import os import subprocess import sys import gradio as gr from assets.i18n.i18n import I18nAuto from core import ( run_preprocess_script, run_extract_script, run_train_script, run_index_script, ) from rvc.configs.config import max_vram_gpu, get_gpu_info from rvc.lib.utils import format_title i18n = I18nAuto() now_dir = os.getcwd() sys.path.append(now_dir) sup_audioext = { "wav", "mp3", "flac", "ogg", "opus", "m4a", "mp4", "aac", "alac", "wma", "aiff", "webm", "ac3", } # Custom Pretraineds pretraineds_custom_path = os.path.join( now_dir, "rvc", "pretraineds", "pretraineds_custom" ) pretraineds_custom_path_relative = os.path.relpath(pretraineds_custom_path, now_dir) if not os.path.exists(pretraineds_custom_path_relative): os.makedirs(pretraineds_custom_path_relative) def get_pretrained_list(suffix): return [ os.path.join(dirpath, filename) for dirpath, _, filenames in os.walk(pretraineds_custom_path_relative) for filename in filenames if filename.endswith(".pth") and suffix in filename ] pretraineds_list_d = get_pretrained_list("D") pretraineds_list_g = get_pretrained_list("G") def refresh_custom_pretraineds(): return ( {"choices": sorted(get_pretrained_list("G")), "__type__": "update"}, {"choices": sorted(get_pretrained_list("D")), "__type__": "update"}, ) # Dataset Creator datasets_path = os.path.join(now_dir, "assets", "datasets") if not os.path.exists(datasets_path): os.makedirs(datasets_path) datasets_path_relative = os.path.relpath(datasets_path, now_dir) def get_datasets_list(): return [ dirpath for dirpath, _, filenames in os.walk(datasets_path_relative) if any(filename.endswith(tuple(sup_audioext)) for filename in filenames) ] def refresh_datasets(): return {"choices": sorted(get_datasets_list()), "__type__": "update"} # Drop Model def save_drop_model(dropbox): if ".pth" not in dropbox: gr.Info( i18n( "The file you dropped is not a valid pretrained file. Please try again." ) ) else: file_name = os.path.basename(dropbox) pretrained_path = os.path.join(pretraineds_custom_path_relative, file_name) if os.path.exists(pretrained_path): os.remove(pretrained_path) os.rename(dropbox, pretrained_path) gr.Info( i18n( "Click the refresh button to see the pretrained file in the dropdown menu." ) ) return None # Drop Dataset def save_drop_dataset_audio(dropbox, dataset_name): if not dataset_name: gr.Info("Please enter a valid dataset name. Please try again.") return None, None else: file_extension = os.path.splitext(dropbox)[1][1:].lower() if file_extension not in sup_audioext: gr.Info("The file you dropped is not a valid audio file. Please try again.") else: dataset_name = format_title(dataset_name) audio_file = format_title(os.path.basename(dropbox)) dataset_path = os.path.join(now_dir, "assets", "datasets", dataset_name) if not os.path.exists(dataset_path): os.makedirs(dataset_path) destination_path = os.path.join(dataset_path, audio_file) if os.path.exists(destination_path): os.remove(destination_path) os.rename(dropbox, destination_path) gr.Info( i18n( "The audio file has been successfully added to the dataset. Please click the preprocess button." ) ) dataset_path = os.path.dirname(destination_path) relative_dataset_path = os.path.relpath(dataset_path, now_dir) return None, relative_dataset_path # Train Tab def train_tab(): with gr.Accordion(i18n("Preprocess")): with gr.Row(): with gr.Column(): model_name = gr.Textbox( label=i18n("Model Name"), placeholder=i18n("Enter model name"), value="my-project", interactive=True, ) dataset_path = gr.Dropdown( label=i18n("Dataset Path"), # placeholder=i18n("Enter dataset path"), choices=get_datasets_list(), allow_custom_value=True, interactive=True, ) refresh_datasets_button = gr.Button(i18n("Refresh Datasets")) dataset_creator = gr.Checkbox( label=i18n("Dataset Creator"), value=False, interactive=True, visible=True, ) with gr.Column(visible=False) as dataset_creator_settings: with gr.Accordion("Dataset Creator"): dataset_name = gr.Textbox( label=i18n("Dataset Name"), placeholder=i18n("Enter dataset name"), interactive=True, ) upload_audio_dataset = gr.File( label=i18n("Upload Audio Dataset"), type="filepath", interactive=True, ) with gr.Column(): sampling_rate = gr.Radio( label=i18n("Sampling Rate"), choices=["32000", "40000", "48000"], value="40000", interactive=True, ) rvc_version = gr.Radio( label=i18n("RVC