mirror of
https://github.com/FunAudioLLM/CosyVoice.git
synced 2026-02-04 17:39:25 +08:00
add train cfg in flow matching
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lyuxiang.lx
78
runtime/python/fastapi/fastapi_client.py
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78
runtime/python/fastapi/fastapi_client.py
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import argparse
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import logging
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import requests
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def saveResponse(path, response):
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# 以二进制写入模式打开文件
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with open(path, 'wb') as file:
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# 将响应的二进制内容写入文件
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file.write(response.content)
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def main():
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api = args.api_base
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if args.mode == 'sft':
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url = api + "/api/inference/sft"
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payload={
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'tts': args.tts_text,
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'role': args.spk_id
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}
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response = requests.request("POST", url, data=payload)
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saveResponse(args.tts_wav, response)
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elif args.mode == 'zero_shot':
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url = api + "/api/inference/zero-shot"
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payload={
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'tts': args.tts_text,
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'prompt': args.prompt_text
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}
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files=[('audio', ('prompt_audio.wav', open(args.prompt_wav,'rb'), 'application/octet-stream'))]
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response = requests.request("POST", url, data=payload, files=files)
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saveResponse(args.tts_wav, response)
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elif args.mode == 'cross_lingual':
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url = api + "/api/inference/cross-lingual"
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payload={
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'tts': args.tts_text,
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}
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files=[('audio', ('prompt_audio.wav', open(args.prompt_wav,'rb'), 'application/octet-stream'))]
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response = requests.request("POST", url, data=payload, files=files)
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saveResponse(args.tts_wav, response)
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else:
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url = api + "/api/inference/instruct"
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payload = {
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'tts': args.tts_text,
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'role': args.spk_id,
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'instruct': args.instruct_text
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}
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response = requests.request("POST", url, data=payload)
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saveResponse(args.tts_wav, response)
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logging.info("Response save to {}", args.tts_wav)
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if __name__ == "__main__":
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parser = argparse.ArgumentParser()
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parser.add_argument('--api_base',
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type=str,
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default='http://127.0.0.1:6006')
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parser.add_argument('--mode',
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default='sft',
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choices=['sft', 'zero_shot', 'cross_lingual', 'instruct'],
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help='request mode')
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parser.add_argument('--tts_text',
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type=str,
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default='你好，我是通义千问语音合成大模型，请问有什么可以帮您的吗？')
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parser.add_argument('--spk_id',
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type=str,
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default='中文女')
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parser.add_argument('--prompt_text',
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type=str,
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default='希望你以后能够做的比我还好呦。')
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parser.add_argument('--prompt_wav',
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type=str,
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default='../../zero_shot_prompt.wav')
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parser.add_argument('--instruct_text',
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type=str,
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default='Theo \'Crimson\', is a fiery, passionate rebel leader. Fights with fervor for justice, but struggles with impulsiveness.')
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parser.add_argument('--tts_wav',
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type=str,
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default='demo.wav')
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args = parser.parse_args()
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prompt_sr, target_sr = 16000, 22050
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main()
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109
runtime/python/fastapi/fastapi_server.py
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109
runtime/python/fastapi/fastapi_server.py
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# Set inference model
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# export MODEL_DIR=pretrained_models/CosyVoice-300M-Instruct
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# For development
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# fastapi dev --port 6006 fastapi_server.py
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# For production deployment
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# fastapi run --port 6006 fastapi_server.py
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import os
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import sys
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import io,time
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from fastapi import FastAPI, Response, File, UploadFile, Form
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from fastapi.responses import HTMLResponse
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from contextlib import asynccontextmanager
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ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
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sys.path.append('{}/../..'.format(ROOT_DIR))
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sys.path.append('{}/../../third_party/Matcha-TTS'.format(ROOT_DIR))
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from cosyvoice.cli.cosyvoice import CosyVoice
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from cosyvoice.utils.file_utils import load_wav
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import numpy as np
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import torch
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import torchaudio
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import logging
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logging.getLogger('matplotlib').setLevel(logging.WARNING)
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class LaunchFailed(Exception):
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pass
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@asynccontextmanager
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async def lifespan(app: FastAPI):
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model_dir = os.getenv("MODEL_DIR", "pretrained_models/CosyVoice-300M-SFT")
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if model_dir:
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logging.info("MODEL_DIR is {}", model_dir)
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app.cosyvoice = CosyVoice('../../'+model_dir)
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# sft usage
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logging.info("Avaliable speakers {}", app.cosyvoice.list_avaliable_spks())
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else:
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raise LaunchFailed("MODEL_DIR environment must set")
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yield
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app = FastAPI(lifespan=lifespan)
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def buildResponse(output):
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buffer = io.BytesIO()
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torchaudio.save(buffer, output, 22050, format="wav")
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buffer.seek(0)
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return Response(content=buffer.read(-1), media_type="audio/wav")
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@app.post("/api/inference/sft")
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@app.get("/api/inference/sft")
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async def sft(tts: str = Form(), role: str = Form()):
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start = time.process_time()
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output = app.cosyvoice.inference_sft(tts, role)
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end = time.process_time()
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logging.info("infer time is {} seconds", end-start)
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return buildResponse(output['tts_speech'])
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@app.post("/api/inference/zero-shot")
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async def zeroShot(tts: str = Form(), prompt: str = Form(), audio: UploadFile = File()):
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start = time.process_time()
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prompt_speech = load_wav(audio.file, 16000)
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prompt_audio = (prompt_speech.numpy() * (2**15)).astype(np.int16).tobytes()
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prompt_speech_16k = torch.from_numpy(np.array(np.frombuffer(prompt_audio, dtype=np.int16))).unsqueeze(dim=0)
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prompt_speech_16k = prompt_speech_16k.float() / (2**15)
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output = app.cosyvoice.inference_zero_shot(tts, prompt, prompt_speech_16k)
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end = time.process_time()
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logging.info("infer time is {} seconds", end-start)
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return buildResponse(output['tts_speech'])
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@app.post("/api/inference/cross-lingual")
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async def crossLingual(tts: str = Form(), audio: UploadFile = File()):
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start = time.process_time()
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prompt_speech = load_wav(audio.file, 16000)
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prompt_audio = (prompt_speech.numpy() * (2**15)).astype(np.int16).tobytes()
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prompt_speech_16k = torch.from_numpy(np.array(np.frombuffer(prompt_audio, dtype=np.int16))).unsqueeze(dim=0)
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prompt_speech_16k = prompt_speech_16k.float() / (2**15)
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output = app.cosyvoice.inference_cross_lingual(tts, prompt_speech_16k)
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end = time.process_time()
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logging.info("infer time is {} seconds", end-start)
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return buildResponse(output['tts_speech'])
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@app.post("/api/inference/instruct")
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@app.get("/api/inference/instruct")
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async def instruct(tts: str = Form(), role: str = Form(), instruct: str = Form()):
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start = time.process_time()
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output = app.cosyvoice.inference_instruct(tts, role, instruct)
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end = time.process_time()
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logging.info("infer time is {} seconds", end-start)
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return buildResponse(output['tts_speech'])
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@app.get("/api/roles")
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async def roles():
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return {"roles": app.cosyvoice.list_avaliable_spks()}
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@app.get("/", response_class=HTMLResponse)
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async def root():
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return """
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<!DOCTYPE html>
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<html lang=zh-cn>
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<head>
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<meta charset=utf-8>
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<title>Api information</title>
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</head>
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<body>
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Get the supported tones from the Roles API first, then enter the tones and textual content in the TTS API for synthesis. <a href='./docs'>Documents of API</a>
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</body>
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</html>
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"""
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