herta-so-vits / 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 re
import json
import subprocess
import edge_tts
import asyncio
from scipy.io import wavfile
import librosa
import torch
import time
import traceback
from itertools import chain
from utils import mix_model
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()):
device_name = torch.cuda.get_device_properties(i).name
cuda[f"CUDA:{i} {device_name}"] = f"cuda:{i}"
def upload_mix_append_file(files,sfiles):
try:
if(sfiles == None):
file_paths = [file.name for file in files]
else:
file_paths = [file.name for file in chain(files,sfiles)]
p = {file:100 for file in file_paths}
return file_paths,mix_model_output1.update(value=json.dumps(p,indent=2))
except Exception as e:
if debug: traceback.print_exc()
raise gr.Error(e)
def mix_submit_click(js,mode):
try:
assert js.lstrip()!=""
modes = {"凸组合":0, "线性组合":1}
mode = modes[mode]
data = json.loads(js)
data = list(data.items())
model_path,mix_rate = zip(*data)
path = mix_model(model_path,mix_rate,mode)
return f"成功,文件被保存在了{path}"
except Exception as e:
if debug: traceback.print_exc()
raise gr.Error(e)
def updata_mix_info(files):
try:
if files == None : return mix_model_output1.update(value="")
p = {file.name:100 for file in files}
return mix_model_output1.update(value=json.dumps(p,indent=2))
except Exception as e:
if debug: traceback.print_exc()
raise gr.Error(e)
def modelAnalysis(model_path,config_path,cluster_model_path,device,enhance):
global model
try:
device = cuda[device] if "CUDA" in device else device
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)
msg = f"成功加载模型到设备{device_name}上\n"
if cluster_model_path is None:
msg += "未加载聚类模型\n"
else:
msg += f"聚类模型{cluster_model_path.name}加载成功\n"
msg += "当前模型的可用音色:\n"
for i in spks:
msg += i + " "
return sid.update(choices = spks,value=spks[0]), msg
except Exception as e:
if debug: traceback.print_exc()
raise gr.Error(e)
def modelUnload():
global model
if model is None:
return sid.update(choices = [],value=""),"没有模型需要卸载!"
else:
model.unload_model()
model = None
torch.cuda.empty_cache()
return sid.update(choices = [],value=""),"模型卸载完毕!"
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,cr_threshold):
global model
try:
if input_audio is None:
raise gr.Error("你需要上传音频")
if model is None:
raise gr.Error("你需要指定模型")
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,cr_threshold)
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()))
filename = sid + "_" + timestamp + ".wav"
output_file = os.path.join("./results", filename)
soundfile.write(output_file, _audio, model.target_sample, format="wav")
return f"推理成功,音频文件保存为results/{filename}", (model.target_sample, _audio)
except Exception as e:
if debug: traceback.print_exc()
return f"文件保存失败,请手动保存", (model.target_sample, _audio)
except Exception as e:
if debug: traceback.print_exc()
raise gr.Error(e)
def tts_func(_text,_rate,_voice):
#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
if ( _voice == "女" ) : voice = "zh-CN-XiaoyiNeural"
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 text_clear(text):
return re.sub(r"[\n\,\(\) ]", "", text)
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,tts_voice,F0_mean_pooling,enhancer_adaptive_key,cr_threshold):
#使用edge-tts把文字转成音频
text2tts=text_clear(text2tts)
output_file=tts_func(text2tts,tts_rate,tts_voice)
#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,cr_threshold)
os.remove(output_file)
os.remove(save_path2)
return a,b
def debug_change():
global debug
debug = debug_button.value
with gr.Blocks(
theme=gr.themes.Base(
primary_hue = gr.themes.colors.green,
font=["Source Sans Pro", "Arial", "sans-serif"],
font_mono=['JetBrains mono', "Consolas", 'Courier New']
),
) as app:
with gr.Tabs():
