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update fastapi

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lyuxiang.lx
2024-09-05 14:09:40 +08:00
parent 2ce724045b
commit 7555afb90a
6 changed files with 131 additions and 148 deletions
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9
cosyvoice/cli/cosyvoice.py
View File

@@ -13,6 +13,7 @@
# limitations under the License.
import os
import time
from tqdm import tqdm
from hyperpyyaml import load_hyperpyyaml
from modelscope import snapshot_download
from cosyvoice.cli.frontend import CosyVoiceFrontEnd
@@ -52,7 +53,7 @@ class CosyVoice:
return spks
def inference_sft(self, tts_text, spk_id, stream=False):
for i in self.frontend.text_normalize(tts_text, split=True):
for i in tqdm(self.frontend.text_normalize(tts_text, split=True)):
model_input = self.frontend.frontend_sft(i, spk_id)
start_time = time.time()
logging.info('synthesis text {}'.format(i))
@@ -64,7 +65,7 @@ class CosyVoice:
def inference_zero_shot(self, tts_text, prompt_text, prompt_speech_16k, stream=False):
prompt_text = self.frontend.text_normalize(prompt_text, split=False)
for i in self.frontend.text_normalize(tts_text, split=True):
for i in tqdm(self.frontend.text_normalize(tts_text, split=True)):
model_input = self.frontend.frontend_zero_shot(i, prompt_text, prompt_speech_16k)
start_time = time.time()
logging.info('synthesis text {}'.format(i))
@@ -77,7 +78,7 @@ class CosyVoice:
def inference_cross_lingual(self, tts_text, prompt_speech_16k, stream=False):
if self.frontend.instruct is True:
raise ValueError('{} do not support cross_lingual inference'.format(self.model_dir))
for i in self.frontend.text_normalize(tts_text, split=True):
for i in tqdm(self.frontend.text_normalize(tts_text, split=True)):
model_input = self.frontend.frontend_cross_lingual(i, prompt_speech_16k)
start_time = time.time()
logging.info('synthesis text {}'.format(i))
@@ -91,7 +92,7 @@ class CosyVoice:
if self.frontend.instruct is False:
raise ValueError('{} do not support instruct inference'.format(self.model_dir))
instruct_text = self.frontend.text_normalize(instruct_text, split=False)
for i in self.frontend.text_normalize(tts_text, split=True):
for i in tqdm(self.frontend.text_normalize(tts_text, split=True)):
model_input = self.frontend.frontend_instruct(i, spk_id, instruct_text)
start_time = time.time()
logging.info('synthesis text {}'.format(i))

2
cosyvoice/hifigan/generator.py
View File

@@ -340,7 +340,7 @@ class HiFTGenerator(nn.Module):
s = self._f02source(f0)
# use cache_source to avoid glitch
if cache_source.shape[2] == 0:
if cache_source.shape[2] != 0:
s[:, :, :cache_source.shape[2]] = cache_source
s_stft_real, s_stft_imag = self._stft(s.squeeze(1))

6
examples/libritts/cosyvoice/run.sh
View File

@@ -103,3 +103,9 @@ if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
--deepspeed.save_states model+optimizer
done
fi
if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
echo "Export your model for inference speedup. Remember copy your llm or flow model to model_dir"
python cosyvoice/bin/export_jit.py --model_dir $pretrained_model_dir
python cosyvoice/bin/export_onnx.py --model_dir $pretrained_model_dir
fi

6
examples/magicdata-read/cosyvoice/run.sh
View File

@@ -103,3 +103,9 @@ if [ ${stage} -le 5 ] && [ ${stop_stage} -ge 5 ]; then
--deepspeed.save_states model+optimizer
done
fi
if [ ${stage} -le 6 ] && [ ${stop_stage} -ge 6 ]; then
echo "Export your model for inference speedup. Remember copy your llm or flow model to model_dir"
python cosyvoice/bin/export_jit.py --model_dir $pretrained_model_dir
python cosyvoice/bin/export_onnx.py --model_dir $pretrained_model_dir
fi

