app.py

import glob
import json
import logging
import os
import re
import subprocess
import sys
import time
import traceback
from itertools import chain
from pathlib import Path

# os.system("wget -P cvec/ https://huggingface.co/spaces/innnky/nanami/resolve/main/checkpoint_best_legacy_500.pt")
import gradio as gr
import librosa
import numpy as np
import soundfile
import torch

from compress_model import removeOptimizer
from edgetts.tts_voices import SUPPORTED_LANGUAGES
from inference.infer_tool import Svc
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

local_model_root = './trained'

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 is 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(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 is None :
            return mix_model_output1(value="")
        p = {file.name:100 for file in files}
        return mix_model_output1(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,diff_model_path,diff_config_path,only_diffusion,use_spk_mix,local_model_enabled,local_model_selection):
    global model
    try:
        device = cuda[device] if "CUDA" in device else device
        cluster_filepath = os.path.split(cluster_model_path.name) if cluster_model_path is not None else "no_cluster"
        # get model and config path
        if (local_model_enabled):
            # local path
            model_path = glob.glob(os.path.join(local_model_selection, '*.pth'))[0]
            config_path = glob.glob(os.path.join(local_model_selection, '*.json'))[0]
        else:
            # upload from webpage
            model_path = model_path.name
            config_path = config_path.name
        fr = ".pkl" in cluster_filepath[1]
        model = Svc(model_path,
                config_path,
                device=device if device != "Auto" else None,
                cluster_model_path = cluster_model_path.name if cluster_model_path is not None else "",
                nsf_hifigan_enhance=enhance,
                diffusion_model_path = diff_model_path.name if diff_model_path is not None else "",
                diffusion_config_path = diff_config_path.name if diff_config_path is not None else "",
                shallow_diffusion = True if diff_model_path is not None else False,
                only_diffusion = only_diffusion,
                spk_mix_enable = use_spk_mix,
                feature_retrieval = fr
                )
        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 += "未加载聚类模型或特征检索模型\n"
        elif fr:
            msg += f"特征检索模型{cluster_filepath[1]}加载成功\n"
        else:
            msg += f"聚类模型{cluster_filepath[1]}加载成功\n"
        if diff_model_path is None:
            msg += "未加载扩散模型\n"
        else:
            msg += f"扩散模型{diff_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 gr.Dropdown(choices = spks,value=spks[0],interactive=True), msg
    except Exception as e:
        if debug:
            traceback.print_exc()
        raise gr.Error(e)

    
def modelUnload():
    global model
    if model is None:
        return sid(choices = [],value=""),"没有模型需要卸载!"
    else:
        model.unload_model()
        model = None
        torch.cuda.empty_cache()
        return sid(choices = [],value=""),"模型卸载完毕!"
    
def vc_infer(output_format, sid, audio_path, truncated_basename, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold, k_step, use_spk_mix, second_encoding, loudness_envelope_adjustment):
    global model
    _audio = model.slice_inference(
        audio_path,
        sid,
        vc_transform,
        slice_db,
        cluster_ratio,
        auto_f0,
        noise_scale,
        pad_seconds,
        cl_num,
        lg_num,
        lgr_num,
        f0_predictor,
        enhancer_adaptive_key,
        cr_threshold,
        k_step,
        use_spk_mix,
        second_encoding,
        loudness_envelope_adjustment
    )  
    model.clear_empty()
    #构建保存文件的路径，并保存到results文件夹内
    str(int(time.time()))
    if not os.path.exists("results"):
        os.makedirs("results")
    key = "auto" if auto_f0 else f"{int(vc_transform)}key"
    cluster = "_" if cluster_ratio == 0 else f"_{cluster_ratio}_"
    isdiffusion = "sovits"
    if model.shallow_diffusion:
        isdiffusion = "sovdiff"

    if model.only_diffusion:
        isdiffusion = "diff"
    
    output_file_name = 'result_'+truncated_basename+f'_{sid}_{key}{cluster}{isdiffusion}.{output_format}'
    output_file = os.path.join("results", output_file_name)
    soundfile.write(output_file, _audio, model.target_sample, format=output_format)
    return output_file

