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webUI.py
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webUI.py
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import io
import os
# os.system("wget -P cvec/ https://huggingface.co/spaces/innnky/nanami/resolve/main/checkpoint_best_legacy_500.pt")
import gradio as gr
import gradio.processing_utils as gr_pu
import librosa
import numpy as np
import soundfile
from inference.infer_tool import Svc
import logging
import traceback
import subprocess
import edge_tts
import asyncio
from scipy.io import wavfile
import librosa
import torch
import time
logging.getLogger('numba').setLevel(logging.WARNING)
logging.getLogger('markdown_it').setLevel(logging.WARNING)
logging.getLogger('urllib3').setLevel(logging.WARNING)
logging.getLogger('matplotlib').setLevel(logging.WARNING)
logging.getLogger('multipart').setLevel(logging.WARNING)
model = None
spk = None
debug=False
cuda = []
if torch.cuda.is_available():
for i in range(torch.cuda.device_count()):
cuda.append("cuda:{}".format(i))
def vc_fn(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,F0_mean_pooling,enhancer_adaptive_key):
global model
try:
if input_audio is None:
return "You need to upload an audio", None
if model is None:
return "You need to upload an model", None
sampling_rate, audio = input_audio
# print(audio.shape,sampling_rate)
audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
if len(audio.shape) > 1:
audio = librosa.to_mono(audio.transpose(1, 0))
temp_path = "temp.wav"
soundfile.write(temp_path, audio, sampling_rate, format="wav")
_audio = model.slice_inference(temp_path, sid, vc_transform, slice_db, cluster_ratio, auto_f0, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,F0_mean_pooling,enhancer_adaptive_key)
model.clear_empty()
os.remove(temp_path)
#构建保存文件的路径，并保存到results文件夹内
try:
timestamp = str(int(time.time()))
output_file = os.path.join("./results", sid + "_" + timestamp + ".wav")
soundfile.write(output_file, _audio, model.target_sample, format="wav")
return "Success", (model.target_sample, _audio)
except Exception as e:
if debug:traceback.print_exc()
return "自动保存失败，请手动保存，音乐输出见下", (model.target_sample, _audio)
except Exception as e:
if debug:traceback.print_exc()
return "异常信息:"+str(e)+"\n请排障后重试",None
def tts_func(_text,_rate):
#使用edge-tts把文字转成音频
# voice = "zh-CN-XiaoyiNeural"#女性，较高音
# voice = "zh-CN-YunxiNeural"#男性
voice = "zh-CN-YunxiNeural"#男性
output_file = _text[0:10]+".wav"
# communicate = edge_tts.Communicate(_text, voice)
# await communicate.save(output_file)
if _rate>=0:
ratestr="+{:.0%}".format(_rate)
elif _rate<0:
ratestr="{:.0%}".format(_rate)#减号自带
p=subprocess.Popen(["edge-tts",
"--text",_text,
"--write-media",output_file,
"--voice",voice,
"--rate="+ratestr]
,shell=True,
stdout=subprocess.PIPE,
stdin=subprocess.PIPE)
p.wait()
return output_file
def vc_fn2(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,text2tts,tts_rate,F0_mean_pooling,enhancer_adaptive_key):
#使用edge-tts把文字转成音频
output_file=tts_func(text2tts,tts_rate)
#调整采样率
sr2=44100
wav, sr = librosa.load(output_file)
wav2 = librosa.resample(wav, orig_sr=sr, target_sr=sr2)
save_path2= text2tts[0:10]+"_44k"+".wav"
wavfile.write(save_path2,sr2,
(wav2 * np.iinfo(np.int16).max).astype(np.int16)
)
#读取音频
sample_rate, data=gr_pu.audio_from_file(save_path2)
vc_input=(sample_rate, data)
a,b=vc_fn(sid, vc_input, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,F0_mean_pooling,enhancer_adaptive_key)
os.remove(output_file)
os.remove(save_path2)
