yi
76 lines
2.6 KiB
Python
76 lines
2.6 KiB
Python
import os
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import shutil
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import ssl
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import tempfile
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import traceback
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from fastapi import FastAPI, File, UploadFile
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from fastapi.middleware.cors import CORSMiddleware
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from fastapi.responses import JSONResponse
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import whisper
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import imageio_ffmpeg
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# Ensure whisper can execute "ffmpeg" command
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_ffmpeg_exe = imageio_ffmpeg.get_ffmpeg_exe()
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_ffmpeg_bin_dir = os.path.join(tempfile.gettempdir(), "dataclaw_ffmpeg_bin")
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_ffmpeg_link = os.path.join(_ffmpeg_bin_dir, "ffmpeg")
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os.makedirs(_ffmpeg_bin_dir, exist_ok=True)
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if not os.path.exists(_ffmpeg_link):
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try:
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os.symlink(_ffmpeg_exe, _ffmpeg_link)
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except OSError:
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shutil.copy2(_ffmpeg_exe, _ffmpeg_link)
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os.chmod(_ffmpeg_link, 0o755)
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os.environ["PATH"] = _ffmpeg_bin_dir + os.pathsep + os.environ.get("PATH", "")
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# Disable SSL verification temporarily to fix UNEXPECTED_EOF_WHILE_READING error during model download
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ssl._create_default_https_context = ssl._create_unverified_context
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app = FastAPI(title="Whisper Transcription Service")
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# Allow CORS for frontend
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"], # Allows all origins
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allow_credentials=True,
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allow_methods=["*"], # Allows all methods
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allow_headers=["*"], # Allows all headers
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)
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# Load the whisper model globally. "small" is a good balance between speed and accuracy.
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print("Loading Whisper model (small)... This may take a moment.")
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model = whisper.load_model("small")
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print("Model loaded successfully.")
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@app.get("/health")
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async def health():
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return {"status": "ok"}
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@app.post("/transcribe")
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async def transcribe_audio(file: UploadFile = File(...)):
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# Save the uploaded file to a temporary file
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with tempfile.NamedTemporaryFile(delete=False, suffix=".webm") 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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try:
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# Convert webm to wav since Whisper's internal ffmpeg dependency can be problematic
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# We will use an alternative approach or just pass the webm if it works natively
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# Transcribe using whisper
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# Forcing language to Chinese for better accuracy on Chinese speech
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result = model.transcribe(tmp_path, language="zh", task="transcribe")
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return {"text": result.get("text", "")}
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except Exception as e:
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print(f"Error during transcription: {e}")
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print(traceback.format_exc())
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return JSONResponse(status_code=500, content={"error": str(e)})
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finally:
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# Clean up the temporary file
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if os.path.exists(tmp_path):
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os.remove(tmp_path)
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8001)
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