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RVC-MAKER / main /app /app.py
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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
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.Row():
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"])
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.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.Row():
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)
audio_input = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"])
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(translations["convert_audio"], visible=configs.get("convert_tab", True)):
gr.Markdown(f"## {translations['convert_audio']}")
with gr.Row():
gr.Markdown(translations["convert_info"])
with gr.Row():
with gr.Column():
with gr.Accordion(translations["model_accordion"], open=True):
with gr.Row():
model_pth = 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)
model_index = 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)
refesh = gr.Button(translations["refesh"])
with gr.Row():
with gr.Column():
audio_select = gr.Dropdown(label=translations["select_separate"], choices=[], value="", interactive=True, allow_custom_value=True, visible=False)
convert_button_2 = gr.Button(translations["convert_audio"], visible=False)
with gr.Row():
with gr.Column():
input0 = gr.File(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"])
play_audio = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"])
with gr.Row():
with gr.Column():
with gr.Row():
index_strength = gr.Slider(label=translations["index_strength"], info=translations["index_strength_info"], minimum=0, maximum=1, value=0.5, step=0.01, interactive=True, visible=model_index.value != "")
with gr.Row():
with gr.Column():
with gr.Accordion(translations["input_output"], open=False):
with gr.Column():
export_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_audio0 = gr.Dropdown(label=translations["audio_path"], value="", choices=paths_for_files, info=translations["provide_audio"], allow_custom_value=True, interactive=True)
output_audio = gr.Textbox(label=translations["output_path"], value="audios/output.wav", placeholder="audios/output.wav", info=translations["output_path_info"], interactive=True)
with gr.Column():
refesh0 = gr.Button(translations["refesh"])
with gr.Accordion(translations["setting"], open=False):
with gr.Row():
cleaner0 = gr.Checkbox(label=translations["clear_audio"], value=False, interactive=True)
autotune = gr.Checkbox(label=translations["autotune"], value=False, interactive=True)
use_audio = gr.Checkbox(label=translations["use_audio"], value=False, interactive=True)
checkpointing = gr.Checkbox(label=translations["memory_efficient_training"], value=False, interactive=True)
with gr.Row():
use_original = gr.Checkbox(label=translations["convert_original"], value=False, interactive=True, visible=use_audio.value)
convert_backing = gr.Checkbox(label=translations["convert_backing"], value=False, interactive=True, visible=use_audio.value)
not_merge_backing = gr.Checkbox(label=translations["not_merge_backing"], value=False, interactive=True, visible=use_audio.value)
merge_instrument = gr.Checkbox(label=translations["merge_instruments"], value=False, interactive=True, visible=use_audio.value)
with gr.Row():
pitch = gr.Slider(minimum=-20, maximum=20, step=1, info=translations["pitch_info"], label=translations["pitch"], value=0, interactive=True)
clean_strength0 = gr.Slider(label=translations["clean_strength"], info=translations["clean_strength_info"], minimum=0, maximum=1, value=0.5, step=0.1, interactive=True, visible=cleaner0.value)
with gr.Accordion(translations["f0_method"], open=False):
with gr.Group():
with gr.Row():
onnx_f0_mode = gr.Checkbox(label=translations["f0_onnx_mode"], info=translations["f0_onnx_mode_info"], value=False, interactive=True)
unlock_full_method = gr.Checkbox(label=translations["f0_unlock"], info=translations["f0_unlock_info"], value=False, interactive=True)
method = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=method_f0+["hybrid"], value="rmvpe", interactive=True)