def vc_fn(sid, input_audio, output_format, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment):
    global model
    try:
        if input_audio is None:
            return "You need to upload an audio", None
        if model is None:
            return "You need to upload an model", None
        if getattr(model, 'cluster_model', None) is None and model.feature_retrieval is False:
            if cluster_ratio != 0:
                return "You need to upload an cluster model or feature retrieval model before assigning cluster ratio!", None
        #print(input_audio)    
        audio, sampling_rate = soundfile.read(input_audio)
        #print(audio.shape,sampling_rate)
        if np.issubdtype(audio.dtype, np.integer):
            audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
        #print(audio.dtype)
        if len(audio.shape) > 1:
            audio = librosa.to_mono(audio.transpose(1, 0))
        # 未知原因Gradio上传的filepath会有一个奇怪的固定后缀，这里去掉
        truncated_basename = Path(input_audio).stem[:-6]
        processed_audio = os.path.join("raw", f"{truncated_basename}.wav")
        soundfile.write(processed_audio, audio, sampling_rate, format="wav")
        output_file = vc_infer(output_format, sid, processed_audio, truncated_basename, vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold, k_step, use_spk_mix, second_encoding, loudness_envelope_adjustment)

        return "Success", output_file
    except Exception as e:
        if debug:
            traceback.print_exc()
        raise gr.Error(e)

def text_clear(text):
    return re.sub(r"[\n\,\(\) ]", "", text)

def vc_fn2(_text, _lang, _gender, _rate, _volume, sid, output_format, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold, k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment):
    global model
    try:
        if model is None:
            return "You need to upload an model", None
        if getattr(model, 'cluster_model', None) is None and model.feature_retrieval is False:
            if cluster_ratio != 0:
                return "You need to upload an cluster model or feature retrieval model before assigning cluster ratio!", None
        _rate = f"+{int(_rate*100)}%" if _rate >= 0 else f"{int(_rate*100)}%"
        _volume = f"+{int(_volume*100)}%" if _volume >= 0 else f"{int(_volume*100)}%"
        if _lang == "Auto":
            _gender = "Male" if _gender == "男" else "Female"
            subprocess.run([sys.executable, "edgetts/tts.py", _text, _lang, _rate, _volume, _gender])
        else:
            subprocess.run([sys.executable, "edgetts/tts.py", _text, _lang, _rate, _volume])
        target_sr = 44100
        y, sr = librosa.load("tts.wav")
        resampled_y = librosa.resample(y, orig_sr=sr, target_sr=target_sr)
        soundfile.write("tts.wav", resampled_y, target_sr, subtype = "PCM_16")
        input_audio = "tts.wav"
        #audio, _ = soundfile.read(input_audio)
        output_file_path = vc_infer(output_format, sid, input_audio, "tts", vc_transform, auto_f0, cluster_ratio, slice_db, noise_scale, pad_seconds, cl_num, lg_num, lgr_num, f0_predictor, enhancer_adaptive_key, cr_threshold, k_step, use_spk_mix, second_encoding, loudness_envelope_adjustment)
        os.remove("tts.wav")
        return "Success", output_file_path
    except Exception as e:
        if debug: traceback.print_exc()  # noqa: E701
        raise gr.Error(e)

def model_compression(_model):
    if _model == "":
        return "请先选择要压缩的模型"
    else:
        model_path = os.path.split(_model.name)
        filename, extension = os.path.splitext(model_path[1])
        output_model_name = f"{filename}_compressed{extension}"
        output_path = os.path.join(os.getcwd(), output_model_name)
        removeOptimizer(_model.name, output_path)
        return f"模型已成功被保存在了{output_path}"

def scan_local_models():
    res = []
    candidates = glob.glob(os.path.join(local_model_root, '**', '*.json'), recursive=True)
    candidates = set([os.path.dirname(c) for c in candidates])
    print(candidates)
    for candidate in candidates:
        jsons = glob.glob(os.path.join(candidate, '*.json'))
        pths = glob.glob(os.path.join(candidate, '*.pth'))
        if (len(jsons) == 1 and len(pths) == 1):
            # must contain exactly one json and one pth file
            res.append(candidate)
    print(res)
    return res

def local_model_refresh_fn():
    choices = scan_local_models()
    #gr.Dropdown(choices=symbols, interactive=True)
    return gr.Dropdown(choices=choices,interactive=True)

