Delete webUI.py

webUI.py

@@ -1,311 +0,0 @@
-import io
-import os
-
-# os.system("wget -P cvec/ https://huggingface.co/spaces/innnky/nanami/resolve/main/checkpoint_best_legacy_500.pt")
-import gradio as gr
-import gradio.processing_utils as gr_pu
-import librosa
-import numpy as np
-import soundfile
-from inference.infer_tool import Svc
-import logging
-import re
-import json
-
-import subprocess
-import edge_tts
-import asyncio
-from scipy.io import wavfile
-import librosa
-import torch
-import time
-import traceback
-from itertools import chain
-from utils import mix_model
-
-logging.getLogger('numba').setLevel(logging.WARNING)
-logging.getLogger('markdown_it').setLevel(logging.WARNING)
-logging.getLogger('urllib3').setLevel(logging.WARNING)
-logging.getLogger('matplotlib').setLevel(logging.WARNING)
-logging.getLogger('multipart').setLevel(logging.WARNING)
-
-model = None
-spk = None
-debug = False
-
-cuda = {}
-if torch.cuda.is_available():
-    for i in range(torch.cuda.device_count()):
-        device_name = torch.cuda.get_device_properties(i).name
-        cuda[f"CUDA:{i} {device_name}"] = f"cuda:{i}"
-
-def upload_mix_append_file(files,sfiles):
-    try:
-        if(sfiles == None):
-            file_paths = [file.name for file in files]
-        else:
-            file_paths = [file.name for file in chain(files,sfiles)]
-        p = {file:100 for file in file_paths}
-        return file_paths,mix_model_output1.update(value=json.dumps(p,indent=2))
-    except Exception as e:
-        if debug: traceback.print_exc()
-        raise gr.Error(e)
-
-def mix_submit_click(js,mode):
-    try:
-        assert js.lstrip()!=""
-        modes = {"凸组合":0, "线性组合":1}
-        mode = modes[mode]
-        data = json.loads(js)
-        data = list(data.items())
-        model_path,mix_rate = zip(*data)
-        path = mix_model(model_path,mix_rate,mode)
-        return f"成功，文件被保存在了{path}"
-    except Exception as e:
-        if debug: traceback.print_exc()
-        raise gr.Error(e)
-
-def updata_mix_info(files):
-    try:
-        if files == None : return mix_model_output1.update(value="")
-        p = {file.name:100 for file in files}
-        return mix_model_output1.update(value=json.dumps(p,indent=2))
-    except Exception as e:
-        if debug: traceback.print_exc()
-        raise gr.Error(e)
-
-def modelAnalysis(model_path,config_path,cluster_model_path,device,enhance):
-    global model
-    try:
-        device = cuda[device] if "CUDA" in device else device
-        model = Svc(model_path.name, config_path.name, device=device if device!="Auto" else None, cluster_model_path = cluster_model_path.name if cluster_model_path != None else "",nsf_hifigan_enhance=enhance)
-        spks = list(model.spk2id.keys())
-        device_name = torch.cuda.get_device_properties(model.dev).name if "cuda" in str(model.dev) else str(model.dev)
-        msg = f"成功加载模型到设备{device_name}上\n"
-        if cluster_model_path is None:
-            msg += "未加载聚类模型\n"
-        else:
-            msg += f"聚类模型{cluster_model_path.name}加载成功\n"
-        msg += "当前模型的可用音色：\n"
-        for i in spks:
-            msg += i + " "
-        return sid.update(choices = spks,value=spks[0]), msg
-    except Exception as e:
-        if debug: traceback.print_exc()
-        raise gr.Error(e)
-
-    
-def modelUnload():
-    global model
-    if model is None:
-        return sid.update(choices = [],value=""),"没有模型需要卸载!"
-    else:
-        model.unload_model()
-        model = None
-        torch.cuda.empty_cache()
-        return sid.update(choices = [],value=""),"模型卸载完毕!"
