Folzo
7c0a0a39ba
- FastAPI server with OpenAI-compatible API - Integration with faster-whisper (large-v3) - Full Docker support with docker-compose - Support for multiple languages (FR/EN/Auto) - Clean, maintainable codebase (~100 lines) - Health endpoint and error handling - Volume caching for models
139 lines
4.6 KiB
Python
139 lines
4.6 KiB
Python
# 🎙️ Effi-STT - Speech-to-Text pour Effi
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# API OpenAI-compatible avec faster-whisper
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from fastapi import FastAPI, File, UploadFile, Form, HTTPException
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from fastapi.responses import JSONResponse
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import uvicorn
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from faster_whisper import WhisperModel
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import tempfile
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import os
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from typing import Optional
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import logging
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# Configuration du logging
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logging.basicConfig(level=logging.INFO)
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logger = logging.getLogger(__name__)
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app = FastAPI(
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title="Effi-STT",
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description="Speech-to-Text API compatible OpenAI pour Effi",
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version="1.0.0"
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)
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# Configuration
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MODEL_SIZE = os.getenv("MODEL_SIZE", "large-v3")
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DEVICE = os.getenv("DEVICE", "cpu")
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COMPUTE_TYPE = os.getenv("COMPUTE_TYPE", "int8")
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LANGUAGE = os.getenv("LANGUAGE", "fr")
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logger.info(f"🎙️ Chargement du modèle Whisper: {MODEL_SIZE}")
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logger.info(f" Device: {DEVICE}, Compute type: {COMPUTE_TYPE}")
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# Chargement du modèle au démarrage
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model = WhisperModel(
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MODEL_SIZE,
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device=DEVICE,
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compute_type=COMPUTE_TYPE,
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cpu_threads=int(os.getenv("CPU_THREADS", "4"))
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)
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logger.info("✅ Modèle chargé avec succès!")
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@app.get("/health")
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async def health():
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"""Endpoint de vérification de santé"""
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return {"status": "healthy", "model": MODEL_SIZE}
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@app.post("/v1/audio/transcriptions")
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async def transcribe(
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file: UploadFile = File(..., description="Fichier audio à transcrire"),
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model_param: Optional[str] = Form(None, alias="model"),
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language: Optional[str] = Form(LANGUAGE, description="Code langue (fr, en, auto)"),
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prompt: Optional[str] = Form(None, description="Prompt optionnel"),
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response_format: Optional[str] = Form("json", description="Format de réponse")
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):
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"""
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Endpoint de transcription compatible API OpenAI
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"""
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try:
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logger.info(f"📝 Transcription demandée: lang={language}, file={file.filename}")
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# Sauvegarder le fichier temporairement
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suffix = os.path.splitext(file.filename)[1] or ".mp3"
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with tempfile.NamedTemporaryFile(delete=False, suffix=suffix) as tmp:
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content = await file.read()
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tmp.write(content)
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tmp_path = tmp.name
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logger.info(f" Fichier sauvegardé: {tmp_path} ({len(content)} bytes)")
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try:
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# Transcription avec faster-whisper
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segments, info = model.transcribe(
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tmp_path,
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language=language if language != "auto" else None,
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task="transcribe",
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beam_size=5,
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best_of=5,
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condition_on_previous_text=True
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)
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# Concaténer tous les segments
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text_parts = []
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for segment in segments:
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text_parts.append(segment.text)
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full_text = " ".join(text_parts).strip()
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logger.info(f"✅ Transcription réussie: '{full_text[:50]}...'")
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logger.info(f" Langue détectée: {info.language}, Probabilité: {info.language_probability:.2f}")
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# Format OpenAI standard
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if response_format == "text":
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return full_text
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elif response_format == "srt":
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# TODO: Implémenter format SRT
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return full_text
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elif response_format == "vtt":
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# TODO: Implémenter format VTT
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return full_text
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else:
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# Format JSON (défaut)
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return JSONResponse(content={"text": full_text})
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finally:
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# Nettoyage
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os.unlink(tmp_path)
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except Exception as e:
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logger.error(f"❌ Erreur transcription: {str(e)}")
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raise HTTPException(status_code=500, detail=str(e))
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@app.get("/v1/models")
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async def list_models():
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"""Liste les modèles disponibles (compatibilité OpenAI)"""
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return {
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"data": [
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{
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"id": "whisper-1",
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"object": "model",
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"created": 1677610602,
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"owned_by": "openai",
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"permission": [],
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"root": "whisper-1",
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"parent": None
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}
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],
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"object": "list"
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}
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if __name__ == "__main__":
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port = int(os.getenv("PORT", "8200"))
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host = os.getenv("HOST", "0.0.0.0")
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logger.info(f"🚀 Démarrage du serveur sur http://{host}:{port}")
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logger.info(f" Endpoints:")
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logger.info(f" - GET /health")
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logger.info(f" - POST /v1/audio/transcriptions")
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logger.info(f" - GET /v1/models")
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uvicorn.run(app, host=host, port=port, log_level="info")
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