92
runtime/python/fastapi/client.py
View File

@@ -1,56 +1,68 @@
# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import argparse
import logging
import requests
import torch
import torchaudio
import numpy as np
def saveResponse(path, response):
# 以二进制写入模式打开文件
with open(path, 'wb') as file:
# 将响应的二进制内容写入文件
file.write(response.content)
def main():
api = args.api_base
url = "http://{}:{}/inference_{}".format(args.host, args.port, args.mode)
if args.mode == 'sft':
url = api + "/api/inference/sft"
payload={
'tts': args.tts_text,
'role': args.spk_id
}
response = requests.request("POST", url, data=payload)
saveResponse(args.tts_wav, response)
elif args.mode == 'zero_shot':
url = api + "/api/inference/zero-shot"
payload={
'tts': args.tts_text,
'prompt': args.prompt_text
}
files=[('audio', ('prompt_audio.wav', open(args.prompt_wav,'rb'), 'application/octet-stream'))]
response = requests.request("POST", url, data=payload, files=files)
saveResponse(args.tts_wav, response)
elif args.mode == 'cross_lingual':
url = api + "/api/inference/cross-lingual"
payload={
'tts': args.tts_text,
}
files=[('audio', ('prompt_audio.wav', open(args.prompt_wav,'rb'), 'application/octet-stream'))]
response = requests.request("POST", url, data=payload, files=files)
saveResponse(args.tts_wav, response)
else:
url = api + "/api/inference/instruct"
payload = {
'tts': args.tts_text,
'role': args.spk_id,
'instruct': args.instruct_text
'tts_text': args.tts_text,
'spk_id': args.spk_id
}
response = requests.request("POST", url, data=payload)
saveResponse(args.tts_wav, response)
logging.info("Response save to {}", args.tts_wav)
response = requests.request("GET", url, data=payload, stream=True)
elif args.mode == 'zero_shot':
payload = {
'tts_text': args.tts_text,
'prompt_text': args.prompt_text
}
files = [('prompt_wav', ('prompt_wav', open(args.prompt_wav, 'rb'), 'application/octet-stream'))]
response = requests.request("GET", url, data=payload, files=files, stream=True)
elif args.mode == 'cross_lingual':
payload = {
'tts_text': args.tts_text,
}
files = [('prompt_wav', ('prompt_wav', open(args.prompt_wav,'rb'), 'application/octet-stream'))]
response = requests.request("GET", url, data=payload, files=files, stream=True)
else:
payload = {
'tts_text': args.tts_text,
'spk_id': args.spk_id,
'instruct_text': args.instruct_text
}
response = requests.request("GET", url, data=payload, stream=True)
tts_audio = b''
for r in response.iter_content(chunk_size=16000):
tts_audio += r
tts_speech = torch.from_numpy(np.array(np.frombuffer(tts_audio, dtype=np.int16))).unsqueeze(dim=0)
logging.info('save response to {}'.format(args.tts_wav))
torchaudio.save(args.tts_wav, tts_speech, target_sr)
logging.info('get response')
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument('--api_base',
parser.add_argument('--host',
type=str,
default='http://127.0.0.1:6006')
default='0.0.0.0')
parser.add_argument('--port',
type=int,
default='50000')
parser.add_argument('--mode',
default='sft',
choices=['sft', 'zero_shot', 'cross_lingual', 'instruct'],

