Update tabs/train/train.py

tabs/train/train.py

@@ -1,1008 +1,1008 @@
-import os
-import shutil
-import sys
-from multiprocessing import cpu_count
-
-import gradio as gr
-
-from assets.i18n.i18n import I18nAuto
-from core import (
-    run_extract_script,
-    run_index_script,
-    run_preprocess_script,
-    run_prerequisites_script,
-    run_train_script,
-)
-from rvc.configs.config import get_gpu_info, get_number_of_gpus, max_vram_gpu
-from rvc.lib.utils import format_title
-from tabs.settings.sections.restart import stop_train
-
-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", "models", "pretraineds", "pretraineds_custom"
-)
-
-pretraineds_custom_path_relative = os.path.relpath(pretraineds_custom_path, now_dir)
-
-custom_embedder_root = os.path.join(
-    now_dir, "rvc", "models", "embedders", "embedders_custom"
-)
-custom_embedder_root_relative = os.path.relpath(custom_embedder_root, now_dir)
-
-os.makedirs(custom_embedder_root, exist_ok=True)
-os.makedirs(pretraineds_custom_path_relative, exist_ok=True)
-
-
-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"}
-
-
-# Model Names
-models_path = os.path.join(now_dir, "logs")
-
-
-def get_models_list():
-    return [
-        os.path.basename(dirpath)
-        for dirpath in os.listdir(models_path)
-        if os.path.isdir(os.path.join(models_path, dirpath))
-        and all(excluded not in dirpath for excluded in ["zips", "mute", "reference"])
-    ]
-
-
-def refresh_models():
-    return {"choices": sorted(get_models_list()), "__type__": "update"}
-
-
-# Refresh Models and Datasets
-def refresh_models_and_datasets():
-    return (
-        {"choices": sorted(get_models_list()), "__type__": "update"},
-        {"choices": sorted(get_datasets_list()), "__type__": "update"},
-    )
-
-
-# Refresh Custom Embedders
-def get_embedder_custom_list():
-    return [
-        os.path.join(dirpath, dirname)
-        for dirpath, dirnames, _ in os.walk(custom_embedder_root_relative)
-        for dirname in dirnames
-    ]
-
-
-def refresh_custom_embedder_list():
-    return {"choices": sorted(get_embedder_custom_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)
-        shutil.copy(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)
-            shutil.copy(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
-
-
-# Drop Custom Embedder
-def create_folder_and_move_files(folder_name, bin_file, config_file):
-    if not folder_name:
-        return "Folder name must not be empty."
-
-    folder_name = os.path.join(custom_embedder_root, folder_name)
-    os.makedirs(folder_name, exist_ok=True)
-
-    if bin_file:
-        bin_file_path = os.path.join(folder_name, os.path.basename(bin_file))
-        shutil.copy(bin_file, bin_file_path)
-
-    if config_file:
-        config_file_path = os.path.join(folder_name, os.path.basename(config_file))
-        shutil.copy(config_file, config_file_path)
-
-    return f"Files moved to folder {folder_name}"
-
-
-def refresh_embedders_folders():
-    custom_embedders = [
-        os.path.join(dirpath, dirname)
-        for dirpath, dirnames, _ in os.walk(custom_embedder_root_relative)
-        for dirname in dirnames
-    ]
-    return custom_embedders
-
-
-# Export
-## Get Pth and Index Files
-def get_pth_list():
-    return [
-        os.path.relpath(os.path.join(dirpath, filename), now_dir)
-        for dirpath, _, filenames in os.walk(models_path)
-        for filename in filenames
-        if filename.endswith(".pth")
-    ]
-
-
-def get_index_list():
-    return [
-        os.path.relpath(os.path.join(dirpath, filename), now_dir)
-        for dirpath, _, filenames in os.walk(models_path)
-        for filename in filenames
-        if filename.endswith(".index") and "trained" not in filename
-    ]
-
-
-def refresh_pth_and_index_list():
-    return (
-        {"choices": sorted(get_pth_list()), "__type__": "update"},
-        {"choices": sorted(get_index_list()), "__type__": "update"},
-    )
-
-
-## Export Pth and Index Files
-def export_pth(pth_path):
-    if pth_path and os.path.exists(pth_path):
-        return pth_path
-    return None
-
-
-def export_index(index_path):
-    if index_path and os.path.exists(index_path):
-        return index_path
-    return None
-
-
-## Upload to Google Drive
-def upload_to_google_drive(pth_path, index_path):
-    def upload_file(file_path):
-        if file_path:
-            try:
-                gr.Info(f"Uploading {pth_path} to Google Drive...")
-                google_drive_folder = "/content/drive/MyDrive/ApplioExported"
-                if not os.path.exists(google_drive_folder):
-                    os.makedirs(google_drive_folder)
-                google_drive_file_path = os.path.join(
-                    google_drive_folder, os.path.basename(file_path)
-                )
-                if os.path.exists(google_drive_file_path):
-                    os.remove(google_drive_file_path)
-                shutil.copy2(file_path, google_drive_file_path)
-                gr.Info("File uploaded successfully.")
-            except Exception as error:
-                print(f"An error occurred uploading to Google Drive: {error}")
-                gr.Info("Error uploading to Google Drive")
-
-    upload_file(pth_path)
-    upload_file(index_path)
-
-
-# Train Tab
-def train_tab():
-    # Model settings section
-    with gr.Accordion(i18n("Model Settings")):
-        with gr.Row():
-            with gr.Column():
-                model_name = gr.Dropdown(
-                    label=i18n("Model Name"),
-                    info=i18n("Name of the new model."),
-                    choices=get_models_list(),
-                    value="my-project",
-                    interactive=True,
-                    allow_custom_value=True,
-                )
-                architecture = gr.Radio(
-                    label=i18n("Architecture"),
-                    info=i18n(
-                        "Choose the model architecture:\n- **RVC (V2)**: Default option, compatible with all clients.\n- **Applio**: Advanced quality with improved vocoders and higher sample rates, Applio-only."
-                    ),
-                    choices=["RVC", "Applio"],
-                    value="RVC",
-                    interactive=True,
-                    visible=True,
-                )
-            with gr.Column():
-                sampling_rate = gr.Radio(
-                    label=i18n("Sampling Rate"),
-                    info=i18n("The sampling rate of the audio files."),
-                    choices=["32000", "40000", "48000"],
-                    value="40000",
-                    interactive=True,
-                )
-                vocoder = gr.Radio(
-                    label=i18n("Vocoder"),
-                    info=i18n(
-                        "Choose the vocoder for audio synthesis:\n- **HiFi-GAN**: Default option, compatible with all clients.\n- **MRF HiFi-GAN**: Higher fidelity, Applio-only.\n- **RefineGAN**: Superior audio quality, Applio-only."
