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| import os | |
| import re | |
| import ssl | |
| import sys | |
| import json | |
| import torch | |
| import codecs | |
| import shutil | |
| import asyncio | |
| import librosa | |
| import logging | |
| import datetime | |
| import platform | |
| import requests | |
| import warnings | |
| import threading | |
| import subprocess | |
| import logging.handlers | |
| import numpy as np | |
| import gradio as gr | |
| import pandas as pd | |
| import soundfile as sf | |
| from time import sleep | |
| from multiprocessing import cpu_count | |
| from main.app.tabs.inference.inference import inference_tabs | |
| sys.path.append(os.getcwd()) | |
| from main.tools import huggingface | |
| from main.configs.config import Config | |
| from main.app.based.utils import * | |
| with gr.Blocks(title=" Ultimate RVC Maker ⚡", theme=theme) as app: | |
| gr.HTML("<h1 style='text-align: center;'>Ultimate RVC Maker ⚡</h1>") | |
| with gr.Tabs(): | |
| with gr.TabItem(translations["separator_tab"], visible=configs.get("separator_tab", True)): | |
| gr.Markdown(f"## {translations['separator_tab']}") | |
| with gr.Row(): | |
| gr.Markdown(translations["4_part"]) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Group(): | |
| with gr.Row(): | |
| cleaner = gr.Checkbox(label=translations["clear_audio"], value=False, interactive=True, min_width=140) | |
| backing = gr.Checkbox(label=translations["separator_backing"], value=False, interactive=True, min_width=140) | |
| reverb = gr.Checkbox(label=translations["dereveb_audio"], value=False, interactive=True, min_width=140) | |
| backing_reverb = gr.Checkbox(label=translations["dereveb_backing"], value=False, interactive=False, min_width=140) | |
| denoise = gr.Checkbox(label=translations["denoise_mdx"], value=False, interactive=False, min_width=140) | |
| with gr.Row(): | |
| separator_model = gr.Dropdown(label=translations["separator_model"], value=uvr_model[0], choices=uvr_model, interactive=True) | |
| separator_backing_model = gr.Dropdown(label=translations["separator_backing_model"], value="Version-1", choices=["Version-1", "Version-2"], interactive=True, visible=backing.value) | |
| with gr.Row(): | |
| with gr.Column(): | |
| separator_button = gr.Button(translations["separator_tab"], variant="primary") | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Group(): | |
| with gr.Row(): | |
| shifts = gr.Slider(label=translations["shift"], info=translations["shift_info"], minimum=1, maximum=20, value=2, step=1, interactive=True) | |
| segment_size = gr.Slider(label=translations["segments_size"], info=translations["segments_size_info"], minimum=32, maximum=3072, value=256, step=32, interactive=True) | |
| with gr.Row(): | |
| mdx_batch_size = gr.Slider(label=translations["batch_size"], info=translations["mdx_batch_size_info"], minimum=1, maximum=64, value=1, step=1, interactive=True, visible=backing.value or reverb.value or separator_model.value in mdx_model) | |
| with gr.Column(): | |
| with gr.Group(): | |
| with gr.Row(): | |
| overlap = gr.Radio(label=translations["overlap"], info=translations["overlap_info"], choices=["0.25", "0.5", "0.75", "0.99"], value="0.25", interactive=True) | |
| with gr.Row(): | |
| mdx_hop_length = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=8192, value=1024, step=1, interactive=True, visible=backing.value or reverb.value or separator_model.value in mdx_model) | |
| with gr.Column(): | |
| with gr.Row(): | |
| clean_strength = gr.Slider(label=translations["clean_strength"], info=translations["clean_strength_info"], minimum=0, maximum=1, value=0.5, step=0.1, interactive=True, visible=cleaner.value) | |
| sample_rate1 = gr.Slider(minimum=8000, maximum=96000, step=1, value=44100, label=translations["sr"], info=translations["sr_info"], interactive=True) | |
| with gr.Column(): | |
| input = gr.File(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"]) | |
| audio_input = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"]) | |
| with gr.Column(): | |
| with gr.Accordion(translations["use_url"], open=False): | |
| url = gr.Textbox(label=translations["url_audio"], value="", placeholder="https://www.youtube.com/...", scale=6) | |
| download_button = gr.Button(translations["downloads"]) | |
| with gr.Column(): | |
| with gr.Accordion(translations["input_output"], open=False): | |
| format = gr.Radio(label=translations["export_format"], info=translations["export_info"], choices=["wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3"], value="wav", interactive=True) | |
| input_audio = gr.Dropdown(label=translations["audio_path"], value="", choices=paths_for_files, allow_custom_value=True, interactive=True) | |
| refesh_separator = gr.Button(translations["refesh"]) | |
| output_separator = gr.Textbox(label=translations["output_folder"], value="audios", placeholder="audios", info=translations["output_folder_info"], interactive=True) | |
| with gr.Row(): | |
| gr.Markdown(translations["output_separator"]) | |
| with gr.Row(): | |
| instruments_audio = gr.Audio(show_download_button=True, interactive=False, label=translations["instruments"]) | |
| original_vocals = gr.Audio(show_download_button=True, interactive=False, label=translations["original_vocal"]) | |
| main_vocals = gr.Audio(show_download_button=True, interactive=False, label=translations["main_vocal"], visible=backing.value) | |
| backing_vocals = gr.Audio(show_download_button=True, interactive=False, label=translations["backing_vocal"], visible=backing.value) | |
| with gr.Row(): | |
| separator_model.change(fn=lambda a, b, c: [visible(a or b or c in mdx_model), visible(a or b or c in mdx_model), valueFalse_interactive(a or b or c in mdx_model), visible(c not in mdx_model)], inputs=[backing, reverb, separator_model], outputs=[mdx_batch_size, mdx_hop_length, denoise, shifts]) | |
| backing.change(fn=lambda a, b, c: [visible(a or b or c in mdx_model), visible(a or b or c in mdx_model), valueFalse_interactive(a or b or c in mdx_model), visible(a), visible(a), visible(a), valueFalse_interactive(a and b)], inputs=[backing, reverb, separator_model], outputs=[mdx_batch_size, mdx_hop_length, denoise, separator_backing_model, main_vocals, backing_vocals, backing_reverb]) | |
| reverb.change(fn=lambda a, b, c: [visible(a or b or c in mdx_model), visible(a or b or c in mdx_model), valueFalse_interactive(a or b or c in mdx_model), valueFalse_interactive(a and b)], inputs=[backing, reverb, separator_model], outputs=[mdx_batch_size, mdx_hop_length, denoise, backing_reverb]) | |
| with gr.Row(): | |
| input_audio.change(fn=lambda audio: audio if os.path.isfile(audio) else None, inputs=[input_audio], outputs=[audio_input]) | |
| cleaner.change(fn=visible, inputs=[cleaner], outputs=[clean_strength]) | |
| with gr.Row(): | |
| input.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[input], outputs=[input_audio]) | |
| refesh_separator.click(fn=change_audios_choices, inputs=[input_audio], outputs=[input_audio]) | |
| with gr.Row(): | |
| download_button.click( | |
| fn=download_url, | |
| inputs=[url], | |
| outputs=[input_audio, audio_input, url], | |
| api_name='download_url' | |
| ) | |
| separator_button.click( | |
| fn=separator_music, | |
| inputs=[ | |
| input_audio, | |
| output_separator, | |
| format, | |
| shifts, | |
| segment_size, | |
| overlap, | |
| cleaner, | |
| clean_strength, | |
| denoise, | |
| separator_model, | |
| separator_backing_model, | |
| backing, | |
| reverb, | |
| backing_reverb, | |
| mdx_hop_length, | |
| mdx_batch_size, | |
| sample_rate1 | |
| ], | |