hybrid_method = gr.Dropdown(label=translations["f0_method_hybrid"], info=translations["f0_method_hybrid_info"], choices=["hybrid[pm+dio]", "hybrid[pm+crepe-tiny]", "hybrid[pm+crepe]", "hybrid[pm+fcpe]", "hybrid[pm+rmvpe]", "hybrid[pm+harvest]", "hybrid[pm+yin]", "hybrid[dio+crepe-tiny]", "hybrid[dio+crepe]", "hybrid[dio+fcpe]", "hybrid[dio+rmvpe]", "hybrid[dio+harvest]", "hybrid[dio+yin]", "hybrid[crepe-tiny+crepe]", "hybrid[crepe-tiny+fcpe]", "hybrid[crepe-tiny+rmvpe]", "hybrid[crepe-tiny+harvest]", "hybrid[crepe+fcpe]", "hybrid[crepe+rmvpe]", "hybrid[crepe+harvest]", "hybrid[crepe+yin]", "hybrid[fcpe+rmvpe]", "hybrid[fcpe+harvest]", "hybrid[fcpe+yin]", "hybrid[rmvpe+harvest]", "hybrid[rmvpe+yin]", "hybrid[harvest+yin]"], value="hybrid[pm+dio]", interactive=True, allow_custom_value=True, visible=method.value == "hybrid")
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(translations["f0_file"], open=False):
upload_f0_file = gr.File(label=translations["upload_f0"], file_types=[".txt"])
f0_file_dropdown = gr.Dropdown(label=translations["f0_file_2"], value="", choices=f0_file, allow_custom_value=True, interactive=True)
refesh_f0_file = gr.Button(translations["refesh"])
with gr.Accordion(translations["hubert_model"], open=False):
embed_mode = gr.Radio(label=translations["embed_mode"], info=translations["embed_mode_info"], value="fairseq", choices=embedders_mode, interactive=True, visible=True)
embedders = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=embedders_model, value="hubert_base", interactive=True)
custom_embedders = gr.Textbox(label=translations["modelname"], info=translations["modelname_info"], value="", placeholder="hubert_base", interactive=True, visible=embedders.value == "custom")
with gr.Accordion(translations["use_presets"], open=False):
with gr.Row():
presets_name = gr.Dropdown(label=translations["file_preset"], choices=presets_file, value=presets_file[0] if len(presets_file) > 0 else '', interactive=True, allow_custom_value=True)
with gr.Row():
load_click = gr.Button(translations["load_file"], variant="primary")
refesh_click = gr.Button(translations["refesh"])
with gr.Accordion(translations["export_file"], open=False):
with gr.Row():
with gr.Column():
with gr.Group():
with gr.Row():
cleaner_chbox = gr.Checkbox(label=translations["save_clean"], value=True, interactive=True)
autotune_chbox = gr.Checkbox(label=translations["save_autotune"], value=True, interactive=True)
pitch_chbox = gr.Checkbox(label=translations["save_pitch"], value=True, interactive=True)
index_strength_chbox = gr.Checkbox(label=translations["save_index_2"], value=True, interactive=True)
resample_sr_chbox = gr.Checkbox(label=translations["save_resample"], value=True, interactive=True)
filter_radius_chbox = gr.Checkbox(label=translations["save_filter"], value=True, interactive=True)
volume_envelope_chbox = gr.Checkbox(label=translations["save_envelope"], value=True, interactive=True)
protect_chbox = gr.Checkbox(label=translations["save_protect"], value=True, interactive=True)
split_audio_chbox = gr.Checkbox(label=translations["save_split"], value=True, interactive=True)
formant_shifting_chbox = gr.Checkbox(label=translations["formantshift"], value=True, interactive=True)
with gr.Row():
with gr.Column():
name_to_save_file = gr.Textbox(label=translations["filename_to_save"])
save_file_button = gr.Button(translations["export_file"])
with gr.Row():
upload_presets = gr.File(label=translations["upload_presets"], file_types=[".json"])
with gr.Column():
with gr.Row():
split_audio = gr.Checkbox(label=translations["split_audio"], value=False, interactive=True)
formant_shifting = gr.Checkbox(label=translations["formantshift"], value=False, interactive=True)
f0_autotune_strength = gr.Slider(minimum=0, maximum=1, label=translations["autotune_rate"], info=translations["autotune_rate_info"], value=1, step=0.1, interactive=True, visible=autotune.value)
resample_sr = gr.Slider(minimum=0, maximum=96000, label=translations["resample"], info=translations["resample_info"], value=0, step=1, interactive=True)
filter_radius = gr.Slider(minimum=0, maximum=7, label=translations["filter_radius"], info=translations["filter_radius_info"], value=3, step=1, interactive=True)
volume_envelope = gr.Slider(minimum=0, maximum=1, label=translations["volume_envelope"], info=translations["volume_envelope_info"], value=1, step=0.1, interactive=True)
protect = gr.Slider(minimum=0, maximum=1, label=translations["protect"], info=translations["protect_info"], value=0.5, step=0.01, interactive=True)
with gr.Row():
formant_qfrency = gr.Slider(value=1.0, label=translations["formant_qfrency"], info=translations["formant_qfrency"], minimum=0.0, maximum=16.0, step=0.1, interactive=True, visible=False)