Version"), choices=["v1", "v2"], value="v2", interactive=True, ) preprocess_output_info = gr.Textbox( label=i18n("Output Information"), value="", max_lines=8, interactive=False, ) with gr.Row(): preprocess_button = gr.Button(i18n("Preprocess Dataset")) preprocess_button.click( run_preprocess_script, [model_name, dataset_path, sampling_rate], preprocess_output_info, api_name="preprocess_dataset", ) with gr.Accordion(i18n("Extract")): with gr.Row(): hop_length = gr.Slider( 1, 512, 128, step=1, label=i18n("Hop Length"), interactive=True ) with gr.Row(): with gr.Column(): f0method = gr.Radio( label=i18n("Pitch extraction algorithm"), choices=["pm", "dio", "crepe", "crepe-tiny", "harvest", "rmvpe"], value="rmvpe", interactive=True, ) extract_output_info = gr.Textbox( label=i18n("Output Information"), value="", max_lines=8, interactive=False, ) extract_button = gr.Button(i18n("Extract Features")) extract_button.click( run_extract_script, [model_name, rvc_version, f0method, hop_length, sampling_rate], extract_output_info, api_name="extract_features", ) with gr.Accordion(i18n("Train")): with gr.Row(): batch_size = gr.Slider( 1, 50, max_vram_gpu(0), step=1, label=i18n("Batch Size"), interactive=True, ) save_every_epoch = gr.Slider( 1, 100, 10, step=1, label=i18n("Save Every Epoch"), interactive=True ) total_epoch = gr.Slider( 1, 1000, 500, step=1, label=i18n("Total Epoch"), interactive=True ) with gr.Row(): pitch_guidance = gr.Checkbox( label=i18n("Pitch Guidance"), value=True, interactive=True ) pretrained = gr.Checkbox( label=i18n("Pretrained"), value=True, interactive=True ) save_only_latest = gr.Checkbox( label=i18n("Save Only Latest"), value=False, interactive=True ) save_every_weights = gr.Checkbox( label=i18n("Save Every Weights"), value=True, interactive=True, ) custom_pretrained = gr.Checkbox( label=i18n("Custom Pretrained"), value=False, interactive=True ) multiple_gpu = gr.Checkbox( label=i18n("GPU Settings"), value=False, interactive=True ) with gr.Row(): with gr.Column(visible=False) as pretrained_custom_settings: with gr.Accordion("Pretrained Custom Settings"): upload_pretrained = gr.File( label=i18n("Upload Pretrained Model"), type="filepath", interactive=True, ) refresh_custom_pretaineds_button = gr.Button( i18n("Refresh Custom Pretraineds") ) g_pretrained_path = gr.Dropdown( label=i18n("Custom Pretrained G"), choices=sorted(pretraineds_list_g), interactive=True, allow_custom_value=True, ) d_pretrained_path = gr.Dropdown( label=i18n("Custom Pretrained D"), choices=sorted(pretraineds_list_d), interactive=True, allow_custom_value=True, ) with gr.Column(visible=False) as gpu_custom_settings: with gr.Accordion("GPU Settings"): gpu = gr.Textbox( label=i18n("GPU Number"), placeholder=i18n("0 to ∞ separated by -"), value="0", interactive=True, ) gr.Textbox( label=i18n("GPU Information"), value=get_gpu_info(), interactive=False, ) with gr.Row(): train_output_info = gr.Textbox( label=i18n("Output Information"), value="", max_lines=8, interactive=False, ) with gr.Row(): train_button = gr.Button(i18n("Start Training")) train_button.click( run_train_script, [ model_name, rvc_version, save_every_epoch, save_only_latest, save_every_weights, total_epoch, sampling_rate, batch_size, gpu, pitch_guidance, pretrained, custom_pretrained, g_pretrained_path, d_pretrained_path, ], train_output_info, api_name="start_training", ) index_button = gr.Button(i18n("Generate Index")) index_button.click( run_index_script, [model_name, rvc_version], train_output_info, api_name="generate_index", ) def toggle_visible(checkbox): return {"visible": checkbox, "__type__": "update"} refresh_datasets_button.click( fn=refresh_datasets, inputs=[], outputs=[dataset_path], ) dataset_creator.change( fn=toggle_visible, inputs=[dataset_creator], outputs=[dataset_creator_settings], ) upload_audio_dataset.upload( fn=save_drop_dataset_audio, inputs=[upload_audio_dataset, dataset_name], outputs=[upload_audio_dataset, dataset_path], ) custom_pretrained.change( fn=toggle_visible, inputs=[custom_pretrained], outputs=[pretrained_custom_settings], ) refresh_custom_pretaineds_button.click( fn=refresh_custom_pretraineds, inputs=[], outputs=[g_pretrained_path, d_pretrained_path], ) upload_pretrained.upload( fn=save_drop_model, inputs=[upload_pretrained], outputs=[upload_pretrained], ) multiple_gpu.change( fn=toggle_visible, inputs=[multiple_gpu], outputs=[gpu_custom_settings], )