Delete tools/webui.py

tools/webui.py

@@ -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. <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>.")
-    )
-    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。 <br>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>模型分为三类： <br>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>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> 3、去混响、去延迟模型（by FoxJoy）：<br>  (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>&emsp;(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>去混响/去延迟，附：<br>1、DeEcho-DeReverb模型的耗时是另外2个DeEcho模型的接近2倍；<br>2、MDX-Net-Dereverb模型挺慢的；<br>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,
-)