-                    ),
-                    choices=["HiFi-GAN", "MRF HiFi-GAN", "RefineGAN"],
-                    value="HiFi-GAN",
-                    interactive=False,
-                    visible=True,
-                )
-        with gr.Accordion(
-            i18n("Advanced Settings"),
-            open=False,
-        ):
-            with gr.Row():
-                with gr.Column():
-                    cpu_cores = gr.Slider(
-                        1,
-                        min(cpu_count(), 32),  # max 32 parallel processes
-                        min(cpu_count(), 32),
-                        step=1,
-                        label=i18n("CPU Cores"),
-                        info=i18n(
-                            "The number of CPU cores to use in the extraction process. The default setting are your cpu cores, which is recommended for most cases."
-                        ),
-                        interactive=True,
-                    )
-
-                with gr.Column():
-                    gpu = gr.Textbox(
-                        label=i18n("GPU Number"),
-                        info=i18n(
-                            "Specify the number of GPUs you wish to utilize for extracting by entering them separated by hyphens (-)."
-                        ),
-                        placeholder=i18n("0 to ∞ separated by -"),
-                        value=str(get_number_of_gpus()),
-                        interactive=True,
-                    )
-                    gr.Textbox(
-                        label=i18n("GPU Information"),
-                        info=i18n("The GPU information will be displayed here."),
-                        value=get_gpu_info(),
-                        interactive=False,
-                    )
-    # Preprocess section
-    with gr.Accordion(i18n("Preprocess")):
-        dataset_path = gr.Dropdown(
-            label=i18n("Dataset Path"),
-            info=i18n("Path to the dataset folder."),
-            # placeholder=i18n("Enter dataset path"),
-            choices=get_datasets_list(),
-            allow_custom_value=True,
-            interactive=True,
-        )
-        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(i18n("Dataset Creator")):
-                dataset_name = gr.Textbox(
-                    label=i18n("Dataset Name"),
-                    info=i18n("Name of the new dataset."),
-                    placeholder=i18n("Enter dataset name"),
-                    interactive=True,
-                )
-                upload_audio_dataset = gr.File(
-                    label=i18n("Upload Audio Dataset"),
-                    type="filepath",
-                    interactive=True,
-                )
-        refresh = gr.Button(i18n("Refresh"))
-
-        with gr.Accordion(i18n("Advanced Settings"), open=False):
-            cut_preprocess = gr.Radio(
-                label=i18n("Audio cutting"),
-                info=i18n(
-                    "Audio file slicing method: Select 'Skip' if the files are already pre-sliced, 'Simple' if excessive silence has already been removed from the files, or 'Automatic' for automatic silence detection and slicing around it."
-                ),
-                choices=["Skip", "Simple", "Automatic"],
-                value="Automatic",
-                interactive=True,
-            )
-            with gr.Row():
-                chunk_len = gr.Slider(
-                    0.5,
-                    5.0,
-                    3.0,
-                    step=0.1,
-                    label=i18n("Chunk length (sec)"),
-                    info=i18n("Length of the audio slice for 'Simple' method."),
-                    interactive=True,
-                )
-                overlap_len = gr.Slider(
-                    0.0,
-                    0.4,
-                    0.3,
-                    step=0.1,
-                    label=i18n("Overlap length (sec)"),
-                    info=i18n(
-                        "Length of the overlap between slices for 'Simple' method."
-                    ),
-                    interactive=True,
-                )
-
-            with gr.Row():
-                process_effects = gr.Checkbox(
-                    label=i18n("Process effects"),
-                    info=i18n(
-                        "It's recommended to deactivate this option if your dataset has already been processed."
-                    ),
-                    value=True,
-                    interactive=True,
-                    visible=True,
-                )
-                noise_reduction = gr.Checkbox(
-                    label=i18n("Noise Reduction"),
-                    info=i18n(
-                        "It's recommended keep deactivate this option if your dataset has already been processed."
-                    ),
-                    value=False,
-                    interactive=True,
-                    visible=True,
-                )
-            clean_strength = gr.Slider(
-                minimum=0,
-                maximum=1,
-                label=i18n("Noise Reduction Strength"),
-                info=i18n(
-                    "Set the clean-up level to the audio you want, the more you increase it the more it will clean up, but it is possible that the audio will be more compressed."
-                ),
-                visible=False,
-                value=0.5,
-                interactive=True,
-            )
-        preprocess_output_info = gr.Textbox(
-            label=i18n("Output Information"),
-            info=i18n("The output information will be displayed here."),
-            value="",
-            max_lines=8,
-            interactive=False,
-        )
-
-        with gr.Row():
-            preprocess_button = gr.Button(i18n("Preprocess Dataset"))
-            preprocess_button.click(
-                fn=run_preprocess_script,
-                inputs=[
-                    model_name,
-                    dataset_path,
-                    sampling_rate,
-                    cpu_cores,
-                    cut_preprocess,
-                    process_effects,
-                    noise_reduction,
-                    clean_strength,
-                    chunk_len,
-                    overlap_len,
-                ],
-                outputs=[preprocess_output_info],
-            )
-
-    # Extract section
-    with gr.Accordion(i18n("Extract")):
-        with gr.Row():
-            f0_method = gr.Radio(
-                label=i18n("Pitch extraction algorithm"),
-                info=i18n(
-                    "Pitch extraction algorithm to use for the audio conversion. The default algorithm is rmvpe, which is recommended for most cases."
-                ),
-                choices=["crepe", "crepe-tiny", "rmvpe"],
-                value="rmvpe",
-                interactive=True,
-            )
-
-            embedder_model = gr.Radio(
-                label=i18n("Embedder Model"),
-                info=i18n("Model used for learning speaker embedding."),
-                choices=[
-                    "contentvec",
-                    "chinese-hubert-base",
-                    "japanese-hubert-base",
-                    "korean-hubert-base",
-                    "custom",
-                ],
-                value="contentvec",
-                interactive=True,
-            )
-        include_mutes = gr.Slider(
-            0,
-            10,
-            2,
-            step=1,
-            label=i18n("Silent training files"),
-            info=i18n(
-                "Adding several silent files to the training set enables the model to handle pure silence in inferred audio files. Select 0 if your dataset is clean and already contains segments of pure silence."
-            ),
-            value=True,
-            interactive=True,
-        )
-        hop_length = gr.Slider(
-            1,
-            512,
-            128,
-            step=1,
-            label=i18n("Hop Length"),
-            info=i18n(
-                "Denotes the duration it takes for the system to transition to a significant pitch change. Smaller hop lengths require more time for inference but tend to yield higher pitch accuracy."