formant_timbre = gr.Slider(value=1.0, label=translations["formant_timbre"], info=translations["formant_timbre"], minimum=0.0, maximum=16.0, step=0.1, interactive=True, visible=False)
with gr.Row():
convert_button = gr.Button(translations["convert_audio"], variant="primary")
gr.Markdown(translations["output_convert"])
with gr.Row():
main_convert = gr.Audio(show_download_button=True, interactive=False, label=translations["main_convert"])
backing_convert = gr.Audio(show_download_button=True, interactive=False, label=translations["convert_backing"], visible=convert_backing.value)
main_backing = gr.Audio(show_download_button=True, interactive=False, label=translations["main_or_backing"], visible=convert_backing.value)
with gr.Row():
original_convert = gr.Audio(show_download_button=True, interactive=False, label=translations["convert_original"], visible=use_original.value)
vocal_instrument = gr.Audio(show_download_button=True, interactive=False, label=translations["voice_or_instruments"], visible=merge_instrument.value)
with gr.Row():
upload_f0_file.upload(fn=lambda inp: shutil.move(inp.name, os.path.join("assets", "f0")), inputs=[upload_f0_file], outputs=[f0_file_dropdown])
refesh_f0_file.click(fn=change_f0_choices, inputs=[], outputs=[f0_file_dropdown])
unlock_full_method.change(fn=unlock_f0, inputs=[unlock_full_method], outputs=[method])
with gr.Row():
load_click.click(
fn=load_presets,
inputs=[
presets_name,
cleaner0,
autotune,
pitch,
clean_strength0,
index_strength,
resample_sr,
filter_radius,
volume_envelope,
protect,
split_audio,
f0_autotune_strength,
formant_qfrency,
formant_timbre
],
outputs=[
cleaner0,
autotune,
pitch,
clean_strength0,
index_strength,
resample_sr,
filter_radius,
volume_envelope,
protect,
split_audio,
f0_autotune_strength,
formant_shifting,
formant_qfrency,
formant_timbre
]
)
refesh_click.click(fn=change_preset_choices, inputs=[], outputs=[presets_name])
save_file_button.click(
fn=save_presets,
inputs=[
name_to_save_file,
cleaner0,
autotune,
pitch,
clean_strength0,
index_strength,
resample_sr,
filter_radius,
volume_envelope,
protect,
split_audio,
f0_autotune_strength,
cleaner_chbox,
autotune_chbox,
pitch_chbox,
index_strength_chbox,
resample_sr_chbox,
filter_radius_chbox,
volume_envelope_chbox,
protect_chbox,
split_audio_chbox,
formant_shifting_chbox,
formant_shifting,
formant_qfrency,
formant_timbre
],
outputs=[presets_name]
)
with gr.Row():
upload_presets.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("assets", "presets")), inputs=[upload_presets], outputs=[presets_name])
autotune.change(fn=visible, inputs=[autotune], outputs=[f0_autotune_strength])
use_audio.change(fn=lambda a: [visible(a), visible(a), visible(a), visible(a), visible(a), valueFalse_interactive(a), valueFalse_interactive(a), valueFalse_interactive(a), valueFalse_interactive(a), visible(not a), visible(not a), visible(not a), visible(not a)], inputs=[use_audio], outputs=[main_backing, use_original, convert_backing, not_merge_backing, merge_instrument, use_original, convert_backing, not_merge_backing, merge_instrument, input_audio0, output_audio, input0, play_audio])
with gr.Row():
convert_backing.change(fn=lambda a,b: [change_backing_choices(a, b), visible(a)], inputs=[convert_backing, not_merge_backing], outputs=[use_original, backing_convert])
use_original.change(fn=lambda audio, original: [visible(original), visible(not original), visible(audio and not original), valueFalse_interactive(not original), valueFalse_interactive(not original)], inputs=[use_audio, use_original], outputs=[original_convert, main_convert, main_backing, convert_backing, not_merge_backing])
cleaner0.change(fn=visible, inputs=[cleaner0], outputs=[clean_strength0])
with gr.Row():
merge_instrument.change(fn=visible, inputs=[merge_instrument], outputs=[vocal_instrument])
not_merge_backing.change(fn=lambda audio, merge, cvb: [visible(audio and not merge), change_backing_choices(cvb, merge)], inputs=[use_audio, not_merge_backing, convert_backing], outputs=[main_backing, use_original])
method.change(fn=lambda method, hybrid: [visible(method == "hybrid"), hoplength_show(method, hybrid)], inputs=[method, hybrid_method], outputs=[hybrid_method, hop_length])
with gr.Row():
hybrid_method.change(fn=hoplength_show, inputs=[method, hybrid_method], outputs=[hop_length])
refesh.click(fn=change_models_choices, inputs=[], outputs=[model_pth, model_index])
