [
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+    {file = "soxr-0.3.7-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d994f1a7690b1b13ab639ea33e0c1d78415b64d88d6df4af705a9443f97b9687"},
+    {file = "soxr-0.3.7-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e87b58bc9e8c2caa16f07726f666bd043f0a49ca937baa803ce7708003b27833"},
+    {file = "soxr-0.3.7-cp38-cp38-win_amd64.whl", hash = "sha256:07f4c0c6125ea1482fa187ad5f007216712ee0a93586a9b2f80e79c0bf944cf7"},
+    {file = "soxr-0.3.7-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:e5267c3ba34d4b873d9bbe3a9e58418b01ae4fd04349a4f944d9943b9ddac0f7"},
+    {file = "soxr-0.3.7-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:6e39668c250e221db888cf3b290a16fbe10a702d9a4eb604a127f720040de583"},
+    {file = "soxr-0.3.7-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f8ceeb74e5a55d903cc286d3bd12c2d8f8c85d02894071e9ec92ab405430907c"},
+    {file = "soxr-0.3.7-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0eed6bf58192dd1bb93becd2444de4d712689713d727b32fd55623ae9aae7df7"},
+    {file = "soxr-0.3.7-cp39-cp39-win_amd64.whl", hash = "sha256:7221302b4547d02a3f38dd3cd15317ab2b78873c75921db5f4a070848f0c71be"},
+    {file = "soxr-0.3.7.tar.gz", hash = "sha256:436ddff00c6eb2c75b79c19cfdca7527b1e31b5fad738652f044045ba6258593"},
+]
+
+[package.dependencies]
+numpy = "*"
+
+[package.extras]
+docs = ["linkify-it-py", "myst-parser", "sphinx", "sphinx-book-theme"]
+test = ["pytest"]
+
 [[package]]
 name = "sqlalchemy"
 version = "2.0.30"
@@ -3568,6 +4216,17 @@ files = [
 doc = ["reno", "sphinx"]
 test = ["pytest", "tornado (>=4.5)", "typeguard"]
 
+[[package]]
+name = "threadpoolctl"
+version = "3.5.0"
+description = "threadpoolctl"
+optional = false
+python-versions = ">=3.8"
+files = [
+    {file = "threadpoolctl-3.5.0-py3-none-any.whl", hash = "sha256:56c1e26c150397e58c4926da8eeee87533b1e32bef131bd4bf6a2f45f3185467"},
+    {file = "threadpoolctl-3.5.0.tar.gz", hash = "sha256:082433502dd922bf738de0d8bcc4fdcbf0979ff44c42bd40f5af8a282f6fa107"},
+]
+
 [[package]]
 name = "tokenizers"
 version = "0.19.1"
@@ -4244,6 +4903,19 @@ files = [
     {file = "websockets-12.0.tar.gz", hash = "sha256:81df9cbcbb6c260de1e007e58c011bfebe2dafc8435107b0537f393dd38c8b1b"},
 ]
 
+[[package]]
+name = "whisper-cpp-python"
+version = "0.2.0"
+description = "A Python wrapper for whisper.cpp"
+optional = false
+python-versions = ">=3.9"
+files = [
+    {file = "whisper_cpp_python-0.2.0.tar.gz", hash = "sha256:4e727040711d9f8adc3767f786525ba1543b5dd34e3a851880a953933b402f55"},
+]
+
+[package.dependencies]
+librosa = ">=0.10.0.post2,<0.11.0"
+
 [[package]]
 name = "wrapt"
 version = "1.16.0"
@@ -4444,4 +5116,4 @@ test = ["big-O", "importlib-resources", "jaraco.functools", "jaraco.itertools",
 [metadata]
 lock-version = "2.0"
 python-versions = "^3.11"
-content-hash = "854913e46a4742275f760698c85f5c0003a5df398805ce81d5a40bb074697baa"
+content-hash = "d9abc569af387cd74eb903c00e081e203ea028606bde4e4e848121ce19a9d2f9"
diff --git a/pyproject.toml b/pyproject.toml
index 4bee240..a5ea6e6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -27,6 +27,9 @@ environs = "^11.0.0"
 fastapi-cache2 = {extras = ["redis"], version = "^0.2.1"}
 aiomcache = "^0.8.2"
 autocorrect = "^2.6.1"
+whisper-cpp-python = "^0.2.0"
+faster-whisper = "^1.0.2"
+sentencepiece = "^0.2.0"
 
 
 [build-system]