81 lines
2.4 KiB
Python
81 lines
2.4 KiB
Python
import os
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import torch
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import numpy as np
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import io
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import wave
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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 faster_qwen3_tts import FasterQwen3TTS
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app = FastAPI(title="Indra tts")
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if not torch.cuda.is_available():
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raise RuntimeError("Mouth cannot find CUDA. Check nvidia-container-toolkit.")
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print(f"Loading model on: {torch.cuda.get_device_name(0)}")
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# Load the Base model for high-fidelity mimicry
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model = FasterQwen3TTS.from_pretrained(
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"Qwen/Qwen3-TTS-12Hz-1.7B-Base",
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device="cuda:0", # Targets GPU 7
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dtype=torch.bfloat16
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)
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class TTSRequest(BaseModel):
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model: str = "tts-1" # ignored by backend, here to satisfy modelix router
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input: str
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voice: str = "oni"
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response_format: str = "wav"
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seed: int = 42
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@app.post("/v1/audio/speech")
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async def generate_speech(request: TTSRequest):
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try:
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voice_file = f"{request.voice}.wav"
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base_path = "/mnt/nvme3n1/swarm/voice-samples"
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ref_path = os.path.join(base_path, voice_file)
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txt_path = os.path.splitext(ref_path)[0] + ".txt"
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ref_text = None
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if os.path.exists(txt_path):
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with open(txt_path, "r") as f:
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ref_text = f.read().strip()
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# Fix the seed for the persona identity
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torch.manual_seed(request.seed)
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full_audio = []
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# Non-streaming call is fine here since it takes <1s on your L40S
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audio_data, sample_rate = model.generate_voice_clone(
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text=request.input,
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language="English",
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ref_audio=ref_path,
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ref_text=ref_text,
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xvec_only=(ref_text is None)
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)
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audio_data = np.array(audio_data)
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audio_data = audio_data.flatten()
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# Convert Float32 to Int16 for standard WAV compatibility
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audio_int16 = (audio_data * 32767).astype(np.int16)
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wav_io = io.BytesIO()
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with wave.open(wav_io, 'wb') as wav_file:
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wav_file.setnchannels(1)
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wav_file.setsampwidth(2)
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wav_file.setframerate(sample_rate)
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wav_file.writeframes(audio_int16.tobytes())
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wav_io.seek(0)
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return Response(content=wav_io.getvalue(), media_type="audio/wav")
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except Exception as e:
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print(f"Indra Mouth Error: {e}")
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raise HTTPException(status_code=500, detail=str(e))
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if __name__ == "__main__":
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import uvicorn
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uvicorn.run(app, host="0.0.0.0", port=8002)
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