-            ),
-            visible=False,
-            interactive=True,
-        )
-        with gr.Row(visible=False) as embedder_custom:
-            with gr.Accordion("Custom Embedder", open=True):
-                with gr.Row():
-                    embedder_model_custom = gr.Dropdown(
-                        label="Select Custom Embedder",
-                        choices=refresh_embedders_folders(),
-                        interactive=True,
-                        allow_custom_value=True,
-                    )
-                    refresh_embedders_button = gr.Button("Refresh embedders")
-                folder_name_input = gr.Textbox(label="Folder Name", interactive=True)
-                with gr.Row():
-                    bin_file_upload = gr.File(
-                        label="Upload .bin", type="filepath", interactive=True
-                    )
-                    config_file_upload = gr.File(
-                        label="Upload .json", type="filepath", interactive=True
-                    )
-                move_files_button = gr.Button("Move files to custom embedder folder")
-
-        extract_output_info = gr.Textbox(
-            label=i18n("Output Information"),
-            info=i18n("The output information will be displayed here."),
-            value="",
-            max_lines=8,
-            interactive=False,
-        )
-        extract_button = gr.Button(i18n("Extract Features"))
-        extract_button.click(
-            fn=run_extract_script,
-            inputs=[
-                model_name,
-                f0_method,
-                hop_length,
-                cpu_cores,
-                gpu,
-                sampling_rate,
-                embedder_model,
-                embedder_model_custom,
-                include_mutes,
-            ],
-            outputs=[extract_output_info],
-        )
-
-    # Training section
-    with gr.Accordion(i18n("Training")):
-        with gr.Row():
-            batch_size = gr.Slider(
-                1,
-                50,
-                max_vram_gpu(0),
-                step=1,
-                label=i18n("Batch Size"),
-                info=i18n(
-                    "It's advisable to align it with the available VRAM of your GPU. A setting of 4 offers improved accuracy but slower processing, while 8 provides faster and standard results."
-                ),
-                interactive=True,
-            )
-            save_every_epoch = gr.Slider(
-                1,
-                100,
-                10,
-                step=1,
-                label=i18n("Save Every Epoch"),
-                info=i18n("Determine at how many epochs the model will saved at."),
-                interactive=True,
-            )
-            total_epoch = gr.Slider(
-                1,
-                10000,
-                500,
-                step=1,
-                label=i18n("Total Epoch"),
-                info=i18n(
-                    "Specifies the overall quantity of epochs for the model training process."
-                ),
-                interactive=True,
-            )
-        with gr.Accordion(i18n("Advanced Settings"), open=False):
-            with gr.Row():
-                with gr.Column():
-                    save_only_latest = gr.Checkbox(
-                        label=i18n("Save Only Latest"),
-                        info=i18n(
-                            "Enabling this setting will result in the G and D files saving only their most recent versions, effectively conserving storage space."
-                        ),
-                        value=True,
-                        interactive=True,
-                    )
-                    save_every_weights = gr.Checkbox(
-                        label=i18n("Save Every Weights"),
-                        info=i18n(
-                            "This setting enables you to save the weights of the model at the conclusion of each epoch."
-                        ),
-                        value=True,
-                        interactive=True,
-                    )
-                    pretrained = gr.Checkbox(
-                        label=i18n("Pretrained"),
-                        info=i18n(
-                            "Utilize pretrained models when training your own. This approach reduces training duration and enhances overall quality."
-                        ),
-                        value=True,
-                        interactive=True,
-                    )
-                with gr.Column():
-                    cleanup = gr.Checkbox(
-                        label=i18n("Fresh Training"),
-                        info=i18n(
-                            "Enable this setting only if you are training a new model from scratch or restarting the training. Deletes all previously generated weights and tensorboard logs."
-                        ),
-                        value=False,
-                        interactive=True,
-                    )
-                    cache_dataset_in_gpu = gr.Checkbox(
-                        label=i18n("Cache Dataset in GPU"),
-                        info=i18n(
-                            "Cache the dataset in GPU memory to speed up the training process."
-                        ),
-                        value=False,
-                        interactive=True,
-                    )
-                    checkpointing = gr.Checkbox(
-                        label=i18n("Checkpointing"),
-                        info=i18n(
-                            "Enables memory-efficient training. This reduces VRAM usage at the cost of slower training speed. It is useful for GPUs with limited memory (e.g., <6GB VRAM) or when training with a batch size larger than what your GPU can normally accommodate."
-                        ),
-                        value=False,
-                        interactive=True,
-                    )
-            with gr.Row():
-                custom_pretrained = gr.Checkbox(
-                    label=i18n("Custom Pretrained"),
-                    info=i18n(
-                        "Utilizing custom pretrained models can lead to superior results, as selecting the most suitable pretrained models tailored to the specific use case can significantly enhance performance."
-                    ),
-                    value=False,
-                    interactive=True,
-                )
-                overtraining_detector = gr.Checkbox(
-                    label=i18n("Overtraining Detector"),
-                    info=i18n(
-                        "Detect overtraining to prevent the model from learning the training data too well and losing the ability to generalize to new data."
-                    ),
-                    value=False,
-                    interactive=True,
-                )
-            with gr.Row():
-                with gr.Column(visible=False) as pretrained_custom_settings:
-                    with gr.Accordion(i18n("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"),
-                            info=i18n(
-                                "Select the custom pretrained model for the generator."
-                            ),
-                            choices=sorted(pretraineds_list_g),
-                            interactive=True,
-                            allow_custom_value=True,
-                        )
-                        d_pretrained_path = gr.Dropdown(
-                            label=i18n("Custom Pretrained D"),
-                            info=i18n(
-                                "Select the custom pretrained model for the discriminator."
-                            ),
-                            choices=sorted(pretraineds_list_d),
-                            interactive=True,
-                            allow_custom_value=True,
-                        )
-
-                with gr.Column(visible=False) as overtraining_settings:
-                    with gr.Accordion(i18n("Overtraining Detector Settings")):
-                        overtraining_threshold = gr.Slider(
-                            1,
-                            100,
-                            50,
-                            step=1,
-                            label=i18n("Overtraining Threshold"),
-                            info=i18n(
-                                "Set the maximum number of epochs you want your model to stop training if no improvement is detected."
-                            ),
-                            interactive=True,
-                        )
-            index_algorithm = gr.Radio(
-                label=i18n("Index Algorithm"),
-                info=i18n(
-                    "KMeans is a clustering algorithm that divides the dataset into K clusters. This setting is particularly useful for large datasets."
-                ),
-                choices=["Auto", "Faiss", "KMeans"],
-                value="Auto",
-                interactive=True,
-            )
-
-        def enforce_terms(terms_accepted, *args):
-            if not terms_accepted:
-                message = "You must agree to the Terms of Use to proceed."
-                gr.Info(message)
-                return message
-            return run_train_script(*args)
-
-        terms_checkbox = gr.Checkbox(
-            label=i18n("I agree to the terms of use"),
-            info=i18n(
-                "Please ensure compliance with the terms and conditions detailed in [this document](https://github.com/IAHispano/Applio/blob/main/TERMS_OF_USE.md) before proceeding with your training."
