vh
06eb487a26
- app.py: thin FastAPI wrapper exposing OpenAI /v1/audio/speech (+ /v1/audio/voices, /healthz) around OmniVoice's Python API; precomputes a voice-clone prompt per voice at startup (loaded Whisper auto-transcribes each reference). Replaces the Gradio demo. - Dockerfile/compose: run the uvicorn wrapper, /healthz healthcheck, project name pinned to "omnivoice" so the asset-engine liveness probe matches. - deploy-omnivoice.yaml: stage chatterbox /refs/*.wav as clone voices (skip _* artifacts) + verify the API surface. - services.yaml: catalog entry (id omnivoice, :8199/v1/audio/speech, voice list sourced live from /v1/audio/voices) + reproducibility_audit row. Verified live on irv-ml1: /healthz ok, 33 voices loaded, test synth -> 24kHz PCM_16 WAV.
112 lines
4.1 KiB
Python
112 lines
4.1 KiB
Python
"""Thin FastAPI wrapper exposing OmniVoice (k2-fsa/OmniVoice) as an OpenAI-style
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TTS for the asset-engine.
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Upstream ships only a Gradio demo; we own this wrapper (same pattern as
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stacks/index-tts/app.py). Voices are reference WAVs staged in
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${OMNIVOICE_VOICES_DIR} (the reused chatterbox /refs/*.wav). A voice-clone prompt
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is precomputed once per voice at startup (the loaded Whisper ASR auto-transcribes
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each reference) and cached, so per-request latency is just generation.
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Endpoints:
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GET /healthz -> readiness (200 once model + >=1 voice are loaded)
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GET /v1/audio/voices -> {"voices": [<name>, ...]}
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POST /v1/audio/speech -> {input, voice, response_format=wav} -> audio/wav
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"""
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import glob
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import io
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import logging
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import os
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from pathlib import Path
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from typing import Optional
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import numpy as np
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import soundfile as sf
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import torch
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from fastapi import FastAPI, HTTPException
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from fastapi.responses import Response
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from pydantic import BaseModel
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from omnivoice import OmniVoice, OmniVoiceGenerationConfig
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logging.basicConfig(level=os.environ.get("OMNIVOICE_LOG_LEVEL", "INFO"))
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log = logging.getLogger("omnivoice-api")
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CKPT = os.environ.get("OMNIVOICE_CKPT", "k2-fsa/OmniVoice")
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VOICES_DIR = os.environ.get("OMNIVOICE_VOICES_DIR", "/app/voices")
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ASR_MODEL = os.environ.get("OMNIVOICE_ASR_MODEL", "openai/whisper-large-v3-turbo")
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NUM_STEP = int(os.environ.get("OMNIVOICE_NUM_STEP", "32"))
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GUIDANCE = float(os.environ.get("OMNIVOICE_GUIDANCE_SCALE", "2.0"))
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app = FastAPI(title="OmniVoice TTS (asset-engine wrapper)")
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MODEL: Optional[OmniVoice] = None
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PROMPTS: dict = {} # voice name -> VoiceClonePrompt
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SR: int = 24000
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class SpeechRequest(BaseModel):
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input: str
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voice: str
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response_format: str = "wav"
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model: Optional[str] = None # ignored (single model); OpenAI-compat field
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@app.on_event("startup")
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def _load() -> None:
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global MODEL, SR
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device = "cuda" if torch.cuda.is_available() else "cpu"
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log.info("loading OmniVoice %s on %s (asr=%s)", CKPT, device, ASR_MODEL)
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MODEL = OmniVoice.from_pretrained(
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CKPT, device_map=device, load_asr=True, asr_model_name=ASR_MODEL
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)
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SR = int(getattr(MODEL, "sampling_rate", 24000))
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for wav in sorted(glob.glob(os.path.join(VOICES_DIR, "*.wav"))):
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name = Path(wav).stem
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if name.startswith("_"):
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continue # skip _*.wav (chatterbox test/deploy artifacts)
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try:
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PROMPTS[name] = MODEL.create_voice_clone_prompt(ref_audio=wav)
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log.info("voice ready: %s", name)
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except Exception as exc: # noqa: BLE001
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log.warning("voice %s failed to load: %s", name, exc)
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log.info("%d voices loaded: %s", len(PROMPTS), sorted(PROMPTS))
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@app.get("/healthz")
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def healthz():
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if MODEL is None or not PROMPTS:
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raise HTTPException(status_code=503, detail="not ready")
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return {"status": "ok", "voices": len(PROMPTS), "sampling_rate": SR}
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@app.get("/v1/audio/voices")
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def voices():
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return {"voices": sorted(PROMPTS.keys())}
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@app.post("/v1/audio/speech")
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def speech(req: SpeechRequest):
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if MODEL is None:
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raise HTTPException(status_code=503, detail="model not loaded")
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if not req.input or not req.input.strip():
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raise HTTPException(status_code=400, detail="input is empty")
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if req.response_format not in ("wav", "", None):
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raise HTTPException(status_code=400, detail="only response_format=wav is supported")
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prompt = PROMPTS.get(req.voice)
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if prompt is None:
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raise HTTPException(
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status_code=404,
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detail=f"unknown voice '{req.voice}'; have {sorted(PROMPTS)}",
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)
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gen = OmniVoiceGenerationConfig(num_step=NUM_STEP, guidance_scale=GUIDANCE, denoise=True)
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out = MODEL.generate(text=req.input.strip(), voice_clone_prompt=prompt, generation_config=gen)
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audio = out[0] if isinstance(out, (list, tuple)) else out
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audio = np.asarray(audio, dtype=np.float32)
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buf = io.BytesIO()
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sf.write(buf, audio, SR, format="WAV", subtype="PCM_16")
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return Response(content=buf.getvalue(), media_type="audio/wav")
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