Add OpenAI-compatible API and Docker deployment

- Add FastAPI-based API in whisperx/api/
- Implement transcription endpoint compatible with OpenAI
- Added Dockerfile and docker-compose.yml for easy deployment
- Updated README with Docker instructions
- Added new script whisperx-serve for running the API

whisperx/api/main.py

@@ -0,0 +1,86 @@
+import os
+import tempfile
+import asyncio
+from contextlib import asynccontextmanager
+from fastapi import FastAPI, UploadFile, File, Form, HTTPException
+from fastapi.responses import JSONResponse
+import torch
+import whisperx
+from whisperx.schema import TranscriptionResult
+
+
+model = None
+align_model_metadata = None
+
+
+def load_transcription_model(model_name: str = "turbo", device: str = None, compute_type: str = "float16"):
+    global model, align_model_metadata
+    if device is None:
+        device = "cuda" if torch.cuda.is_available() else "cpu"
+    print(f"Loading WhisperX model: {model_name} on {device} with {compute_type}")
+    model = whisperx.load_model(model_name, device, compute_type=compute_type)
+    # For alignment, load the metadata
+    align_model_metadata = whisperx.alignment.DEFAULT_ALIGN_MODELS_HF
+    print("Model loaded and ready.")
+
+
+@asynccontextmanager
+async def lifespan(app: FastAPI):
+    # Load the model at startup
+    model_name = os.getenv("WHISPERX_MODEL", "turbo")
+    device = os.getenv("WHISPERX_DEVICE", "cuda")
+    compute_type = os.getenv("WHISPERX_COMPUTE_TYPE", "float16")
+    load_transcription_model(model_name, device, compute_type)
+    yield
+    # Cleanup if needed
+    print("Shutting down API")
+
+
+app = FastAPI(
+    title="WhisperX API",
+    description="OpenAI-compatible API for speech transcription using WhisperX",
+    version="1.0.0",
+    lifespan=lifespan
+)
+
+
+@app.get("/")
+async def root():
+    return {"message": "WhisperX API is running"}
+
+
+@app.post("/v1/audio/transcriptions")
+async def transcribe_audio(
+    file: UploadFile = File(...),
+    model_name: str = Form("whisper-1"),  # OpenAI uses 'whisper-1', we ignore this
+    language: str = Form(None),
+    response_format: str = Form("json"),
+    temperature: float = Form(0.0),  # We don't use temperature for now
+    prompt: str = Form(None)  # Not used
+):
+    if model is None:
+        raise HTTPException(status_code=500, detail="Model not loaded")
+
+    if not file.filename.lower().endswith(('.wav', '.mp3', '.flac', '.m4a', '.webm', '.mp4', '.mpga', '.ogg', '.opus')):
+        raise HTTPException(status_code=400, detail="Unsupported audio format")
+
+    # Save uploaded file to temp file
+    with tempfile.NamedTemporaryFile(delete=False, suffix=".wav") as temp_file:
+        temp_file.write(await file.read())
+        audio_path = temp_file.name
+
+    try:
+        # Load audio
+        audio = whisperx.load_audio(audio_path)
+
+        # Transcribe
+        result = model(audio, batch_size=16, language=language)
+        text = " ".join([segment['text'] for segment in result["segments"]]).strip()
+
+        # If we have segments, might want to return more info, but for OpenAI compatibility, just text
+
+        return JSONResponse({"text": text})
+    except Exception as e:
+        raise HTTPException(status_code=500, detail=f"Transcription failed: {str(e)}")
+    finally:
+        os.unlink(audio_path)

whisperx/api/serve.py

@@ -0,0 +1,16 @@
+import uvicorn
+
+
+def serve(host: str = "0.0.0.0", port: int = 8000, workers: int = 1):
+    """Run the WhisperX API server"""
+    uvicorn.run(
+        "whisperx.api.main:app",
+        host=host,
+        port=port,
+        workers=workers,
+        reload=False  # No reload for production
+    )
+
+
+if __name__ == "__main__":
+    serve()