| outputs=[original_vocals, instruments_audio, main_vocals, backing_vocals], | |
| api_name='separator_music' | |
| ) | |
| with gr.TabItem("Inference"): | |
| inference_tabs() | |
| with gr.TabItem(translations["downloads"], visible=configs.get("downloads_tab", True)): | |
| gr.Markdown(translations["download_markdown"]) | |
| with gr.Row(): | |
| gr.Markdown(translations["download_markdown_2"]) | |
| with gr.Row(): | |
| with gr.Accordion(translations["model_download"], open=True): | |
| with gr.Row(): | |
| downloadmodel = gr.Radio(label=translations["model_download_select"], choices=[translations["download_url"], translations["download_from_csv"], translations["search_models"], translations["upload"]], interactive=True, value=translations["download_url"]) | |
| with gr.Row(): | |
| gr.Markdown("___") | |
| with gr.Column(): | |
| with gr.Row(): | |
| url_input = gr.Textbox(label=translations["model_url"], value="", placeholder="https://...", scale=6) | |
| download_model_name = gr.Textbox(label=translations["modelname"], value="", placeholder=translations["modelname"], scale=2) | |
| url_download = gr.Button(value=translations["downloads"], scale=2) | |
| with gr.Column(): | |
| model_browser = gr.Dropdown(choices=models.keys(), label=translations["model_warehouse"], scale=8, allow_custom_value=True, visible=False) | |
| download_from_browser = gr.Button(value=translations["get_model"], scale=2, variant="primary", visible=False) | |
| with gr.Column(): | |
| search_name = gr.Textbox(label=translations["name_to_search"], placeholder=translations["modelname"], interactive=True, scale=8, visible=False) | |
| search = gr.Button(translations["search_2"], scale=2, visible=False) | |
| search_dropdown = gr.Dropdown(label=translations["select_download_model"], value="", choices=[], allow_custom_value=True, interactive=False, visible=False) | |
| download = gr.Button(translations["downloads"], variant="primary", visible=False) | |
| with gr.Column(): | |
| model_upload = gr.File(label=translations["drop_model"], file_types=[".pth", ".onnx", ".index", ".zip"], visible=False) | |
| with gr.Row(): | |
| with gr.Accordion(translations["download_pretrained_2"], open=False): | |
| with gr.Row(): | |
| pretrain_download_choices = gr.Radio(label=translations["model_download_select"], choices=[translations["download_url"], translations["list_model"], translations["upload"]], value=translations["download_url"], interactive=True) | |
| with gr.Row(): | |
| gr.Markdown("___") | |
| with gr.Column(): | |
| with gr.Row(): | |
| pretrainD = gr.Textbox(label=translations["pretrained_url"].format(dg="D"), value="", info=translations["only_huggingface"], placeholder="https://...", interactive=True, scale=4) | |
| pretrainG = gr.Textbox(label=translations["pretrained_url"].format(dg="G"), value="", info=translations["only_huggingface"], placeholder="https://...", interactive=True, scale=4) | |
| download_pretrain_button = gr.Button(translations["downloads"], scale=2) | |
| with gr.Column(): | |
| with gr.Row(): | |
| pretrain_choices = gr.Dropdown(label=translations["select_pretrain"], info=translations["select_pretrain_info"], choices=list(fetch_pretrained_data().keys()), value="Titan_Medium", allow_custom_value=True, interactive=True, scale=6, visible=False) | |
| sample_rate_pretrain = gr.Dropdown(label=translations["pretrain_sr"], info=translations["pretrain_sr"], choices=["48k", "40k", "32k"], value="48k", interactive=True, visible=False) | |
| download_pretrain_choices_button = gr.Button(translations["downloads"], scale=2, variant="primary", visible=False) | |
| with gr.Row(): | |
| pretrain_upload_g = gr.File(label=translations["drop_pretrain"].format(dg="G"), file_types=[".pth"], visible=False) | |
| pretrain_upload_d = gr.File(label=translations["drop_pretrain"].format(dg="D"), file_types=[".pth"], visible=False) | |
| with gr.Row(): | |
| url_download.click( | |
| fn=download_model, | |
| inputs=[ | |
| url_input, | |
| download_model_name | |
| ], | |
| outputs=[url_input], | |
| api_name="download_model" | |
| ) | |
| download_from_browser.click( | |
| fn=lambda model: download_model(models[model], model), | |
| inputs=[model_browser], | |
| outputs=[model_browser], | |
| api_name="download_browser" | |
| ) | |
| with gr.Row(): | |
| downloadmodel.change(fn=change_download_choices, inputs=[downloadmodel], outputs=[url_input, download_model_name, url_download, model_browser, download_from_browser, search_name, search, search_dropdown, download, model_upload]) | |
| search.click(fn=search_models, inputs=[search_name], outputs=[search_dropdown, download]) | |
| model_upload.upload(fn=save_drop_model, inputs=[model_upload], outputs=[model_upload]) | |
| download.click( | |
| fn=lambda model: download_model(model_options[model], model), | |
| inputs=[search_dropdown], | |
| outputs=[search_dropdown], | |
| api_name="search_models" | |
| ) | |
| with gr.Row(): | |
| pretrain_download_choices.change(fn=change_download_pretrained_choices, inputs=[pretrain_download_choices], outputs=[pretrainD, pretrainG, download_pretrain_button, pretrain_choices, sample_rate_pretrain, download_pretrain_choices_button, pretrain_upload_d, pretrain_upload_g]) | |
| pretrain_choices.change(fn=update_sample_rate_dropdown, inputs=[pretrain_choices], outputs=[sample_rate_pretrain]) | |
| with gr.Row(): | |
| download_pretrain_button.click( | |
| fn=download_pretrained_model, | |
| inputs=[ | |
| pretrain_download_choices, | |
| pretrainD, | |
| pretrainG | |
| ], | |
| outputs=[pretrainD], | |
| api_name="download_pretrain_link" | |
| ) | |
| download_pretrain_choices_button.click( | |
| fn=download_pretrained_model, | |
| inputs=[ | |
| pretrain_download_choices, | |
| pretrain_choices, | |
| sample_rate_pretrain | |
| ], | |
| outputs=[pretrain_choices], | |
| api_name="download_pretrain_choices" | |
| ) | |
| pretrain_upload_g.upload( | |
| fn=lambda pretrain_upload_g: shutil.move(pretrain_upload_g.name, os.path.join("assets", "models", "pretrained_custom")), | |
| inputs=[pretrain_upload_g], | |
| outputs=[], | |
| api_name="upload_pretrain_g" | |
| ) | |
| pretrain_upload_d.upload( | |
| fn=lambda pretrain_upload_d: shutil.move(pretrain_upload_d.name, os.path.join("assets", "models", "pretrained_custom")), | |
| inputs=[pretrain_upload_d], | |
| outputs=[], | |
| api_name="upload_pretrain_d" | |
| ) | |
| with gr.TabItem(translations["training_model"], visible=configs.get("training_tab", True)): | |
| gr.Markdown(f"## {translations['training_model']}") | |
| with gr.Row(): | |
| gr.Markdown(translations["training_markdown"]) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| training_name = gr.Textbox(label=translations["modelname"], info=translations["training_model_name"], value="", placeholder=translations["modelname"], interactive=True) | |
| training_sr = gr.Radio(label=translations["sample_rate"], info=translations["sample_rate_info"], choices=["32k", "40k", "48k"], value="48k", interactive=True) | |
| training_ver = gr.Radio(label=translations["training_version"], info=translations["training_version_info"], choices=["v1", "v2"], value="v2", interactive=True) | |
| with gr.Row(): | |
| clean_dataset = gr.Checkbox(label=translations["clear_dataset"], value=False, interactive=True) | |
| preprocess_cut = gr.Checkbox(label=translations["split_audio"], value=True, interactive=True) | |
| process_effects = gr.Checkbox(label=translations["preprocess_effect"], value=False, interactive=True) | |
| checkpointing1 = gr.Checkbox(label=translations["memory_efficient_training"], value=False, interactive=True) | |
