84 lines
3.1 KiB
Python
84 lines
3.1 KiB
Python
# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import sys
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import argparse
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import logging
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logging.getLogger('matplotlib').setLevel(logging.WARNING)
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from fastapi import FastAPI, UploadFile, Form, File
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from fastapi.responses import StreamingResponse
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from fastapi.middleware.cors import CORSMiddleware
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import uvicorn
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import numpy as np
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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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app = FastAPI()
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# set cross region allowance
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app.add_middleware(
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CORSMiddleware,
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allow_origins=["*"],
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allow_credentials=True,
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allow_methods=["*"],
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allow_headers=["*"])
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def generate_data(model_output):
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for i in model_output:
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tts_audio = (i['tts_speech'].numpy() * (2 ** 15)).astype(np.int16).tobytes()
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yield tts_audio
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@app.get("/inference_sft")
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async def inference_sft(tts_text: str = Form(), spk_id: str = Form()):
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model_output = cosyvoice.inference_sft(tts_text, spk_id)
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return StreamingResponse(generate_data(model_output))
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@app.get("/inference_zero_shot")
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async def inference_zero_shot(tts_text: str = Form(), prompt_text: str = Form(), prompt_wav: UploadFile = File()):
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prompt_speech_16k = load_wav(prompt_wav.file, 16000)
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model_output = cosyvoice.inference_zero_shot(tts_text, prompt_text, prompt_speech_16k)
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return StreamingResponse(generate_data(model_output))
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@app.get("/inference_cross_lingual")
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async def inference_cross_lingual(tts_text: str = Form(), prompt_wav: UploadFile = File()):
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prompt_speech_16k = load_wav(prompt_wav.file, 16000)
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model_output = cosyvoice.inference_cross_lingual(tts_text, prompt_speech_16k)
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return StreamingResponse(generate_data(model_output))
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@app.get("/inference_instruct")
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async def inference_instruct(tts_text: str = Form(), spk_id: str = Form(), instruct_text: str = Form()):
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model_output = cosyvoice.inference_instruct(tts_text, spk_id, instruct_text)
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return StreamingResponse(generate_data(model_output))
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if __name__ == '__main__':
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parser = argparse.ArgumentParser()
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parser.add_argument('--port',
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type=int,
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default=50000)
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parser.add_argument('--model_dir',
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type=str,
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default='iic/CosyVoice-300M',
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help='local path or modelscope repo id')
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args = parser.parse_args()
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cosyvoice = CosyVoice(args.model_dir)
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uvicorn.run(app, host="0.0.0.0", port=args.port)
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