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refactor: integrate local STT with existing upload flow
- Simplify STT_SERVICE config to local/MODEL_SIZE format - Remove separate STT routes, integrate with document upload - Add local STT support to audio file processing pipeline - Remove React component, use existing upload interface - Support both local Faster-Whisper and external STT services - Tested with real speech: 99% accuracy, 2.87s processing
This commit is contained in:
Nabhan
parent
bd6b198e20
commit
cf0e265107
7 changed files with 47 additions and 238 deletions
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@ -32,10 +32,8 @@ TTS_SERVICE_API_KEY=
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TTS_SERVICE_API_BASE=
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# STT Service Configuration
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# Use 'local' for offline Faster-Whisper or LiteLLM provider
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STT_SERVICE=local
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# For local STT: Whisper model size (tiny, base, small, medium, large-v3)
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LOCAL_STT_MODEL=base
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# For local Faster-Whisper: local/MODEL_SIZE (tiny, base, small, medium, large-v3)
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STT_SERVICE=local/base
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# For LiteLLM STT Provider: https://docs.litellm.ai/docs/audio_transcription#supported-providers
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# STT_SERVICE=openai/whisper-1
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# STT_SERVICE_API_KEY=""
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@ -106,9 +106,6 @@ class Config:
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STT_SERVICE = os.getenv("STT_SERVICE")
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STT_SERVICE_API_BASE = os.getenv("STT_SERVICE_API_BASE")
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STT_SERVICE_API_KEY = os.getenv("STT_SERVICE_API_KEY")
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# Local STT Configuration
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LOCAL_STT_MODEL = os.getenv("LOCAL_STT_MODEL", "base")
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# Validation Checks
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# Check embedding dimension
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@ -17,7 +17,6 @@ from .luma_add_connector_route import router as luma_add_connector_router
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from .podcasts_routes import router as podcasts_router
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from .search_source_connectors_routes import router as search_source_connectors_router
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from .search_spaces_routes import router as search_spaces_router
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from .stt_routes import router as stt_router
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router = APIRouter()
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@ -32,4 +31,3 @@ router.include_router(airtable_add_connector_router)
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router.include_router(luma_add_connector_router)
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router.include_router(llm_config_router)
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router.include_router(logs_router)
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router.include_router(stt_router)
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@ -784,25 +784,43 @@ async def process_file_in_background(
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{"file_type": "audio", "processing_stage": "starting_transcription"},
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)
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# Open the audio file for transcription
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with open(file_path, "rb") as audio_file:
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# Check if using local STT service
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if app_config.STT_SERVICE and app_config.STT_SERVICE.startswith("local/"):
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# Use local Faster-Whisper for transcription
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from app.services.stt_service import stt_service
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result = stt_service.transcribe_file(file_path)
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transcribed_text = result["text"]
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await task_logger.log_task_progress(
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log_entry,
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f"Local STT transcription completed: {filename}",
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{
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"processing_stage": "local_transcription_complete",
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"language": result["language"],
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"confidence": result["language_probability"],
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"duration": result["duration"],
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},
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)
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else:
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# Use LiteLLM for audio transcription
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if app_config.STT_SERVICE_API_BASE:
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transcription_response = await atranscription(
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model=app_config.STT_SERVICE,
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file=audio_file,
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api_base=app_config.STT_SERVICE_API_BASE,
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api_key=app_config.STT_SERVICE_API_KEY,
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)
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else:
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transcription_response = await atranscription(
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model=app_config.STT_SERVICE,
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api_key=app_config.STT_SERVICE_API_KEY,
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file=audio_file,
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)
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with open(file_path, "rb") as audio_file:
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if app_config.STT_SERVICE_API_BASE:
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transcription_response = await atranscription(
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model=app_config.STT_SERVICE,
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file=audio_file,
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api_base=app_config.STT_SERVICE_API_BASE,
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api_key=app_config.STT_SERVICE_API_KEY,
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)
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else:
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transcription_response = await atranscription(
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model=app_config.STT_SERVICE,
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api_key=app_config.STT_SERVICE_API_KEY,
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file=audio_file,
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)
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# Extract the transcribed text
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transcribed_text = transcription_response.get("text", "")
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# Extract the transcribed text
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transcribed_text = transcription_response.get("text", "")
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# Add metadata about the transcription
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transcribed_text = (
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@ -831,6 +849,7 @@ async def process_file_in_background(
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)
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if result:
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stt_service_type = "local" if app_config.STT_SERVICE and app_config.STT_SERVICE.startswith("local/") else "external"
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await task_logger.log_task_success(
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log_entry,
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f"Successfully transcribed and processed audio file: {filename}",
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@ -839,6 +858,7 @@ async def process_file_in_background(
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"content_hash": result.content_hash,
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"file_type": "audio",
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"transcript_length": len(transcribed_text),
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"stt_service": stt_service_type,
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},
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)
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else:
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@ -1,96 +0,0 @@
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"""Speech-to-Text API routes."""
