diff --git a/LazyImport.py b/LazyImport.py new file mode 100644 index 000000000..5bdb05ddd --- /dev/null +++ b/LazyImport.py @@ -0,0 +1,13 @@ +from importlib.util import find_spec, LazyLoader, module_from_spec +from sys import modules + +def lazyload(name): + if name in modules: + return modules[name] + else: + spec = find_spec(name) + loader = LazyLoader(spec.loader) + module = module_from_spec(spec) + modules[name] = module + loader.exec_module(module) + return module \ No newline at end of file diff --git a/infer-web.py b/infer-web.py index a9246bba3..979c0fbe5 100644 --- a/infer-web.py +++ b/infer-web.py @@ -1,43 +1,37 @@ -from importlib.util import find_spec, LazyLoader, module_from_spec import sys -def lazyload(name): - spec = find_spec(name) - loader = LazyLoader(spec.loader) - spec.loader = loader - module = module_from_spec(spec) - sys.modules[name] = module - loader.exec_module(module) - return module - -import shutil +from shutil import rmtree import json # Mangio fork using json for preset saving -math = lazyload('math') + from glob import glob1 from signal import SIGTERM import os now_dir = os.getcwd() sys.path.append(now_dir) + +from LazyImport import lazyload + +math = lazyload('math') + import traceback import warnings tensorlowest = lazyload('tensorlowest') import faiss ffmpeg = lazyload('ffmpeg') -import numpy as np +np = lazyload("numpy") torch = lazyload('torch') re = lazyload('regex') os.environ["OPENBLAS_NUM_THREADS"] = "1" os.environ["no_proxy"] = "localhost, 127.0.0.1, ::1" import logging -import threading from random import shuffle from subprocess import Popen -from time import sleep - -import gradio as gr -from soundfile import write as SFWrite +gr = lazyload("gradio") +SF = lazyload("soundfile") +SFWrite = SF.write +import onnx from config import Config from fairseq import checkpoint_utils from i18n import I18nAuto @@ -55,6 +49,12 @@ def lazyload(name): from vc_infer_pipeline import VC from sklearn.cluster import MiniBatchKMeans +import time +import threading +from safetensors.torch import load_file + +from numba import jit + from shlex import quote as SQuote RQuote = lambda val: SQuote(str(val)) @@ -63,9 +63,9 @@ def lazyload(name): runtime_dir = os.path.join(now_dir, "runtime/Lib/site-packages") directories = ['logs', 'audios', 'datasets', 'weights'] -shutil.rmtree(tmp, ignore_errors=True) -shutil.rmtree(os.path.join(runtime_dir, "infer_pack"), ignore_errors=True) -shutil.rmtree(os.path.join(runtime_dir, "uvr5_pack"), ignore_errors=True) +rmtree(tmp, ignore_errors=True) +rmtree(os.path.join(runtime_dir, "infer_pack"), ignore_errors=True) +rmtree(os.path.join(runtime_dir, "uvr5_pack"), ignore_errors=True) os.makedirs(tmp, exist_ok=True) for folder in directories: @@ -131,14 +131,14 @@ def load_hubert(): index_root = "./logs/" audio_root = "audios" -names = [name for name in os.listdir(weight_root) if name.endswith(".pth")] +names = [name for name in os.listdir(weight_root) if name.endswith((".pth", ".onnx"))] -indexes_list = ["%s/%s" % (root, name) +indexes_list = [f"{root}/{name}" for root, _, files in os.walk(index_root, topdown=False) for name in files if name.endswith(".index") and "trained" not in name] -audio_paths = ["%s/%s" % (root, name) +audio_paths = [f"{root}/{name}" for root, _, files in os.walk(audio_root, topdown=False) for name in files] @@ -168,7 +168,6 @@ def get_fshift_presets(): return fshift_presets_list if fshift_presets_list else '' - def vc_single( sid, input_audio_path0, @@ -346,8 +345,6 @@ def uvr(model_name, inp_root, save_root_vocal, paths, save_root_ins, agg, format yield "\n".join(infos) - -# 一个选项卡全局只能有一个音色 def get_vc(sid, to_return_protect0, to_return_protect1): global n_spk, tgt_sr, net_g, vc, cpt, version, hubert_model if not sid: @@ -366,7 +363,7 @@ def get_vc(sid, to_return_protect0, to_return_protect1): cpt = None return ({"visible": False, "__type__": "update"},) * 3 - person = "%s/%s" % (weight_root, sid) + person = f"{weight_root}/{sid}" print(f"loading {person}") cpt = torch.load(person, map_location="cpu") tgt_sr = cpt["config"][-1] @@ -398,7 +395,7 @@ def get_vc(sid, to_return_protect0, to_return_protect1): def change_choices(): - names = [name for name in os.listdir(weight_root) if name.endswith(".pth")] + names = [name for name in os.listdir(weight_root) if name.endswith(".pth", ".onnx")] indexes_list = [os.path.join(root, name) for root, _, files in os.walk(index_root, topdown=False) for name in files if name.endswith(".index") and "trained" not in name] audio_paths = [os.path.join(audio_root, file) for file in os.listdir(os.path.join(os.getcwd(), "audios"))] @@ -414,17 +411,16 @@ def change_choices(): "48k": 48000, } - +@jit def if_done(done, p): while p.poll() is None: - sleep(0.5) + time.sleep(0.5) done[0] = True - def if_done_multi(done, ps): while not all(p.poll() is not None for p in ps): - sleep(0.5) + time.sleep(0.5) done[0] = True def formant_enabled(cbox, qfrency, tmbre, frmntapply, formantpreset, formant_refresh_button): @@ -468,11 +464,13 @@ def update_fshift_presets(preset, qfrency, tmbre): {"value": tmbre, "__type__": "update"}, ) - def preprocess_dataset(trainset_dir, exp_dir, sr, n_p): sr = sr_dict[sr] - log_dir = f"{now_dir}\\logs\\{exp_dir}" - log_file = f"{log_dir}\\preprocess.log" + + tmpdir = os.path.join(now_dir, "logs", exp_dir).split("/") + log_dir = os.path.join(*tmpdir, "logs", exp_dir) + log_file = os.path.join(log_dir, "preprocess.log") + os.makedirs(log_dir, exist_ok=True) @@ -497,7 +495,7 @@ def preprocess_dataset(trainset_dir, exp_dir, sr, n_p): while not done[0]: with open(log_file, "r") as f: yield f.read() - sleep(1) + time.sleep(1) with open(log_file, "r") as f: log = f.read() @@ -505,7 +503,6 @@ def preprocess_dataset(trainset_dir, exp_dir, sr, n_p): print(log) yield log - def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, echl): gpus = gpus.split("-") log_dir = f"{now_dir}/logs/{exp_dir}" @@ -526,7 +523,7 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, echl): while not done[0]: with open(log_file, "r") as f: yield f.read() - sleep(1) + time.sleep(1) leng = len(gpus) ps = [] @@ -546,7 +543,7 @@ def extract_f0_feature(gpus, n_p, f0method, if_f0, exp_dir, version19, echl): while not done[0]: with open(log_file, "r") as f: yield f.read() - sleep(1) + time.sleep(1) with open(log_file, "r") as f: log = f.read() @@ -640,18 +637,18 @@ def click_train( if_save_every_weights18, version19, ): + + CSVutil('csvdb/stop.csv', 'w+', 'formanting', False) - log_dir = f"%s\\logs\\%s" % ( - now_dir, exp_dir1 - ) + log_dir = os.path.join(now_dir, "logs", exp_dir1) + os.makedirs(log_dir, exist_ok=True) - gt_wavs_dir = f"%s\\0_gt_wavs" % (log_dir) + gt_wavs_dir = os.path.join(log_dir, "0_gt_wavs") feature_dim = "256" if version19 == "v1" else "768" - feature_dir = f"%s\\3_feature%s" % ( - log_dir, feature_dim - ) + + feature_dir = os.path.join(log_dir, "3_feature", feature_dim) log_interval = set_log_interval(log_dir, batch_size12) @@ -693,14 +690,11 @@ def generate_paths(name): G_train = f"-pg {pretrained_G14}" if pretrained_G14 else "" D_train = f"-pd {pretrained_D15}" if pretrained_D15 else "" - save_latest = 1 if if_save_latest13 else 0 - cache_gpu = 1 if if_cache_gpu17 else 0 - save_every_weights = 1 if if_save_every_weights18 else 0 cmd = ( f"{config.python_cmd} train_nsf_sim_cache_sid_load_pretrain.py -e {exp_dir1} -sr {sr2} -f0 {int(if_f0_3)} -bs {batch_size12}" - f" -g {gpus16 if gpus16 is not None else ''} -te {total_epoch11} -se {save_epoch10} {G_train} {D_train} -l {save_latest}" - f" -c {cache_gpu} -sw {save_every_weights} -v {version19} -li {log_interval}" + f" -g {gpus16 if gpus16 is not None else ''} -te {total_epoch11} -se {save_epoch10} {G_train} {D_train} -l {int(if_save_latest13)}" + f" -c {int(if_cache_gpu17)} -sw {int(if_save_every_weights18)} -v {version19} -li {log_interval}" ) print(cmd) @@ -793,7 +787,6 @@ def change_info_(ckpt_path): print(f"Exception occurred: {str(e)}, Traceback: {traceback.format_exc()}") return {"__type__": "update"}, {"__type__": "update"}, {"__type__": "update"} - def export_onnx(model_path, exported_path): device = torch.device("cpu") checkpoint = torch.load(model_path, map_location=device) @@ -862,7 +855,7 @@ def cli_infer(com): output_path = f'audio-outputs/{output_file_name}' print(f"{output_message} Starting the inference...") - vc_data = get_vc(model_name, protection_amnt, protect1) + vc_data = get_vc(model_name, protection_amnt, protection_amnt) print(vc_data) print(f"{output_message} Performing inference...") @@ -871,7 +864,7 @@ def cli_infer(com): source_audio_path, source_audio_path, transposition, - f0_file, + None, # f0 file support not implemented f0_method, feature_index_path, feature_index_path, @@ -968,7 +961,7 @@ def preset_apply(preset, qfer, tmbr): print("An unexpected error occurred", e) return ({"value": qfer, "__type__": "update"}, {"value": tmbr, "__type__": "update"}) - +@jit(nopython=True) def print_page_details(): page_description = { 'HOME': @@ -1040,12 +1033,13 @@ def print_page_details(): '\nExample: logs/mi-test/G_168000.pth MyModel 40k 1 "Created by Cole Mangio" v2' } print(page_description.get(cli_current_page, 'Invalid page')) - + + def change_page(page): global cli_current_page cli_current_page = page return 0 - +@jit def execute_command(com): command_to_page = { "go home": "HOME", @@ -1080,11 +1074,9 @@ def cli_navigation_loop(): while True: print(f"\nYou are currently in '{cli_current_page}':") print_page_details() - print(cli_current_page + ": ", end="") - try: - execute_command(input()) - except Exception as e: - print(f"An error occurred: {traceback.format_exc()}") + print(f"{cli_current_page}: ", end="") + try: execute_command(input()) + except Exception as e: print(f"An error occurred: {traceback.format_exc()}") if(config.is_cli): print( @@ -1142,1029 +1134,1033 @@ def stoptraining(mim): faq_file = "docs/faq.md" if tab_faq == "常见问题解答" else "docs/faq_en.md" weights_dir = 'weights/' -#Change your Gradio Theme here. 👇 👇 👇 👇 Example: " theme='HaleyCH/HaleyCH_Theme' " -with gr.Blocks(theme=gr.themes.Soft(), title='Mangio-RVC-Web 💻') as app: - gr.HTML("

The Mangio-RVC-Fork 💻

") - 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. 详见根目录使用需遵守的协议-LICENSE.txt." +def GradioSetup(): + #Change your Gradio Theme here. 👇 👇 👇 👇 Example: " theme='HaleyCH/HaleyCH_Theme' " + with gr.Blocks(theme=gr.themes.Soft(), title='Mangio-RVC-Web 💻') as app: + gr.HTML("

The Mangio-RVC-Fork 💻

") + 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. 详见根目录使用需遵守的协议-LICENSE.txt." + ) ) - ) - with gr.Tabs(): - - with gr.TabItem(i18n("模型推理")): - # Inference Preset Row - # with gr.Row(): - # mangio_preset = gr.Dropdown(label="Inference Preset", choices=sorted(get_presets())) - # mangio_preset_name_save = gr.Textbox( - # label="Your preset name" - # ) - # mangio_preset_save_btn = gr.Button('Save Preset', variant="primary") - - # Other RVC stuff - with gr.Row(): - - sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names), value='') - - refresh_button = gr.Button(i18n("Refresh voice list, index path and audio files"), variant="primary") - clean_button = gr.Button(i18n("卸载音色省显存"), variant="primary") - spk_item = gr.Slider( - minimum=0, - maximum=2333, - step=1, - label=i18n("请选择说话人id"), - value=0, - visible=False, - interactive=True, - ) - clean_button.click(fn=lambda: ({"value": "", "__type__": "update"}), inputs=[], outputs=[sid0]) + with gr.Tabs(): + + with gr.TabItem(i18n("模型推理")): + # Inference Preset Row + # with gr.Row(): + # mangio_preset = gr.Dropdown(label="Inference Preset", choices=sorted(get_presets())) + # mangio_preset_name_save = gr.Textbox( + # label="Your preset name" + # ) + # mangio_preset_save_btn = gr.Button('Save Preset', variant="primary") + + # Other RVC stuff + with gr.Row(): + + sid0 = gr.Dropdown(label=i18n("推理音色"), choices=sorted(names), value='') + + refresh_button = gr.Button(i18n("Refresh voice list, index path and audio files"), variant="primary") + clean_button = gr.Button(i18n("卸载音色省显存"), variant="primary") + spk_item = gr.Slider( + minimum=0, + maximum=2333, + step=1, + label=i18n("请选择说话人id"), + value=0, + visible=False, + interactive=True, + ) + clean_button.click(fn=lambda: ({"value": "", "__type__": "update"}), inputs=[], outputs=[sid0]) - with gr.Group(): + with gr.Group(): + gr.Markdown( + value=i18n("男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ") + ) + with gr.Row(): + with gr.Column(): + vc_transform0 = gr.Number( + label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 + ) + input_audio0 = gr.Textbox( + label=i18n("Add audio's name to the path to the audio file to be processed (default is the correct format example) Remove the path to use an audio from the dropdown list:"), + value=os.path.abspath(os.getcwd()).replace('\\', '/') + "/audios/" + "audio.wav", + ) + input_audio1 = gr.Dropdown( + label=i18n("Auto detect audio path and select from the dropdown:"), + choices=sorted(audio_paths), + value='', + interactive=True, + ) + input_audio1.change(fn=lambda:'',inputs=[],outputs=[input_audio0]) + f0method0 = gr.Radio( + label=i18n( + "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU" + ), + choices=["pm", "harvest", "dio", "crepe", "crepe-tiny", "mangio-crepe", "mangio-crepe-tiny", "rmvpe"], # Fork Feature. Add Crepe-Tiny + value="rmvpe", + interactive=True, + ) + crepe_hop_length = gr.Slider( + minimum=1, + maximum=512, + step=1, + label=i18n("crepe_hop_length"), + value=120, + interactive=True, + visible=False, + ) + f0method0.change( + fn=lambda radio: ( + { + "visible": radio in ['mangio-crepe', 'mangio-crepe-tiny'], + "__type__": "update" + } + ), + inputs=[f0method0], + outputs=[crepe_hop_length] + ) + filter_radius0 = gr.Slider( + minimum=0, + maximum=7, + label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"), + value=3, + step=1, + interactive=True, + ) + with gr.Column(): + file_index1 = gr.Textbox( + label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"), + value="", + interactive=True, + ) + + file_index2 = gr.Dropdown( + label="3. Path to your added.index file (if it didn't automatically find it.)", + choices=get_indexes(), + interactive=True, + allow_custom_value=True, + ) + #sid0.select(fn=match_index, inputs=sid0, outputs=file_index2) + + + + + refresh_button.click( + fn=change_choices, inputs=[], outputs=[sid0, file_index2, input_audio1] + ) + # file_big_npy1 = gr.Textbox( + # label=i18n("特征文件路径"), + # value="E:\\codes\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy", + # interactive=True, + # ) + index_rate1 = gr.Slider( + minimum=0, + maximum=1, + label=i18n("检索特征占比"), + value=0.75, + interactive=True, + ) + with gr.Column(): + resample_sr0 = gr.Slider( + minimum=0, + maximum=48000, + label=i18n("后处理重采样至最终采样率，0为不进行重采样"), + value=0, + step=1, + interactive=True, + ) + rms_mix_rate0 = gr.Slider( + minimum=0, + maximum=1, + label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"), + value=0.25, + interactive=True, + ) + protect0 = gr.Slider( + minimum=0, + maximum=0.5, + label=i18n( + "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果" + ), + value=0.33, + step=0.01, + interactive=True, + ) + formanting = gr.Checkbox( + value=bool(DoFormant), + label="[EXPERIMENTAL] Formant shift inference audio", + info="Used for male to female and vice-versa conversions", + interactive=True, + visible=True, + ) + + formant_preset = gr.Dropdown( + value='', + choices=get_fshift_presets(), + label='Browse presets for formanting', + info='Presets are located in formantshiftcfg/ folder', + visible=bool(DoFormant), + ) + + formant_refresh_button = gr.Button( + value='\U0001f504', + visible=bool(DoFormant), + variant='primary', + ) + + qfrency = gr.Slider( + value=Quefrency, + info="Default value is 1.0", + label="Quefrency for formant shifting", + minimum=0.0, + maximum=16.0, + step=0.1, + visible=bool(DoFormant), + interactive=True, + ) + + tmbre = gr.Slider( + value=Timbre, + info="Default value is 1.0", + label="Timbre for formant shifting", + minimum=0.0, + maximum=16.0, + step=0.1, + visible=bool(DoFormant), + interactive=True, + ) + + formant_preset.change(fn=preset_apply, inputs=[formant_preset, qfrency, tmbre], outputs=[qfrency, tmbre]) + frmntbut = gr.Button("Apply", variant="primary", visible=bool(DoFormant)) + formanting.change(fn=formant_enabled,inputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button],outputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button]) + frmntbut.click(fn=formant_apply,inputs=[qfrency, tmbre], outputs=[qfrency, tmbre]) + formant_refresh_button.click(fn=update_fshift_presets,inputs=[formant_preset, qfrency, tmbre],outputs=[formant_preset, qfrency, tmbre]) + + f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调")) + but0 = gr.Button(i18n("转换"), variant="primary") + with gr.Row(): + vc_output1 = gr.Textbox(label=i18n("输出信息")) + vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)")) + but0.click( + vc_single, + [ + spk_item, + input_audio0, + input_audio1, + vc_transform0, + f0_file, + f0method0, + file_index1, + file_index2, + # file_big_npy1, + index_rate1, + filter_radius0, + resample_sr0, + rms_mix_rate0, + protect0, + crepe_hop_length + ], + [vc_output1, vc_output2], + ) + with gr.Group(): + gr.Markdown( + value=i18n("批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ") + ) + with gr.Row(): + with gr.Column(): + vc_transform1 = gr.Number( + label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 + ) + opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt") + f0method1 = gr.Radio( + label=i18n( + "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU" + ), + choices=["pm", "harvest", "crepe", "rmvpe"], + value="rmvpe", + interactive=True, + ) + + filter_radius1 = gr.Slider( + minimum=0, + maximum=7, + label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"), + value=3, + step=1, + interactive=True, + ) + with gr.Column(): + file_index3 = gr.Textbox( + label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"), + value="", + interactive=True, + ) + file_index4 = gr.Dropdown( #file index dropdown for batch + label=i18n("自动检测index路径,下拉式选择(dropdown)"), + choices=get_indexes(), + interactive=True, + ) + sid0.select(fn=match_index, inputs=[sid0], outputs=[file_index2, file_index4]) + refresh_button.click( + fn=lambda: change_choices()[1], + inputs=[], + outputs=file_index4, + ) + # file_big_npy2 = gr.Textbox( + # label=i18n("特征文件路径"), + # value="E:\\codes\\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy", + # interactive=True, + # ) + index_rate2 = gr.Slider( + minimum=0, + maximum=1, + label=i18n("检索特征占比"), + value=1, + interactive=True, + ) + with gr.Column(): + resample_sr1 = gr.Slider( + minimum=0, + maximum=48000, + label=i18n("后处理重采样至最终采样率，0为不进行重采样"), + value=0, + step=1, + interactive=True, + ) + rms_mix_rate1 = gr.Slider( + minimum=0, + maximum=1, + label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"), + value=1, + interactive=True, + ) + protect1 = gr.Slider( + minimum=0, + maximum=0.5, + label=i18n( + "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果" + ), + value=0.33, + step=0.01, + interactive=True, + ) + with gr.Column(): + dir_input = gr.Textbox( + label=i18n("输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)"), + value=os.path.abspath(os.getcwd()).replace('\\', '/') + "/audios/", + ) + 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.Row(): + format1 = gr.Radio( + label=i18n("导出文件格式"), + choices=["wav", "flac", "mp3", "m4a"], + value="flac", + interactive=True, + ) + but1 = gr.Button(i18n("转换"), variant="primary") + vc_output3 = gr.Textbox(label=i18n("输出信息")) + but1.click( + vc_multi, + [ + spk_item, + dir_input, + opt_input, + inputs, + vc_transform1, + f0method1, + file_index3, + file_index4, + # file_big_npy2, + index_rate2, + filter_radius1, + resample_sr1, + rms_mix_rate1, + protect1, + format1, + crepe_hop_length, + ], + [vc_output3], + ) + sid0.change( + fn=get_vc, + inputs=[sid0, protect0, protect1], + outputs=[spk_item, protect0, protect1], + ) + 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"), + value=((os.getcwd()).replace('\\', '/') + "/audios/") + ) + 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="opt" + ) + opt_ins_root = gr.Textbox( + label=i18n("指定输出非主人声文件夹"), value="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], + ) + with gr.TabItem(i18n("训练")): gr.Markdown( - value=i18n("男转女推荐+12key, 女转男推荐-12key, 如果音域爆炸导致音色失真也可以自己调整到合适音域. ") + value=i18n( + "step1: 填写实验配置. 实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件. " + ) ) with gr.Row(): - with gr.Column(): - vc_transform0 = gr.Number( - label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 + exp_dir1 = gr.Textbox(label=i18n("输入实验名"), value="mi-test") + sr2 = gr.Radio( + label=i18n("目标采样率"), + choices=["40k", "48k", "32k"], + value="40k", + interactive=True, + ) + if_f0_3 = gr.Checkbox( + label="Whether the model has pitch guidance.", + value=True, + interactive=True, + ) + version19 = gr.Radio( + label=i18n("版本"), + choices=["v1", "v2"], + value="v2", + interactive=True, + visible=True, + ) + np7 = gr.Slider( + minimum=0, + maximum=config.n_cpu, + step=1, + label=i18n("提取音高和处理数据使用的CPU进程数"), + value=int(np.ceil(config.n_cpu / 1.5)), + interactive=True, + ) + with gr.Group(): # 暂时单人的, 后面支持最多4人的#数据处理 + gr.Markdown( + value=i18n( + "step2a: 自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练. " ) - input_audio0 = gr.Textbox( - label=i18n("Add audio's name to the path to the audio file to be processed (default is the correct format example) Remove the path to use an audio from the dropdown list:"), - value=os.path.abspath(os.getcwd()).replace('\\', '/') + "/audios/" + "audio.wav", + ) + with gr.Row(): + trainset_dir4 = gr.Textbox( + label=i18n("输入训练文件夹路径"), value=os.path.abspath(os.getcwd()) + "\\datasets\\" ) - input_audio1 = gr.Dropdown( - label=i18n("Auto detect audio path and select