112 lines
3.3 KiB
Python
112 lines
3.3 KiB
Python
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#!/usr/bin/env python3
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"""OpenAI-compatible TTS wrapper around chatterbox-tts, for Open Notebook.
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Implements exactly the endpoint esperanto's OpenAICompatibleTextToSpeechModel calls:
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POST /audio/speech {model, voice, input, response_format} -> raw audio bytes
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Start:
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~/miniforge3/envs/chatterbox/bin/python tts_server.py
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Env vars:
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TTS_HOST (default 0.0.0.0), TTS_PORT (default 8901)
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CUDA_VISIBLE_DEVICES should be set by the caller (e.g. "1,2") to keep GPU 0 free.
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"""
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from __future__ import annotations
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import os
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import subprocess
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import sys
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import tempfile
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from pathlib import Path
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sys.path.insert(0, str(Path.home() / "chatterbox-tts-cli"))
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import chatterbox_cli_v4 as tts # noqa: E402
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import torch
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import torchaudio as ta
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from fastapi import FastAPI, HTTPException, Response
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from pydantic import BaseModel
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app = FastAPI(title="Chatterbox TTS (OpenAI-compatible)", version="1.0")
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_DEVICE = tts.get_device(None)
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_model_cache: dict[str, tuple] = {}
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def _get_model(lang: str):
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key = "en" if lang == "en" else "multi"
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if key not in _model_cache:
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_model_cache[key] = tts.load_model(lang, _DEVICE, t3_model="v3")
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return _model_cache[key]
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class SpeechRequest(BaseModel):
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model: str | None = None
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voice: str = "de"
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input: str
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response_format: str = "mp3"
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_FORMAT_CONTENT_TYPE = {
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"mp3": "audio/mpeg",
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"wav": "audio/wav",
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"opus": "audio/opus",
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"flac": "audio/flac",
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"aac": "audio/aac",
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}
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@app.get("/health")
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def health():
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return {"status": "ok", "device": _DEVICE}
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@app.get("/audio/voices")
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def voices():
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return {"voices": [{"id": lang, "name": lang} for lang in sorted(tts.SUPPORTED_LANGS)]}
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@app.post("/audio/speech")
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def speech(req: SpeechRequest):
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lang = req.voice if req.voice in tts.SUPPORTED_LANGS else "de"
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raw = tts.clean_raw_text(req.input)
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raw_chunks = tts.split_into_sentences(raw, max_len=400)
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chunks = [tts.preprocess_tts_text(c, lang=lang, pronunciation_dict=None) for c in raw_chunks]
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chunks = [c for c in chunks if c.strip()]
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if not chunks:
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raise HTTPException(status_code=422, detail="Kein synthetisierbarer Text übrig.")
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model, model_kind, sr = _get_model(lang)
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wavs = []
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for chunk in chunks:
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wavs.append(tts.generate_chunk(model, model_kind, chunk, lang, None))
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final = wavs[0] if len(wavs) == 1 else torch.cat(wavs, dim=-1)
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with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as wav_tmp:
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wav_path = wav_tmp.name
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ta.save(wav_path, final, sr)
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fmt = req.response_format if req.response_format in _FORMAT_CONTENT_TYPE else "wav"
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try:
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if fmt == "wav":
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audio_bytes = Path(wav_path).read_bytes()
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else:
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out_path = wav_path.replace(".wav", f".{fmt}")
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subprocess.run(
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["ffmpeg", "-y", "-i", wav_path, out_path],
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check=True, capture_output=True,
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)
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audio_bytes = Path(out_path).read_bytes()
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os.unlink(out_path)
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finally:
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os.unlink(wav_path)
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return Response(content=audio_bytes, media_type=_FORMAT_CONTENT_TYPE[fmt])
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host=os.environ.get("TTS_HOST", "0.0.0.0"),
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port=int(os.environ.get("TTS_PORT", "8901")))
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