-            ),
-            value=False,
-            interactive=True,
-        )
-        train_output_info = gr.Textbox(
-            label=i18n("Output Information"),
-            info=i18n("The output information will be displayed here."),
-            value="",
-            max_lines=8,
-            interactive=False,
-        )
-
-        with gr.Row():
-            train_button = gr.Button(i18n("Start Training"))
-            train_button.click(
-                fn=enforce_terms,
-                inputs=[
-                    terms_checkbox,
-                    model_name,
-                    save_every_epoch,
-                    save_only_latest,
-                    save_every_weights,
-                    total_epoch,
-                    sampling_rate,
-                    batch_size,
-                    gpu,
-                    overtraining_detector,
-                    overtraining_threshold,
-                    pretrained,
-                    cleanup,
-                    index_algorithm,
-                    cache_dataset_in_gpu,
-                    custom_pretrained,
-                    g_pretrained_path,
-                    d_pretrained_path,
-                    vocoder,
-                    checkpointing,
-                ],
-                outputs=[train_output_info],
-            )
-
-            stop_train_button = gr.Button(i18n("Stop Training"), visible=False)
-            stop_train_button.click(
-                fn=stop_train,
-                inputs=[model_name],
-                outputs=[],
-            )
-
-            index_button = gr.Button(i18n("Generate Index"))
-            index_button.click(
-                fn=run_index_script,
-                inputs=[model_name, index_algorithm],
-                outputs=[train_output_info],
-            )
-
-    # Export Model section
-    with gr.Accordion(i18n("Export Model"), open=False):
-        if not os.name == "nt":
-            gr.Markdown(
-                i18n(
-                    "The button 'Upload' is only for google colab: Uploads the exported files to the ApplioExported folder in your Google Drive."
-                )
-            )
-        with gr.Row():
-            with gr.Column():
-                pth_file_export = gr.File(
-                    label=i18n("Exported Pth file"),
-                    type="filepath",
-                    value=None,
-                    interactive=False,
-                )
-                pth_dropdown_export = gr.Dropdown(
-                    label=i18n("Pth file"),
-                    info=i18n("Select the pth file to be exported"),
-                    choices=get_pth_list(),
-                    value=None,
-                    interactive=True,
-                    allow_custom_value=True,
-                )
-            with gr.Column():
-                index_file_export = gr.File(
-                    label=i18n("Exported Index File"),
-                    type="filepath",
-                    value=None,
-                    interactive=False,
-                )
-                index_dropdown_export = gr.Dropdown(
-                    label=i18n("Index File"),
-                    info=i18n("Select the index file to be exported"),
-                    choices=get_index_list(),
-                    value=None,
-                    interactive=True,
-                    allow_custom_value=True,
-                )
-        with gr.Row():
-            with gr.Column():
-                refresh_export = gr.Button(i18n("Refresh"))
-                if not os.name == "nt":
-                    upload_exported = gr.Button(i18n("Upload"))
-                    upload_exported.click(
-                        fn=upload_to_google_drive,
-                        inputs=[pth_dropdown_export, index_dropdown_export],
-                        outputs=[],
-                    )
-
-            def toggle_visible(checkbox):
-                return {"visible": checkbox, "__type__": "update"}
-
-            def toggle_visible_hop_length(f0_method):
-                if f0_method == "crepe" or f0_method == "crepe-tiny":
-                    return {"visible": True, "__type__": "update"}
-                return {"visible": False, "__type__": "update"}
-
-            def toggle_pretrained(pretrained, custom_pretrained):
-                if custom_pretrained == False:
-                    return {"visible": pretrained, "__type__": "update"}, {
-                        "visible": False,
-                        "__type__": "update",
-                    }
-                else:
-                    return {"visible": pretrained, "__type__": "update"}, {
-                        "visible": pretrained,
-                        "__type__": "update",
-                    }
-
-            def enable_stop_train_button():
-                return {"visible": False, "__type__": "update"}, {
-                    "visible": True,
-                    "__type__": "update",
-                }
-
-            def disable_stop_train_button():
-                return {"visible": True, "__type__": "update"}, {
-                    "visible": False,
-                    "__type__": "update",
-                }
-
-            def download_prerequisites():
-                gr.Info(
-                    "Checking for prerequisites with pitch guidance... Missing files will be downloaded. If you already have them, this step will be skipped."
-                )
-                run_prerequisites_script(
-                    pretraineds_hifigan=True,
-                    models=False,
-                    exe=False,
-                )
-                gr.Info(
-                    "Prerequisites check complete. Missing files were downloaded, and you may now start preprocessing."
-                )
-
-            def toggle_visible_embedder_custom(embedder_model):
-                if embedder_model == "custom":
-                    return {"visible": True, "__type__": "update"}
-                return {"visible": False, "__type__": "update"}
-
-            def toggle_architecture(architecture):
-                if architecture == "Applio":
-                    return {
-                        "choices": ["32000", "40000", "44100", "48000"],
-                        "__type__": "update",
-                    }, {
-                        "interactive": True,
-                        "__type__": "update",
-                    }
-                else:
-                    return {
-                        "choices": ["32000", "40000", "48000"],
-                        "__type__": "update",
-                        "value": "40000",
-                    }, {"interactive": False, "__type__": "update", "value": "HiFi-GAN"}
-
-            def update_slider_visibility(noise_reduction):
-                return gr.update(visible=noise_reduction)
-
-            noise_reduction.change(
-                fn=update_slider_visibility,
-                inputs=noise_reduction,
-                outputs=clean_strength,
-            )
-            architecture.change(
-                fn=toggle_architecture,
-                inputs=[architecture],
-                outputs=[sampling_rate, vocoder],
-            )
-            refresh.click(
-                fn=refresh_models_and_datasets,
-                inputs=[],
-                outputs=[model_name, 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],
-            )
-            f0_method.change(
-                fn=toggle_visible_hop_length,
-                inputs=[f0_method],
-                outputs=[hop_length],
-            )
-            embedder_model.change(
-                fn=toggle_visible_embedder_custom,
-                inputs=[embedder_model],
-                outputs=[embedder_custom],
-            )
-            embedder_model.change(
-                fn=toggle_visible_embedder_custom,
-                inputs=[embedder_model],
-                outputs=[embedder_custom],
-            )
-            move_files_button.click(
-                fn=create_folder_and_move_files,
-                inputs=[folder_name_input, bin_file_upload, config_file_upload],
-                outputs=[],
-            )
-            refresh_embedders_button.click(
-                fn=refresh_embedders_folders, inputs=[], outputs=[embedder_model_custom]
-            )
-            pretrained.change(