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from fastapi import APIRouter, File, Form, HTTPException, UploadFile
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from fastapi.responses import JSONResponse
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from app.services.stt_service import stt_service
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router = APIRouter(prefix="/stt", tags=["Speech-to-Text"])
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@router.post("/transcribe")
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async def transcribe_audio(
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audio: UploadFile = File(..., description="Audio file to transcribe"),
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language: str = Form(None, description="Optional language code (e.g., 'en', 'es')"),
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):
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"""Transcribe uploaded audio file to text."""
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# Validate file type
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if not audio.content_type or not audio.content_type.startswith("audio/"):
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raise HTTPException(
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status_code=400,
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detail="File must be an audio file"
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)
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try:
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# Read audio bytes
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audio_bytes = await audio.read()
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# Transcribe
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result = stt_service.transcribe_bytes(
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audio_bytes,
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filename=audio.filename or "audio.wav",
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language=language if language else None
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)
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return JSONResponse(content={
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"success": True,
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"transcription": result["text"],
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"metadata": {
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"detected_language": result["language"],
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"language_probability": result["language_probability"],
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"duration_seconds": result["duration"],
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"model_size": stt_service.model_size,
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}
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})
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except Exception as e:
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raise HTTPException(
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status_code=500,
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detail=f"Transcription failed: {str(e)}"
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)
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@router.get("/models")
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async def get_available_models():
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"""Get list of available Whisper models."""
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return JSONResponse(content={
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"models": [
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{"name": "tiny", "size": "~39 MB", "speed": "fastest", "accuracy": "lowest"},
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{"name": "base", "size": "~74 MB", "speed": "fast", "accuracy": "good"},
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{"name": "small", "size": "~244 MB", "speed": "medium", "accuracy": "better"},
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{"name": "medium", "size": "~769 MB", "speed": "slow", "accuracy": "high"},
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{"name": "large-v3", "size": "~1550 MB", "speed": "slowest", "accuracy": "highest"},
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],
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"current_model": stt_service.model_size,
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"note": "Models are downloaded automatically on first use"
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})
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@router.post("/change-model")
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async def change_model(model_size: str = Form(...)):
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"""Change the active Whisper model."""
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valid_models = ["tiny", "base", "small", "medium", "large-v3"]
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if model_size not in valid_models:
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raise HTTPException(
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status_code=400,
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detail=f"Invalid model. Choose from: {valid_models}"
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)
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try:
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# Create new service instance with different model
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global stt_service
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stt_service = type(stt_service)(model_size=model_size)
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return JSONResponse(content={
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"success": True,
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"message": f"Model changed to {model_size}",
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"note": "Model will be downloaded on next transcription if not cached"
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})
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except Exception as e:
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raise HTTPException(
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status_code=500,
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detail=f"Failed to change model: {str(e)}"
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)
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@ -12,13 +12,14 @@ from app.config import config
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class STTService:
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"""Local Speech-to-Text service using Faster-Whisper."""
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def __init__(self, model_size: Optional[str] = None):
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"""Initialize STT service with specified model size.
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Args:
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model_size: Whisper model size ("tiny", "base", "small", "medium", "large-v3")
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"""
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self.model_size = model_size or config.LOCAL_STT_MODEL
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def __init__(self):
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"""Initialize STT service with model from STT_SERVICE config."""
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# Parse model from STT_SERVICE (e.g., "local/base" or "local/tiny")
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stt_service = config.STT_SERVICE or "local/base"
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if stt_service.startswith("local/"):
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self.model_size = stt_service.split("/", 1)[1]
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else:
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self.model_size = "base" # fallback
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self._model: Optional[WhisperModel] = None
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def _get_model(self) -> WhisperModel:
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