webui.py

# main.py

import gradio as gr
from gradio import State
from gradio_utils import *
from pathlib import Path
import argparse
from tools.i18n.i18n import I18nAuto
from config import is_share, webui_port_main
from functions.core_functions import convert_voice, process_srt_and_generate_audio, load_model, run_tts
from functions.slice_utils import open_slice, close_slice
from functions.logging_utils import remove_log_file, read_logs
from multiprocessing import cpu_count
import os
from subprocess import Popen


def launch():
    parser = argparse.ArgumentParser(
        description="""XTTS fine-tuning demo\n\n"""
        """
        Example runs:
        python3 TTS/demos/xtts_ft_demo/xtts_demo.py --port 
        """,
        formatter_class=argparse.RawTextHelpFormatter,
    )
    parser.add_argument(
        "--port",
        type=int,
        help="Port to run the gradio demo. Default: 5003",
        default=5003,
    )
    parser.add_argument(
        "--out_path",
        type=str,
        help="Output path (where data and checkpoints will be saved) Default: output/",
        default=str(Path.cwd() / "finetune_models"),
    )

    parser.add_argument(
        "--num_epochs",
        type=int,
        help="Number of epochs to train. Default: 6",
        default=6,
    )
    parser.add_argument(
        "--batch_size",
        type=int,
        help="Batch size. Default: 2",
        default=2,
    )
    parser.add_argument(
        "--grad_acumm",
        type=int,
        help="Grad accumulation steps. Default: 1",
        default=1,
    )
    parser.add_argument(
        "--max_audio_length",
        type=int,
        help="Max permitted audio size in seconds. Default: 11",
        default=11,
    )

    args = parser.parse_args()
    i18n = I18nAuto()

    demo = gr.Blocks()
    with demo:
        with gr.Tab("0 - Audio Slicing"):
            gr.Markdown(value=i18n("语音切分工具"))
            with gr.Row():
                slice_inp_path = gr.Textbox(label=i18n("音频自动切分输入路径，可文件可文件夹"), value="")
                slice_opt_root = gr.Textbox(label=i18n("切分后的子音频的输出根目录"), value="output/slicer_opt")
                threshold = gr.Textbox(label=i18n("threshold:音量小于这个值视作静音的备选切割点"), value="-34")
                min_length = gr.Textbox(label=i18n("min_length:每段最小多长，如果第一段太短一直和后面段连起来直到超过这个值"), value="4000")
                min_interval = gr.Textbox(label=i18n("min_interval:最短切割间隔"), value="300")
                hop_size = gr.Textbox(label=i18n("hop_size:怎么算音量曲线，越小精度越大计算量越高（不是精度越大效果越好）"), value="10")
                max_sil_kept = gr.Textbox(label=i18n("max_sil_kept:切完后静音最多留多长"), value="500")
            with gr.Row():
                open_slicer_button = gr.Button(i18n("开启语音切割"), variant="primary", visible=True)
                close_slicer_button = gr.Button(i18n("终止语音切割"), variant="primary", visible=False)
                _max = gr.Slider(minimum=0, maximum=1, step=0.05, label=i18n("max:归一化后最大值多少"), value=0.9, interactive=True)
                alpha = gr.Slider(minimum=0, maximum=1, step=0.05, label=i18n("alpha_mix:混多少比例归一化后音频进来"), value=0.25, interactive=True)
                n_process = gr.Slider(minimum=1, maximum=cpu_count(), step=1, label=i18n("切割使用的进程数"), value=4, interactive=True)
                slicer_info = gr.Textbox(label=i18n("语音切割进程输出信息"))

            open_slicer_button.click(open_slice, [slice_inp_path, slice_opt_root, threshold, min_length, min_interval, hop_size, max_sil_kept, _max, alpha, n_process], [slicer_info, open_slicer_button, close_slicer_button])
            close_slicer_button.click(close_slice, [], [slicer_info, open_slicer_button, close_slicer_button])
        

        with gr.Tab("1 - Data processing"):
            out_path = gr.Textbox(label="Output path (where data and checkpoints will be saved):", value=args.out_path)
            upload_file = gr.File(file_count="multiple", label="Select here the audio files that you want to use for XTTS trainining (Supported formats: wav, mp3, and flac)")
            folder_path = gr.Textbox(label="Or input the path of a folder containing audio files")
            whisper_model = gr.Dropdown(label="Whisper Model", value="large-v3", choices=["large-v3", "large-v2", "large", "medium", "small"])
            lang = gr.Dropdown(label="Dataset Language", value="en", choices=["en", "es", "fr", "de", "it", "pt", "pl", "tr", "ru", "nl", "cs", "ar", "zh", "hu", "ko", "ja"])
            progress_data = gr.Label(label="Progress:")
            #train_csv = gr.Textbox(visible=False)
            #eval_csv = gr.Textbox(visible=False)
            prompt_compute_btn = gr.Button(value="Step 1 - Create dataset")
            