| training_f0 = gr.Checkbox(label=translations["training_pitch"], value=True, interactive=True) | |
| upload = gr.Checkbox(label=translations["upload_dataset"], value=False, interactive=True) | |
| with gr.Row(): | |
| clean_dataset_strength = gr.Slider(label=translations["clean_strength"], info=translations["clean_strength_info"], minimum=0, maximum=1, value=0.7, step=0.1, interactive=True, visible=clean_dataset.value) | |
| with gr.Column(): | |
| preprocess_button = gr.Button(translations["preprocess_button"], scale=2) | |
| upload_dataset = gr.Files(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"], visible=upload.value) | |
| preprocess_info = gr.Textbox(label=translations["preprocess_info"], value="", interactive=False) | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Accordion(label=translations["f0_method"], open=False): | |
| with gr.Group(): | |
| with gr.Row(): | |
| onnx_f0_mode2 = gr.Checkbox(label=translations["f0_onnx_mode"], info=translations["f0_onnx_mode_info"], value=False, interactive=True) | |
| unlock_full_method4 = gr.Checkbox(label=translations["f0_unlock"], info=translations["f0_unlock_info"], value=False, interactive=True) | |
| extract_method = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=method_f0, value="rmvpe", interactive=True) | |
| extract_hop_length = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=512, value=128, step=1, interactive=True, visible=False) | |
| with gr.Accordion(label=translations["hubert_model"], open=False): | |
| with gr.Group(): | |
| embed_mode2 = gr.Radio(label=translations["embed_mode"], info=translations["embed_mode_info"], value="fairseq", choices=embedders_mode, interactive=True, visible=True) | |
| extract_embedders = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=embedders_model, value="hubert_base", interactive=True) | |
| with gr.Row(): | |
| extract_embedders_custom = gr.Textbox(label=translations["modelname"], info=translations["modelname_info"], value="", placeholder="hubert_base", interactive=True, visible=extract_embedders.value == "custom") | |
| with gr.Column(): | |
| extract_button = gr.Button(translations["extract_button"], scale=2) | |
| extract_info = gr.Textbox(label=translations["extract_info"], value="", interactive=False) | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Column(): | |
| total_epochs = gr.Slider(label=translations["total_epoch"], info=translations["total_epoch_info"], minimum=1, maximum=10000, value=300, step=1, interactive=True) | |
| save_epochs = gr.Slider(label=translations["save_epoch"], info=translations["save_epoch_info"], minimum=1, maximum=10000, value=50, step=1, interactive=True) | |
| with gr.Column(): | |
| with gr.Row(): | |
| index_button = gr.Button(f"3. {translations['create_index']}", variant="primary", scale=2) | |
| training_button = gr.Button(f"4. {translations['training_model']}", variant="primary", scale=2) | |
| with gr.Row(): | |
| with gr.Accordion(label=translations["setting"], open=False): | |
| with gr.Row(): | |
| index_algorithm = gr.Radio(label=translations["index_algorithm"], info=translations["index_algorithm_info"], choices=["Auto", "Faiss", "KMeans"], value="Auto", interactive=True) | |
| with gr.Row(): | |
| custom_dataset = gr.Checkbox(label=translations["custom_dataset"], info=translations["custom_dataset_info"], value=False, interactive=True) | |
| overtraining_detector = gr.Checkbox(label=translations["overtraining_detector"], info=translations["overtraining_detector_info"], value=False, interactive=True) | |
| clean_up = gr.Checkbox(label=translations["cleanup_training"], info=translations["cleanup_training_info"], value=False, interactive=True) | |
| cache_in_gpu = gr.Checkbox(label=translations["cache_in_gpu"], info=translations["cache_in_gpu_info"], value=False, interactive=True) | |
| with gr.Column(): | |
| dataset_path = gr.Textbox(label=translations["dataset_folder"], value="dataset", interactive=True, visible=custom_dataset.value) | |
| with gr.Column(): | |
| threshold = gr.Slider(minimum=1, maximum=100, value=50, step=1, label=translations["threshold"], interactive=True, visible=overtraining_detector.value) | |
| with gr.Accordion(translations["setting_cpu_gpu"], open=False): | |
| with gr.Column(): | |
| gpu_number = gr.Textbox(label=translations["gpu_number"], value=str("-".join(map(str, range(torch.cuda.device_count()))) if torch.cuda.is_available() else "-"), info=translations["gpu_number_info"], interactive=True) | |
| gpu_info = gr.Textbox(label=translations["gpu_info"], value=get_gpu_info(), info=translations["gpu_info_2"], interactive=False) | |
| cpu_core = gr.Slider(label=translations["cpu_core"], info=translations["cpu_core_info"], minimum=0, maximum=cpu_count(), value=cpu_count(), step=1, interactive=True) | |
| train_batch_size = gr.Slider(label=translations["batch_size"], info=translations["batch_size_info"], minimum=1, maximum=64, value=8, step=1, interactive=True) | |
| with gr.Row(): | |
| save_only_latest = gr.Checkbox(label=translations["save_only_latest"], info=translations["save_only_latest_info"], value=True, interactive=True) | |
| save_every_weights = gr.Checkbox(label=translations["save_every_weights"], info=translations["save_every_weights_info"], value=True, interactive=True) | |
| not_use_pretrain = gr.Checkbox(label=translations["not_use_pretrain_2"], info=translations["not_use_pretrain_info"], value=False, interactive=True) | |
| custom_pretrain = gr.Checkbox(label=translations["custom_pretrain"], info=translations["custom_pretrain_info"], value=False, interactive=True) | |
| with gr.Row(): | |
| vocoders = gr.Radio(label=translations["vocoder"], info=translations["vocoder_info"], choices=["Default", "MRF-HiFi-GAN", "RefineGAN"], value="Default", interactive=True) | |
| with gr.Row(): | |
| deterministic = gr.Checkbox(label=translations["deterministic"], info=translations["deterministic_info"], value=False, interactive=True) | |
| benchmark = gr.Checkbox(label=translations["benchmark"], info=translations["benchmark_info"], value=False, interactive=True) | |
| with gr.Row(): | |
| model_author = gr.Textbox(label=translations["training_author"], info=translations["training_author_info"], value="", placeholder=translations["training_author"], interactive=True) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Accordion(translations["custom_pretrain_info"], open=False, visible=custom_pretrain.value and not not_use_pretrain.value) as pretrain_setting: | |
| pretrained_D = gr.Dropdown(label=translations["pretrain_file"].format(dg="D"), choices=pretrainedD, value=pretrainedD[0] if len(pretrainedD) > 0 else '', interactive=True, allow_custom_value=True) | |
| pretrained_G = gr.Dropdown(label=translations["pretrain_file"].format(dg="G"), choices=pretrainedG, value=pretrainedG[0] if len(pretrainedG) > 0 else '', interactive=True, allow_custom_value=True) | |
| refesh_pretrain = gr.Button(translations["refesh"], scale=2) | |
| with gr.Row(): | |
| training_info = gr.Textbox(label=translations["train_info"], value="", interactive=False) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Accordion(translations["export_model"], open=False): | |
| with gr.Row(): | |
| model_file= gr.Dropdown(label=translations["model_name"], choices=model_name, value=model_name[0] if len(model_name) >= 1 else "", interactive=True, allow_custom_value=True) | |
| index_file = gr.Dropdown(label=translations["index_path"], choices=index_path, value=index_path[0] if len(index_path) >= 1 else "", interactive=True, allow_custom_value=True) | |
| with gr.Row(): | |
| refesh_file = gr.Button(f"1. {translations['refesh']}", scale=2) | |
