so-vits-svc/webUI.py

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import json
import logging
import os
import re
import subprocess
import time
import traceback
from itertools import chain
from pathlib import Path
# os.system("wget -P cvec/ https://huggingface.co/spaces/innnky/nanami/resolve/main/checkpoint_best_legacy_500.pt")
import gradio as gr
import librosa
import numpy as np
import soundfile
import torch
from compress_model import removeOptimizer
from edgetts.tts_voices import SUPPORTED_LANGUAGES
from inference.infer_tool import Svc
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 is 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 is 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,diff_model_path,diff_config_path,only_diffusion,use_spk_mix):
global model
try:
device = cuda[device] if "CUDA" in device else device
cluster_filepath = os.path.split(cluster_model_path.name) if cluster_model_path is not None else "no_cluster"
fr = ".pkl" in cluster_filepath[1]
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 is not None else "",
nsf_hifigan_enhance=enhance,
diffusion_model_path = diff_model_path.name if diff_model_path is not None else "",
diffusion_config_path = diff_config_path.name if diff_config_path is not None else "",
shallow_diffusion = True if diff_model_path is not None else False,
only_diffusion = only_diffusion,
spk_mix_enable = use_spk_mix,
feature_retrieval = fr
)
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}\n"
if cluster_model_path is None:
msg += "未加载聚类模型或特征检索模型\n"
elif fr:
msg += f"特征检索模型{cluster_filepath[1]}加载成功\n"
else:
msg += f"聚类模型{cluster_filepath[1]}加载成功\n"
if diff_model_path is None:
msg += "未加载扩散模型\n"
else:
msg += f"扩散模型{diff_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_infer(output_format, sid, audio_path, truncated_basename, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold, k_step, use_spk_mix, second_encoding, loudness_envelope_adjustment):
global model
_audio = model.slice_inference(
audio_path,
sid,
vc_transform,
slice_db,
cluster_ratio,
auto_f0,
noise_scale,
pad_seconds,
cl_num,
lg_num,
lgr_num,
f0_predictor,
enhancer_adaptive_key,
cr_threshold,
k_step,
use_spk_mix,
second_encoding,
loudness_envelope_adjustment
)
model.clear_empty()
#构建保存文件的路径并保存到results文件夹内
str(int(time.time()))
if not os.path.exists("results"):
os.makedirs("results")
key = "auto" if auto_f0 else f"{int(vc_transform)}key"
cluster = "_" if cluster_ratio == 0 else f"_{cluster_ratio}_"
isdiffusion = "sovits"
if model.shallow_diffusion:
isdiffusion = "sovdiff"
if model.only_diffusion:
isdiffusion = "diff"
output_file_name = 'result_'+truncated_basename+f'_{sid}_{key}{cluster}{isdiffusion}.{output_format}'
output_file = os.path.join("results", output_file_name)
soundfile.write(output_file, _audio, model.target_sample, format=output_format)
return output_file
def vc_fn(sid, input_audio, output_format, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment):
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
if getattr(model, 'cluster_model', None) is None and model.feature_retrieval is False:
if cluster_ratio != 0:
return "You need to upload an cluster model or feature retrieval model before assigning cluster ratio!", None
#print(input_audio)
audio, sampling_rate = soundfile.read(input_audio)
#print(audio.shape,sampling_rate)
if np.issubdtype(audio.dtype, np.integer):
audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
#print(audio.dtype)
if len(audio.shape) > 1:
audio = librosa.to_mono(audio.transpose(1, 0))
# 未知原因Gradio上传的filepath会有一个奇怪的固定后缀这里去掉
truncated_basename = Path(input_audio).stem[:-6]
processed_audio = os.path.join("raw", f"{truncated_basename}.wav")
soundfile.write(processed_audio, audio, sampling_rate, format="wav")
output_file = vc_infer(output_format, sid, processed_audio, truncated_basename, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold, k_step, use_spk_mix, second_encoding, loudness_envelope_adjustment)
return "Success", output_file
except Exception as e:
if debug:
traceback.print_exc()
raise gr.Error(e)
def text_clear(text):
return re.sub(r"[\n\,\(\) ]", "", text)
def vc_fn2(_text, _lang, _gender, _rate, _volume, sid, output_format, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold, k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment):
global model
try:
if model is None:
return "You need to upload an model", None
if getattr(model, 'cluster_model', None) is None and model.feature_retrieval is False:
if cluster_ratio != 0:
return "You need to upload an cluster model or feature retrieval model before assigning cluster ratio!", None
_rate = f"+{int(_rate*100)}%" if _rate >= 0 else f"{int(_rate*100)}%"
_volume = f"+{int(_volume*100)}%" if _volume >= 0 else f"{int(_volume*100)}%"
if _lang == "Auto":
_gender = "Male" if _gender == "" else "Female"
subprocess.run([r"python", "edgetts/tts.py", _text, _lang, _rate, _volume, _gender])
else:
subprocess.run([r"python", "edgetts/tts.py", _text, _lang, _rate, _volume])
target_sr = 44100
y, sr = librosa.load("tts.wav")
resampled_y = librosa.resample(y, orig_sr=sr, target_sr=target_sr)
soundfile.write("tts.wav", resampled_y, target_sr, subtype = "PCM_16")
input_audio = "tts.wav"
#audio, _ = soundfile.read(input_audio)
output_file_path = vc_infer(output_format, sid, input_audio, "tts", vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold, k_step, use_spk_mix, second_encoding, loudness_envelope_adjustment)
os.remove("tts.wav")
return "Success", output_file_path
except Exception as e:
if debug: traceback.print_exc() # noqa: E701
raise gr.Error(e)
def model_compression(_model):
if _model == "":
return "请先选择要压缩的模型"
else:
model_path = os.path.split(_model.name)
filename, extension = os.path.splitext(model_path[1])
output_model_name = f"{filename}_compressed{extension}"
output_path = os.path.join(os.getcwd(), output_model_name)
removeOptimizer(_model.name, output_path)
return f"模型已成功被保存在了{output_path}"
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>
""")
with gr.Row():
model_path = gr.File(label="选择模型文件")
config_path = gr.File(label="选择配置文件")
with gr.Row():
diff_model_path = gr.File(label="选择扩散模型文件")
diff_config_path = gr.File(label="选择扩散模型配置文件")
cluster_model_path = gr.File(label="选择聚类模型或特征检索文件(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)
only_diffusion = gr.Checkbox(label="是否使用全扩散推理开启后将不使用So-VITS模型仅使用扩散模型进行完整扩散推理默认关闭", 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_predictor = gr.Dropdown(label="选择F0预测器,可选择crepe,pm,dio,harvest,默认为pm(注意crepe为原F0使用均值滤波器)", choices=["pm","dio","harvest","crepe"], value="pm")
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/特征检索混合比例0-1之间0即不启用聚类/特征检索。使用聚类/特征检索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)
output_format = gr.Radio(label="音频输出格式", choices=["wav", "flac", "mp3"], value = "wav")
noise_scale = gr.Number(label="noise_scale 建议不要动,会影响音质,玄学参数", value=0.4)
k_step = gr.Slider(label="浅扩散步数,只有使用了扩散模型才有效,步数越大越接近扩散模型的结果", value=100, minimum = 1, maximum = 1000)
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crepe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)
loudness_envelope_adjustment = gr.Number(label="输入源响度包络替换输出响度包络融合比例越靠近1越使用输出响度包络", value = 0)
second_encoding = gr.Checkbox(label = "二次编码,浅扩散前会对原始音频进行二次编码,玄学选项,效果时好时差,默认关闭", value=False)
use_spk_mix = gr.Checkbox(label = "动态声线融合", value = False, interactive = False)
with gr.Tabs():
with gr.TabItem("音频转音频"):
vc_input3 = gr.Audio(label="选择音频", type="filepath")
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")
with gr.Row():
tts_gender = gr.Radio(label = "说话人性别", choices = ["",""], value = "")
tts_lang = gr.Dropdown(label = "选择语言Auto为根据输入文字自动识别", choices=SUPPORTED_LANGUAGES, value = "Auto")
tts_rate = gr.Slider(label = "TTS语音变速倍速相对值", minimum = -1, maximum = 3, value = 0, step = 0.1)
tts_volume = gr.Slider(label = "TTS语音音量相对值", minimum = -1, maximum = 1.5, value = 0, step = 0.1)
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")
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.TabItem("模型压缩工具"):
gr.Markdown(value="""
该工具可以实现对模型的体积压缩,在**不影响模型推理功能**的情况下将原本约600M的So-VITS模型压缩至约200M, 大大减少了硬盘的压力。
**注意:压缩后的模型将无法继续训练,请在确认封炉后再压缩。**
""")
model_to_compress = gr.File(label="模型上传")
compress_model_btn = gr.Button("压缩模型", variant="primary")
compress_model_output = gr.Textbox(label="输出信息", value="")
compress_model_btn.click(model_compression, [model_to_compress], [compress_model_output])
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, output_format, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment], [vc_output1, vc_output2])
vc_submit2.click(vc_fn2, [text2tts, tts_lang, tts_gender, tts_rate, tts_volume, sid, output_format, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment], [vc_output1, vc_output2])
debug_button.change(debug_change,[],[])
model_load_button.click(modelAnalysis,[model_path,config_path,cluster_model_path,device,enhance,diff_model_path,diff_config_path,only_diffusion,use_spk_mix],[sid,sid_output])
model_unload_button.click(modelUnload,[],[sid,sid_output])
os.system("start http://127.0.0.1:7860")
app.launch()