model_pth.change(fn=get_index, inputs=[model_pth], outputs=[model_index])
with gr.Row():
input0.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[input0], outputs=[input_audio0])
input_audio0.change(fn=lambda audio: audio if os.path.isfile(audio) else None, inputs=[input_audio0], outputs=[play_audio])
formant_shifting.change(fn=lambda a: [visible(a)]*2, inputs=[formant_shifting], outputs=[formant_qfrency, formant_timbre])
with gr.Row():
embedders.change(fn=lambda embedders: visible(embedders == "custom"), inputs=[embedders], outputs=[custom_embedders])
refesh0.click(fn=change_audios_choices, inputs=[input_audio0], outputs=[input_audio0])
model_index.change(fn=index_strength_show, inputs=[model_index], outputs=[index_strength])
with gr.Row():
audio_select.change(fn=lambda: visible(True), inputs=[], outputs=[convert_button_2])
convert_button.click(fn=lambda: visible(False), inputs=[], outputs=[convert_button])
convert_button_2.click(fn=lambda: [visible(False), visible(False)], inputs=[], outputs=[audio_select, convert_button_2])
with gr.Row():
convert_button.click(
fn=convert_selection,
inputs=[
cleaner0,
autotune,
use_audio,
use_original,
convert_backing,
not_merge_backing,
merge_instrument,
pitch,
clean_strength0,
model_pth,
model_index,
index_strength,
input_audio0,
output_audio,
export_format,
method,
hybrid_method,
hop_length,
embedders,
custom_embedders,
resample_sr,
filter_radius,
volume_envelope,
protect,
split_audio,
f0_autotune_strength,
checkpointing,
onnx_f0_mode,
formant_shifting,
formant_qfrency,
formant_timbre,
f0_file_dropdown,
embed_mode
],
outputs=[audio_select, main_convert, backing_convert, main_backing, original_convert, vocal_instrument, convert_button],
api_name="convert_selection"
)
embed_mode.change(fn=visible_embedders, inputs=[embed_mode], outputs=[embedders])
convert_button_2.click(
fn=convert_audio,
inputs=[
cleaner0,
autotune,
use_audio,
use_original,
convert_backing,
not_merge_backing,
merge_instrument,
pitch,
clean_strength0,
model_pth,
model_index,
index_strength,
input_audio0,
output_audio,
export_format,
method,
hybrid_method,
hop_length,
embedders,
custom_embedders,
resample_sr,
filter_radius,
volume_envelope,
protect,
split_audio,
f0_autotune_strength,
audio_select,
checkpointing,
onnx_f0_mode,
formant_shifting,
formant_qfrency,
formant_timbre,
f0_file_dropdown,
embed_mode
],
outputs=[main_convert, backing_convert, main_backing, original_convert, vocal_instrument, convert_button],
api_name="convert_audio"
)
with gr.TabItem(translations["convert_with_whisper"], visible=configs.get("convert_with_whisper", True)):
gr.Markdown(f"## {translations['convert_with_whisper']}")
with gr.Row():
gr.Markdown(translations["convert_with_whisper_info"])
with gr.Row():
with gr.Column():
with gr.Group():
with gr.Row():
cleaner2 = gr.Checkbox(label=translations["clear_audio"], value=False, interactive=True)
autotune2 = gr.Checkbox(label=translations["autotune"], value=False, interactive=True)
checkpointing2 = gr.Checkbox(label=translations["memory_efficient_training"], value=False, interactive=True)
formant_shifting2 = gr.Checkbox(label=translations["formantshift"], value=False, interactive=True)
with gr.Row():
num_spk = gr.Slider(minimum=2, maximum=8, step=1, info=translations["num_spk_info"], label=translations["num_spk"], value=2, interactive=True)
with gr.Row():
with gr.Column():
convert_button3 = gr.Button(translations["convert_audio"], variant="primary")
with gr.Row():
with gr.Column():
with gr.Accordion(translations["model_accordion"] + " 1", open=True):
with gr.Row():
model_pth2 = 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)
model_index2 = 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():
refesh2 = gr.Button(translations["refesh"])
with gr.Row():
pitch3 = gr.Slider(minimum=-20, maximum=20, step=1, info=translations["pitch_info"], label=translations["pitch"], value=0, interactive=True)
index_strength2 = gr.Slider(label=translations["index_strength"], info=translations["index_strength_info"], minimum=0, maximum=1, value=0.5, step=0.01, interactive=True, visible=model_index2.value != "")
with gr.Accordion(translations["input_output"], open=False):
with gr.Column():
export_format2 = 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_audio1 = gr.Dropdown(label=translations["audio_path"], value="", choices=paths_for_files, info=translations["provide_audio"], allow_custom_value=True, interactive=True)