| zip_model = gr.Button(translations["zip_model"], variant="primary", scale=2) | |
| with gr.Row(): | |
| zip_output = gr.File(label=translations["output_zip"], file_types=[".zip"], interactive=False, visible=False) | |
| with gr.Row(): | |
| vocoders.change(fn=pitch_guidance_lock, inputs=[vocoders], outputs=[training_f0]) | |
| training_f0.change(fn=vocoders_lock, inputs=[training_f0, vocoders], outputs=[vocoders]) | |
| unlock_full_method4.change(fn=unlock_f0, inputs=[unlock_full_method4], outputs=[extract_method]) | |
| with gr.Row(): | |
| refesh_file.click(fn=change_models_choices, inputs=[], outputs=[model_file, index_file]) | |
| zip_model.click(fn=zip_file, inputs=[training_name, model_file, index_file], outputs=[zip_output]) | |
| dataset_path.change(fn=lambda folder: os.makedirs(folder, exist_ok=True), inputs=[dataset_path], outputs=[]) | |
| with gr.Row(): | |
| upload.change(fn=visible, inputs=[upload], outputs=[upload_dataset]) | |
| overtraining_detector.change(fn=visible, inputs=[overtraining_detector], outputs=[threshold]) | |
| clean_dataset.change(fn=visible, inputs=[clean_dataset], outputs=[clean_dataset_strength]) | |
| with gr.Row(): | |
| custom_dataset.change(fn=lambda custom_dataset: [visible(custom_dataset), "dataset"],inputs=[custom_dataset], outputs=[dataset_path, dataset_path]) | |
| training_ver.change(fn=unlock_vocoder, inputs=[training_ver, vocoders], outputs=[vocoders]) | |
| vocoders.change(fn=unlock_ver, inputs=[training_ver, vocoders], outputs=[training_ver]) | |
| upload_dataset.upload( | |
| fn=lambda files, folder: [shutil.move(f.name, os.path.join(folder, os.path.split(f.name)[1])) for f in files] if folder != "" else gr_warning(translations["dataset_folder1"]), | |
| inputs=[upload_dataset, dataset_path], | |
| outputs=[], | |
| api_name="upload_dataset" | |
| ) | |
| with gr.Row(): | |
| not_use_pretrain.change(fn=lambda a, b: visible(a and not b), inputs=[custom_pretrain, not_use_pretrain], outputs=[pretrain_setting]) | |
| custom_pretrain.change(fn=lambda a, b: visible(a and not b), inputs=[custom_pretrain, not_use_pretrain], outputs=[pretrain_setting]) | |
| refesh_pretrain.click(fn=change_pretrained_choices, inputs=[], outputs=[pretrained_D, pretrained_G]) | |
| with gr.Row(): | |
| preprocess_button.click( | |
| fn=preprocess, | |
| inputs=[ | |
| training_name, | |
| training_sr, | |
| cpu_core, | |
| preprocess_cut, | |
| process_effects, | |
| dataset_path, | |
| clean_dataset, | |
| clean_dataset_strength | |
| ], | |
| outputs=[preprocess_info], | |
| api_name="preprocess" | |
| ) | |
| with gr.Row(): | |
| embed_mode2.change(fn=visible_embedders, inputs=[embed_mode2], outputs=[extract_embedders]) | |
| extract_method.change(fn=hoplength_show, inputs=[extract_method], outputs=[extract_hop_length]) | |
| extract_embedders.change(fn=lambda extract_embedders: visible(extract_embedders == "custom"), inputs=[extract_embedders], outputs=[extract_embedders_custom]) | |
| with gr.Row(): | |
| extract_button.click( | |
| fn=extract, | |
| inputs=[ | |
| training_name, | |
| training_ver, | |
| extract_method, | |
| training_f0, | |
| extract_hop_length, | |
| cpu_core, | |
| gpu_number, | |
| training_sr, | |
| extract_embedders, | |
| extract_embedders_custom, | |
| onnx_f0_mode2, | |
| embed_mode2 | |
| ], | |
| outputs=[extract_info], | |
| api_name="extract" | |
| ) | |
| with gr.Row(): | |
| index_button.click( | |
| fn=create_index, | |
| inputs=[ | |
| training_name, | |
| training_ver, | |
| index_algorithm | |
| ], | |
| outputs=[training_info], | |
| api_name="create_index" | |
| ) | |
| with gr.Row(): | |
| training_button.click( | |
| fn=training, | |
| inputs=[ | |
| training_name, | |
| training_ver, | |
| save_epochs, | |
| save_only_latest, | |
| save_every_weights, | |
| total_epochs, | |
| training_sr, | |
| train_batch_size, | |
| gpu_number, | |
| training_f0, | |
| not_use_pretrain, | |
| custom_pretrain, | |
| pretrained_G, | |
| pretrained_D, | |
| overtraining_detector, | |
| threshold, | |
| clean_up, | |
| cache_in_gpu, | |
| model_author, | |
| vocoders, | |
| checkpointing1, | |
| deterministic, | |
| benchmark | |
| ], | |
| outputs=[training_info], | |
| api_name="training_model" | |
| ) | |
| with gr.TabItem(translations["audio_editing"], visible=configs.get("audioldm2", True)): | |
| gr.Markdown(translations["audio_editing_info"]) | |
| with gr.Row(): | |
| gr.Markdown(translations["audio_editing_markdown"]) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Group(): | |
| with gr.Row(): | |
| save_compute = gr.Checkbox(label=translations["save_compute"], value=True, interactive=True) | |
| tar_prompt = gr.Textbox(label=translations["target_prompt"], info=translations["target_prompt_info"], placeholder="Piano and violin cover", lines=5, interactive=True) | |
| with gr.Column(): | |
| cfg_scale_src = gr.Slider(value=3, minimum=0.5, maximum=25, label=translations["cfg_scale_src"], info=translations["cfg_scale_src_info"], interactive=True) | |
| cfg_scale_tar = gr.Slider(value=12, minimum=0.5, maximum=25, label=translations["cfg_scale_tar"], info=translations["cfg_scale_tar_info"], interactive=True) | |
| with gr.Row(): | |
| edit_button = gr.Button(translations["editing"], variant="primary") | |
| with gr.Row(): | |
| with gr.Column(): | |
| drop_audio_file = gr.File(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"]) | |
| display_audio = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"]) | |
| with gr.Column(): | |
| with gr.Accordion(translations["input_output"], open=False): | |
| with gr.Column(): | |
| export_audio_format = gr.Radio(label=translations["export_format"], info=translations["export_info"], choices=["wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3"], value="wav", interactive=True) | |
| input_audiopath = gr.Dropdown(label=translations["audio_path"], value="", choices=paths_for_files, info=translations["provide_audio"], allow_custom_value=True, interactive=True) | |
| output_audiopath = gr.Textbox(label=translations["output_path"], value="audios/output.wav", placeholder="audios/output.wav", info=translations["output_path_info"], interactive=True) | |
| with gr.Column(): | |
| refesh_audio = gr.Button(translations["refesh"]) | |
| with gr.Accordion(translations["setting"], open=False): | |
| audioldm2_model = gr.Radio(label=translations["audioldm2_model"], info=translations["audioldm2_model_info"], choices=["audioldm2", "audioldm2-large", "audioldm2-music"], value="audioldm2-music", interactive=True) | |
| with gr.Row(): | |
| src_prompt = gr.Textbox(label=translations["source_prompt"], lines=2, interactive=True, info=translations["source_prompt_info"], placeholder="A recording of a happy upbeat classical music piece") | |
| with gr.Row(): | |
| with gr.Column(): | |
| audioldm2_sample_rate = gr.Slider(minimum=8000, maximum=96000, label=translations["sr"], info=translations["sr_info"], value=44100, step=1, interactive=True) | |
| t_start = gr.Slider(minimum=15, maximum=85, value=45, step=1, label=translations["t_start"], interactive=True, info=translations["t_start_info"]) | |
| steps = gr.Slider(value=50, step=1, minimum=10, maximum=300, label=translations["steps_label"], info=translations["steps_info"], interactive=True) | |
| with gr.Row(): | |
| gr.Markdown(translations["output_audio"]) | |
| with gr.Row(): | |
| output_audioldm2 = gr.Audio(show_download_button=True, interactive=False, label=translations["output_audio"]) | |
| with gr.Row(): | |
| refesh_audio.click(fn=change_audios_choices, inputs=[input_audiopath], outputs=[input_audiopath]) | |
