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@@ -1,119 +1,77 @@
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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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-
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+# 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 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 fastapi.middleware.cors import CORSMiddleware #引入 CORS中间件模块
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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 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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+from cosyvoice.cli.cosyvoice import CosyVoice
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+from cosyvoice.utils.file_utils import load_wav
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-class LaunchFailed(Exception):
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- pass
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-
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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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-
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-app = FastAPI(lifespan=lifespan)
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-
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-#设置允许访问的域名
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-origins = ["*"] #"*"，即为所有,也可以改为允许的特定ip。
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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=origins, #设置允许的origins来源
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+ CORSMiddleware,
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+ allow_origins=["*"],
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allow_credentials=True,
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- allow_methods=["*"], # 设置允许跨域的http方法，比如 get、post、put等。
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- allow_headers=["*"]) #允许跨域的headers，可以用来鉴别来源等作用。
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-
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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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-
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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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-
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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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+ allow_methods=["*"],
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+ allow_headers=["*"])
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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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+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.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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+@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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- 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.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.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("/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("/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("/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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-@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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+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="127.0.0.1", port=args.port)
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Xiang Lyu