output_audio2 = gr.Textbox(label=translations["output_path"], value="audios/output.wav", placeholder="audios/output.wav", info=translations["output_path_info"], interactive=True)
with gr.Column():
refesh4 = gr.Button(translations["refesh"])
with gr.Row():
input2 = gr.File(label=translations["drop_audio"], file_types=[".wav", ".mp3", ".flac", ".ogg", ".opus", ".m4a", ".mp4", ".aac", ".alac", ".wma", ".aiff", ".webm", ".ac3"])
with gr.Column():
with gr.Accordion(translations["model_accordion"] + " 2", open=True):
with gr.Row():
model_pth3 = 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)
model_index3 = 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():
refesh3 = gr.Button(translations["refesh"])
with gr.Row():
pitch4 = gr.Slider(minimum=-20, maximum=20, step=1, info=translations["pitch_info"], label=translations["pitch"], value=0, interactive=True)
index_strength3 = gr.Slider(label=translations["index_strength"], info=translations["index_strength_info"], minimum=0, maximum=1, value=0.5, step=0.01, interactive=True, visible=model_index3.value != "")
with gr.Accordion(translations["setting"], open=False):
with gr.Row():
model_size = gr.Radio(label=translations["model_size"], info=translations["model_size_info"], choices=["tiny", "tiny.en", "base", "base.en", "small", "small.en", "medium", "medium.en", "large-v1", "large-v2", "large-v3", "large-v3-turbo"], value="medium", interactive=True)
with gr.Accordion(translations["f0_method"], open=False):
with gr.Group():
with gr.Row():
onnx_f0_mode4 = gr.Checkbox(label=translations["f0_onnx_mode"], info=translations["f0_onnx_mode_info"], value=False, interactive=True)
unlock_full_method2 = gr.Checkbox(label=translations["f0_unlock"], info=translations["f0_unlock_info"], value=False, interactive=True)
method3 = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=method_f0+["hybrid"], value="rmvpe", interactive=True)
hybrid_method3 = gr.Dropdown(label=translations["f0_method_hybrid"], info=translations["f0_method_hybrid_info"], choices=["hybrid[pm+dio]", "hybrid[pm+crepe-tiny]", "hybrid[pm+crepe]", "hybrid[pm+fcpe]", "hybrid[pm+rmvpe]", "hybrid[pm+harvest]", "hybrid[pm+yin]", "hybrid[dio+crepe-tiny]", "hybrid[dio+crepe]", "hybrid[dio+fcpe]", "hybrid[dio+rmvpe]", "hybrid[dio+harvest]", "hybrid[dio+yin]", "hybrid[crepe-tiny+crepe]", "hybrid[crepe-tiny+fcpe]", "hybrid[crepe-tiny+rmvpe]", "hybrid[crepe-tiny+harvest]", "hybrid[crepe+fcpe]", "hybrid[crepe+rmvpe]", "hybrid[crepe+harvest]", "hybrid[crepe+yin]", "hybrid[fcpe+rmvpe]", "hybrid[fcpe+harvest]", "hybrid[fcpe+yin]", "hybrid[rmvpe+harvest]", "hybrid[rmvpe+yin]", "hybrid[harvest+yin]"], value="hybrid[pm+dio]", interactive=True, allow_custom_value=True, visible=method3.value == "hybrid")
hop_length3 = 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(translations["hubert_model"], open=False):
embed_mode3 = gr.Radio(label=translations["embed_mode"], info=translations["embed_mode_info"], value="fairseq", choices=embedders_mode, interactive=True, visible=True)
embedders3 = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=embedders_model, value="hubert_base", interactive=True)
custom_embedders3 = gr.Textbox(label=translations["modelname"], info=translations["modelname_info"], value="", placeholder="hubert_base", interactive=True, visible=embedders3.value == "custom")
with gr.Column():
clean_strength3 = gr.Slider(label=translations["clean_strength"], info=translations["clean_strength_info"], minimum=0, maximum=1, value=0.5, step=0.1, interactive=True, visible=cleaner2.value)
f0_autotune_strength3 = gr.Slider(minimum=0, maximum=1, label=translations["autotune_rate"], info=translations["autotune_rate_info"], value=1, step=0.1, interactive=True, visible=autotune.value)
resample_sr3 = gr.Slider(minimum=0, maximum=96000, label=translations["resample"], info=translations["resample_info"], value=0, step=1, interactive=True)
filter_radius3 = gr.Slider(minimum=0, maximum=7, label=translations["filter_radius"], info=translations["filter_radius_info"], value=3, step=1, interactive=True)
volume_envelope3 = gr.Slider(minimum=0, maximum=1, label=translations["volume_envelope"], info=translations["volume_envelope_info"], value=1, step=0.1, interactive=True)
protect3 = gr.Slider(minimum=0, maximum=1, label=translations["protect"], info=translations["protect_info"], value=0.5, step=0.01, interactive=True)
with gr.Row():