| drop_audio_file.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[drop_audio_file], outputs=[input_audiopath]) | |
| input_audiopath.change(fn=lambda audio: audio if os.path.isfile(audio) else None, inputs=[input_audiopath], outputs=[display_audio]) | |
| with gr.Row(): | |
| edit_button.click( | |
| fn=run_audioldm2, | |
| inputs=[ | |
| input_audiopath, | |
| output_audiopath, | |
| export_audio_format, | |
| audioldm2_sample_rate, | |
| audioldm2_model, | |
| src_prompt, | |
| tar_prompt, | |
| steps, | |
| cfg_scale_src, | |
| cfg_scale_tar, | |
| t_start, | |
| save_compute | |
| ], | |
| outputs=[output_audioldm2], | |
| api_name="audioldm2" | |
| ) | |
| with gr.TabItem(translations["audio_effects"], visible=configs.get("effects_tab", True)): | |
| gr.Markdown(translations["apply_audio_effects"]) | |
| with gr.Row(): | |
| gr.Markdown(translations["audio_effects_edit"]) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| reverb_check_box = gr.Checkbox(label=translations["reverb"], value=False, interactive=True) | |
| chorus_check_box = gr.Checkbox(label=translations["chorus"], value=False, interactive=True) | |
| delay_check_box = gr.Checkbox(label=translations["delay"], value=False, interactive=True) | |
| phaser_check_box = gr.Checkbox(label=translations["phaser"], value=False, interactive=True) | |
| compressor_check_box = gr.Checkbox(label=translations["compressor"], value=False, interactive=True) | |
| more_options = gr.Checkbox(label=translations["more_option"], value=False, interactive=True) | |
| with gr.Row(): | |
| with gr.Accordion(translations["input_output"], open=False): | |
| with gr.Row(): | |
| upload_audio = gr.File(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"]) | |
| with gr.Row(): | |
| audio_in_path = gr.Dropdown(label=translations["input_audio"], value="", choices=paths_for_files, info=translations["provide_audio"], interactive=True, allow_custom_value=True) | |
| audio_out_path = gr.Textbox(label=translations["output_audio"], value="audios/audio_effects.wav", placeholder="audios/audio_effects.wav", info=translations["provide_output"], interactive=True) | |
| with gr.Row(): | |
| with gr.Column(): | |
| audio_combination = gr.Checkbox(label=translations["merge_instruments"], value=False, interactive=True) | |
| audio_combination_input = gr.Dropdown(label=translations["input_audio"], value="", choices=paths_for_files, info=translations["provide_audio"], interactive=True, allow_custom_value=True, visible=audio_combination.value) | |
| with gr.Row(): | |
| audio_effects_refesh = gr.Button(translations["refesh"]) | |
| with gr.Row(): | |
| audio_output_format = gr.Radio(label=translations["export_format"], info=translations["export_info"], choices=["wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3"], value="wav", interactive=True) | |
| with gr.Row(): | |
| apply_effects_button = gr.Button(translations["apply"], variant="primary", scale=2) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Accordion(translations["reverb"], open=False, visible=reverb_check_box.value) as reverb_accordion: | |
| reverb_freeze_mode = gr.Checkbox(label=translations["reverb_freeze"], info=translations["reverb_freeze_info"], value=False, interactive=True) | |
| reverb_room_size = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.15, label=translations["room_size"], info=translations["room_size_info"], interactive=True) | |
| reverb_damping = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.7, label=translations["damping"], info=translations["damping_info"], interactive=True) | |
| reverb_wet_level = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.2, label=translations["wet_level"], info=translations["wet_level_info"], interactive=True) | |
| reverb_dry_level = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.8, label=translations["dry_level"], info=translations["dry_level_info"], interactive=True) | |
| reverb_width = gr.Slider(minimum=0, maximum=1, step=0.01, value=1, label=translations["width"], info=translations["width_info"], interactive=True) | |
| with gr.Row(): | |
| with gr.Accordion(translations["chorus"], open=False, visible=chorus_check_box.value) as chorus_accordion: | |
| chorus_depth = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["chorus_depth"], info=translations["chorus_depth_info"], interactive=True) | |
| chorus_rate_hz = gr.Slider(minimum=0.1, maximum=10, step=0.1, value=1.5, label=translations["chorus_rate_hz"], info=translations["chorus_rate_hz_info"], interactive=True) | |
| chorus_mix = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["chorus_mix"], info=translations["chorus_mix_info"], interactive=True) | |
| chorus_centre_delay_ms = gr.Slider(minimum=0, maximum=50, step=1, value=10, label=translations["chorus_centre_delay_ms"], info=translations["chorus_centre_delay_ms_info"], interactive=True) | |
| chorus_feedback = gr.Slider(minimum=-1, maximum=1, step=0.01, value=0, label=translations["chorus_feedback"], info=translations["chorus_feedback_info"], interactive=True) | |
| with gr.Row(): | |
| with gr.Accordion(translations["delay"], open=False, visible=delay_check_box.value) as delay_accordion: | |
| delay_second = gr.Slider(minimum=0, maximum=5, step=0.01, value=0.5, label=translations["delay_seconds"], info=translations["delay_seconds_info"], interactive=True) | |
| delay_feedback = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["delay_feedback"], info=translations["delay_feedback_info"], interactive=True) | |
| delay_mix = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["delay_mix"], info=translations["delay_mix_info"], interactive=True) | |
| with gr.Column(): | |
| with gr.Row(): | |
| with gr.Accordion(translations["more_option"], open=False, visible=more_options.value) as more_accordion: | |
| with gr.Row(): | |
| fade = gr.Checkbox(label=translations["fade"], value=False, interactive=True) | |
| bass_or_treble = gr.Checkbox(label=translations["bass_or_treble"], value=False, interactive=True) | |
| limiter = gr.Checkbox(label=translations["limiter"], value=False, interactive=True) | |
| resample_checkbox = gr.Checkbox(label=translations["resample"], value=False, interactive=True) | |
| with gr.Row(): | |
| distortion_checkbox = gr.Checkbox(label=translations["distortion"], value=False, interactive=True) | |
| gain_checkbox = gr.Checkbox(label=translations["gain"], value=False, interactive=True) | |
| bitcrush_checkbox = gr.Checkbox(label=translations["bitcrush"], value=False, interactive=True) | |
| clipping_checkbox = gr.Checkbox(label=translations["clipping"], value=False, interactive=True) | |
| with gr.Accordion(translations["fade"], open=True, visible=fade.value) as fade_accordion: | |
| with gr.Row(): | |
| fade_in = gr.Slider(minimum=0, maximum=10000, step=100, value=0, label=translations["fade_in"], info=translations["fade_in_info"], interactive=True) | |
| fade_out = gr.Slider(minimum=0, maximum=10000, step=100, value=0, label=translations["fade_out"], info=translations["fade_out_info"], interactive=True) | |
| with gr.Accordion(translations["bass_or_treble"], open=True, visible=bass_or_treble.value) as bass_treble_accordion: | |
| with gr.Row(): | |
| bass_boost = gr.Slider(minimum=0, maximum=20, step=1, value=0, label=translations["bass_boost"], info=translations["bass_boost_info"], interactive=True) | |
| bass_frequency = gr.Slider(minimum=20, maximum=200, step=10, value=100, label=translations["bass_frequency"], info=translations["bass_frequency_info"], interactive=True) | |
| with gr.Row(): | |
