GPT-SoVITS/tools/uvr5/webui.py

import os
import traceback,gradio as gr
import logging
from tools.i18n.i18n import I18nAuto
i18n = I18nAuto()

logger = logging.getLogger(__name__)
import ffmpeg
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]


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":
            pre_fun = MDXNetDereverb(15, device)
        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,
            )
        is_hp3 = "HP3" in model_name
        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)
            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=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
                    )
                infos.append("%s->Success" % (os.path.basename(inp_path)))
                yield "\n".join(infos)
            except:
                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()
        if torch.cuda.is_available():
            torch.cuda.empty_cache()
            logger.info("Executed torch.cuda.empty_cache()")
    yield "\n".join(infos)


with gr.Blocks(title="RVC WebUI") as app:
    gr.Markdown(
        value=
            "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="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,
    server_port=9873,
    quiet=True,
)