formant_qfrency3 = gr.Slider(value=1.0, label=translations["formant_qfrency"] + " 1", info=translations["formant_qfrency"], minimum=0.0, maximum=16.0, step=0.1, interactive=True, visible=False)
formant_timbre3 = gr.Slider(value=1.0, label=translations["formant_timbre"] + " 1", info=translations["formant_timbre"], minimum=0.0, maximum=16.0, step=0.1, interactive=True, visible=False)
with gr.Row():
formant_qfrency4 = gr.Slider(value=1.0, label=translations["formant_qfrency"] + " 2", info=translations["formant_qfrency"], minimum=0.0, maximum=16.0, step=0.1, interactive=True, visible=False)
formant_timbre4 = gr.Slider(value=1.0, label=translations["formant_timbre"] + " 2", info=translations["formant_timbre"], minimum=0.0, maximum=16.0, step=0.1, interactive=True, visible=False)
with gr.Row():
gr.Markdown(translations["input_output"])
with gr.Row():
play_audio2 = gr.Audio(show_download_button=True, interactive=False, label=translations["input_audio"])
play_audio3 = gr.Audio(show_download_button=True, interactive=False, label=translations["output_file_tts_convert"])
with gr.Row():
autotune2.change(fn=visible, inputs=[autotune2], outputs=[f0_autotune_strength3])
cleaner2.change(fn=visible, inputs=[cleaner2], outputs=[clean_strength3])
method3.change(fn=lambda method, hybrid: [visible(method == "hybrid"), hoplength_show(method, hybrid)], inputs=[method3, hybrid_method3], outputs=[hybrid_method3, hop_length3])
with gr.Row():
hybrid_method3.change(fn=hoplength_show, inputs=[method3, hybrid_method3], outputs=[hop_length3])
refesh2.click(fn=change_models_choices, inputs=[], outputs=[model_pth2, model_index2])
model_pth2.change(fn=get_index, inputs=[model_pth2], outputs=[model_index2])
with gr.Row():
refesh3.click(fn=change_models_choices, inputs=[], outputs=[model_pth3, model_index3])
model_pth3.change(fn=get_index, inputs=[model_pth3], outputs=[model_index3])
input2.upload(fn=lambda audio_in: shutil.move(audio_in.name, os.path.join("audios")), inputs=[input2], outputs=[input_audio1])
with gr.Row():
input_audio1.change(fn=lambda audio: audio if os.path.isfile(audio) else None, inputs=[input_audio1], outputs=[play_audio2])
formant_shifting2.change(fn=lambda a: [visible(a)]*4, inputs=[formant_shifting2], outputs=[formant_qfrency3, formant_timbre3, formant_qfrency4, formant_timbre4])
embedders3.change(fn=lambda embedders: visible(embedders == "custom"), inputs=[embedders3], outputs=[custom_embedders3])
with gr.Row():
refesh4.click(fn=change_audios_choices, inputs=[input_audio1], outputs=[input_audio1])
model_index2.change(fn=index_strength_show, inputs=[model_index2], outputs=[index_strength2])
model_index3.change(fn=index_strength_show, inputs=[model_index3], outputs=[index_strength3])
with gr.Row():
unlock_full_method2.change(fn=unlock_f0, inputs=[unlock_full_method2], outputs=[method3])
embed_mode3.change(fn=visible_embedders, inputs=[embed_mode3], outputs=[embedders3])
convert_button3.click(
fn=convert_with_whisper,
inputs=[
num_spk,
model_size,
cleaner2,
clean_strength3,
autotune2,
f0_autotune_strength3,
checkpointing2,
model_pth2,
model_pth3,
model_index2,
model_index3,
pitch3,
pitch4,
index_strength2,
index_strength3,
export_format2,
input_audio1,
output_audio2,
onnx_f0_mode4,
method3,
hybrid_method3,
hop_length3,
embed_mode3,
embedders3,
custom_embedders3,
resample_sr3,
filter_radius3,
volume_envelope3,
protect3,
formant_shifting2,
formant_qfrency3,
formant_timbre3,
formant_qfrency4,
formant_timbre4,
],
outputs=[play_audio3],
api_name="convert_with_whisper"
)
with gr.TabItem(translations["convert_text"], visible=configs.get("tts_tab", True)):
gr.Markdown(translations["convert_text_markdown"])
with gr.Row():
gr.Markdown(translations["convert_text_markdown_2"])
with gr.Row():
with gr.Column():
with gr.Group():
with gr.Row():
use_txt = gr.Checkbox(label=translations["input_txt"], value=False, interactive=True)
google_tts_check_box = gr.Checkbox(label=translations["googletts"], value=False, interactive=True)
prompt = gr.Textbox(label=translations["text_to_speech"], value="", placeholder="Hello Words", lines=3)
with gr.Column():
speed = gr.Slider(label=translations["voice_speed"], info=translations["voice_speed_info"], minimum=-100, maximum=100, value=0, step=1)
pitch0 = gr.Slider(minimum=-20, maximum=20, step=1, info=translations["pitch_info"], label=translations["pitch"], value=0, interactive=True)
with gr.Row():
tts_button = gr.Button(translations["tts_1"], variant="primary", scale=2)