| treble_boost = gr.Slider(minimum=0, maximum=20, step=1, value=0, label=translations["treble_boost"], info=translations["treble_boost_info"], interactive=True) | |
| treble_frequency = gr.Slider(minimum=1000, maximum=10000, step=500, value=3000, label=translations["treble_frequency"], info=translations["treble_frequency_info"], interactive=True) | |
| with gr.Accordion(translations["limiter"], open=True, visible=limiter.value) as limiter_accordion: | |
| with gr.Row(): | |
| limiter_threashold_db = gr.Slider(minimum=-60, maximum=0, step=1, value=-1, label=translations["limiter_threashold_db"], info=translations["limiter_threashold_db_info"], interactive=True) | |
| limiter_release_ms = gr.Slider(minimum=10, maximum=1000, step=1, value=100, label=translations["limiter_release_ms"], info=translations["limiter_release_ms_info"], interactive=True) | |
| with gr.Column(): | |
| pitch_shift_semitones = gr.Slider(minimum=-20, maximum=20, step=1, value=0, label=translations["pitch"], info=translations["pitch_info"], interactive=True) | |
| audio_effect_resample_sr = gr.Slider(minimum=0, maximum=96000, step=1, value=0, label=translations["resample"], info=translations["resample_info"], interactive=True, visible=resample_checkbox.value) | |
| distortion_drive_db = gr.Slider(minimum=0, maximum=50, step=1, value=20, label=translations["distortion"], info=translations["distortion_info"], interactive=True, visible=distortion_checkbox.value) | |
| gain_db = gr.Slider(minimum=-60, maximum=60, step=1, value=0, label=translations["gain"], info=translations["gain_info"], interactive=True, visible=gain_checkbox.value) | |
| clipping_threashold_db = gr.Slider(minimum=-60, maximum=0, step=1, value=-1, label=translations["clipping_threashold_db"], info=translations["clipping_threashold_db_info"], interactive=True, visible=clipping_checkbox.value) | |
| bitcrush_bit_depth = gr.Slider(minimum=1, maximum=24, step=1, value=16, label=translations["bitcrush_bit_depth"], info=translations["bitcrush_bit_depth_info"], interactive=True, visible=bitcrush_checkbox.value) | |
| with gr.Row(): | |
| with gr.Accordion(translations["phaser"], open=False, visible=phaser_check_box.value) as phaser_accordion: | |
| phaser_depth = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["phaser_depth"], info=translations["phaser_depth_info"], interactive=True) | |
| phaser_rate_hz = gr.Slider(minimum=0.1, maximum=10, step=0.1, value=1, label=translations["phaser_rate_hz"], info=translations["phaser_rate_hz_info"], interactive=True) | |
| phaser_mix = gr.Slider(minimum=0, maximum=1, step=0.01, value=0.5, label=translations["phaser_mix"], info=translations["phaser_mix_info"], interactive=True) | |
| phaser_centre_frequency_hz = gr.Slider(minimum=50, maximum=5000, step=10, value=1000, label=translations["phaser_centre_frequency_hz"], info=translations["phaser_centre_frequency_hz_info"], interactive=True) | |
| phaser_feedback = gr.Slider(minimum=-1, maximum=1, step=0.01, value=0, label=translations["phaser_feedback"], info=translations["phaser_feedback_info"], interactive=True) | |
| with gr.Row(): | |
| with gr.Accordion(translations["compressor"], open=False, visible=compressor_check_box.value) as compressor_accordion: | |
| compressor_threashold_db = gr.Slider(minimum=-60, maximum=0, step=1, value=-20, label=translations["compressor_threashold_db"], info=translations["compressor_threashold_db_info"], interactive=True) | |
| compressor_ratio = gr.Slider(minimum=1, maximum=20, step=0.1, value=1, label=translations["compressor_ratio"], info=translations["compressor_ratio_info"], interactive=True) | |
| compressor_attack_ms = gr.Slider(minimum=0.1, maximum=100, step=0.1, value=10, label=translations["compressor_attack_ms"], info=translations["compressor_attack_ms_info"], interactive=True) | |
| compressor_release_ms = gr.Slider(minimum=10, maximum=1000, step=1, value=100, label=translations["compressor_release_ms"], info=translations["compressor_release_ms_info"], interactive=True) | |
| with gr.Row(): | |
| gr.Markdown(translations["output_audio"]) | |
| with gr.Row(): | |
| audio_play_input = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"]) | |
| audio_play_output = gr.Audio(show_download_button=True, interactive=False, label=translations["output_audio"]) | |
| with gr.Row(): | |
| reverb_check_box.change(fn=visible, inputs=[reverb_check_box], outputs=[reverb_accordion]) | |
| chorus_check_box.change(fn=visible, inputs=[chorus_check_box], outputs=[chorus_accordion]) | |
| delay_check_box.change(fn=visible, inputs=[delay_check_box], outputs=[delay_accordion]) | |
| with gr.Row(): | |
| compressor_check_box.change(fn=visible, inputs=[compressor_check_box], outputs=[compressor_accordion]) | |
| phaser_check_box.change(fn=visible, inputs=[phaser_check_box], outputs=[phaser_accordion]) | |
| more_options.change(fn=visible, inputs=[more_options], outputs=[more_accordion]) | |
| with gr.Row(): | |
| fade.change(fn=visible, inputs=[fade], outputs=[fade_accordion]) | |
| bass_or_treble.change(fn=visible, inputs=[bass_or_treble], outputs=[bass_treble_accordion]) | |
| limiter.change(fn=visible, inputs=[limiter], outputs=[limiter_accordion]) | |
| resample_checkbox.change(fn=visible, inputs=[resample_checkbox], outputs=[audio_effect_resample_sr]) | |
| with gr.Row(): | |
| distortion_checkbox.change(fn=visible, inputs=[distortion_checkbox], outputs=[distortion_drive_db]) | |
| gain_checkbox.change(fn=visible, inputs=[gain_checkbox], outputs=[gain_db]) | |
| clipping_checkbox.change(fn=visible, inputs=[clipping_checkbox], outputs=[clipping_threashold_db]) | |
| bitcrush_checkbox.change(fn=visible, inputs=[bitcrush_checkbox], outputs=[bitcrush_bit_depth]) | |
| with gr.Row(): | |
| upload_audio.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[upload_audio], outputs=[audio_in_path]) | |
| audio_in_path.change(fn=lambda audio: audio if audio else None, inputs=[audio_in_path], outputs=[audio_play_input]) | |
| audio_effects_refesh.click(fn=lambda a, b: [change_audios_choices(a), change_audios_choices(b)], inputs=[audio_in_path, audio_combination_input], outputs=[audio_in_path, audio_combination_input]) | |
| with gr.Row(): | |
| more_options.change(fn=lambda: [False]*8, inputs=[], outputs=[fade, bass_or_treble, limiter, resample_checkbox, distortion_checkbox, gain_checkbox, clipping_checkbox, bitcrush_checkbox]) | |
| audio_combination.change(fn=visible, inputs=[audio_combination], outputs=[audio_combination_input]) | |
| with gr.Row(): | |
| apply_effects_button.click( | |
| fn=audio_effects, | |
| inputs=[ | |
| audio_in_path, | |
| audio_out_path, | |
| resample_checkbox, | |
| audio_effect_resample_sr, | |
| chorus_depth, | |
| chorus_rate_hz, | |
| chorus_mix, | |
| chorus_centre_delay_ms, | |
| chorus_feedback, | |
| distortion_drive_db, | |
| reverb_room_size, | |
| reverb_damping, | |
| reverb_wet_level, | |
| reverb_dry_level, | |
| reverb_width, | |
| reverb_freeze_mode, | |
| pitch_shift_semitones, | |
| delay_second, | |
| delay_feedback, | |
| delay_mix, | |
| compressor_threashold_db, | |
| compressor_ratio, | |
| compressor_attack_ms, | |
| compressor_release_ms, | |
| limiter_threashold_db, | |
| limiter_release_ms, | |
| gain_db, | |
| bitcrush_bit_depth, | |
| clipping_threashold_db, | |
| phaser_rate_hz, | |
| phaser_depth, | |
| phaser_centre_frequency_hz, | |
| phaser_feedback, | |
| phaser_mix, | |
| bass_boost, | |
| bass_frequency, | |
| treble_boost, | |
| treble_frequency, | |
| fade_in, | |
| fade_out, | |
| audio_output_format, | |
| chorus_check_box, | |
| distortion_checkbox, | |
| reverb_check_box, | |
| delay_check_box, | |
| compressor_check_box, | |
| limiter, | |