-
-
-def vc_fn(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,F0_mean_pooling,enhancer_adaptive_key,cr_threshold):
-    global model
-    try:
-        if input_audio is None:
-            raise gr.Error("你需要上传音频")
-        if model is None:
-            raise gr.Error("你需要指定模型")
-        sampling_rate, audio = input_audio
-        # print(audio.shape,sampling_rate)
-        audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
-        if len(audio.shape) > 1:
-            audio = librosa.to_mono(audio.transpose(1, 0))
-        temp_path = "temp.wav"
-        soundfile.write(temp_path, audio, sampling_rate, format="wav")
-        _audio = model.slice_inference(temp_path, sid, vc_transform, slice_db, cluster_ratio, auto_f0, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,F0_mean_pooling,enhancer_adaptive_key,cr_threshold)
-        model.clear_empty()
-        os.remove(temp_path)
-        #构建保存文件的路径，并保存到results文件夹内
-        try:
-            timestamp = str(int(time.time()))
-            filename = sid + "_" + timestamp + ".wav"
-            output_file = os.path.join("./results", filename)
-            soundfile.write(output_file, _audio, model.target_sample, format="wav")
-            return f"推理成功，音频文件保存为results/{filename}", (model.target_sample, _audio)
-        except Exception as e:
-            if debug: traceback.print_exc()
-            return f"文件保存失败，请手动保存", (model.target_sample, _audio)
-    except Exception as e:
-        if debug: traceback.print_exc()
-        raise gr.Error(e)
-
-
-def tts_func(_text,_rate,_voice):
-    #使用edge-tts把文字转成音频
-    # voice = "zh-CN-XiaoyiNeural"#女性，较高音
-    # voice = "zh-CN-YunxiNeural"#男性
-    voice = "zh-CN-YunxiNeural"#男性
-    if ( _voice == "女" ) : voice = "zh-CN-XiaoyiNeural"
-    output_file = _text[0:10]+".wav"
-    # communicate = edge_tts.Communicate(_text, voice)
-    # await communicate.save(output_file)
-    if _rate>=0:
-        ratestr="+{:.0%}".format(_rate)
-    elif _rate<0:
-        ratestr="{:.0%}".format(_rate)#减号自带
-
-    p=subprocess.Popen("edge-tts "+
-                        " --text "+_text+
-                        " --write-media "+output_file+
-                        " --voice "+voice+
-                        " --rate="+ratestr
-                        ,shell=True,
-                        stdout=subprocess.PIPE,
-                        stdin=subprocess.PIPE)
-    p.wait()
-    return output_file
-
-def text_clear(text):
-    return re.sub(r"[\n\,\(\) ]", "", text)
-
-def vc_fn2(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,text2tts,tts_rate,tts_voice,F0_mean_pooling,enhancer_adaptive_key,cr_threshold):
-    #使用edge-tts把文字转成音频
-    text2tts=text_clear(text2tts)
-    output_file=tts_func(text2tts,tts_rate,tts_voice)
-
-    #调整采样率
-    sr2=44100
-    wav, sr = librosa.load(output_file)
-    wav2 = librosa.resample(wav, orig_sr=sr, target_sr=sr2)
-    save_path2= text2tts[0:10]+"_44k"+".wav"
-    wavfile.write(save_path2,sr2,
-                (wav2 * np.iinfo(np.int16).max).astype(np.int16)
-                )
-
-    #读取音频
-    sample_rate, data=gr_pu.audio_from_file(save_path2)
-    vc_input=(sample_rate, data)
-
-    a,b=vc_fn(sid, vc_input, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,F0_mean_pooling,enhancer_adaptive_key,cr_threshold)
-    os.remove(output_file)
-    os.remove(save_path2)
-    return a,b
-
-def debug_change():
-    global debug
-    debug = debug_button.value
-
-with gr.Blocks(
-    theme=gr.themes.Base(
-        primary_hue = gr.themes.colors.green,
