mirror of
https://github.com/jamiepine/voicebox.git
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100 lines
3.5 KiB
Python
100 lines
3.5 KiB
Python
"""Transcription endpoints."""
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import asyncio
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import tempfile
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from pathlib import Path
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from fastapi import APIRouter, File, Form, HTTPException, UploadFile
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from .. import models
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from ..backends import transcribe_with_metadata
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from ..languages import normalize_capture_language
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from ..services import transcribe
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from ..services.task_queue import create_background_task
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from ..utils.tasks import get_task_manager
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router = APIRouter()
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UPLOAD_CHUNK_SIZE = 1024 * 1024 # 1MB
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# Same set profiles.py accepts for voice samples. librosa picks its decoder from the
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# file extension, so the temp file has to keep the uploaded one.
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ALLOWED_AUDIO_EXTS = {".wav", ".mp3", ".m4a", ".ogg", ".flac", ".aac", ".webm", ".opus"}
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@router.post("/transcribe", response_model=models.TranscriptionResponse)
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async def transcribe_audio(
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file: UploadFile = File(...),
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language: str | None = Form(None),
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model: str | None = Form(None),
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):
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"""Transcribe audio file to text."""
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uploaded_ext = Path(file.filename or "").suffix.lower()
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file_suffix = uploaded_ext if uploaded_ext in ALLOWED_AUDIO_EXTS else ".wav"
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with tempfile.NamedTemporaryFile(suffix=file_suffix, delete=False) as tmp:
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while chunk := await file.read(UPLOAD_CHUNK_SIZE):
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tmp.write(chunk)
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tmp_path = tmp.name
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try:
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from ..utils.audio import load_audio
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from ..backends import WHISPER_HF_REPOS
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language = normalize_capture_language(language)
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audio, sr = await asyncio.to_thread(load_audio, tmp_path)
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duration = len(audio) / sr
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whisper_model = transcribe.get_whisper_model()
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model_size = model if model else whisper_model.model_size
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valid_sizes = list(WHISPER_HF_REPOS.keys())
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if model_size not in valid_sizes:
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raise HTTPException(
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status_code=400,
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detail=f"Invalid model size '{model_size}'. Must be one of: {', '.join(valid_sizes)}",
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)
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already_loaded = whisper_model.is_loaded() and whisper_model.model_size == model_size
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if not already_loaded and not whisper_model._is_model_cached(model_size):
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progress_model_name = f"whisper-{model_size}"
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task_manager = get_task_manager()
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async def download_whisper_background():
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try:
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await whisper_model.load_model_async(model_size)
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task_manager.complete_download(progress_model_name)
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except Exception as e:
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task_manager.error_download(progress_model_name, str(e))
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task_manager.start_download(progress_model_name)
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create_background_task(download_whisper_background())
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raise HTTPException(
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status_code=202,
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detail={
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"message": f"Whisper model {model_size} is being downloaded. Please wait and try again.",
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"model_name": progress_model_name,
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"downloading": True,
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},
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)
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transcription = await transcribe_with_metadata(
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whisper_model, tmp_path, language, model_size
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)
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return models.TranscriptionResponse(
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text=transcription.text,
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duration=duration,
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language=transcription.language,
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)
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except HTTPException:
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raise
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except ValueError as e:
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raise HTTPException(status_code=400, detail=str(e)) from e
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except Exception as e:
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raise HTTPException(status_code=500, detail=str(e))
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finally:
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Path(tmp_path).unlink(missing_ok=True)
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