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48 lines
1.4 KiB
Python
48 lines
1.4 KiB
Python
from fastapi import FastAPI, File, UploadFile, Form
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from fastapi.responses import JSONResponse
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import os
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import shutil
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from whisper.transcribe import transcribe
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from whisper import load_model
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from whisper.tokenizer import LANGUAGES
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import torch
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app = FastAPI()
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# Cargar modelo solo una vez al iniciar el contenedor
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MODEL_NAME = os.environ.get("WHISPER_MODEL", "turbo")
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DEVICE = "cuda" if torch.cuda.is_available() else "cpu"
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model = load_model(MODEL_NAME, device=DEVICE)
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@app.post("/transcribe")
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def transcribe_audio(
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file: UploadFile = File(...),
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task: str = Form("transcribe"),
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language: str = Form(None),
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temperature: float = Form(0.0),
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word_timestamps: bool = Form(False)
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):
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temp_path = f"temp_{file.filename}"
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with open(temp_path, "wb") as buffer:
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shutil.copyfileobj(file.file, buffer)
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try:
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result = transcribe(
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model,
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temp_path,
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task=task,
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language=language,
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temperature=temperature,
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word_timestamps=word_timestamps,
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verbose=False
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)
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os.remove(temp_path)
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return JSONResponse({
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"text": result["text"],
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"segments": result.get("segments", []),
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"language": result.get("language", "")
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})
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except Exception as e:
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if os.path.exists(temp_path):
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os.remove(temp_path)
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return JSONResponse({"error": str(e)}, status_code=500)
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