            train_csv_state = State()
            eval_csv_state = State()
            prompt_compute_btn.click(preprocess_dataset, inputs=[upload_file, folder_path, lang, whisper_model, out_path, train_csv_state, eval_csv_state], outputs=[progress_data, train_csv_state, eval_csv_state])           
            #prompt_compute_btn.click(preprocess_dataset, inputs=[upload_file, folder_path, lang, whisper_model, out_path, train_csv, eval_csv], outputs=[progress_data, train_csv, eval_csv])
            
        with gr.Tab("2 - Fine-tuning XTTS Encoder"):
            load_params_btn = gr.Button(value="Load Params from output folder")
            version = gr.Dropdown(
                label="XTTS base version",
                value="v2.0.2",
                choices=[
                    "v2.0.3",
                    "v2.0.2",
                    "v2.0.1",
                    "v2.0.0",
                    "main"
                ],
            )
            train_csv = gr.Textbox(
                label="Train CSV:",
            )
            eval_csv = gr.Textbox(
                label="Eval CSV:",
            )
            custom_model = gr.Textbox(
                label="(Optional) Custom model.pth file , leave blank if you want to use the base file.",
                value="",
            )
            num_epochs =  gr.Slider(
                label="Number of epochs:",
                minimum=1,
                maximum=100,
                step=1,
                value=args.num_epochs,
            )
            batch_size = gr.Slider(
                label="Batch size:",
                minimum=2,
                maximum=512,
                step=1,
                value=args.batch_size,
            )
            grad_acumm = gr.Slider(
                label="Grad accumulation steps:",
                minimum=2,
                maximum=128,
                step=1,
                value=args.grad_acumm,
            )
            max_audio_length = gr.Slider(
                label="Max permitted audio size in seconds:",
                minimum=2,
                maximum=20,
                step=1,
                value=args.max_audio_length,
            )
            clear_train_data = gr.Dropdown(
                label="Clear train data, you will delete selected folder, after optimizing",
                value="run",
                choices=[
                    "none",
                    "run",
                    "dataset",
                    "all"
                ])
            
            progress_train = gr.Label(
                label="Progress:"
            )
            
            train_btn = gr.Button(value="Step 2 - Run the training")
            optimize_model_btn = gr.Button(value="Step 2.5 - Optimize the model")
            
            load_params_btn.click(load_params, inputs=[out_path], outputs=[progress_train, train_csv, eval_csv, lang])
            train_output_state = State()
            optimize_output_state = State()
            train_btn.click(train_model, inputs=[custom_model, version, lang, train_csv_state, eval_csv_state, num_epochs, batch_size, grad_acumm, out_path, max_audio_length], outputs=[progress_train, train_output_state])
            optimize_model_btn.click(optimize_model, inputs=[out_path, clear_train_data], outputs=[progress_train, optimize_output_state])
 
            #train_btn.click(train_model, inputs=[custom_model, version, lang, train_csv_state, eval_csv_state, num_epochs, batch_size, grad_acumm, out_path, max_audio_length], outputs=[progress_train, train_output_state])
           # train_btn.click(train_model, inputs=[custom_model, version, lang, train_csv, eval_csv, num_epochs, batch_size, grad_acumm, out_path, max_audio_length], outputs=[progress_train, "temp", "temp", "temp", "temp", "temp"])
            #optimize_model_btn.click(optimize_model, inputs=[out_path, clear_train_data], outputs=[progress_train, "temp"])
        
        with gr.Tab("3 - Inference"):
            with gr.Row():
                with gr.Column() as col1:
                    load_params_tts_btn = gr.Button(value="Load params for TTS from output folder")
                    xtts_checkpoint = gr.Textbox(
                        label="XTTS checkpoint path:",
                        value="",
                    )
                    xtts_config = gr.Textbox(
                        label="XTTS config path:",
                        value="",
                    )

                    xtts_vocab = gr.Textbox(
                        label="XTTS vocab path:",
                        value="",
                    )
                    xtts_speaker = gr.Textbox(
                        label="XTTS speaker path:",
                        value="",
                    )
                    progress_load = gr.Label(
                        label="Progress:"
                    )
                    load_btn = gr.Button(value="Step 3 - Load Fine-tuned XTTS model")