| gain_checkbox, | |
| bitcrush_checkbox, | |
| clipping_checkbox, | |
| phaser_check_box, | |
| bass_or_treble, | |
| fade, | |
| audio_combination, | |
| audio_combination_input | |
| ], | |
| outputs=[audio_play_output], | |
| api_name="audio_effects" | |
| ) | |
| with gr.TabItem(translations["createdataset"], visible=configs.get("create_dataset_tab", True)): | |
| gr.Markdown(translations["create_dataset_markdown"]) | |
| with gr.Row(): | |
| gr.Markdown(translations["create_dataset_markdown_2"]) | |
| with gr.Row(): | |
| dataset_url = gr.Textbox(label=translations["url_audio"], info=translations["create_dataset_url"], value="", placeholder="https://www.youtube.com/...", interactive=True) | |
| output_dataset = gr.Textbox(label=translations["output_data"], info=translations["output_data_info"], value="dataset", placeholder="dataset", interactive=True) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Group(): | |
| with gr.Row(): | |
| separator_reverb = gr.Checkbox(label=translations["dereveb_audio"], value=False, interactive=True) | |
| denoise_mdx = gr.Checkbox(label=translations["denoise"], value=False, interactive=True) | |
| with gr.Row(): | |
| kim_vocal_version = gr.Radio(label=translations["model_ver"], info=translations["model_ver_info"], choices=["Version-1", "Version-2"], value="Version-2", interactive=True) | |
| kim_vocal_overlap = gr.Radio(label=translations["overlap"], info=translations["overlap_info"], choices=["0.25", "0.5", "0.75", "0.99"], value="0.25", interactive=True) | |
| with gr.Row(): | |
| kim_vocal_hop_length = gr.Slider(label="Hop length", info=translations["hop_length_info"], minimum=1, maximum=8192, value=1024, step=1, interactive=True) | |
| kim_vocal_batch_size = gr.Slider(label=translations["batch_size"], info=translations["mdx_batch_size_info"], minimum=1, maximum=64, value=1, step=1, interactive=True) | |
| with gr.Row(): | |
| kim_vocal_segments_size = gr.Slider(label=translations["segments_size"], info=translations["segments_size_info"], minimum=32, maximum=3072, value=256, step=32, interactive=True) | |
| with gr.Row(): | |
| sample_rate0 = gr.Slider(minimum=8000, maximum=96000, step=1, value=44100, label=translations["sr"], info=translations["sr_info"], interactive=True) | |
| with gr.Column(): | |
| create_button = gr.Button(translations["createdataset"], variant="primary", scale=2, min_width=4000) | |
| with gr.Group(): | |
| with gr.Row(): | |
| clean_audio = gr.Checkbox(label=translations["clear_audio"], value=False, interactive=True) | |
| skip = gr.Checkbox(label=translations["skip"], value=False, interactive=True) | |
| with gr.Row(): | |
| dataset_clean_strength = gr.Slider(minimum=0, maximum=1, step=0.1, value=0.5, label=translations["clean_strength"], info=translations["clean_strength_info"], interactive=True, visible=clean_audio.value) | |
| with gr.Row(): | |
| skip_start = gr.Textbox(label=translations["skip_start"], info=translations["skip_start_info"], value="", placeholder="0,...", interactive=True, visible=skip.value) | |
| skip_end = gr.Textbox(label=translations["skip_end"], info=translations["skip_end_info"], value="", placeholder="0,...", interactive=True, visible=skip.value) | |
| create_dataset_info = gr.Textbox(label=translations["create_dataset_info"], value="", interactive=False) | |
| with gr.Row(): | |
| clean_audio.change(fn=visible, inputs=[clean_audio], outputs=[dataset_clean_strength]) | |
| skip.change(fn=lambda a: [valueEmpty_visible1(a)]*2, inputs=[skip], outputs=[skip_start, skip_end]) | |
| with gr.Row(): | |
| create_button.click( | |
| fn=create_dataset, | |
| inputs=[ | |
| dataset_url, | |
| output_dataset, | |
| clean_audio, | |
| dataset_clean_strength, | |
| separator_reverb, | |
| kim_vocal_version, | |
| kim_vocal_overlap, | |
| kim_vocal_segments_size, | |
| denoise_mdx, | |
| skip, | |
| skip_start, | |
| skip_end, | |
| kim_vocal_hop_length, | |
| kim_vocal_batch_size, | |
| sample_rate0 | |
| ], | |
| outputs=[create_dataset_info], | |
| api_name="create_dataset" | |
| ) | |
| with gr.TabItem(translations["fushion"], visible=configs.get("fushion_tab", True)): | |
| gr.Markdown(translations["fushion_markdown"]) | |
| with gr.Row(): | |
| gr.Markdown(translations["fushion_markdown_2"]) | |
| with gr.Row(): | |
| name_to_save = gr.Textbox(label=translations["modelname"], placeholder="Model.pth", value="", max_lines=1, interactive=True) | |
| with gr.Row(): | |
| fushion_button = gr.Button(translations["fushion"], variant="primary", scale=4) | |
| with gr.Column(): | |
| with gr.Row(): | |
| model_a = gr.File(label=f"{translations['model_name']} 1", file_types=[".pth", ".onnx"]) | |
| model_b = gr.File(label=f"{translations['model_name']} 2", file_types=[".pth", ".onnx"]) | |
| with gr.Row(): | |
| model_path_a = gr.Textbox(label=f"{translations['model_path']} 1", value="", placeholder="assets/weights/Model_1.pth") | |
| model_path_b = gr.Textbox(label=f"{translations['model_path']} 2", value="", placeholder="assets/weights/Model_2.pth") | |
| with gr.Row(): | |
| ratio = gr.Slider(minimum=0, maximum=1, label=translations["model_ratio"], info=translations["model_ratio_info"], value=0.5, interactive=True) | |
| with gr.Row(): | |
| output_model = gr.File(label=translations["output_model_path"], file_types=[".pth", ".onnx"], interactive=False, visible=False) | |
| with gr.Row(): | |
| model_a.upload(fn=lambda model: shutil.move(model.name, os.path.join("assets", "weights")), inputs=[model_a], outputs=[model_path_a]) | |
| model_b.upload(fn=lambda model: shutil.move(model.name, os.path.join("assets", "weights")), inputs=[model_b], outputs=[model_path_b]) | |
| with gr.Row(): | |
| fushion_button.click( | |
| fn=fushion_model, | |
| inputs=[ | |
| name_to_save, | |
| model_path_a, | |
| model_path_b, | |
| ratio | |
| ], | |
| outputs=[name_to_save, output_model], | |
| api_name="fushion_model" | |
| ) | |
| fushion_button.click(fn=lambda: visible(True), inputs=[], outputs=[output_model]) | |
| with gr.TabItem(translations["read_model"], visible=configs.get("read_tab", True)): | |
| gr.Markdown(translations["read_model_markdown"]) | |
| with gr.Row(): | |
| gr.Markdown(translations["read_model_markdown_2"]) | |
| with gr.Row(): | |
| model = gr.File(label=translations["drop_model"], file_types=[".pth", ".onnx"]) | |
| with gr.Row(): | |
| read_button = gr.Button(translations["readmodel"], variant="primary", scale=2) | |
| with gr.Column(): | |
| model_path = gr.Textbox(label=translations["model_path"], value="", placeholder="assets/weights/Model.pth", info=translations["model_path_info"], interactive=True) | |
| output_info = gr.Textbox(label=translations["modelinfo"], value="", interactive=False, scale=6) | |
| with gr.Row(): | |
| model.upload(fn=lambda model: shutil.move(model.name, os.path.join("assets", "weights")), inputs=[model], outputs=[model_path]) | |
| read_button.click( | |
| fn=model_info, | |
| inputs=[model_path], | |
| outputs=[output_info], | |
| api_name="read_model" | |
| ) | |
| with gr.TabItem(translations["convert_model"], visible=configs.get("onnx_tab", True)): | |
| gr.Markdown(translations["pytorch2onnx"]) | |
| with gr.Row(): | |
| gr.Markdown(translations["pytorch2onnx_markdown"]) | |
| with gr.Row(): | |
| model_pth_upload = gr.File(label=translations["drop_model"], file_types=[".pth"]) | |
| with gr.Row(): | |
| convert_onnx = gr.Button(translations["convert_model"], variant="primary", scale=2) | |
| with gr.Row(): | |
| model_pth_path = gr.Textbox(label=translations["model_path"], value="", placeholder="assets/weights/Model.pth", info=translations["model_path_info"], interactive=True) | |