-                fn=toggle_pretrained,
-                inputs=[pretrained, custom_pretrained],
-                outputs=[custom_pretrained, pretrained_custom_settings],
-            )
-            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],
-            )
-            overtraining_detector.change(
-                fn=toggle_visible,
-                inputs=[overtraining_detector],
-                outputs=[overtraining_settings],
-            )
-            train_button.click(
-                fn=enable_stop_train_button,
-                inputs=[],
-                outputs=[train_button, stop_train_button],
-            )
-            train_output_info.change(
-                fn=disable_stop_train_button,
-                inputs=[],
-                outputs=[train_button, stop_train_button],
-            )
-            pth_dropdown_export.change(
-                fn=export_pth,
-                inputs=[pth_dropdown_export],
-                outputs=[pth_file_export],
-            )
-            index_dropdown_export.change(
-                fn=export_index,
-                inputs=[index_dropdown_export],
-                outputs=[index_file_export],
-            )
-            refresh_export.click(
-                fn=refresh_pth_and_index_list,
-                inputs=[],
-                outputs=[pth_dropdown_export, index_dropdown_export],
-            )
+import os
+import shutil
+import sys
+from multiprocessing import cpu_count
+
+import gradio as gr
+
+from assets.i18n.i18n import I18nAuto
+from core import (
+    run_extract_script,
+    run_index_script,
+    run_preprocess_script,
+    run_prerequisites_script,
+    run_train_script,
+)
+from rvc.configs.config import get_gpu_info, get_number_of_gpus, max_vram_gpu
+from rvc.lib.utils import format_title
+from tabs.settings.sections.restart import stop_train
+
+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", "models", "pretraineds", "pretraineds_custom"
+)
+
+pretraineds_custom_path_relative = os.path.relpath(pretraineds_custom_path, now_dir)
+
+custom_embedder_root = os.path.join(
+    now_dir, "rvc", "models", "embedders", "embedders_custom"
+)
+custom_embedder_root_relative = os.path.relpath(custom_embedder_root, now_dir)
+
+os.makedirs(custom_embedder_root, exist_ok=True)
+os.makedirs(pretraineds_custom_path_relative, exist_ok=True)
+
+
+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"}
+
+
+# Model Names
+models_path = os.path.join(now_dir, "logs")
+
+
+def get_models_list():
+    return [
+        os.path.basename(dirpath)
+        for dirpath in os.listdir(models_path)
+        if os.path.isdir(os.path.join(models_path, dirpath))
+        and all(excluded not in dirpath for excluded in ["zips", "mute", "reference"])
+    ]
+
+
+def refresh_models():
+    return {"choices": sorted(get_models_list()), "__type__": "update"}
+
+
+# Refresh Models and Datasets
+def refresh_models_and_datasets():
+    return (
+        {"choices": sorted(get_models_list()), "__type__": "update"},
+        {"choices": sorted(get_datasets_list()), "__type__": "update"},
+    )
+
+
+# Refresh Custom Embedders
+def get_embedder_custom_list():
+    return [
+        os.path.join(dirpath, dirname)
+        for dirpath, dirnames, _ in os.walk(custom_embedder_root_relative)
+        for dirname in dirnames
+    ]
+
+
+def refresh_custom_embedder_list():
+    return {"choices": sorted(get_embedder_custom_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)
+        shutil.copy(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)
+            shutil.copy(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
+
+
+# Drop Custom Embedder
+def create_folder_and_move_files(folder_name, bin_file, config_file):
+    if not folder_name:
+        return "Folder name must not be empty."
+
+    folder_name = os.path.join(custom_embedder_root, folder_name)
+    os.makedirs(folder_name, exist_ok=True)
+
+    if bin_file:
+        bin_file_path = os.path.join(folder_name, os.path.basename(bin_file))
+        shutil.copy(bin_file, bin_file_path)
+
+    if config_file:
+        config_file_path = os.path.join(folder_name, os.path.basename(config_file))
+        shutil.copy(config_file, config_file_path)
+
+    return f"Files moved to folder {folder_name}"
+
+
+def refresh_embedders_folders():
+    custom_embedders = [
+        os.path.join(dirpath, dirname)
+        for dirpath, dirnames, _ in os.walk(custom_embedder_root_relative)
+        for dirname in dirnames
+    ]
+    return custom_embedders
+
+
+# Export
+## Get Pth and Index Files
+def get_pth_list():
+    return [
+        os.path.relpath(os.path.join(dirpath, filename), now_dir)
+        for dirpath, _, filenames in os.walk(models_path)
+        for filename in filenames
+        if filename.endswith(".pth")
+    ]
+
+
+def get_index_list():
+    return [
+        os.path.relpath(os.path.join(dirpath, filename), now_dir)
+        for dirpath, _, filenames in os.walk(models_path)
+        for filename in filenames
+        if filename.endswith(".index") and "trained" not in filename
+    ]
+
+
+def refresh_pth_and_index_list():
+    return (
+        {"choices": sorted(get_pth_list()), "__type__": "update"},
+        {"choices": sorted(get_index_list()), "__type__": "update"},
+    )
+
+
+## Export Pth and Index Files
+def export_pth(pth_path):
+    if pth_path and os.path.exists(pth_path):
+        return pth_path
+    return None
+
+
+def export_index(index_path):
+    if index_path and os.path.exists(index_path):
+        return index_path
+    return None
+
+
+## Upload to Google Drive
+def upload_to_google_drive(pth_path, index_path):
+    def upload_file(file_path):
+        if file_path:
+            try:
+                gr.Info(f"Uploading {pth_path} to Google Drive...")
+                google_drive_folder = "/content/drive/MyDrive/ApplioExported"
+                if not os.path.exists(google_drive_folder):
+                    os.makedirs(google_drive_folder)
+                google_drive_file_path = os.path.join(
+                    google_drive_folder, os.path.basename(file_path)
+                )
+                if os.path.exists(google_drive_file_path):
+                    os.remove(google_drive_file_path)
+                shutil.copy2(file_path, google_drive_file_path)
+                gr.Info("File uploaded successfully.")
+            except Exception as error:
+                print(f"An error occurred uploading to Google Drive: {error}")
+                gr.Info("Error uploading to Google Drive")
+
+    upload_file(pth_path)
+    upload_file(index_path)
+
+
+# Train Tab
+def train_tab():
+    # Model settings section
+    with gr.Accordion(i18n("Model Settings")):
+        with gr.Row():
+            with gr.Column():
+                model_name = gr.Dropdown(
+                    label=i18n("Model Name"),
+                    info=i18n("Name of the new model."),
+                    choices=get_models_list(),
+                    value="my-project",
+                    interactive=True,
+                    allow_custom_value=True,
+                )
+                architecture = gr.Radio(
+                    label=i18n("Architecture"),
+                    info=i18n(
+                        "Choose the model architecture:\n- **RVC (V2)**: Default option, compatible with all clients.\n- **Applio**: Advanced quality with improved vocoders and higher sample rates, Applio-only."
+                    ),
+                    choices=["RVC", "Applio"],
+                    value="RVC",
+                    interactive=True,
+                    visible=True,
+                )
+            with gr.Column():
+                sampling_rate = gr.Radio(
+                    label=i18n("Sampling Rate"),
+                    info=i18n("The sampling rate of the audio files."),
+                    choices=["32000", "40000", "48000"],
+                    value="40000",
+                    interactive=True,
+                )
+                vocoder = gr.Radio(
+                    label=i18n("Vocoder"),
+                    info=i18n(
+                        "Choose the vocoder for audio synthesis:\n- **HiFi-GAN**: Default option, compatible with all clients.\n- **MRF HiFi-GAN**: Higher fidelity, Applio-only.\n- **RefineGAN**: Superior audio quality, Applio-only."