from the dropdown:"), - choices=sorted(audio_paths), - value='', + spk_id5 = gr.Slider( + minimum=0, + maximum=4, + step=1, + label=i18n("请指定说话人id"), + value=0, interactive=True, ) - input_audio1.change(fn=lambda:'',inputs=[],outputs=[input_audio0]) - f0method0 = gr.Radio( - label=i18n( - "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU" - ), - choices=["pm", "harvest", "dio", "crepe", "crepe-tiny", "mangio-crepe", "mangio-crepe-tiny", "rmvpe"], # Fork Feature. Add Crepe-Tiny - value="rmvpe", - interactive=True, + but1 = gr.Button(i18n("处理数据"), variant="primary") + info1 = gr.Textbox(label=i18n("输出信息"), value="") + but1.click( + preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1] ) - crepe_hop_length = gr.Slider( + with gr.Group(): + step2b = gr.Markdown(value=i18n("step2b: 使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号)")) + with gr.Row(): + with gr.Column(): + gpus6 = gr.Textbox( + label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"), + value=gpus, + interactive=True, + ) + gpu_info9 = gr.Textbox(label=i18n("显卡信息"), value=gpu_info) + with gr.Column(): + f0method8 = gr.Radio( + label=i18n( + "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢" + ), + choices=["pm", "harvest", "dio", "crepe", "mangio-crepe", "rmvpe"], # Fork feature: Crepe on f0 extraction for training. + value="rmvpe", + interactive=True, + ) + + extraction_crepe_hop_length = gr.Slider( + minimum=1, + maximum=512, + step=1, + label=i18n("crepe_hop_length"), + value=64, + interactive=True, + visible=False, + ) + + f0method8.change( + fn=lambda radio: ( + { + "visible": radio in ['mangio-crepe', 'mangio-crepe-tiny'], + "__type__": "update" + } + ), + inputs=[f0method8], + outputs=[extraction_crepe_hop_length] + ) + but2 = gr.Button(i18n("特征提取"), variant="primary") + info2 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8, interactive=False) + but2.click( + extract_f0_feature, + [gpus6, np7, f0method8, if_f0_3, exp_dir1, version19, extraction_crepe_hop_length], + [info2], + ) + with gr.Group(): + gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引")) + with gr.Row(): + save_epoch10 = gr.Slider( minimum=1, - maximum=512, + maximum=50, step=1, - label=i18n("crepe_hop_length"), - value=120, + label=i18n("保存频率save_every_epoch"), + value=5, interactive=True, - visible=False, - ) - f0method0.change( - fn=lambda radio: ( - { - "visible": radio in ['mangio-crepe', 'mangio-crepe-tiny'], - "__type__": "update" - } - ), - inputs=[f0method0], - outputs=[crepe_hop_length] + visible=True, ) - filter_radius0 = gr.Slider( - minimum=0, - maximum=7, - label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"), - value=3, + total_epoch11 = gr.Slider( + minimum=1, + maximum=10000, step=1, + label=i18n("总训练轮数total_epoch"), + value=20, interactive=True, ) - with gr.Column(): - file_index1 = gr.Textbox( - label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"), - value="", + batch_size12 = gr.Slider( + minimum=1, + maximum=40, + step=1, + label=i18n("每张显卡的batch_size"), + value=default_batch_size, interactive=True, ) - - file_index2 = gr.Dropdown( - label="3. Path to your added.index file (if it didn't automatically find it.)", - choices=get_indexes(), + if_save_latest13 = gr.Checkbox( + label="Whether to save only the latest .ckpt file to save hard drive space", + value=True, interactive=True, - allow_custom_value=True, - ) - #sid0.select(fn=match_index, inputs=sid0, outputs=file_index2) - - - - - refresh_button.click( - fn=change_choices, inputs=[], outputs=[sid0, file_index2, input_audio1] - ) - # file_big_npy1 = gr.Textbox( - # label=i18n("特征文件路径"), - # value="E:\\codes\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy", - # interactive=True, - # ) - index_rate1 = gr.Slider( - minimum=0, - maximum=1, - label=i18n("检索特征占比"), - value=0.75, + ) + if_cache_gpu17 = gr.Checkbox( + label="Cache all training sets to GPU memory. Caching small datasets (less than 10 minutes) can speed up training, but caching large datasets will consume a lot of GPU memory and may not provide much speed improvement", + value=False, interactive=True, ) - with gr.Column(): - resample_sr0 = gr.Slider( - minimum=0, - maximum=48000, - label=i18n("后处理重采样至最终采样率，0为不进行重采样"), - value=0, - step=1, + if_save_every_weights18 = gr.Checkbox( + label="Save a small final model to the 'weights' folder at each save point", + value=True, interactive=True, ) - rms_mix_rate0 = gr.Slider( - minimum=0, - maximum=1, - label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"), - value=0.25, + with gr.Row(): + pretrained_G14 = gr.Textbox( + lines=2, + label=i18n("加载预训练底模G路径"), + value="pretrained/f0G40k.pth", interactive=True, ) - protect0 = gr.Slider( - minimum=0, - maximum=0.5, - label=i18n( - "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果" - ), - value=0.33, - step=0.01, + pretrained_D15 = gr.Textbox( + lines=2, + label=i18n("加载预训练底模D路径"), + value="pretrained/f0D40k.pth", interactive=True, ) - formanting = gr.Checkbox( - value=bool(DoFormant), - label="[EXPERIMENTAL] Formant shift inference audio", - info="Used for male to female and vice-versa conversions", + sr2.change( + change_sr2, + [sr2, if_f0_3, version19], + [pretrained_G14, pretrained_D15], + ) + version19.change( + change_version19, + [sr2, if_f0_3, version19], + [pretrained_G14, pretrained_D15, sr2], + ) + if_f0_3.change( + fn=change_f0, + inputs=[if_f0_3, sr2, version19], + outputs=[f0method8, pretrained_G14, pretrained_D15], + ) + if_f0_3.change(fn=lambda radio: ( + { + "visible": radio in ['mangio-crepe', 'mangio-crepe-tiny'], + "__type__": "update" + } + ), inputs=[f0method8], outputs=[extraction_crepe_hop_length]) + gpus16 = gr.Textbox( + label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"), + value=gpus, interactive=True, - visible=True, ) - - formant_preset = gr.Dropdown( - value='', - choices=get_fshift_presets(), - label='Browse presets for formanting', - info='Presets are located in formantshiftcfg/ folder', - visible=bool(DoFormant), + butstop = gr.Button( + "Stop Training", + variant='primary', + visible=False, ) + but3 = gr.Button(i18n("训练模型"), variant="primary", visible=True) + but3.click(fn=stoptraining, inputs=[gr.Number(value=0, visible=False)], outputs=[but3, butstop]) + butstop.click(fn=stoptraining, inputs=[gr.Number(value=1, visible=False)], outputs=[butstop, but3]) + - formant_refresh_button = gr.Button( - value='\U0001f504', - visible=bool(DoFormant), - variant='primary', + but4 = gr.Button(i18n("训练特征索引"), variant="primary") + #but5 = gr.Button(i18n("一键训练"), variant="primary") + info3 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=10) + + if_save_every_weights18.change( + fn=lambda if_save_every_weights: ( + { + "visible": if_save_every_weights, + "__type__": "update" + } + ), + inputs=[if_save_every_weights18], + outputs=[save_epoch10] ) - qfrency = gr.Slider( - value=Quefrency, - info="Default value is 1.0", - label="Quefrency for formant shifting", - minimum=0.0, - maximum=16.0, - step=0.1, - visible=bool(DoFormant), - interactive=True, + but3.click( + click_train, + [ + exp_dir1, + sr2, + if_f0_3, + spk_id5, + save_epoch10, + total_epoch11, + batch_size12, + if_save_latest13, + pretrained_G14, + pretrained_D15, + gpus16, + if_cache_gpu17, + if_save_every_weights18, + version19, + ], + [info3, butstop, but3], ) - tmbre = gr.Slider( - value=Timbre, - info="Default value is 1.0", - label="Timbre for formant shifting", - minimum=0.0, - maximum=16.0, - step=0.1, - visible=bool(DoFormant), - interactive=True, - ) + but4.click(train_index, [exp_dir1, version19], info3) - formant_preset.change(fn=preset_apply, inputs=[formant_preset, qfrency, tmbre], outputs=[qfrency, tmbre]) - frmntbut = gr.Button("Apply", variant="primary", visible=bool(DoFormant)) - formanting.change(fn=formant_enabled,inputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button],outputs=[formanting,qfrency,tmbre,frmntbut,formant_preset,formant_refresh_button]) - frmntbut.click(fn=formant_apply,inputs=[qfrency, tmbre], outputs=[qfrency, tmbre]) - formant_refresh_button.click(fn=update_fshift_presets,inputs=[formant_preset, qfrency, tmbre],outputs=[formant_preset, qfrency, tmbre]) - f0_file = gr.File(label=i18n("F0曲线文件, 可选, 一行一个音高, 代替默认F0及升降调")) - but0 = gr.Button(i18n("转换"), variant="primary") - with gr.Row(): - vc_output1 = gr.Textbox(label=i18n("输出信息")) - vc_output2 = gr.Audio(label=i18n("输出音频(右下角三个点,点了可以下载)")) - but0.click( - vc_single, - [ - spk_item, - input_audio0, - input_audio1, - vc_transform0, - f0_file, - f0method0, - file_index1, - file_index2, - # file_big_npy1, - index_rate1, - filter_radius0, - resample_sr0, - rms_mix_rate0, - protect0, - crepe_hop_length - ], - [vc_output1, vc_output2], + + #but5.click( + # train1key, + # [ + # exp_dir1, + # sr2, + # if_f0_3, + # trainset_dir4, + # spk_id5, + # np7, + # f0method8, + # save_epoch10, + # total_epoch11, + # batch_size12, + # if_save_latest13, + # pretrained_G14, + # pretrained_D15, + # gpus16, + # if_cache_gpu17, + # if_save_every_weights18, + # version19, + # extraction_crepe_hop_length + # ], + # info3, + #) + with gr.Group(): + + gr.Markdown(value= + 'Step 4: Export lowest points on a graph of the model ' + 'After clicking on Export lowest points of a model, ' + 'The new files will be located in logs/[yourmodelname]/lowestvals/ folder' ) - with gr.Group(): - gr.Markdown( - value=i18n("批量转换, 输入待转换音频文件夹, 或上传多个音频文件, 在指定文件夹(默认opt)下输出转换的音频. ") - ) - with gr.Row(): - with gr.Column(): - vc_transform1 = gr.Number( - label=i18n("变调(整数, 半音数量, 升八度12降八度-12)"), value=0 - ) - opt_input = gr.Textbox(label=i18n("指定输出文件夹"), value="opt") - f0method1 = gr.Radio( - label=i18n( - "选择音高提取算法,输入歌声可用pm提速,harvest低音好但巨慢无比,crepe效果好但吃GPU" - ), - choices=["pm", "harvest", "crepe", "rmvpe"], - value="rmvpe", - interactive=True, - ) + + with gr.Row(): + with gr.Accordion(label='Lowest points export'): - filter_radius1 = gr.Slider( + lowestval_weight_dir = gr.Textbox(visible=False) + ds = gr.Textbox(visible=False) + weights_dir1 = gr.Textbox(visible=False, value=weights_dir) + + + with gr.Row(): + amntlastmdls = gr.Slider( + minimum=1, + maximum=25, + label='How many lowest points to save', + value=3, + step=1, + interactive=True, + ) + lpexport = gr.Button( + value='Export lowest points of a model', + variant='primary', + ) + lw_mdls = gr.File( + file_count="multiple", + label="Output models", + interactive=False, + ) ##### + + with gr.Row(): + infolpex = gr.Textbox(label="Output information:", value="", max_lines=10) + mdlbl = gr.Dataframe(label='Stats of selected models', datatype='number', type='pandas') + + lpexport.click( + lambda model_name: os.path.join("logs", model_name, "lowestvals"), + inputs=[exp_dir1], + outputs=[lowestval_weight_dir] + ) + + lpexport.click(fn=tensorlowest.main, inputs=[exp_dir1, save_epoch10, amntlastmdls], outputs=[ds]) + + ds.change( + fn=tensorlowest.selectweights, + inputs=[exp_dir1, ds, weights_dir1, lowestval_weight_dir], + outputs=[infolpex, lw_mdls, mdlbl], + ) + with gr.TabItem(i18n("ckpt处理")): + with gr.Group(): + gr.Markdown(value=i18n("模型融合, 可用于测试音色融合")) + with gr.Row(): + ckpt_a = gr.Textbox(label=i18n("A模型路径"), value="", interactive=True, placeholder="Path to your model A.") + ckpt_b = gr.Textbox(label=i18n("B模型路径"), value="", interactive=True, placeholder="Path to your model B.") + alpha_a = gr.Slider( minimum=0, - maximum=7, - label=i18n(">=3则使用对harvest音高识别的结果使用中值滤波，数值为滤波半径，使用可以削弱哑音"), - value=3, - step=1, - interactive=True, - ) - with gr.Column(): - file_index3 = gr.Textbox( - label=i18n("特征检索库文件路径,为空则使用下拉的选择结果"), - value="", + maximum=1, + label=i18n("A模型权重"), + value=0.5, interactive=True, ) - file_index4 = gr.Dropdown( #file index dropdown for batch - label=i18n("自动检测index路径,下拉式选择(dropdown)"), - choices=get_indexes(), + with gr.Row(): + sr_ = gr.Radio( + label=i18n("目标采样率"), + choices=["40k", "48k"], + value="40k", interactive=True, ) - sid0.select(fn=match_index, inputs=[sid0], outputs=[file_index2, file_index4]) - refresh_button.click( - fn=lambda: change_choices()[1], - inputs=[], - outputs=file_index4, - ) - # file_big_npy2 = gr.Textbox( - # label=i18n("特征文件路径"), - # value="E:\\codes\\py39\\vits_vc_gpu_train\\logs\\mi-test-1key\\total_fea.npy", - # interactive=True, - # ) - index_rate2 = gr.Slider( - minimum=0, - maximum=1, - label=i18n("检索特征占比"), - value=1, + if_f0_ = gr.Checkbox( + label="Whether the model has pitch guidance.", + value=True, interactive=True, ) - with gr.Column(): - resample_sr1 = gr.Slider( - minimum=0, - maximum=48000, - label=i18n("后处理重采样至最终采样率，0为不进行重采样"), - value=0, - step=1, - interactive=True, + info__ = gr.Textbox( + label=i18n("要置入的模型信息"), value="", max_lines=8, interactive=True, placeholder="Model information to be placed." ) - rms_mix_rate1 = gr.Slider( - minimum=0, - maximum=1, - label=i18n("输入源音量包络替换输出音量包络融合比例，越靠近1越使用输出包络"), - value=1, + name_to_save0 = gr.Textbox( + label=i18n("保存的模型名不带后缀"), + value="", + placeholder="Name for saving.", + max_lines=1, interactive=True, ) - protect1 = gr.Slider( - minimum=0, - maximum=0.5, - label=i18n( - "保护清辅音和呼吸声，防止电音撕裂等artifact，拉满0.5不开启，调低加大保护力度但可能降低索引效果" - ), - value=0.33, - step=0.01, + version_2 = gr.Radio( + label=i18n("模型版本型号"), + choices=["v1", "v2"], + value="v1", interactive=True, ) - with gr.Column(): - dir_input = gr.Textbox( - label=i18n("输入待处理音频文件夹路径(去文件管理器地址栏拷就行了)"), - value=os.path.abspath(os.getcwd()).replace('\\', '/') + "/audios/", - ) - 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.Row(): - format1 = gr.Radio( - label=i18n("导出文件格式"), - choices=["wav", "flac", "mp3", "m4a"], - value="flac", - interactive=True, - ) - but1 = gr.Button(i18n("转换"), variant="primary") - vc_output3 = gr.Textbox(label=i18n("输出信息")) - but1.click( - vc_multi, + but6 = gr.Button(i18n("融合"), variant="primary") + info4 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) + but6.click( + merge, [ - spk_item, - dir_input, - opt_input, - inputs, - vc_transform1, - f0method1, - file_index3, - file_index4, - # file_big_npy2, - index_rate2, - filter_radius1, - resample_sr1, - rms_mix_rate1, - protect1, - format1, - crepe_hop_length, + ckpt_a, + ckpt_b, + alpha_a, + sr_, + if_f0_, + info__, + name_to_save0, + version_2, ], - [vc_output3], - ) - sid0.change( - fn=get_vc, - inputs=[sid0, protect0, protect1], - outputs=[spk_item, protect0, protect1], - ) - 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"), - value=((os.getcwd()).replace('\\', '/') + "/audios/") + info4, + ) # def merge(path1,path2,alpha1,sr,f0,info): + with gr.Group(): + gr.Markdown(value=i18n("修改模型信息(仅支持weights文件夹下提取的小模型文件)")) + with gr.Row(): ###### + ckpt_path0 = gr.Textbox( + label=i18n("模型路径"), placeholder="Path to your Model.", value="", interactive=True ) - 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, + info_ = gr.Textbox( + label=i18n("要改的模型信息"), value="", max_lines=8, interactive=True, placeholder="Model information to be changed." + ) + name_to_save1 = gr.Textbox( + label=i18n("保存的文件名, 默认空为和源文件同名"), + placeholder="Either leave empty or put in the Name of the Model to be saved.", + value="", + max_lines=8, 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="opt" + with gr.Row(): + but7 = gr.Button(i18n("修改"), variant="primary") + info5 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) + but7.click(change_info, [ckpt_path0, info_, name_to_save1], info5) + with gr.Group(): + gr.Markdown(value=i18n("查看模型信息(仅支持weights文件夹下提取的小模型文件)")) + with gr.Row(): + ckpt_path1 = gr.Textbox( + label=i18n("模型路径"), value="", interactive=True, placeholder="Model path here." ) - opt_ins_root = gr.Textbox( - label=i18n("指定输出非主人声文件夹"), value="opt" + but8 = gr.Button(i18n("查看"), variant="primary") + info6 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) + but8.click(show_info, [ckpt_path1], info6) + with gr.Group(): + gr.Markdown( + value=i18n( + "模型提取(输入logs文件夹下大文件模型路径),适用于训一半不想训了模型没有自动提取保存小文件模型,或者想测试中间模型的情况" ) - format0 = gr.Radio( - label=i18n("导出文件格式"), - choices=["wav", "flac", "mp3", "m4a"], - value="flac", + ) + with gr.Row(): + ckpt_path2 = gr.Textbox( + lines=3, + label=i18n("模型路径"), + value=os.path.abspath(os.getcwd()).replace('\\', '/') + "/logs/[YOUR_MODEL]/G_23333.pth", 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], - ) - with gr.TabItem(i18n("训练")): - gr.Markdown( - value=i18n( - "step1: 填写实验配置. 实验数据放在logs下, 每个实验一个文件夹, 需手工输入实验名路径, 内含实验配置, 日志, 训练得到的模型文件. " - ) - ) - with gr.Row(): - exp_dir1 = gr.Textbox(label=i18n("输入实验名"), value="mi-test") - sr2 = gr.Radio( - label=i18n("目标采样率"), - choices=["40k", "48k", "32k"], - value="40k", - interactive=True, - ) - if_f0_3 = gr.Checkbox( - label="Whether the model has pitch guidance.", - value=True, - interactive=True, - ) - version19 = gr.Radio( - label=i18n("版本"), - choices=["v1", "v2"], - value="v2", - interactive=True, - visible=True, - ) - np7 = gr.Slider( - minimum=0, - maximum=config.n_cpu, - step=1, - label=i18n("提取音高和处理数据使用的CPU进程数"), - value=int(np.ceil(config.n_cpu / 1.5)), - interactive=True, - ) - with gr.Group(): # 暂时单人的, 后面支持最多4人的#数据处理 - gr.Markdown( - value=i18n( - "step2a: 自动遍历训练文件夹下所有可解码成音频的文件并进行切片归一化, 在实验目录下生成2个wav文件夹; 暂时只支持单人训练. " - ) - ) - with gr.Row(): - trainset_dir4 = gr.Textbox( - label=i18n("输入训练文件夹路径"), value=os.path.abspath(os.getcwd()) + "\\datasets\\" - ) - spk_id5 = gr.Slider( - minimum=0, - maximum=4, - step=1, - label=i18n("请指定说话人id"), - value=0, - interactive=True, - ) - but1 = gr.Button(i18n("处理数据"), variant="primary") - info1 = gr.Textbox(label=i18n("输出信息"), value="") - but1.click( - preprocess_dataset, [trainset_dir4, exp_dir1, sr2, np7], [info1] - ) - with gr.Group(): - step2b = gr.Markdown(value=i18n("step2b: 使用CPU提取音高(如果模型带音高), 使用GPU提取特征(选择卡号)")) - with gr.Row(): - with gr.Column(): - gpus6 = gr.Textbox( - label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"), - value=gpus, + save_name = gr.Textbox( + label=i18n("保存名"), value="", interactive=True, + placeholder="Your filename here.", + ) + sr__ = gr.Radio( + label=i18n("目标采样率"), + choices=["32k", "40k", "48k"], + value="40k", interactive=True, ) - gpu_info9 = gr.Textbox(label=i18n("显卡信息"), value=gpu_info) - with gr.Column(): - f0method8 = gr.Radio( - label=i18n( - "选择音高提取算法:输入歌声可用pm提速,高质量语音但CPU差可用dio提速,harvest质量更好但慢" - ), - choices=["pm", "harvest", "dio", "crepe", "mangio-crepe", "rmvpe"], # Fork feature: Crepe on f0 extraction for training. - value="rmvpe", + if_f0__ = gr.Checkbox( + label="Whether the model has pitch guidance.", + value=True, interactive=True, ) - - extraction_crepe_hop_length = gr.Slider( - minimum=1, - maximum=512, - step=1, - label=i18n("crepe_hop_length"), - value=64, + version_1 = gr.Radio( + label=i18n("模型版本型号"), + choices=["v1", "v2"], + value="v2", interactive=True, - visible=False, ) - - f0method8.change( - fn=lambda radio: ( - { - "visible": radio in ['mangio-crepe', 'mangio-crepe-tiny'], - "__type__": "update" - } - ), - inputs=[f0method8], - outputs=[extraction_crepe_hop_length] + info___ = gr.Textbox( + label=i18n("要置入的模型信息"), value="", max_lines=8, interactive=True, placeholder="Model info here." ) - but2 = gr.Button(i18n("特征提取"), variant="primary") - info2 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8, interactive=False) - but2.click( - extract_f0_feature, - [gpus6, np7, f0method8, if_f0_3, exp_dir1, version19, extraction_crepe_hop_length], - [info2], - ) - with gr.Group(): - gr.Markdown(value=i18n("step3: 填写训练设置, 开始训练模型和索引")) - with gr.Row(): - save_epoch10 = gr.Slider( - minimum=1, - maximum=50, - step=1, - label=i18n("保存频率save_every_epoch"), - value=5, - interactive=True, - visible=True, - ) - total_epoch11 = gr.Slider( - minimum=1, - maximum=10000, - step=1, - label=i18n("总训练轮数total_epoch"), - value=20, - interactive=True, - ) - batch_size12 = gr.Slider( - minimum=1, - maximum=40, - step=1, - label=i18n("每张显卡的batch_size"), - value=default_batch_size, - interactive=True, - ) - if_save_latest13 = gr.Checkbox( - label="Whether to save only the latest .ckpt file to save hard drive space", - value=True, - interactive=True, - ) - if_cache_gpu17 = gr.Checkbox( - label="Cache all training sets to GPU memory. Caching small datasets (less than 10 minutes) can speed up training, but caching large datasets will consume a lot of GPU memory and may not provide much speed improvement", - value=False, - interactive=True, - ) - if_save_every_weights18 = gr.Checkbox( - label="Save a small final model to the 'weights' folder at each save point", - value=True, - interactive=True, - ) - with gr.Row(): - pretrained_G14 = gr.Textbox( - lines=2, - label=i18n("加载预训练底模G路径"), - value="pretrained/f0G40k.pth", - interactive=True, - ) - pretrained_D15 = gr.Textbox( - lines=2, - label=i18n("加载预训练底模D路径"), - value="pretrained/f0D40k.pth", - interactive=True, - ) - sr2.change( - change_sr2, - [sr2, if_f0_3, version19], - [pretrained_G14, pretrained_D15], - ) - version19.change( - change_version19, - [sr2, if_f0_3, version19], - [pretrained_G14, pretrained_D15, sr2], - ) - if_f0_3.change( - fn=change_f0, - inputs=[if_f0_3, sr2, version19], - outputs=[f0method8, pretrained_G14, pretrained_D15], - ) - if_f0_3.change(fn=lambda radio: ( - { - "visible": radio in ['mangio-crepe', 'mangio-crepe-tiny'], - "__type__": "update" - } - ), inputs=[f0method8], outputs=[extraction_crepe_hop_length]) - gpus16 = gr.Textbox( - label=i18n("以-分隔输入使用的卡号, 例如 0-1-2 使用卡0和卡1和卡2"), - value=gpus, - interactive=True, - ) - butstop = gr.Button( - "Stop Training", - variant='primary', - visible=False, - ) - but3 = gr.Button(i18n("训练模型"), variant="primary", visible=True) - but3.click(fn=stoptraining, inputs=[gr.Number(value=0, visible=False)], outputs=[but3, butstop]) - butstop.click(fn=stoptraining, inputs=[gr.Number(value=1, visible=False)], outputs=[butstop, but3]) - - - but4 = gr.Button(i18n("训练特征索引"), variant="primary") - #but5 = gr.Button(i18n("一键训练"), variant="primary") - info3 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=10) - - if_save_every_weights18.change( - fn=lambda