diff --git a/tools/webui.py b/tools/webui.py deleted file mode 100644 index 41ec588..0000000 --- a/tools/webui.py +++ /dev/null @@ -1,178 +0,0 @@ -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=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(" ") - ) - if model_name == "onnx_dereverb_By_FoxJoy": - from MDXNet import MDXNetDereverb - - 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 - ) - 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 - ) - 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, -)