+                    ),
+                    choices=["HiFi-GAN", "MRF HiFi-GAN", "RefineGAN"],
+                    value="HiFi-GAN",
+                    interactive=False,
+                    visible=True,
+                )
+        with gr.Accordion(
+            i18n("Advanced Settings"),
+            open=False,
+        ):
+            with gr.Row():
+                with gr.Column():
+                    cpu_cores = gr.Slider(
+                        1,
+                        min(cpu_count(), 32),  # max 32 parallel processes
+                        min(cpu_count(), 32),
+                        step=1,
+                        label=i18n("CPU Cores"),
+                        info=i18n(
+                            "The number of CPU cores to use in the extraction process. The default setting are your cpu cores, which is recommended for most cases."
+                        ),
+                        interactive=True,
+                    )
+
+                with gr.Column():
+                    gpu = gr.Textbox(
+                        label=i18n("GPU Number"),
+                        info=i18n(
+                            "Specify the number of GPUs you wish to utilize for extracting by entering them separated by hyphens (-)."
+                        ),
+                        placeholder=i18n("0 to ∞ separated by -"),
+                        value=str(get_number_of_gpus()),
+                        interactive=True,
+                    )
+                    gr.Textbox(
+                        label=i18n("GPU Information"),
+                        info=i18n("The GPU information will be displayed here."),
+                        value=get_gpu_info(),
+                        interactive=False,
+                    )
+    # Preprocess section
+    with gr.Accordion(i18n("Preprocess")):
+        dataset_path = gr.Dropdown(
+            label=i18n("Dataset Path"),
+            info=i18n("Path to the dataset folder."),
+            # placeholder=i18n("Enter dataset path"),
+            choices=get_datasets_list(),
+            allow_custom_value=True,
+            interactive=True,
+        )
+        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(i18n("Dataset Creator")):
+                dataset_name = gr.Textbox(
+                    label=i18n("Dataset Name"),
+                    info=i18n("Name of the new dataset."),
+                    placeholder=i18n("Enter dataset name"),
+                    interactive=True,
+                )
+                upload_audio_dataset = gr.File(
+                    label=i18n("Upload Audio Dataset"),
+                    type="filepath",
+                    interactive=True,
+                )
+        refresh = gr.Button(i18n("Refresh"))
+
+        with gr.Accordion(i18n("Advanced Settings"), open=False):
+            cut_preprocess = gr.Radio(
+                label=i18n("Audio cutting"),
+                info=i18n(
+                    "Audio file slicing method: Select 'Skip' if the files are already pre-sliced, 'Simple' if excessive silence has already been removed from the files, or 'Automatic' for automatic silence detection and slicing around it."
+                ),
+                choices=["Skip", "Simple", "Automatic"],
+                value="Automatic",
+                interactive=True,
+            )
+            with gr.Row():
+                chunk_len = gr.Slider(
+                    0.5,
+                    5.0,
+                    3.0,
+                    step=0.1,
+                    label=i18n("Chunk length (sec)"),
+                    info=i18n("Length of the audio slice for 'Simple' method."),
+                    interactive=True,
+                )
+                overlap_len = gr.Slider(
+                    0.0,
+                    0.4,
+                    0.3,
+                    step=0.1,
+                    label=i18n("Overlap length (sec)"),
+                    info=i18n(
+                        "Length of the overlap between slices for 'Simple' method."
+                    ),
+                    interactive=True,
+                )
+
+            with gr.Row():
+                process_effects = gr.Checkbox(
+                    label=i18n("Process effects"),
+                    info=i18n(
+                        "It's recommended to deactivate this option if your dataset has already been processed."
+                    ),
+                    value=True,
+                    interactive=True,
+                    visible=True,
+                )
+                noise_reduction = gr.Checkbox(
+                    label=i18n("Noise Reduction"),
+                    info=i18n(
+                        "It's recommended keep deactivate this option if your dataset has already been processed."
+                    ),
+                    value=False,
+                    interactive=True,
+                    visible=True,
+                )
+            clean_strength = gr.Slider(
+                minimum=0,
+                maximum=1,
+                label=i18n("Noise Reduction Strength"),
+                info=i18n(
+                    "Set the clean-up level to the audio you want, the more you increase it the more it will clean up, but it is possible that the audio will be more compressed."
+                ),
+                visible=False,
+                value=0.5,
+                interactive=True,
+            )
+        preprocess_output_info = gr.Textbox(
+            label=i18n("Output Information"),
+            info=i18n("The output information will be displayed here."),
+            value="",
+            max_lines=8,
+            interactive=False,
+        )
+
+        with gr.Row():
+            preprocess_button = gr.Button(i18n("Preprocess Dataset"))
+            preprocess_button.click(
+                fn=run_preprocess_script,
+                inputs=[
+                    model_name,
+                    dataset_path,
+                    sampling_rate,
+                    cpu_cores,
+                    cut_preprocess,
+                    process_effects,
+                    noise_reduction,
+                    clean_strength,
+                    chunk_len,
+                    overlap_len,
+                ],
+                outputs=[preprocess_output_info],
+            )
+
+    # Extract section
+    with gr.Accordion(i18n("Extract")):
+        with gr.Row():
+            f0_method = gr.Radio(
+                label=i18n("Pitch extraction algorithm"),
+                info=i18n(
+                    "Pitch extraction algorithm to use for the audio conversion. The default algorithm is rmvpe, which is recommended for most cases."
+                ),
+                choices=["crepe", "crepe-tiny", "rmvpe"],
+                value="rmvpe",
+                interactive=True,
+            )
+
+            embedder_model = gr.Radio(
+                label=i18n("Embedder Model"),
+                info=i18n("Model used for learning speaker embedding."),
+                choices=[
+                    "contentvec",
+                    "chinese-hubert-base",
+                    "japanese-hubert-base",
+                    "korean-hubert-base",
+                    "custom",
+                ],
+                value="contentvec",
+                interactive=True,
+            )
+        include_mutes = gr.Slider(
+            0,
+            10,
+            2,
+            step=1,
+            label=i18n("Silent training files"),
+            info=i18n(
+                "Adding several silent files to the training set enables the model to handle pure silence in inferred audio files. Select 0 if your dataset is clean and already contains segments of pure silence."
+            ),
+            value=True,
+            interactive=True,
+        )
+        hop_length = gr.Slider(
+            1,
+            512,
+            128,
+            step=1,
+            label=i18n("Hop Length"),
+            info=i18n(
+                "Denotes the duration it takes for the system to transition to a significant pitch change. Smaller hop lengths require more time for inference but tend to yield higher pitch accuracy."
