Deploy WhisperX service

app.py

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+from fastapi import FastAPI, File, UploadFile, Form
+from fastapi.responses import JSONResponse
+import gradio as gr
+import whisperx
+import torch
+import tempfile
+import os
+import uvicorn
+from threading import Thread
+
+# Device setup
+device = "cuda" if torch.cuda.is_available() else "cpu"
+compute_type = "float16" if device == "cuda" else "int8"
+
+print(f"🚀 Device: {device}, Compute: {compute_type}")
+
+# Create FastAPI app
+app = FastAPI(title="WhisperX Alignment API")
+
+def process_audio(audio_path: str, language: str = "en"):
+    """Core alignment logic"""
+    try:
+        print(f"📝 Processing {audio_path} ({language})...")
+        
+        # Load model
+        model = whisperx.load_model("base", device=device, compute_type=compute_type)
+        
+        # Transcribe
+        result = model.transcribe(audio_path, language=language)
+        
+        # Align
+        align_model, metadata = whisperx.load_align_model(language_code=language, device=device)
+        aligned = whisperx.align(result["segments"], align_model, metadata, audio_path, device=device)
+        
+        # Extract word segments
+        word_segments = []
+        for segment in aligned["segments"]:
+            for word in segment.get("words", []):
+                word_segments.append({
+                    "word": word["word"].strip(),
+                    "start": round(word["start"], 2),
+                    "end": round(word["end"], 2)
+                })
+        
+        duration = aligned["segments"][-1]["end"] if aligned["segments"] else 0
+        
+        return {
+            "word_segments": word_segments,
+            "duration": round(duration, 2),
+            "word_count": len(word_segments),
+            "language": language,
+            "device": device
+        }
+    except Exception as e:
+        print(f"❌ Error: {e}")
+        return {"error": str(e)}
+
+# FastAPI endpoint
+@app.post("/align")
+async def align_audio_api(
+    audio_file: UploadFile = File(...),
+    language: str = Form("en")
+):
+    """REST API endpoint for audio alignment"""
+    temp_path = None
+    try:
+        # Save temp file
+        with tempfile.NamedTemporaryFile(suffix=".mp3", delete=False) as tmp:
+            content = await audio_file.read()
+            tmp.write(content)
+            temp_path = tmp.name
+        
+        # Process
+        result = process_audio(temp_path, language)
+        return JSONResponse(result)
+    
+    finally:
+        if temp_path and os.path.exists(temp_path):
+            os.unlink(temp_path)
+
+@app.get("/")
+def health():
+    return {"status": "healthy", "device": device}
+
+# Gradio interface
+def align_gradio(audio_file, language="en"):
+    """Gradio UI wrapper"""
+    if not audio_file:
+        return {"error": "No file"}
+    return process_audio(audio_file, language)
+
+gradio_app = gr.Interface(
+    fn=align_gradio,
+    inputs=[
+        gr.Audio(type="filepath", label="Audio"),
+        gr.Textbox(value="en", label="Language")
+    ],
+    outputs=gr.JSON(label="Result"),
+    title="🎯 WhisperX Alignment",
+    description="Upload audio for word-level timestamps"
+)
+
+# Mount Gradio to FastAPI
+app = gr.mount_gradio_app(app, gradio_app, path="/")
+
+# Launch
+if __name__ == "__main__":
+    uvicorn.run(app, host="0.0.0.0", port=7860)
+
+

packages.txt

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+ffmpeg

requirements.txt

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+git+https://github.com/m-bain/whisperx.git
+fastapi
+uvicorn[standard]
+python-multipart
+
+
+
+
+