162
runtime/python/fastapi/server.py
View File

@@ -1,119 +1,77 @@
# Set inference model
# export MODEL_DIR=pretrained_models/CosyVoice-300M-Instruct
# For development
# fastapi dev --port 6006 fastapi_server.py
# For production deployment
# fastapi run --port 6006 fastapi_server.py
# Copyright (c) 2024 Alibaba Inc (authors: Xiang Lyu)
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import sys
import io,time
from fastapi import FastAPI, Response, File, UploadFile, Form
from fastapi.responses import HTMLResponse
from fastapi.middleware.cors import CORSMiddleware #引入 CORS中间件模块
from contextlib import asynccontextmanager
ROOT_DIR = os.path.dirname(os.path.abspath(__file__))
sys.path.append('{}/../../..'.format(ROOT_DIR))
sys.path.append('{}/../../../third_party/Matcha-TTS'.format(ROOT_DIR))
from cosyvoice.cli.cosyvoice import CosyVoice
from cosyvoice.utils.file_utils import load_wav
import numpy as np
import torch
import torchaudio
import argparse
import logging
logging.getLogger('matplotlib').setLevel(logging.WARNING)
from fastapi import FastAPI, UploadFile, Form, File
from fastapi.responses import StreamingResponse
from fastapi.middleware.cors import CORSMiddleware
import uvicorn
import numpy as np
from cosyvoice.cli.cosyvoice import CosyVoice
from cosyvoice.utils.file_utils import load_wav
class LaunchFailed(Exception):
pass
@asynccontextmanager
async def lifespan(app: FastAPI):
model_dir = os.getenv("MODEL_DIR", "pretrained_models/CosyVoice-300M-SFT")
if model_dir:
logging.info("MODEL_DIR is {}", model_dir)
app.cosyvoice = CosyVoice(model_dir)
# sft usage
logging.info("Avaliable speakers {}", app.cosyvoice.list_avaliable_spks())
else:
raise LaunchFailed("MODEL_DIR environment must set")
yield
app = FastAPI(lifespan=lifespan)
#设置允许访问的域名
origins = ["*"] #"*"，即为所有,也可以改为允许的特定ip。
app = FastAPI()
# set cross region allowance
app.add_middleware(
CORSMiddleware,
allow_origins=origins, #设置允许的origins来源
allow_origins=["*"],
allow_credentials=True,
allow_methods=["*"], # 设置允许跨域的http方法，比如 get、post、put等。
allow_headers=["*"]) #允许跨域的headers，可以用来鉴别来源等作用。
allow_methods=["*"],
allow_headers=["*"])
def buildResponse(output):
buffer = io.BytesIO()
torchaudio.save(buffer, output, 22050, format="wav")
buffer.seek(0)
return Response(content=buffer.read(-1), media_type="audio/wav")
def generate_data(model_output):
for i in model_output:
tts_audio = (i['tts_speech'].numpy() * (2 ** 15)).astype(np.int16).tobytes()
yield tts_audio
@app.post("/api/inference/sft")
@app.get("/api/inference/sft")
async def sft(tts: str = Form(), role: str = Form()):
start = time.process_time()
output = app.cosyvoice.inference_sft(tts, role)
end = time.process_time()
logging.info("infer time is {} seconds", end-start)
return buildResponse(output['tts_speech'])
@app.get("/inference_sft")
async def inference_sft(tts_text: str = Form(), spk_id: str = Form()):
model_output = cosyvoice.inference_sft(tts_text, spk_id)
return StreamingResponse(generate_data(model_output))
@app.post("/api/inference/zero-shot")
async def zeroShot(tts: str = Form(), prompt: str = Form(), audio: UploadFile = File()):
start = time.process_time()
prompt_speech = load_wav(audio.file, 16000)
prompt_audio = (prompt_speech.numpy() * (2**15)).astype(np.int16).tobytes()
prompt_speech_16k = torch.from_numpy(np.array(np.frombuffer(prompt_audio, dtype=np.int16))).unsqueeze(dim=0)
prompt_speech_16k = prompt_speech_16k.float() / (2**15)
@app.get("/inference_zero_shot")
async def inference_zero_shot(tts_text: str = Form(), prompt_text: str = Form(), prompt_wav: UploadFile = File()):
prompt_speech_16k = load_wav(prompt_wav.file, 16000)
model_output = cosyvoice.inference_zero_shot(tts_text, prompt_text, prompt_speech_16k)
return StreamingResponse(generate_data(model_output))
output = app.cosyvoice.inference_zero_shot(tts, prompt, prompt_speech_16k)
end = time.process_time()
logging.info("infer time is {} seconds", end-start)
return buildResponse(output['tts_speech'])
@app.get("/inference_cross_lingual")
async def inference_cross_lingual(tts_text: str = Form(), prompt_wav: UploadFile = File()):
prompt_speech_16k = load_wav(prompt_wav.file, 16000)
model_output = cosyvoice.inference_cross_lingual(tts_text, prompt_speech_16k)
return StreamingResponse(generate_data(model_output))
@app.post("/api/inference/cross-lingual")
async def crossLingual(tts: str = Form(), audio: UploadFile = File()):
start = time.process_time()
prompt_speech = load_wav(audio.file, 16000)
prompt_audio = (prompt_speech.numpy() * (2**15)).astype(np.int16).tobytes()
prompt_speech_16k = torch.from_numpy(np.array(np.frombuffer(prompt_audio, dtype=np.int16))).unsqueeze(dim=0)
prompt_speech_16k = prompt_speech_16k.float() / (2**15)
@app.get("/inference_instruct")
async def inference_instruct(tts_text: str = Form(), spk_id: str = Form(), instruct_text: str = Form()):
model_output = cosyvoice.inference_instruct(tts_text, spk_id, instruct_text)
return StreamingResponse(generate_data(model_output))
output = app.cosyvoice.inference_cross_lingual(tts, prompt_speech_16k)
end = time.process_time()
logging.info("infer time is {} seconds", end-start)
return buildResponse(output['tts_speech'])
@app.post("/api/inference/instruct")
@app.get("/api/inference/instruct")
async def instruct(tts: str = Form(), role: str = Form(), instruct: str = Form()):
start = time.process_time()
output = app.cosyvoice.inference_instruct(tts, role, instruct)
end = time.process_time()
logging.info("infer time is {} seconds", end-start)
return buildResponse(output['tts_speech'])
@app.get("/api/roles")
async def roles():
return {"roles": app.cosyvoice.list_avaliable_spks()}
@app.get("/", response_class=HTMLResponse)
async def root():
return """
<!DOCTYPE html>
<html lang=zh-cn>
<head>
<meta charset=utf-8>
<title>Api information</title>
</head>
<body>
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>
</body>
</html>
"""
if __name__=='__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--port',
type=int,
default=50000)
parser.add_argument('--model_dir',
type=str,
default='iic/CosyVoice-300M',
help='local path or modelscope repo id')
args = parser.parse_args()
cosyvoice = CosyVoice(args.model_dir)
uvicorn.run(app, host="127.0.0.1", port=args.port)