+            ),
+            visible=False,
+            interactive=True,
+        )
+        with gr.Row(visible=False) as embedder_custom:
+            with gr.Accordion("Custom Embedder", open=True):
+                with gr.Row():
+                    embedder_model_custom = gr.Dropdown(
+                        label="Select Custom Embedder",
+                        choices=refresh_embedders_folders(),
+                        interactive=True,
+                        allow_custom_value=True,
+                    )
+                    refresh_embedders_button = gr.Button("Refresh embedders")
+                folder_name_input = gr.Textbox(label="Folder Name", interactive=True)
+                with gr.Row():
+                    bin_file_upload = gr.File(
+                        label="Upload .bin", type="filepath", interactive=True
+                    )
+                    config_file_upload = gr.File(
+                        label="Upload .json", type="filepath", interactive=True
+                    )
+                move_files_button = gr.Button("Move files to custom embedder folder")
+
+        extract_output_info = gr.Textbox(
+            label=i18n("Output Information"),
+            info=i18n("The output information will be displayed here."),
+            value="",
+            max_lines=8,
+            interactive=False,
+        )
+        extract_button = gr.Button(i18n("Extract Features"))
+        extract_button.click(
+            fn=run_extract_script,
+            inputs=[
+                model_name,
+                f0_method,
+                hop_length,
+                cpu_cores,
+                gpu,
+                sampling_rate,
+                embedder_model,
+                embedder_model_custom,
+                include_mutes,
+            ],
+            outputs=[extract_output_info],
+        )
+
+    # Training section
+    with gr.Accordion(i18n("Training")):
+        with gr.Row():
+            batch_size = gr.Slider(
+                1,
+                50,
+                max_vram_gpu(0),
+                step=1,
+                label=i18n("Batch Size"),
+                info=i18n(
+                    "It's advisable to align it with the available VRAM of your GPU. A setting of 4 offers improved accuracy but slower processing, while 8 provides faster and standard results."
+                ),
+                interactive=True,
+            )
+            save_every_epoch = gr.Slider(
+                1,
+                100,
+                10,
+                step=1,
+                label=i18n("Save Every Epoch"),
+                info=i18n("Determine at how many epochs the model will saved at."),
+                interactive=True,
+            )
+            total_epoch = gr.Slider(
+                1,
+                10000,
+                500,
+                step=1,
+                label=i18n("Total Epoch"),
+                info=i18n(
+                    "Specifies the overall quantity of epochs for the model training process."
+                ),
+                interactive=True,
+            )
+        with gr.Accordion(i18n("Advanced Settings"), open=False):
+            with gr.Row():
+                with gr.Column():
+                    save_only_latest = gr.Checkbox(
+                        label=i18n("Save Only Latest"),
+                        info=i18n(
+                            "Enabling this setting will result in the G and D files saving only their most recent versions, effectively conserving storage space."
+                        ),
+                        value=True,
+                        interactive=True,
+                    )
+                    save_every_weights = gr.Checkbox(
+                        label=i18n("Save Every Weights"),
+                        info=i18n(
+                            "This setting enables you to save the weights of the model at the conclusion of each epoch."
+                        ),
+                        value=True,
+                        interactive=True,
+                    )
+                    pretrained = gr.Checkbox(
+                        label=i18n("Pretrained"),
+                        info=i18n(
+                            "Utilize pretrained models when training your own. This approach reduces training duration and enhances overall quality."
+                        ),
+                        value=True,
+                        interactive=True,
+                    )
+                with gr.Column():
+                    cleanup = gr.Checkbox(
+                        label=i18n("Fresh Training"),
+                        info=i18n(
+                            "Enable this setting only if you are training a new model from scratch or restarting the training. Deletes all previously generated weights and tensorboard logs."
+                        ),
+                        value=False,
+                        interactive=True,
+                    )
+                    cache_dataset_in_gpu = gr.Checkbox(
+                        label=i18n("Cache Dataset in GPU"),
+                        info=i18n(
+                            "Cache the dataset in GPU memory to speed up the training process."
+                        ),
+                        value=False,
+                        interactive=True,
+                    )
+                    checkpointing = gr.Checkbox(
+                        label=i18n("Checkpointing"),
+                        info=i18n(
+                            "Enables memory-efficient training. This reduces VRAM usage at the cost of slower training speed. It is useful for GPUs with limited memory (e.g., <6GB VRAM) or when training with a batch size larger than what your GPU can normally accommodate."
+                        ),
+                        value=False,
+                        interactive=True,
+                    )
+            with gr.Row():
+                custom_pretrained = gr.Checkbox(
+                    label=i18n("Custom Pretrained"),
+                    info=i18n(
+                        "Utilizing custom pretrained models can lead to superior results, as selecting the most suitable pretrained models tailored to the specific use case can significantly enhance performance."
+                    ),
+                    value=False,
+                    interactive=True,
+                )
+                overtraining_detector = gr.Checkbox(
+                    label=i18n("Overtraining Detector"),
+                    info=i18n(
+                        "Detect overtraining to prevent the model from learning the training data too well and losing the ability to generalize to new data."
+                    ),
+                    value=False,
+                    interactive=True,
+                )
+            with gr.Row():
+                with gr.Column(visible=False) as pretrained_custom_settings:
+                    with gr.Accordion(i18n("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"),
+                            info=i18n(
+                                "Select the custom pretrained model for the generator."
+                            ),
+                            choices=sorted(pretraineds_list_g),
+                            interactive=True,
+                            allow_custom_value=True,
+                        )
+                        d_pretrained_path = gr.Dropdown(
+                            label=i18n("Custom Pretrained D"),
+                            info=i18n(
+                                "Select the custom pretrained model for the discriminator."
+                            ),
+                            choices=sorted(pretraineds_list_d),
+                            interactive=True,
+                            allow_custom_value=True,
+                        )
+
+                with gr.Column(visible=False) as overtraining_settings:
+                    with gr.Accordion(i18n("Overtraining Detector Settings")):
+                        overtraining_threshold = gr.Slider(
+                            1,
+                            100,
+                            50,
+                            step=1,
+                            label=i18n("Overtraining Threshold"),
+                            info=i18n(
+                                "Set the maximum number of epochs you want your model to stop training if no improvement is detected."
+                            ),
+                            interactive=True,
+                        )
+            index_algorithm = gr.Radio(
+                label=i18n("Index Algorithm"),
+                info=i18n(
+                    "KMeans is a clustering algorithm that divides the dataset into K clusters. This setting is particularly useful for large datasets."
+                ),
+                choices=["Auto", "Faiss", "KMeans"],
+                value="Auto",
+                interactive=True,
+            )
+
+        def enforce_terms(terms_accepted, *args):
+            if not terms_accepted:
+                message = "You must agree to the Terms of Use to proceed."
+                gr.Info(message)
+                return message
+            return run_train_script(*args)
+
+        terms_checkbox = gr.Checkbox(
+            label=i18n("I agree to the terms of use"),
+            info=i18n(
+                "Please ensure compliance with the terms and conditions detailed in [this document](https://github.com/IAHispano/Applio/blob/main/TERMS_OF_USE.md) before proceeding with your training."