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>
                        """)
                    with gr.Tabs():
                        # invisible checkbox that tracks tab status
                        local_model_enabled = gr.Checkbox(value=False, visible=False)
                        with gr.TabItem('上传') as local_model_tab_upload:
                            with gr.Row():
                                model_path = gr.File(label="选择模型文件")
                                config_path = gr.File(label="选择配置文件")
                        with gr.TabItem('本地') as local_model_tab_local:
                            gr.Markdown(f'模型应当放置于{local_model_root}文件夹下')
                            local_model_refresh_btn = gr.Button('刷新本地模型列表')
                            local_model_selection = gr.Dropdown(label='选择模型文件夹', choices=[], interactive=True)
                    with gr.Row():
                        diff_model_path = gr.File(label="选择扩散模型文件")
                        diff_config_path = gr.File(label="选择扩散模型配置文件")
                    cluster_model_path = gr.File(label="选择聚类模型或特征检索文件（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)
                    only_diffusion = gr.Checkbox(label="是否使用全扩散推理，开启后将不使用So-VITS模型，仅使用扩散模型进行完整扩散推理，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_predictor = gr.Dropdown(label="选择F0预测器,可选择crepe,pm,dio,harvest,rmvpe,dspitch默认为pm(注意：crepe为原F0使用均值滤波器,dspitch是添加了diffsinger的提取器)", choices=["pm","dio","harvest","crepe","rmvpe","dspitch"], value="pm",interactive=True)
                    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/特征检索混合比例，0-1之间，0即不启用聚类/特征检索。使用聚类/特征检索能提升音色相似度，但会导致咬字下降（如果使用建议0.5左右）", value=0)
                    slice_db = gr.Number(label="切片阈值", value=-40)
                    output_format = gr.Radio(label="音频输出格式", choices=["wav", "flac", "mp3"], value = "wav")
                    noise_scale = gr.Number(label="noise_scale 建议不要动，会影响音质，玄学参数", value=0.4)
                    k_step = gr.Slider(label="浅扩散步数，只有使用了扩散模型才有效，步数越大越接近扩散模型的结果", value=100, minimum = 1, maximum = 1000)
                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crepe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)
                    loudness_envelope_adjustment = gr.Number(label="输入源响度包络替换输出响度包络融合比例，越靠近1越使用输出响度包络", value = 0)
                    second_encoding = gr.Checkbox(label = "二次编码，浅扩散前会对原始音频进行二次编码，玄学选项，效果时好时差，默认关闭", value=False)
                    use_spk_mix = gr.Checkbox(label = "动态声线融合", value = False, interactive = False)
            with gr.Tabs():
                with gr.TabItem("音频转音频"):
                    vc_input3 = gr.Audio(label="选择音频", type="filepath")
                    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")
                    with gr.Row():
                        tts_gender = gr.Radio(label = "说话人性别", choices = ["男","女"], value = "男")
                        tts_lang = gr.Dropdown(label = "选择语言，Auto为根据输入文字自动识别", choices=SUPPORTED_LANGUAGES, value = "Auto")
                        tts_rate = gr.Slider(label = "TTS语音变速（倍速相对值）", minimum = -1, maximum = 3, value = 0, step = 0.1)
                        tts_volume = gr.Slider(label = "TTS语音音量（相对值）", minimum = -1, maximum = 1.5, value = 0, step = 0.1)
                    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")
                    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.TabItem("模型压缩工具"):
                    gr.Markdown(value="""
                        该工具可以实现对模型的体积压缩，在**不影响模型推理功能**的情况下，将原本约600M的So-VITS模型压缩至约200M, 大大减少了硬盘的压力。
                        **注意：压缩后的模型将无法继续训练，请在确认封炉后再压缩。**
                    """)
                    model_to_compress = gr.File(label="模型上传")
                    compress_model_btn = gr.Button("压缩模型", variant="primary")
                    compress_model_output = gr.Textbox(label="输出信息", value="")

                    compress_model_btn.click(model_compression, [model_to_compress], [compress_model_output])
                    
                    
    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)
        # refresh local model list
        local_model_refresh_btn.click(local_model_refresh_fn, outputs=local_model_selection)
        # set local enabled/disabled on tab switch
        local_model_tab_upload.select(lambda: False, outputs=local_model_enabled)
        local_model_tab_local.select(lambda: True, outputs=local_model_enabled)
        
        vc_submit.click(vc_fn, [sid, vc_input3, output_format, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment], [vc_output1, vc_output2])
        vc_submit2.click(vc_fn2, [text2tts, tts_lang, tts_gender, tts_rate, tts_volume, sid, output_format, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale,pad_seconds,cl_num,lg_num,lgr_num,f0_predictor,enhancer_adaptive_key,cr_threshold,k_step,use_spk_mix,second_encoding,loudness_envelope_adjustment], [vc_output1, vc_output2])

        debug_button.change(debug_change,[],[])
        model_load_button.click(modelAnalysis,[model_path,config_path,cluster_model_path,device,enhance,diff_model_path,diff_config_path,only_diffusion,use_spk_mix,local_model_enabled,local_model_selection],[sid,sid_output])
        model_unload_button.click(modelUnload,[],[sid,sid_output])
    os.system("start http://127.0.0.1:7860")
    app.launch(server_port=6006)