convert_button0 = gr.Button(translations["tts_2"], variant="secondary", scale=2)
with gr.Row():
with gr.Column():
txt_input = gr.File(label=translations["drop_text"], file_types=[".txt", ".srt"], visible=use_txt.value)
tts_voice = gr.Dropdown(label=translations["voice"], choices=edgetts, interactive=True, value="vi-VN-NamMinhNeural")
tts_pitch = gr.Slider(minimum=-20, maximum=20, step=1, info=translations["pitch_info_2"], label=translations["pitch"], value=0, interactive=True)
with gr.Column():
with gr.Accordion(translations["model_accordion"], open=True):
with gr.Row():
model_pth0 = 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)
model_index0 = 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():
refesh1 = gr.Button(translations["refesh"])
with gr.Row():
index_strength0 = gr.Slider(label=translations["index_strength"], info=translations["index_strength_info"], minimum=0, maximum=1, value=0.5, step=0.01, interactive=True, visible=model_index0.value != "")
with gr.Accordion(translations["output_path"], open=False):
export_format0 = 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)
output_audio0 = gr.Textbox(label=translations["output_tts"], value="audios/tts.wav", placeholder="audios/tts.wav", info=translations["tts_output"], interactive=True)
output_audio1 = gr.Textbox(label=translations["output_tts_convert"], value="audios/tts-convert.wav", placeholder="audios/tts-convert.wav", info=translations["tts_output"], interactive=True)
with gr.Accordion(translations["setting"], open=False):
with gr.Accordion(translations["f0_method"], open=False):
with gr.Group():
with gr.Row():
onnx_f0_mode1 = gr.Checkbox(label=translations["f0_onnx_mode"], info=translations["f0_onnx_mode_info"], value=False, interactive=True)
unlock_full_method3 = gr.Checkbox(label=translations["f0_unlock"], info=translations["f0_unlock_info"], value=False, interactive=True)
method0 = gr.Radio(label=translations["f0_method"], info=translations["f0_method_info"], choices=method_f0+["hybrid"], value="rmvpe", interactive=True)
hybrid_method0 = gr.Dropdown(label=translations["f0_method_hybrid"], info=translations["f0_method_hybrid_info"], choices=["hybrid[pm+dio]", "hybrid[pm+crepe-tiny]", "hybrid[pm+crepe]", "hybrid[pm+fcpe]", "hybrid[pm+rmvpe]", "hybrid[pm+harvest]", "hybrid[pm+yin]", "hybrid[dio+crepe-tiny]", "hybrid[dio+crepe]", "hybrid[dio+fcpe]", "hybrid[dio+rmvpe]", "hybrid[dio+harvest]", "hybrid[dio+yin]", "hybrid[crepe-tiny+crepe]", "hybrid[crepe-tiny+fcpe]", "hybrid[crepe-tiny+rmvpe]", "hybrid[crepe-tiny+harvest]", "hybrid[crepe+fcpe]", "hybrid[crepe+rmvpe]", "hybrid[crepe+harvest]", "hybrid[crepe+yin]", "hybrid[fcpe+rmvpe]", "hybrid[fcpe+harvest]", "hybrid[fcpe+yin]", "hybrid[rmvpe+harvest]", "hybrid[rmvpe+yin]", "hybrid[harvest+yin]"], value="hybrid[pm+dio]", interactive=True, allow_custom_value=True, visible=method0.value == "hybrid")
hop_length0 = 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(translations["f0_file"], open=False):
upload_f0_file0 = gr.File(label=translations["upload_f0"], file_types=[".txt"])
f0_file_dropdown0 = gr.Dropdown(label=translations["f0_file_2"], value="", choices=f0_file, allow_custom_value=True, interactive=True)
refesh_f0_file0 = gr.Button(translations["refesh"])
with gr.Accordion(translations["hubert_model"], open=False):
embed_mode1 = gr.Radio(label=translations["embed_mode"], info=translations["embed_mode_info"], value="fairseq", choices=embedders_mode, interactive=True, visible=True)
embedders0 = gr.Radio(label=translations["hubert_model"], info=translations["hubert_info"], choices=embedders_model, value="hubert_base", interactive=True)
custom_embedders0 = gr.Textbox(label=translations["modelname"], info=translations["modelname_info"], value="", placeholder="hubert_base", interactive=True, visible=embedders0.value == "custom")
with gr.Group():
with gr.Row():
formant_shifting1 = gr.Checkbox(label=translations["formantshift"], value=False, interactive=True)
split_audio0 = gr.Checkbox(label=translations["split_audio"], value=False, interactive=True)
cleaner1 = gr.Checkbox(label=translations["clear_audio"], value=False, interactive=True)
autotune3 = gr.Checkbox(label=translations["autotune"], value=False, interactive=True)
checkpointing0 = gr.Checkbox(label=translations["memory_efficient_training"], value=False, interactive=True)
with gr.Column():
