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import os
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import traceback,gradio as gr
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import logging
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from tools.i18n.i18n import I18nAuto
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from tools.my_utils import clean_path
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i18n = I18nAuto()
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logger = logging.getLogger(__name__)
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import librosa,ffmpeg
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import soundfile as sf
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import torch
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import sys
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from mdxnet import MDXNetDereverb
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from vr import AudioPre, AudioPreDeEcho
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from bsroformer import BsRoformer_Loader
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try:
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import gradio.analytics as analytics
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analytics.version_check = lambda:None
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except:...
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weight_uvr5_root = "tools/uvr5/uvr5_weights"
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uvr5_names = []
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for name in os.listdir(weight_uvr5_root):
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if name.endswith(".pth") or name.endswith(".ckpt") or "onnx" in name:
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uvr5_names.append(name.replace(".pth", "").replace(".ckpt", ""))
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device=sys.argv[1]
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is_half=eval(sys.argv[2])
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webui_port_uvr5=int(sys.argv[3])
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is_share=eval(sys.argv[4])
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def html_left(text, label='p'):
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return f"""<div style="text-align: left; margin: 0; padding: 0;">
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<{label} style="margin: 0; padding: 0;">{text}</{label}>
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</div>"""
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def html_center(text, label='p'):
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return f"""<div style="text-align: center; margin: 100; padding: 50;">
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<{label} style="margin: 0; padding: 0;">{text}</{label}>
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</div>"""
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def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format0):
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infos = []
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try:
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inp_root = clean_path(inp_root)
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save_root_vocal = clean_path(save_root_vocal)
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save_root_ins = clean_path(save_root_ins)
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is_hp3 = "HP3" in model_name
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if model_name == "onnx_dereverb_By_FoxJoy":
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pre_fun = MDXNetDereverb(15)
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elif model_name == "Bs_Roformer" or "bs_roformer" in model_name.lower():
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func = BsRoformer_Loader
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pre_fun = func(
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model_path = os.path.join(weight_uvr5_root, model_name + ".ckpt"),
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device = device,
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is_half=is_half
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)
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else:
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func = AudioPre if "DeEcho" not in model_name else AudioPreDeEcho
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pre_fun = func(
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agg=int(agg),
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model_path=os.path.join(weight_uvr5_root, model_name + ".pth"),
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device=device,
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is_half=is_half,
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)
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if inp_root != "":
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paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)]
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else:
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paths = [path.name for path in paths]
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for path in paths:
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inp_path = os.path.join(inp_root, path)
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if(os.path.isfile(inp_path)==False):continue
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need_reformat = 1
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done = 0
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try:
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info = ffmpeg.probe(inp_path, cmd="ffprobe")
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if (
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info["streams"][0]["channels"] == 2
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and info["streams"][0]["sample_rate"] == "44100"
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):
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need_reformat = 0
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pre_fun._path_audio_(
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inp_path, save_root_ins, save_root_vocal, format0,is_hp3
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)
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done = 1
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except:
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need_reformat = 1
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traceback.print_exc()
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if need_reformat == 1:
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tmp_path = "%s/%s.reformatted.wav" % (
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os.path.join(os.environ["TEMP"]),
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os.path.basename(inp_path),
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)
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os.system(
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f'ffmpeg -i "{inp_path}" -vn -acodec pcm_s16le -ac 2 -ar 44100 "{tmp_path}" -y'
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)
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inp_path = tmp_path
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try:
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if done == 0:
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pre_fun._path_audio_(
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inp_path, save_root_ins, save_root_vocal, format0,is_hp3
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)
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infos.append("%s->Success" % (os.path.basename(inp_path)))
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yield "\n".join(infos)
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except:
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infos.append(
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"%s->%s" % (os.path.basename(inp_path), traceback.format_exc())
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)
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yield "\n".join(infos)
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except:
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infos.append(traceback.format_exc())
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yield "\n".join(infos)
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finally:
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try:
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if model_name == "onnx_dereverb_By_FoxJoy":
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del pre_fun.pred.model
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del pre_fun.pred.model_
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else:
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del pre_fun.model
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del pre_fun
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except:
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traceback.print_exc()
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print("clean_empty_cache")
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if torch.cuda.is_available():
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torch.cuda.empty_cache()
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yield "\n".join(infos)
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with gr.Blocks(title="UVR5 WebUI") as app:
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gr.Markdown(
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value=
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i18n("本软件以MIT协议开源, 作者不对软件具备任何控制力, 使用软件者、传播软件导出的声音者自负全责. <br>如不认可该条款, 则不能使用或引用软件包内任何代码和文件. 详见根目录<b>LICENSE</b>.")