if_save_every_weights: ( - { - "visible": if_save_every_weights, - "__type__": "update" - } - ), - inputs=[if_save_every_weights18], - outputs=[save_epoch10] - ) - - but3.click( - click_train, - [ - exp_dir1, - sr2, - if_f0_3, - spk_id5, - save_epoch10, - total_epoch11, - batch_size12, - if_save_latest13, - pretrained_G14, - pretrained_D15, - gpus16, - if_cache_gpu17, - if_save_every_weights18, - version19, - ], - [info3, butstop, but3], - ) - - but4.click(train_index, [exp_dir1, version19], info3) - - - - #but5.click( - # train1key, - # [ - # exp_dir1, - # sr2, - # if_f0_3, - # trainset_dir4, - # spk_id5, - # np7, - # f0method8, - # save_epoch10, - # total_epoch11, - # batch_size12, - # if_save_latest13, - # pretrained_G14, - # pretrained_D15, - # gpus16, - # if_cache_gpu17, - # if_save_every_weights18, - # version19, - # extraction_crepe_hop_length - # ], - # info3, - #) - with gr.Group(): - - gr.Markdown(value= - 'Step 4: Export lowest points on a graph of the model ' - 'After clicking on Export lowest points of a model, ' - 'The new files will be located in logs/[yourmodelname]/lowestvals/ folder' - ) - - with gr.Row(): - with gr.Accordion(label='Lowest points export'): - - lowestval_weight_dir = gr.Textbox(visible=False) - ds = gr.Textbox(visible=False) - weights_dir1 = gr.Textbox(visible=False, value=weights_dir) - - - with gr.Row(): - amntlastmdls = gr.Slider( - minimum=1, - maximum=25, - label='How many lowest points to save', - value=3, - step=1, - interactive=True, - ) - lpexport = gr.Button( - value='Export lowest points of a model', - variant='primary', - ) - lw_mdls = gr.File( - file_count="multiple", - label="Output models", - interactive=False, - ) ##### - - with gr.Row(): - infolpex = gr.Textbox(label="Output information:", value="", max_lines=10) - mdlbl = gr.Dataframe(label='Stats of selected models', datatype='number', type='pandas') - - lpexport.click( - lambda model_name: os.path.join("logs", model_name, "lowestvals"), - inputs=[exp_dir1], - outputs=[lowestval_weight_dir] + but9 = gr.Button(i18n("提取"), variant="primary") + info7 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) + ckpt_path2.change( + change_info_, [ckpt_path2], [sr__, if_f0__, version_1] ) - - lpexport.click(fn=tensorlowest.main, inputs=[exp_dir1, save_epoch10, amntlastmdls], outputs=[ds]) - - ds.change( - fn=tensorlowest.selectweights, - inputs=[exp_dir1, ds, weights_dir1, lowestval_weight_dir], - outputs=[infolpex, lw_mdls, mdlbl], - ) - with gr.TabItem(i18n("ckpt处理")): - with gr.Group(): - gr.Markdown(value=i18n("模型融合, 可用于测试音色融合")) - with gr.Row(): - ckpt_a = gr.Textbox(label=i18n("A模型路径"), value="", interactive=True, placeholder="Path to your model A.") - ckpt_b = gr.Textbox(label=i18n("B模型路径"), value="", interactive=True, placeholder="Path to your model B.") - alpha_a = gr.Slider( - minimum=0, - maximum=1, - label=i18n("A模型权重"), - value=0.5, - interactive=True, + but9.click( + extract_small_model, + [ckpt_path2, save_name, sr__, if_f0__, info___, version_1], + info7, ) + + with gr.TabItem(i18n("Onnx导出")): with gr.Row(): - sr_ = gr.Radio( - label=i18n("目标采样率"), - choices=["40k", "48k"], - value="40k", - interactive=True, - ) - if_f0_ = gr.Checkbox( - label="Whether the model has pitch guidance.", - value=True, - interactive=True, - ) - info__ = gr.Textbox( - label=i18n("要置入的模型信息"), value="", max_lines=8, interactive=True, placeholder="Model information to be placed." - ) - name_to_save0 = gr.Textbox( - label=i18n("保存的模型名不带后缀"), - value="", - placeholder="Name for saving.", - max_lines=1, - interactive=True, - ) - version_2 = gr.Radio( - label=i18n("模型版本型号"), - choices=["v1", "v2"], - value="v1", - interactive=True, - ) + ckpt_dir = gr.Textbox(label=i18n("RVC模型路径"), value="", interactive=True, placeholder="RVC model path.") with gr.Row(): - but6 = gr.Button(i18n("融合"), variant="primary") - info4 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) - but6.click( - merge, - [ - ckpt_a, - ckpt_b, - alpha_a, - sr_, - if_f0_, - info__, - name_to_save0, - version_2, - ], - info4, - ) # def merge(path1,path2,alpha1,sr,f0,info): - with gr.Group(): - gr.Markdown(value=i18n("修改模型信息(仅支持weights文件夹下提取的小模型文件)")) - with gr.Row(): ###### - ckpt_path0 = gr.Textbox( - label=i18n("模型路径"), placeholder="Path to your Model.", value="", interactive=True - ) - info_ = gr.Textbox( - label=i18n("要改的模型信息"), value="", max_lines=8, interactive=True, placeholder="Model information to be changed." - ) - name_to_save1 = gr.Textbox( - label=i18n("保存的文件名, 默认空为和源文件同名"), - placeholder="Either leave empty or put in the Name of the Model to be saved.", - value="", - max_lines=8, - interactive=True, + onnx_dir = gr.Textbox( + label=i18n("Onnx输出路径"), value="", interactive=True, placeholder="Onnx model output path." ) with gr.Row(): - but7 = gr.Button(i18n("修改"), variant="primary") - info5 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) - but7.click(change_info, [ckpt_path0, info_, name_to_save1], info5) - with gr.Group(): - gr.Markdown(value=i18n("查看模型信息(仅支持weights文件夹下提取的小模型文件)")) + infoOnnx = gr.Label(label="info") with gr.Row(): - ckpt_path1 = gr.Textbox( - label=i18n("模型路径"), value="", interactive=True, placeholder="Model path here." - ) - but8 = gr.Button(i18n("查看"), variant="primary") - info6 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) - but8.click(show_info, [ckpt_path1], info6) - with gr.Group(): - gr.Markdown( - value=i18n( - "模型提取(输入logs文件夹下大文件模型路径),适用于训一半不想训了模型没有自动提取保存小文件模型,或者想测试中间模型的情况" - ) - ) - with gr.Row(): - ckpt_path2 = gr.Textbox( - lines=3, - label=i18n("模型路径"), - value=os.path.abspath(os.getcwd()).replace('\\', '/') + "/logs/[YOUR_MODEL]/G_23333.pth", - interactive=True, - ) - save_name = gr.Textbox( - label=i18n("保存名"), value="", interactive=True, - placeholder="Your filename here.", - ) - sr__ = gr.Radio( - label=i18n("目标采样率"), - choices=["32k", "40k", "48k"], - value="40k", - interactive=True, - ) - if_f0__ = gr.Checkbox( - label="Whether the model has pitch guidance.", - value=True, - interactive=True, - ) - version_1 = gr.Radio( - label=i18n("模型版本型号"), - choices=["v1", "v2"], - value="v2", - interactive=True, - ) - info___ = gr.Textbox( - label=i18n("要置入的模型信息"), value="", max_lines=8, interactive=True, placeholder="Model info here." - ) - but9 = gr.Button(i18n("提取"), variant="primary") - info7 = gr.Textbox(label=i18n("输出信息"), value="", max_lines=8) - ckpt_path2.change( - change_info_, [ckpt_path2], [sr__, if_f0__, version_1] - ) - but9.click( - extract_small_model, - [ckpt_path2, save_name, sr__, if_f0__, info___, version_1], - info7, - ) + butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary") + butOnnx.click(export_onnx, [ckpt_dir, onnx_dir], infoOnnx) - with gr.TabItem(i18n("Onnx导出")): - with gr.Row(): - ckpt_dir = gr.Textbox(label=i18n("RVC模型路径"), value="", interactive=True, placeholder="RVC model path.") - with gr.Row(): - onnx_dir = gr.Textbox( - label=i18n("Onnx输出路径"), value="", interactive=True, placeholder="Onnx model output path." - ) - with gr.Row(): - infoOnnx = gr.Label(label="info") - with gr.Row(): - butOnnx = gr.Button(i18n("导出Onnx模型"), variant="primary") - butOnnx.click(export_onnx, [ckpt_dir, onnx_dir], infoOnnx) + with gr.TabItem(tab_faq): + try: + with open(faq_file, "r", encoding="utf8") as