import os import traceback,gradio as gr import logging from tools.i18n.i18n import I18nAuto i18n = I18nAuto() logger = logging.getLogger(__name__) import librosa,ffmpeg import soundfile as sf import torch import sys from mdxnet import MDXNetDereverb from vr import AudioPre, AudioPreDeEcho weight_uvr5_root = "tools/uvr5/uvr5_weights" uvr5_names = [] for name in os.listdir(weight_uvr5_root): if name.endswith(".pth") or "onnx" in name: uvr5_names.append(name.replace(".pth", "")) device=sys.argv[1] is_half=eval(sys.argv[2]) webui_port_uvr5=int(sys.argv[3]) is_share=eval(sys.argv[4]) def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format0): infos = [] try: inp_root = inp_root.strip(" ").strip('"').strip("\n").strip('"').strip(" ") save_root_vocal = ( save_root_vocal.strip(" ").strip('"').strip("\n").strip('"').strip(" ") ) save_root_ins = ( save_root_ins.strip(" ").strip('"').strip("\n").strip('"').strip(" ") ) is_hp3 = "HP3" in model_name if model_name == "onnx_dereverb_By_FoxJoy": pre_fun = MDXNetDereverb(15) else: func = AudioPre if "DeEcho" not in model_name else AudioPreDeEcho pre_fun = func( agg=int(agg), model_path=os.path.join(weight_uvr5_root, model_name + ".pth"), device=device, is_half=is_half, ) if inp_root != "": paths = [os.path.join(inp_root, name) for name in os.listdir(inp_root)] else: paths = [path.name for path in paths] for path in paths: inp_path = os.path.join(inp_root, path) if(os.path.isfile(inp_path)==False):continue need_reformat = 1 done = 0 try: info = ffmpeg.probe(inp_path, cmd="ffprobe") if ( info["streams"][0]["channels"] == 2 and info["streams"][0]["sample_rate"] == "44100" ): need_reformat = 0 pre_fun._path_audio_( inp_path, save_root_ins, save_root_vocal, format0,is_hp3 ) done = 1 except: need_reformat = 1 traceback.print_exc() if need_reformat == 1: tmp_path = "%s/%s.reformatted.wav" % ( os.path.join(os.environ["TEMP"]), os.path.basename(inp_path), ) os.system( "ffmpeg -i %s -vn -acodec pcm_s16le -ac 2 -ar 44100 %s -y" % (inp_path, tmp_path) ) inp_path = tmp_path try: if done == 0: pre_fun._path_audio_( inp_path, save_root_ins, save_root_vocal, format0,is_hp3 ) infos.append("%s->Success" % (os.path.basename(inp_path))) yield "\n".join(infos) except: infos.append( "%s->%s" % (os.path.basename(inp_path), traceback.format_exc()) ) yield "\n".join(infos) except: infos.append(traceback.format_exc()) yield "\n".join(infos) finally: try: if model_name == "onnx_dereverb_By_FoxJoy": del pre_fun.pred.model del pre_fun.pred.model_ else: del pre_fun.model del pre_fun except: traceback.print_exc() print("clean_empty_cache") if torch.cuda.is_available(): torch.cuda.empty_cache() yield "\n".join(infos) with gr.Blocks(title="UVR5 WebUI") as app: gr.Markdown( value= 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.
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LICENSE.") ) with gr.Tabs(): with gr.TabItem(i18n("伴奏人声分离&去混响&去回声")): with gr.Group(): gr.Markdown( value=i18n( "人声伴奏分离批量处理, 使用UVR5模型。
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)。
模型分为三类:
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;
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;
3、去混响、去延迟模型(by FoxJoy):
  (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;
 (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。
去混响/去延迟,附:
1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍;
2、MDX-Net-Dereverb模型挺慢的;
3、个人推荐的最干净的配置是先MDX-Net再DeEcho-Aggressive。" ) ) with gr.Row(): with gr.Column(): dir_wav_input = gr.Textbox( label=i18n("输入待处理音频文件夹路径"), placeholder="C:\\Users\\Desktop\\todo-songs", ) wav_inputs = gr.File( file_count="multiple", label=i18n("也可批量输入音频文件, 二选一, 优先读文件夹") ) with gr.Column(): model_choose = gr.Dropdown(label=i18n("模型"), choices=uvr5_names) agg = gr.Slider( minimum=0, maximum=20, step=1, label=i18n("人声提取激进程度"), value=10, interactive=True, visible=False, # 先不开放调整 ) opt_vocal_root = gr.Textbox( label=i18n("指定输出主人声文件夹"), value="output/uvr5_opt" ) opt_ins_root = gr.Textbox( label=i18n("指定输出非主人声文件夹"), value="output/uvr5_opt" ) format0 = gr.Radio( label=i18n("导出文件格式"), choices=["wav", "flac", "mp3", "m4a"], value="flac", interactive=True, ) but2 = gr.Button(i18n("转换"), variant="primary") vc_output4 = gr.Textbox(label=i18n("输出信息")) but2.click( uvr, [ model_choose, dir_wav_input, opt_vocal_root, wav_inputs, opt_ins_root, agg, format0, ], [vc_output4], api_name="uvr_convert", ) app.queue(concurrency_count=511, max_size=1022).launch( server_name="0.0.0.0", inbrowser=True, share=is_share, server_port=webui_port_uvr5, quiet=True, )