| with gr.Row(): | |
| output_model2 = gr.File(label=translations["output_model_path"], file_types=[".pth", ".onnx"], interactive=False, visible=False) | |
| with gr.Row(): | |
| model_pth_upload.upload(fn=lambda model_pth_upload: shutil.move(model_pth_upload.name, os.path.join("assets", "weights")), inputs=[model_pth_upload], outputs=[model_pth_path]) | |
| convert_onnx.click( | |
| fn=onnx_export, | |
| inputs=[model_pth_path], | |
| outputs=[output_model2, output_info], | |
| api_name="model_onnx_export" | |
| ) | |
| convert_onnx.click(fn=lambda: visible(True), inputs=[], outputs=[output_model2]) | |
| with gr.TabItem(translations["f0_extractor_tab"], visible=configs.get("f0_extractor_tab", True)): | |
| gr.Markdown(translations["f0_extractor_markdown"]) | |
| with gr.Row(): | |
| gr.Markdown(translations["f0_extractor_markdown_2"]) | |
| with gr.Row(): | |
| extractor_button = gr.Button(translations["extract_button"].replace("2. ", ""), variant="primary") | |
| with gr.Row(): | |
| with gr.Column(): | |
| upload_audio_file = gr.File(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"]) | |
| audioplay = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"]) | |
| with gr.Column(): | |
| with gr.Accordion(translations["f0_method"], open=False): | |
| with gr.Group(): | |
| onnx_f0_mode3 = gr.Checkbox(label=translations["f0_onnx_mode"], info=translations["f0_onnx_mode_info"], value=False, interactive=True) | |
| f0_method_extract = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=method_f0, value="rmvpe", interactive=True) | |
| with gr.Accordion(translations["audio_path"], open=True): | |
| input_audio_path = gr.Dropdown(label=translations["audio_path"], value="", choices=paths_for_files, allow_custom_value=True, interactive=True) | |
| refesh_audio_button = gr.Button(translations["refesh"]) | |
| with gr.Row(): | |
| gr.Markdown("___") | |
| with gr.Row(): | |
| file_output = gr.File(label="", file_types=[".txt"], interactive=False) | |
| image_output = gr.Image(label="", interactive=False, show_download_button=True) | |
| with gr.Row(): | |
| upload_audio_file.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[upload_audio_file], outputs=[input_audio_path]) | |
| input_audio_path.change(fn=lambda audio: audio if os.path.isfile(audio) else None, inputs=[input_audio_path], outputs=[audioplay]) | |
| refesh_audio_button.click(fn=change_audios_choices, inputs=[input_audio_path], outputs=[input_audio_path]) | |
| with gr.Row(): | |
| extractor_button.click( | |
| fn=f0_extract, | |
| inputs=[ | |
| input_audio_path, | |
| f0_method_extract, | |
| onnx_f0_mode3 | |
| ], | |
| outputs=[file_output, image_output], | |
| api_name="f0_extract" | |
| ) | |
| with gr.TabItem(translations["settings"], visible=configs.get("settings_tab", True)): | |
| gr.Markdown(translations["settings_markdown"]) | |
| with gr.Row(): | |
| gr.Markdown(translations["settings_markdown_2"]) | |
| with gr.Row(): | |
| toggle_button = gr.Button(translations["change_light_dark"], variant="secondary", scale=2) | |
| with gr.Row(): | |
| with gr.Column(): | |
| language_dropdown = gr.Dropdown(label=translations["lang"], interactive=True, info=translations["lang_restart"], choices=configs.get("support_language", "vi-VN"), value=language) | |
| change_lang = gr.Button(translations["change_lang"], variant="primary", scale=2) | |
| with gr.Column(): | |
| theme_dropdown = gr.Dropdown(label=translations["theme"], interactive=True, info=translations["theme_restart"], choices=configs.get("themes", theme), value=theme, allow_custom_value=True) | |
| changetheme = gr.Button(translations["theme_button"], variant="primary", scale=2) | |
| with gr.Row(): | |
| with gr.Column(): | |
| fp_choice = gr.Radio(choices=["fp16","fp32"], value="fp16" if configs.get("fp16", False) else "fp32", label=translations["precision"], info=translations["precision_info"], interactive=True) | |
| fp_button = gr.Button(translations["update_precision"], variant="secondary", scale=2) | |
| with gr.Column(): | |
| font_choice = gr.Textbox(label=translations["font"], info=translations["font_info"], value=font, interactive=True) | |
| font_button = gr.Button(translations["change_font"]) | |
| with gr.Row(): | |
| with gr.Column(): | |
| with gr.Accordion(translations["stop"], open=False): | |
| separate_stop = gr.Button(translations["stop_separate"]) | |
| convert_stop = gr.Button(translations["stop_convert"]) | |
| create_dataset_stop = gr.Button(translations["stop_create_dataset"]) | |
| audioldm2_stop = gr.Button(translations["stop_audioldm2"]) | |
| with gr.Accordion(translations["stop_training"], open=False): | |
| model_name_stop = gr.Textbox(label=translations["modelname"], info=translations["training_model_name"], value="", placeholder=translations["modelname"], interactive=True) | |
| preprocess_stop = gr.Button(translations["stop_preprocess"]) | |
| extract_stop = gr.Button(translations["stop_extract"]) | |
| train_stop = gr.Button(translations["stop_training"]) | |
| with gr.Row(): | |
| toggle_button.click(fn=None, js="() => {document.body.classList.toggle('dark')}") | |
| fp_button.click(fn=change_fp, inputs=[fp_choice], outputs=[fp_choice]) | |
| with gr.Row(): | |
| change_lang.click(fn=change_language, inputs=[language_dropdown], outputs=[]) | |
| changetheme.click(fn=change_theme, inputs=[theme_dropdown], outputs=[]) | |
| font_button.click(fn=change_font, inputs=[font_choice], outputs=[]) | |
| with gr.Row(): | |
| change_lang.click(fn=None, js="setTimeout(function() {location.reload()}, 15000)", inputs=[], outputs=[]) | |
| changetheme.click(fn=None, js="setTimeout(function() {location.reload()}, 15000)", inputs=[], outputs=[]) | |
| font_button.click(fn=None, js="setTimeout(function() {location.reload()}, 15000)", inputs=[], outputs=[]) | |
| with gr.Row(): | |
| separate_stop.click(fn=lambda: stop_pid("separate_pid", None, False), inputs=[], outputs=[]) | |
| convert_stop.click(fn=lambda: stop_pid("convert_pid", None, False), inputs=[], outputs=[]) | |
| create_dataset_stop.click(fn=lambda: stop_pid("create_dataset_pid", None, False), inputs=[], outputs=[]) | |
| with gr.Row(): | |
| preprocess_stop.click(fn=lambda model_name_stop: stop_pid("preprocess_pid", model_name_stop, False), inputs=[model_name_stop], outputs=[]) | |
| extract_stop.click(fn=lambda model_name_stop: stop_pid("extract_pid", model_name_stop, False), inputs=[model_name_stop], outputs=[]) | |
| train_stop.click(fn=lambda model_name_stop: stop_pid("train_pid", model_name_stop, True), inputs=[model_name_stop], outputs=[]) | |
| with gr.Row(): | |
| audioldm2_stop.click(fn=lambda: stop_pid("audioldm2_pid", None, False), inputs=[], outputs=[]) | |
| with gr.Row(): | |
| gr.Markdown(translations["terms_of_use"]) | |
| gr.Markdown(translations["exemption"]) | |
| logger.info(translations["start_app"]) | |
| logger.info(translations["set_lang"].format(lang=language)) | |
| port = configs.get("app_port", 7860) | |
| for i in range(configs.get("num_of_restart", 5)): | |
| try: | |
| app.queue().launch( | |
| favicon_path=os.path.join("assets", "ico.png"), | |
| server_name=configs.get("server_name", "0.0.0.0"), | |
| server_port=port, | |
| show_error=configs.get("app_show_error", False), | |
| inbrowser="--open" in sys.argv, | |
| share="--share" in sys.argv, | |
| allowed_paths=allow_disk | |
| ) | |
| break | |
| except OSError: | |
| logger.debug(translations["port"].format(port=port)) | |
| port -= 1 | |
| except Exception as e: | |
| logger.error(translations["error_occurred"].format(e=e)) | |
| sys.exit(1) | |