TTS-RVC-API
是的，我们可以将Coqui与RVC一起使用！
为什么将这两个框架结合在一起？
Coqui是文本到语音框架（Vocoder和编码器），但是克隆自己的声音需要数十年，并且不能保证更好的结果。这就是为什么我们使用RVC（基于检索的语音转换），这仅适用于语音到语音。您可以使用Hubert（一种预先训练的模型快速微调并提供更好的结果），因此只需2-3分钟的数据集训练模型。
安装
如何使用Coqui + RVC API？
https : // github . com / skshadan / TTS - RVC - API . git python - m venv . venv
. . venv / bin / activate
pip install - r requirements . txt
pip install TTS
python - m uvicorn app . main : app现在，用相对路径更新config.toml model_dir路径或在请求主体中设置speaker_name
RVC V2模型安装在容器上的位置：
/
└── models
└── speaker1
├── speaker1 . pth
└── speaker1 . index现在运行此
python - m uvicorn app . main : app 发表请求
http : // localhost : 8000 / generate emotions : happy , sad , angry , dull
speed = 1.0 - 2.0 {
"speaker_name" : "speaker3" ,
"input_text" : "Hey there! Welcome to the world" ,
"emotion" : "Surprise" ,
"speed" : 1.0
}代码段
import requests
import json
import time
url = "http://127.*0*.*0.1:8000/generate"
payload = json . dumps ({
"speaker_name" : "speaker3" ,
"input_text" : "Are you mad? The way you've betrayed me is beyond comprehension, a slap in the face that's left me boiling with an anger so intense it's as if you've thrown gasoline on a fire, utterly destroying any trust that was left." ,
"emotion" : "Dull" ,
"speed" : 1.0
})
headers = {
'Content-Type' : 'application/json'
}
start_time = time . time () # Start the timer
response = requests . request ( "POST" , url , headers = headers , data = payload )
end_time = time . time () # Stop the timer
if response . status_code == 200 :
audio_content = response . content
# Save the audio to a file
with open ( "generated_audio.wav" , "wb" ) as audio_file :
audio_file . write ( audio_content )
print ( "Audio saved successfully." )
print ( "Time taken:" , end_time - start_time , "seconds" )
else :
print ( "Error:" , response . text )反馈
如果您有任何反馈，请联系shadankhantech@gmail.com
下载源码
通过命令行克隆项目:
git clone https://github.com/skshadan/TTS-RVC-API.git