-        font=["Source Sans Pro", "Arial", "sans-serif"],
-        font_mono=['JetBrains mono', "Consolas", 'Courier New']
-    ),
-) as app:
-    with gr.Tabs():
-        with gr.TabItem("推理"):
-            gr.Markdown(value="""
-                So-vits-svc 4.0 推理 webui
-                """)
-            with gr.Row(variant="panel"):
-                with gr.Column():
-                    gr.Markdown(value="""
-                        <font size=2> 模型设置</font>
-                        """)
-                    model_path = gr.File(label="选择模型文件")
-                    config_path = gr.File(label="选择配置文件")
-                    cluster_model_path = gr.File(label="选择聚类模型文件（没有可以不选）")
-                    device = gr.Dropdown(label="推理设备，默认为自动选择CPU和GPU", choices=["Auto",*cuda.keys(),"CPU"], value="Auto")
-                    enhance = gr.Checkbox(label="是否使用NSF_HIFIGAN增强,该选项对部分训练集少的模型有一定的音质增强效果，但是对训练好的模型有反面效果，默认关闭", value=False)
-                with gr.Column():
-                    gr.Markdown(value="""
-                        <font size=3>左侧文件全部选择完毕后(全部文件模块显示download)，点击“加载模型”进行解析：</font>
-                        """)
-                    model_load_button = gr.Button(value="加载模型", variant="primary")
-                    model_unload_button = gr.Button(value="卸载模型", variant="primary")
-                    sid = gr.Dropdown(label="音色（说话人）")
-                    sid_output = gr.Textbox(label="Output Message")
-
-
-            with gr.Row(variant="panel"):
-                with gr.Column():
-                    gr.Markdown(value="""
-                        <font size=2> 推理设置</font>
-                        """)
-                    auto_f0 = gr.Checkbox(label="自动f0预测，配合聚类模型f0预测效果更好,会导致变调功能失效（仅限转换语音，歌声勾选此项会究极跑调）", value=False)
-                    F0_mean_pooling = gr.Checkbox(label="是否对F0使用均值滤波器(池化)，对部分哑音有改善。注意，启动该选项会导致推理速度下降，默认关闭", value=False)
-                    vc_transform = gr.Number(label="变调（整数，可以正负，半音数量，升高八度就是12）", value=0)
-                    cluster_ratio = gr.Number(label="聚类模型混合比例，0-1之间，0即不启用聚类。使用聚类模型能提升音色相似度，��会导致咬字下降（如果使用建议0.5左右）", value=0)
-                    slice_db = gr.Number(label="切片阈值", value=-40)
-                    noise_scale = gr.Number(label="noise_scale 建议不要动，会影响音质，玄学参数", value=0.4)
-                with gr.Column():
-                    pad_seconds = gr.Number(label="推理音频pad秒数，由于未知原因开头结尾会有异响，pad一小段静音段后就不会出现", value=0.5)
-                    cl_num = gr.Number(label="音频自动切片，0为不切片，单位为秒(s)", value=0)
-                    lg_num = gr.Number(label="两端音频切片的交叉淡入长度，如果自动切片后出现人声不连贯可调整该数值，如果连贯建议采用默认值0，注意，该设置会影响推理速度，单位为秒/s", value=0)
-                    lgr_num = gr.Number(label="自动音频切片后，需要舍弃每段切片的头尾。该参数设置交叉长度保留的比例，范围0-1,左开右闭", value=0.75)
-                    enhancer_adaptive_key = gr.Number(label="使增强器适应更高的音域(单位为半音数)|默认为0", value=0)
-                    cr_threshold = gr.Number(label="F0过滤阈值，只有启动f0_mean_pooling时有效. 数值范围从0-1. 降低该值可减少跑调概率，但会增加哑音", value=0.05)
-            with gr.Tabs():
-                with gr.TabItem("音频转音频"):
-                    vc_input3 = gr.Audio(label="选择音频")
-                    vc_submit = gr.Button("音频转换", variant="primary")
-                with gr.TabItem("文字转音频"):
-                    text2tts=gr.Textbox(label="在此输入要转译的文字。注意，使用该功能建议打开F0预测，不然会很怪")
-                    tts_rate = gr.Number(label="tts语速", value=0)
-                    tts_voice = gr.Radio(label="性别",choices=["男","女"], value="男")
-                    vc_submit2 = gr.Button("文字转换", variant="primary")
-            with gr.Row():
