mirror of
https://github.com/jamiepine/voicebox.git
synced 2026-09-20 07:10:40 -07:00
James Pine
b3012ed10c
Move 9 business-logic modules from the backend root into services/: channels, effects, history, profiles, stories, versions, export_import, transcribe, tts. Move platform_detect.py into utils/. Backend root now contains only infrastructure (app, main, config, server, models, build_binary) and docs. All 94 routes verified.
80 lines
2.5 KiB
Python
80 lines
2.5 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 ..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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@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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):
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"""Transcribe audio file to text."""
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) 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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from ..utils.audio import load_audio
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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 = whisper_model.model_size
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whisper_hf_repos = {
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"large": "openai/whisper-large-v3",
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"turbo": "openai/whisper-large-v3-turbo",
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}
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model_name = whisper_hf_repos.get(model_size, f"openai/whisper-{model_size}")
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from huggingface_hub import constants as hf_constants
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repo_cache = Path(hf_constants.HF_HUB_CACHE) / ("models--" + model_name.replace("/", "--"))
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if not repo_cache.exists():
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progress_model_name = f"whisper-{model_size}"
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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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except Exception as e:
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get_task_manager().error_download(progress_model_name, str(e))
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get_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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text = await whisper_model.transcribe(tmp_path, language)
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return models.TranscriptionResponse(
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text=text,
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duration=duration,
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)
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except HTTPException:
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raise
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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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