return a,b
app = gr.Blocks()
with app:
with gr.Tabs():
with gr.TabItem("Sovits4.0"):
gr.Markdown(value="""
Sovits4.0 WebUI
""")
gr.Markdown(value="""
<font size=3>下面是模型文件选择：</font>
""")
model_path = gr.File(label="模型文件")
gr.Markdown(value="""
<font size=3>下面是配置文件选择：</font>
""")
config_path = gr.File(label="配置文件")
gr.Markdown(value="""
<font size=3>下面是聚类模型文件选择，没有可以不填：</font>
""")
cluster_model_path = gr.File(label="聚类模型文件")
device = gr.Dropdown(label="推理设备，默认为自动选择cpu和gpu",choices=["Auto",*cuda,"cpu"],value="Auto")
enhance = gr.Checkbox(label="是否使用NSF_HIFIGAN增强,该选项对部分训练集少的模型有一定的音质增强效果，但是对训练好的模型有反面效果，默认关闭", value=False)
gr.Markdown(value="""
<font size=3>全部上传完毕后(全部文件模块显示download),点击模型解析进行解析：</font>
""")
model_analysis_button = gr.Button(value="模型解析")
model_unload_button = gr.Button(value="模型卸载")
sid = gr.Dropdown(label="音色（说话人）")
sid_output = gr.Textbox(label="Output Message")
text2tts=gr.Textbox(label="在此输入要转译的文字。注意，使用该功能建议打开F0预测，不然会很怪")
tts_rate = gr.Number(label="tts语速", value=0)
vc_input3 = gr.Audio(label="上传音频")
vc_transform = gr.Number(label="变调（整数，可以正负，半音数量，升高八度就是12）", value=0)
cluster_ratio = gr.Number(label="聚类模型混合比例，0-1之间，默认为0不启用聚类，能提升音色相似度，但会导致咬字下降（如果使用建议0.5左右）", value=0)
auto_f0 = gr.Checkbox(label="自动f0预测，配合聚类模型f0预测效果更好,会导致变调功能失效（仅限转换语音，歌声不要勾选此项会究极跑调）", value=False)
F0_mean_pooling = gr.Checkbox(label="是否对F0使用均值滤波器(池化)，对部分哑音有改善。注意，启动该选项会导致推理速度下降，默认关闭", value=False)
slice_db = gr.Number(label="切片阈值", value=-40)
noise_scale = gr.Number(label="noise_scale 建议不要动，会影响音质，玄学参数", value=0.4)
cl_num = gr.Number(label="音频自动切片，0为不切片，单位为秒/s", value=0)
pad_seconds = gr.Number(label="推理音频pad秒数，由于未知原因开头结尾会有异响，pad一小段静音段后就不会出现", value=0.5)
lg_num = gr.Number(label="两端音频切片的交叉淡入长度，如果自动切片后出现人声不连贯可调整该数值，如果连贯建议采用默认值0，注意，该设置会影响推理速度，单位为秒/s", value=0)
lgr_num = gr.Number(label="自动音频切片后，需要舍弃每段切片的头尾。该参数设置交叉长度保留的比例，范围0-1,左开右闭", value=0.75,interactive=True)
enhancer_adaptive_key = gr.Number(label="使增强器适应更高的音域(单位为半音数)|默认为0", value=0,interactive=True)
vc_submit = gr.Button("音频直接转换", variant="primary")
vc_submit2 = gr.Button("文字转音频+转换", variant="primary")
vc_output1 = gr.Textbox(label="Output Message")
vc_output2 = gr.Audio(label="Output Audio")
def modelAnalysis(model_path,config_path,cluster_model_path,device,enhance):
global model
try:
model = Svc(model_path.name, config_path.name,device=device if device!="Auto" else None,cluster_model_path= cluster_model_path.name if cluster_model_path!=None else "",nsf_hifigan_enhance=enhance)
spks = list(model.spk2id.keys())
device_name = torch.cuda.get_device_properties(model.dev).name if "cuda" in str(model.dev) else str(model.dev)
return sid.update(choices = spks,value=spks[0]),"ok,模型被加载到了设备{}之上".format(device_name)
except Exception as e:
if debug:traceback.print_exc()
return "","异常信息:"+str(e)+"\n请排障后重试"
def modelUnload():
global model
if model is None:
return sid.update(choices = [],value=""),"没有模型需要卸载!"
else:
model = None
torch.cuda.empty_cache()
return sid.update(choices = [],value=""),"模型卸载完毕!"
vc_submit.click(vc_fn, [sid, vc_input3, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,F0_mean_pooling,enhancer_adaptive_key], [vc_output1, vc_output2])
vc_submit2.click(vc_fn2, [sid, vc_input3, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,text2tts,tts_rate,F0_mean_pooling,enhancer_adaptive_key], [vc_output1, vc_output2])
model_analysis_button.click(modelAnalysis,[model_path,config_path,cluster_model_path,device,enhance],[sid,sid_output])
model_unload_button.click(modelUnload,[],[sid,sid_output])
app.launch()