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)
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with gr.Group():
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gr.Markdown(html_center(i18n("伴奏人声分离&去混响&去回声"),'h2'))
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with gr.Group():
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gr.Markdown(
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value=html_left(i18n("人声伴奏分离批量处理， 使用UVR5模型。") + "<br>" + \
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i18n("合格的文件夹路径格式举例： E:\\codes\\py39\\vits_vc_gpu\\白鹭霜华测试样例(去文件管理器地址栏拷就行了)。")+ "<br>" + \
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i18n("模型分为三类：") + "<br>" + \
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i18n("1、保留人声：不带和声的音频选这个，对主人声保留比HP5更好。内置HP2和HP3两个模型，HP3可能轻微漏伴奏但对主人声保留比HP2稍微好一丁点；") + "<br>" + \
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i18n("2、仅保留主人声：带和声的音频选这个，对主人声可能有削弱。内置HP5一个模型；") + "<br>" + \
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i18n("3、去混响、去延迟模型（by FoxJoy）：") + "<br> " + \
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i18n("(1)MDX-Net(onnx_dereverb):对于双通道混响是最好的选择，不能去除单通道混响；") + "<br> " + \
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i18n("(234)DeEcho:去除延迟效果。Aggressive比Normal去除得更彻底，DeReverb额外去除混响，可去除单声道混响，但是对高频重的板式混响去不干净。") + "<br>" + \
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i18n("去混响/去延迟，附：") + "<br>" + \
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i18n("1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；") + "<br>" + \
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i18n("2、MDX-Net-Dereverb模型挺慢的；") + "<br>" + \
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i18n("3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。"),'h4')
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)
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with gr.Row():
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with gr.Column():
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model_choose = gr.Dropdown(label=i18n("模型"), choices=uvr5_names)
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dir_wav_input = gr.Textbox(
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label=i18n("输入待处理音频文件夹路径"),
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placeholder="C:\\Users\\Desktop\\todo-songs",
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)
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wav_inputs = gr.File(
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file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹")
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)
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with gr.Column():
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agg = gr.Slider(
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minimum=0,
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maximum=20,
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step=1,
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label=i18n("人声提取激进程度"),
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value=10,
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interactive=True,
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visible=False, # 先不开放调整
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)
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opt_vocal_root = gr.Textbox(
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label=i18n("指定输出主人声文件夹"), value="output/uvr5_opt"
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)
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opt_ins_root = gr.Textbox(
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label=i18n("指定输出非主人声文件夹"), value="output/uvr5_opt"
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)
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format0 = gr.Radio(
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label=i18n("导出文件格式"),
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choices=["wav", "flac", "mp3", "m4a"],
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value="flac",
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interactive=True,
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)
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with gr.Column():
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with gr.Row():
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but2 = gr.Button(i18n("转换"), variant="primary")
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with gr.Row():
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vc_output4 = gr.Textbox(label=i18n("输出信息"),lines=3)
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but2.click(
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uvr,
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[
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model_choose,
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dir_wav_input,
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opt_vocal_root,
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wav_inputs,
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opt_ins_root,
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agg,
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format0,
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],
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[vc_output4],
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api_name="uvr_convert",
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)
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app.queue().launch(#concurrency_count=511, max_size=1022
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server_name="0.0.0.0",
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inbrowser=True,
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share=is_share,
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server_port=webui_port_uvr5,
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quiet=True,
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)
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