with gr.TabItem("推理"):
gr.Markdown(value="""
So-vits-svc 4.0 推理 webui
""")
with gr.Row(variant="panel"):
with gr.Column():
gr.Markdown(value="""
<font size=2> 模型设置</font>
""")
model_path = gr.File(label="选择模型文件")
config_path = gr.File(label="选择配置文件")
cluster_model_path = gr.File(label="选择聚类模型文件(没有可以不选)")
device = gr.Dropdown(label="推理设备,默认为自动选择CPU和GPU", choices=["Auto",*cuda.keys(),"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)
with gr.Column():
gr.Markdown(value="""
<font size=3>左侧文件全部选择完毕后(全部文件模块显示download),点击“加载模型”进行解析:</font>
""")
model_load_button = gr.Button(value="加载模型", variant="primary")
model_unload_button = gr.Button(value="卸载模型", variant="primary")
sid = gr.Dropdown(label="音色(说话人)")
sid_output = gr.Textbox(label="Output Message")
with gr.Row(variant="panel"):
with gr.Column():
gr.Markdown(value="""
<font size=2> 推理设置</font>
""")
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)
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,0即不启用聚类。使用聚类模型能提升音色相似度,但会导致咬字下降(如果使用建议0.5左右)", value=0)
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)
with gr.Column():
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)
cl_num = gr.Number(label="音频自动切片,0为不切片,单位为秒(s)", value=0)
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)
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)
cr_threshold = gr.Number(label="F0过滤阈值,只有启动f0_mean_pooling时有效. 数值范围从0-1. 降低该值可减少跑调概率,但会增加哑音", value=0.05)
with gr.Tabs():
with gr.TabItem("音频转音频"):
vc_input3 = gr.Audio(label="选择音频")
vc_submit = gr.Button("音频转换", variant="primary")
with gr.TabItem("文字转音频"):
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)
tts_voice = gr.Radio(label="性别",choices=["男","女"], value="男")
vc_submit2 = gr.Button("文字转换", variant="primary")
with gr.Row():
with gr.Column():
vc_output1 = gr.Textbox(label="Output Message")
with gr.Column():
vc_output2 = gr.Audio(label="Output Audio", interactive=False)
with gr.TabItem("小工具/实验室特性"):
gr.Markdown(value="""
<font size=2> So-vits-svc 4.0 小工具/实验室特性</font>
""")
with gr.Tabs():
with gr.TabItem("静态声线融合"):
gr.Markdown(value="""
<font size=2> 介绍:该功能可以将多个声音模型合成为一个声音模型(多个模型参数的凸组合或线性组合),从而制造出现实中不存在的声线
注意:
1.该功能仅支持单说话人的模型
2.如果强行使用多说话人模型,需要保证多个模型的说话人数量相同,这样可以混合同一个SpaekerID下的声音
3.保证所有待混合模型的config.json中的model字段是相同的
4.输出的混合模型可以使用待合成模型的任意一个config.json,但聚类模型将不能使用
5.批量上传模型的时候最好把模型放到一个文件夹选中后一起上传
6.混合比例调整建议大小在0-100之间,也可以调为其他数字,但在线性组合模式下会出现未知的效果
7.混合完毕后,文件将会保存在项目根目录中,文件名为output.pth
8.凸组合模式会将混合比例执行Softmax使混合比例相加为1,而线性组合模式不会
</font>
""")
mix_model_path = gr.Files(label="选择需要混合模型文件")
mix_model_upload_button = gr.UploadButton("选择/追加需要混合模型文件", file_count="multiple", variant="primary")
mix_model_output1 = gr.Textbox(
label="混合比例调整,单位/%",
interactive = True
)
mix_mode = gr.Radio(choices=["凸组合", "线性组合"], label="融合模式",value="凸组合",interactive = True)
mix_submit = gr.Button("声线融合启动", variant="primary")
mix_model_output2 = gr.Textbox(
label="Output Message"
)
mix_model_path.change(updata_mix_info,[mix_model_path],[mix_model_output1])
mix_model_upload_button.upload(upload_mix_append_file, [mix_model_upload_button,mix_model_path], [mix_model_path,mix_model_output1])
mix_submit.click(mix_submit_click, [mix_model_output1,mix_mode], [mix_model_output2])
with gr.Tabs():
with gr.Row(variant="panel"):
with gr.Column():
gr.Markdown(value="""
<font size=2> WebUI设置</font>
""")
debug_button = gr.Checkbox(label="Debug模式,如果向社区反馈BUG需要打开,打开后控制台可以显示具体错误提示", value=debug)
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,cr_threshold], [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,tts_voice,F0_mean_pooling,enhancer_adaptive_key,cr_threshold], [vc_output1, vc_output2])
debug_button.change(debug_change,[],[])
model_load_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()