-                with gr.Column():
-                    vc_output1 = gr.Textbox(label="Output Message")
-                with gr.Column():
-                    vc_output2 = gr.Audio(label="Output Audio", interactive=False)
-
-        with gr.TabItem("小工具/实验室特性"):
-            gr.Markdown(value="""
-                        <font size=2> So-vits-svc 4.0 小工具/实验室特性</font>
-                        """)
-            with gr.Tabs():
-                with gr.TabItem("静态声线融合"):
-                    gr.Markdown(value="""
-                        <font size=2> 介绍:该功能可以将多个声音模型合成为一个声音模型(多个模型参数的凸组合或线性组合)，从而制造出现实中不存在的声线 
-                                          注意：
-                                          1.该功能仅支持单说话人的模型
-                                          2.如果强行使用多说话人模型，需要保证多个模型的说话人数量相同，这样可以混合同一个SpaekerID下的声音
-                                          3.保证所有待混合模型的config.json中的model字段是相同的
-                                          4.输出的混合模型可以使用待合成模型的任意一个config.json，但聚类模型将不能使用
-                                          5.批量上传模型的时候最好把模型放到一个文件夹选中后一起上传
-                                          6.混合比例调整建议大小在0-100之间，也可以调为其他数字，但在线性组合模式下会出现未知的效果
-                                          7.混合完毕后，文件将会保存在项目根目录中，文件名为output.pth
-                                          8.凸组合模式会将混合比例执行Softmax使混合比例相加为1，而线性组合模式不会
-                        </font>
-                        """)
-                    mix_model_path = gr.Files(label="选择需要混合模型文件")
-                    mix_model_upload_button = gr.UploadButton("选择/追加需要混合模型文件", file_count="multiple", variant="primary")
-                    mix_model_output1 = gr.Textbox(
-                                            label="混合比例调整，单位/%",
-                                            interactive = True
-                                         )
-                    mix_mode = gr.Radio(choices=["凸组合", "线性组合"], label="融合模式",value="凸组合",interactive = True)
-                    mix_submit = gr.Button("声线融合启动", variant="primary")
-                    mix_model_output2 = gr.Textbox(
-                                            label="Output Message"
-                                         )
-                    mix_model_path.change(updata_mix_info,[mix_model_path],[mix_model_output1])
-                    mix_model_upload_button.upload(upload_mix_append_file, [mix_model_upload_button,mix_model_path], [mix_model_path,mix_model_output1])
-                    mix_submit.click(mix_submit_click, [mix_model_output1,mix_mode], [mix_model_output2])
-                    
-                    
-    with gr.Tabs():
-        with gr.Row(variant="panel"):
-            with gr.Column():
-                gr.Markdown(value="""
-                    <font size=2> WebUI设置</font>
-                    """)
-                debug_button = gr.Checkbox(label="Debug模式，如果向社区反馈BUG需要打开，打开后控制台可以显示具体错误提示", value=debug)
-        vc_submit.click(vc_fn, [sid, vc_input3, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,F0_mean_pooling,enhancer_adaptive_key,cr_threshold], [vc_output1, vc_output2])
-        vc_submit2.click(vc_fn2, [sid, vc_input3, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,text2tts,tts_rate,tts_voice,F0_mean_pooling,enhancer_adaptive_key,cr_threshold], [vc_output1, vc_output2])
-        debug_button.change(debug_change,[],[])
-        model_load_button.click(modelAnalysis,[model_path,config_path,cluster_model_path,device,enhance],[sid,sid_output])
-        model_unload_button.click(modelUnload,[],[sid,sid_output])
-    app.launch()
-
-
-