+            ),
+            value=True,
+            interactive=True,
+        )
+        train_output_info = gr.Textbox(
+            label=i18n("Output Information"),
+            info=i18n("The output information will be displayed here."),
+            value="",
+            max_lines=8,
+            interactive=False,
+        )
+
+        with gr.Row():
+            train_button = gr.Button(i18n("Start Training"))
+            train_button.click(
+                fn=enforce_terms,
+                inputs=[
+                    terms_checkbox,
+                    model_name,
+                    save_every_epoch,
+                    save_only_latest,
+                    save_every_weights,
+                    total_epoch,
+                    sampling_rate,
+                    batch_size,
+                    gpu,
+                    overtraining_detector,
+                    overtraining_threshold,
+                    pretrained,
+                    cleanup,
+                    index_algorithm,
+                    cache_dataset_in_gpu,
+                    custom_pretrained,
+                    g_pretrained_path,
+                    d_pretrained_path,
+                    vocoder,
+                    checkpointing,
+                ],
+                outputs=[train_output_info],
+            )
+
+            stop_train_button = gr.Button(i18n("Stop Training"), visible=False)
+            stop_train_button.click(
+                fn=stop_train,
+                inputs=[model_name],
+                outputs=[],
+            )
+
+            index_button = gr.Button(i18n("Generate Index"))
+            index_button.click(
+                fn=run_index_script,
+                inputs=[model_name, index_algorithm],
+                outputs=[train_output_info],
+            )
+
+    # Export Model section
+    with gr.Accordion(i18n("Export Model"), open=False):
+        if not os.name == "nt":
+            gr.Markdown(
+                i18n(
+                    "The button 'Upload' is only for google colab: Uploads the exported files to the ApplioExported folder in your Google Drive."
+                )
+            )
+        with gr.Row():
+            with gr.Column():
+                pth_file_export = gr.File(
+                    label=i18n("Exported Pth file"),
+                    type="filepath",
+                    value=None,
+                    interactive=False,
+                )
+                pth_dropdown_export = gr.Dropdown(
+                    label=i18n("Pth file"),
+                    info=i18n("Select the pth file to be exported"),
+                    choices=get_pth_list(),
+                    value=None,
+                    interactive=True,
+                    allow_custom_value=True,
+                )
+            with gr.Column():
+                index_file_export = gr.File(
+                    label=i18n("Exported Index File"),
+                    type="filepath",
+                    value=None,
+                    interactive=False,
+                )
+                index_dropdown_export = gr.Dropdown(
+                    label=i18n("Index File"),
+                    info=i18n("Select the index file to be exported"),
+                    choices=get_index_list(),
+                    value=None,
+                    interactive=True,
+                    allow_custom_value=True,
+                )
+        with gr.Row():
+            with gr.Column():
+                refresh_export = gr.Button(i18n("Refresh"))
+                if not os.name == "nt":
+                    upload_exported = gr.Button(i18n("Upload"))
+                    upload_exported.click(
+                        fn=upload_to_google_drive,
+                        inputs=[pth_dropdown_export, index_dropdown_export],
+                        outputs=[],
+                    )
+
+            def toggle_visible(checkbox):
+                return {"visible": checkbox, "__type__": "update"}
+
+            def toggle_visible_hop_length(f0_method):
+                if f0_method == "crepe" or f0_method == "crepe-tiny":
+                    return {"visible": True, "__type__": "update"}
+                return {"visible": False, "__type__": "update"}
+
+            def toggle_pretrained(pretrained, custom_pretrained):
+                if custom_pretrained == False:
+                    return {"visible": pretrained, "__type__": "update"}, {
+                        "visible": False,
+                        "__type__": "update",
+                    }
+                else:
+                    return {"visible": pretrained, "__type__": "update"}, {
+                        "visible": pretrained,
+                        "__type__": "update",
+                    }
+
+            def enable_stop_train_button():
+                return {"visible": False, "__type__": "update"}, {
+                    "visible": True,
+                    "__type__": "update",
+                }
+
+            def disable_stop_train_button():
+                return {"visible": True, "__type__": "update"}, {
+                    "visible": False,
+                    "__type__": "update",
+                }
+
+            def download_prerequisites():
+                gr.Info(
+                    "Checking for prerequisites with pitch guidance... Missing files will be downloaded. If you already have them, this step will be skipped."
+                )
+                run_prerequisites_script(
+                    pretraineds_hifigan=True,
+                    models=False,
+                    exe=False,
+                )
+                gr.Info(
+                    "Prerequisites check complete. Missing files were downloaded, and you may now start preprocessing."
+                )
+
+            def toggle_visible_embedder_custom(embedder_model):
+                if embedder_model == "custom":
+                    return {"visible": True, "__type__": "update"}
+                return {"visible": False, "__type__": "update"}
+
+            def toggle_architecture(architecture):
+                if architecture == "Applio":
+                    return {
+                        "choices": ["32000", "40000", "44100", "48000"],
+                        "__type__": "update",
+                    }, {
+                        "interactive": True,
+                        "__type__": "update",
+                    }
+                else:
+                    return {
+                        "choices": ["32000", "40000", "48000"],
+                        "__type__": "update",
+                        "value": "40000",
+                    }, {"interactive": False, "__type__": "update", "value": "HiFi-GAN"}
+
+            def update_slider_visibility(noise_reduction):
+                return gr.update(visible=noise_reduction)
+
+            noise_reduction.change(
+                fn=update_slider_visibility,
+                inputs=noise_reduction,
+                outputs=clean_strength,
+            )
+            architecture.change(
+                fn=toggle_architecture,
+                inputs=[architecture],
+                outputs=[sampling_rate, vocoder],
+            )
+            refresh.click(
+                fn=refresh_models_and_datasets,
+                inputs=[],
+                outputs=[model_name, 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],
+            )
+            f0_method.change(
+                fn=toggle_visible_hop_length,
+                inputs=[f0_method],
+                outputs=[hop_length],
+            )
+            embedder_model.change(
+                fn=toggle_visible_embedder_custom,
+                inputs=[embedder_model],
+                outputs=[embedder_custom],
+            )
+            embedder_model.change(
+                fn=toggle_visible_embedder_custom,
+                inputs=[embedder_model],
+                outputs=[embedder_custom],
+            )
+            move_files_button.click(
+                fn=create_folder_and_move_files,
+                inputs=[folder_name_input, bin_file_upload, config_file_upload],
+                outputs=[],
+            )
+            refresh_embedders_button.click(
+                fn=refresh_embedders_folders, inputs=[], outputs=[embedder_model_custom]
+            )
+            pretrained.change(
+                fn=toggle_pretrained,
+                inputs=[pretrained, custom_pretrained],
+                outputs=[custom_pretrained, pretrained_custom_settings],
+            )
+            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],
+            )
+            overtraining_detector.change(
+                fn=toggle_visible,
+                inputs=[overtraining_detector],
+                outputs=[overtraining_settings],
+            )
+            train_button.click(
+                fn=enable_stop_train_button,
+                inputs=[],
+                outputs=[train_button, stop_train_button],
+            )
+            train_output_info.change(
+                fn=disable_stop_train_button,
+                inputs=[],
+                outputs=[train_button, stop_train_button],
+            )
+            pth_dropdown_export.change(
+                fn=export_pth,
+                inputs=[pth_dropdown_export],
+                outputs=[pth_file_export],
+            )
+            index_dropdown_export.change(
+                fn=export_index,
+                inputs=[index_dropdown_export],
+                outputs=[index_file_export],
+            )
+            refresh_export.click(
+                fn=refresh_pth_and_index_list,
+                inputs=[],
+                outputs=[pth_dropdown_export, index_dropdown_export],
+            )