f0_autotune_strength0 = gr.Slider(minimum=0, maximum=1, label=translations["autotune_rate"], info=translations["autotune_rate_info"], value=1, step=0.1, interactive=True, visible=autotune3.value)
clean_strength1 = gr.Slider(label=translations["clean_strength"], info=translations["clean_strength_info"], minimum=0, maximum=1, value=0.5, step=0.1, interactive=True, visible=cleaner1.value)
resample_sr0 = gr.Slider(minimum=0, maximum=96000, label=translations["resample"], info=translations["resample_info"], value=0, step=1, interactive=True)
filter_radius0 = gr.Slider(minimum=0, maximum=7, label=translations["filter_radius"], info=translations["filter_radius_info"], value=3, step=1, interactive=True)
volume_envelope0 = gr.Slider(minimum=0, maximum=1, label=translations["volume_envelope"], info=translations["volume_envelope_info"], value=1, step=0.1, interactive=True)
protect0 = gr.Slider(minimum=0, maximum=1, label=translations["protect"], info=translations["protect_info"], value=0.5, step=0.01, interactive=True)
with gr.Row():
formant_qfrency1 = gr.Slider(value=1.0, label=translations["formant_qfrency"], info=translations["formant_qfrency"], minimum=0.0, maximum=16.0, step=0.1, interactive=True, visible=False)
formant_timbre1 = gr.Slider(value=1.0, label=translations["formant_timbre"], info=translations["formant_timbre"], minimum=0.0, maximum=16.0, step=0.1, interactive=True, visible=False)
with gr.Row():
gr.Markdown(translations["output_tts_markdown"])
with gr.Row():
tts_voice_audio = gr.Audio(show_download_button=True, interactive=False, label=translations["output_text_to_speech"])
tts_voice_convert = gr.Audio(show_download_button=True, interactive=False, label=translations["output_file_tts_convert"])
with gr.Row():
unlock_full_method3.change(fn=unlock_f0, inputs=[unlock_full_method3], outputs=[method0])
upload_f0_file0.upload(fn=lambda inp: shutil.move(inp.name, os.path.join("assets", "f0")), inputs=[upload_f0_file0], outputs=[f0_file_dropdown0])
refesh_f0_file0.click(fn=change_f0_choices, inputs=[], outputs=[f0_file_dropdown0])
with gr.Row():
embed_mode1.change(fn=visible_embedders, inputs=[embed_mode1], outputs=[embedders0])
autotune3.change(fn=visible, inputs=[autotune3], outputs=[f0_autotune_strength0])
model_pth0.change(fn=get_index, inputs=[model_pth0], outputs=[model_index0])
with gr.Row():
cleaner1.change(fn=visible, inputs=[cleaner1], outputs=[clean_strength1])
method0.change(fn=lambda method, hybrid: [visible(method == "hybrid"), hoplength_show(method, hybrid)], inputs=[method0, hybrid_method0], outputs=[hybrid_method0, hop_length0])
hybrid_method0.change(fn=hoplength_show, inputs=[method0, hybrid_method0], outputs=[hop_length0])
with gr.Row():
refesh1.click(fn=change_models_choices, inputs=[], outputs=[model_pth0, model_index0])
embedders0.change(fn=lambda embedders: visible(embedders == "custom"), inputs=[embedders0], outputs=[custom_embedders0])
formant_shifting1.change(fn=lambda a: [visible(a)]*2, inputs=[formant_shifting1], outputs=[formant_qfrency1, formant_timbre1])
with gr.Row():
model_index0.change(fn=index_strength_show, inputs=[model_index0], outputs=[index_strength0])
txt_input.upload(fn=process_input, inputs=[txt_input], outputs=[prompt])
use_txt.change(fn=visible, inputs=[use_txt], outputs=[txt_input])
with gr.Row():
google_tts_check_box.change(fn=change_tts_voice_choices, inputs=[google_tts_check_box], outputs=[tts_voice])
tts_button.click(
fn=TTS,
inputs=[
prompt,
tts_voice,
speed,
output_audio0,
tts_pitch,
google_tts_check_box,
txt_input
],
outputs=[tts_voice_audio],
api_name="text-to-speech"
)
convert_button0.click(
fn=convert_tts,
inputs=[
cleaner1,
autotune3,
pitch0,
clean_strength1,
model_pth0,
model_index0,
index_strength0,
output_audio0,
output_audio1,
export_format0,
method0,
hybrid_method0,
hop_length0,
embedders0,
custom_embedders0,
resample_sr0,
filter_radius0,
volume_envelope0,
protect0,
split_audio0,
f0_autotune_strength0,
checkpointing0,
onnx_f0_mode1,
formant_shifting1,
formant_qfrency1,
formant_timbre1,
f0_file_dropdown0,
embed_mode1
],
outputs=[tts_voice_convert],
api_name="convert_tts"
)
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["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["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["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["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"])
with gr.Row():
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)