                with gr.Column() as col2:
                    speaker_reference_audio = gr.Textbox(
                        label="Speaker reference audio:",
                        value="",
                    )
                    tts_language = gr.Dropdown(
                        label="Language",
                        value="en",
                        choices=[
                            "en",
                            "es",
                            "fr",
                            "de",
                            "it",
                            "pt",
                            "pl",
                            "tr",
                            "ru",
                            "nl",
                            "cs",
                            "ar",
                            "zh",
                            "hu",
                            "ko",
                            "ja",
                        ]
                    )
                    tts_text = gr.Textbox(
                        label="Input Text.",
                        value="This model sounds really good and above all, it's reasonably fast.",
                    )
                    with gr.Accordion("Advanced settings", open=False) as acr:
                        temperature = gr.Slider(
                            label="temperature",
                            minimum=0,
                            maximum=1,
                            step=0.05,
                            value=0.75,
                        )
                        length_penalty  = gr.Slider(
                            label="length_penalty",
                            minimum=-10.0,
                            maximum=10.0,
                            step=0.5,
                            value=1,
                        )
                        repetition_penalty = gr.Slider(
                            label="repetition penalty",
                            minimum=1,
                            maximum=10,
                            step=0.5,
                            value=5,
                        )
                        top_k = gr.Slider(
                            label="top_k",
                            minimum=1,
                            maximum=100,
                            step=1,
                            value=50,
                        )
                        top_p = gr.Slider(
                            label="top_p",
                            minimum=0,
                            maximum=1,
                            step=0.05,
                            value=0.85,
                        )                        
                        sentence_split = gr.Checkbox(
                            label="Enable text splitting",
                            value=True,
                        )
                        use_config = gr.Checkbox(
                            label="Use Inference settings from config, if disabled use the settings above",
                            value=False,
                        )
                    tts_btn = gr.Button(value="Step 4 - Inference")

                with gr.Column() as col3:
                    progress_gen = gr.Label(
                        label="Progress:"
                    )
                    tts_output_audio = gr.Audio(label="Generated Audio.")
                    reference_audio = gr.Audio(label="Reference audio used.")


                with gr.Column() as col4:
                    srt_upload = gr.File(label="Upload SRT File")
                    generate_srt_audio_btn = gr.Button(value="Generate Audio from SRT")
                    srt_output_audio = gr.Audio(label="Combined Audio from SRT")
                    error_message = gr.Textbox(label="Error Message", visible=False)  

            generate_srt_audio_btn.click(process_srt_and_generate_audio, inputs=[srt_upload, tts_language, speaker_reference_audio, temperature, length_penalty, repetition_penalty, top_k, top_p, sentence_split, use_config], outputs=[srt_output_audio])
            load_btn.click(load_model, inputs=[xtts_checkpoint, xtts_config, xtts_vocab, xtts_speaker], outputs=[progress_load])
            tts_btn.click(run_tts, inputs=[tts_language, tts_text, speaker_reference_audio, temperature, length_penalty, repetition_penalty, top_k, top_p, sentence_split, use_config], outputs=[progress_gen, tts_output_audio, reference_audio])
            load_params_tts_btn.click(load_params_tts, inputs=[out_path, version], outputs=[progress_load, xtts_checkpoint, xtts_config, xtts_vocab, xtts_speaker, speaker_reference_audio])
        
        with gr.Tab("4 - Voice conversion"):
            with gr.Column() as col0:
                gr.Markdown("## OpenVoice Conversion Tool")
                voice_convert_seed = gr.File(label="Upload Reference Speaker Audio being generated")
                audio_to_convert = gr.Textbox(
                    label="Input the to-be-convert audio location",
                    value="",
                )
                convert_button = gr.Button("Convert Voice")
                converted_audio = gr.Audio(label="Converted Audio")

            convert_button.click(convert_voice, inputs=[voice_convert_seed, audio_to_convert], outputs=[converted_audio])
       
        with gr.Tab("5 - Logs"):
            # 添加一个按钮来读取日志
            read_logs_btn = gr.Button("Read Logs")
            log_output = gr.Textbox(label="Log Output")
            read_logs_btn.click(fn=read_logs, inputs=None, outputs=log_output)

    #demo.launch(share=is_share, server_port=webui_port_main, server_name="0.0.0.0")
    demo.launch(
        #share=False,
        share=True,
        debug=False,
        server_port=args.port,
        #server_name="localhost"
        server_name="0.0.0.0"
    )    
    '''
    demo.launch(
        server_name="0.0.0.0",
        inbrowser=True,
        share=is_share,
        server_port=webui_port_main,
        quiet=True,
    )  
    '''
if __name__ == "__main__":
    remove_log_file("logs/main.log")
    launch()