f: + info = f.read() + gr.Markdown(value=info) + except: + gr.Markdown(traceback.format_exc()) - with gr.TabItem(tab_faq): - try: - with open(faq_file, "r", encoding="utf8") as f: - info = f.read() - gr.Markdown(value=info) - except: - gr.Markdown(traceback.format_exc()) - - - #region Mangio Preset Handler Region - def save_preset( - preset_name, - sid0, - vc_transform, - input_audio0, - input_audio1, - f0method, - crepe_hop_length, - filter_radius, - file_index1, - file_index2, - index_rate, - resample_sr, - rms_mix_rate, - protect, - f0_file - ): - data = None - with open('../inference-presets.json', 'r') as file: - data = json.load(file) - preset_json = { - 'name': preset_name, - 'model': sid0, - 'transpose': vc_transform, - 'audio_file': input_audio0, - 'auto_audio_file': input_audio1, - 'f0_method': f0method, - 'crepe_hop_length': crepe_hop_length, - 'median_filtering': filter_radius, - 'feature_path': file_index1, - 'auto_feature_path': file_index2, - 'search_feature_ratio': index_rate, - 'resample': resample_sr, - 'volume_envelope': rms_mix_rate, - 'protect_voiceless': protect, - 'f0_file_path': f0_file - } - data['presets'].append(preset_json) - with open('../inference-presets.json', 'w') as file: - json.dump(data, file) - file.flush() - print("Saved Preset %s into inference-presets.json!" % preset_name) - - - def on_preset_changed(preset_name): - print("Changed Preset to %s!" % preset_name) - data = None - with open('../inference-presets.json', 'r') as file: - data = json.load(file) - - print("Searching for " + preset_name) - returning_preset = None - for preset in data['presets']: - if(preset['name'] == preset_name): - print("Found a preset") - returning_preset = preset - # return all new input values - return ( - # returning_preset['model'], - # returning_preset['transpose'], - # returning_preset['audio_file'], - # returning_preset['f0_method'], - # returning_preset['crepe_hop_length'], - # returning_preset['median_filtering'], - # returning_preset['feature_path'], - # returning_preset['auto_feature_path'], - # returning_preset['search_feature_ratio'], - # returning_preset['resample'], - # returning_preset['volume_envelope'], - # returning_preset['protect_voiceless'], - # returning_preset['f0_file_path'] - ) - # Preset State Changes - - # This click calls save_preset that saves the preset into inference-presets.json with the preset name - # mangio_preset_save_btn.click( - # fn=save_preset, - # inputs=[ - # mangio_preset_name_save, - # sid0, - # vc_transform0, - # input_audio0, - # f0method0, - # crepe_hop_length, - # filter_radius0, - # file_index1, - # file_index2, - # index_rate1, - # resample_sr0, - # rms_mix_rate0, - # protect0, - # f0_file - # ], - # outputs=[] - # ) - - # mangio_preset.change( - # on_preset_changed, - # inputs=[ - # # Pass inputs here - # mangio_preset - # ], - # outputs=[ - # # Pass Outputs here. These refer to the gradio elements that we want to directly change - # # sid0, - # # vc_transform0, - # # input_audio0, - # # f0method0, - # # crepe_hop_length, - # # filter_radius0, - # # file_index1, - # # file_index2, - # # index_rate1, - # # resample_sr0, - # # rms_mix_rate0, - # # protect0, - # # f0_file - # ] - # ) - #endregion - - # with gr.TabItem(i18n("招募音高曲线前端编辑器")): - # gr.Markdown(value=i18n("加开发群联系我xxxxx")) - # with gr.TabItem(i18n("点击查看交流、问题反馈群号")): - # gr.Markdown(value=i18n("xxxxx")) - - if config.iscolab or config.paperspace: # Share gradio link for colab and paperspace (FORK FEATURE) - app.queue(concurrency_count=511, max_size=1022).launch(share=True) - else: - app.queue(concurrency_count=511, max_size=1022).launch( - server_name="0.0.0.0", - inbrowser=not config.noautoopen, - server_port=config.listen_port, - quiet=False, - ) + #region Mangio Preset Handler Region + def save_preset( + preset_name, + sid0, + vc_transform, + input_audio0, + input_audio1, + f0method, + crepe_hop_length, + filter_radius, + file_index1, + file_index2, + index_rate, + resample_sr, + rms_mix_rate, + protect, + f0_file + ): + data = None + with open('../inference-presets.json', 'r') as file: + data = json.load(file) + preset_json = { + 'name': preset_name, + 'model': sid0, + 'transpose': vc_transform, + 'audio_file': input_audio0, + 'auto_audio_file': input_audio1, + 'f0_method': f0method, + 'crepe_hop_length': crepe_hop_length, + 'median_filtering': filter_radius, + 'feature_path': file_index1, + 'auto_feature_path': file_index2, + 'search_feature_ratio': index_rate, + 'resample': resample_sr, + 'volume_envelope': rms_mix_rate, + 'protect_voiceless': protect, + 'f0_file_path': f0_file + } + data['presets'].append(preset_json) + with open('../inference-presets.json', 'w') as file: + json.dump(data, file) + file.flush() + print("Saved Preset %s into inference-presets.json!" % preset_name) + + + def on_preset_changed(preset_name): + print("Changed Preset to %s!" % preset_name) + data = None + with open('../inference-presets.json', 'r') as file: + data = json.load(file) + + print("Searching for " + preset_name) + returning_preset = None + for preset in data['presets']: + if(preset['name'] == preset_name): + print("Found a preset") + returning_preset = preset + # return all new input values + return ( + # returning_preset['model'], + # returning_preset['transpose'], + # returning_preset['audio_file'], + # returning_preset['f0_method'], + # returning_preset['crepe_hop_length'], + # returning_preset['median_filtering'], + # returning_preset['feature_path'], + # returning_preset['auto_feature_path'], + # returning_preset['search_feature_ratio'], + # returning_preset['resample'], + # returning_preset['volume_envelope'], + # returning_preset['protect_voiceless'], + # returning_preset['f0_file_path'] + ) + + # Preset State Changes + + # This click calls save_preset that saves the preset into inference-presets.json with the preset name + # mangio_preset_save_btn.click( + # fn=save_preset, + # inputs=[ + # mangio_preset_name_save, + # sid0, + # vc_transform0, + # input_audio0, + # f0method0, + # crepe_hop_length, + # filter_radius0, + # file_index1, + # file_index2, + # index_rate1, + # resample_sr0, + # rms_mix_rate0, + # protect0, + # f0_file + # ], + # outputs=[] + # ) + + # mangio_preset.change( + # on_preset_changed, + # inputs=[ + # # Pass inputs here + # mangio_preset + # ], + # outputs=[ + # # Pass Outputs here. These refer to the gradio elements that we want to directly change + # # sid0, + # # vc_transform0, + # # input_audio0, + # # f0method0, + # # crepe_hop_length, + # # filter_radius0, + # # file_index1, + # # file_index2, + # # index_rate1, + # # resample_sr0, + # # rms_mix_rate0, + # # protect0, + # # f0_file + # ] + # ) + #endregion + + # with gr.TabItem(i18n("招募音高曲线前端编辑器")): + # gr.Markdown(value=i18n("加开发群联系我xxxxx")) + # with gr.TabItem(i18n("点击查看交流、问题反馈群号")): + # gr.Markdown(value=i18n("xxxxx")) + + if config.iscolab or config.paperspace: # Share gradio link for colab and paperspace (FORK FEATURE) + app.queue(concurrency_count=511, max_size=1022).launch(share=True) + else: + app.queue(concurrency_count=511, max_size=1022).launch( + server_name="0.0.0.0", + inbrowser=not config.noautoopen, + server_port=config.listen_port, + quiet=False, + ) #endregion + +if __name__ == "__main__": + GradioSetup() \ No newline at end of file