xtts_my_model.py

import argparse
import os
import sys
import tempfile

import gradio as gr
import librosa.display
import numpy as np

import os
import torch
import torchaudio
import traceback
from TTS.demos.xtts_ft_demo.utils.formatter import format_audio_list
from TTS.demos.xtts_ft_demo.utils.gpt_train import train_gpt

from TTS.tts.configs.xtts_config import XttsConfig
from TTS.tts.models.xtts import Xtts


def clear_gpu_cache():
    # clear the GPU cache
    if torch.cuda.is_available():
        torch.cuda.empty_cache()

XTTS_MODEL = None
def load_model(xtts_checkpoint, xtts_config, xtts_vocab):
    global XTTS_MODEL
    clear_gpu_cache()
    if not xtts_checkpoint or not xtts_config or not xtts_vocab:
        return "You need to run the previous steps or manually set the `XTTS checkpoint path`, `XTTS config path`, and `XTTS vocab path` fields !!"
    config = XttsConfig()
    config.load_json(xtts_config)
    XTTS_MODEL = Xtts.init_from_config(config)
    print("Loading XTTS model! ")
    XTTS_MODEL.load_checkpoint(config, checkpoint_path=xtts_checkpoint, vocab_path=xtts_vocab, use_deepspeed=False)
    if torch.cuda.is_available():
        XTTS_MODEL.cuda()

    print("Model Loaded!")
    return "Model Loaded!"

def run_tts(lang, tts_text, speaker_audio_file):
    if XTTS_MODEL is None or not speaker_audio_file:
        return "You need to run the previous step to load the model !!", None, None


    # Access the list element
    speaker_audio_file = speaker_audio_file[0]

    # Remove brackets and quotes
    speaker_audio_file = speaker_audio_file.replace("[", "").replace("]", "").replace("'", "")
    
    gpt_cond_latent, speaker_embedding = XTTS_MODEL.get_conditioning_latents(audio_path=speaker_audio_file, gpt_cond_len=XTTS_MODEL.config.gpt_cond_len, max_ref_length=XTTS_MODEL.config.max_ref_len, sound_norm_refs=XTTS_MODEL.config.sound_norm_refs)
    out = XTTS_MODEL.inference(
        text=tts_text,
        language=lang,
        gpt_cond_latent=gpt_cond_latent,
        speaker_embedding=speaker_embedding,
        temperature=XTTS_MODEL.config.temperature, # Add custom parameters here
        length_penalty=XTTS_MODEL.config.length_penalty,
        repetition_penalty=XTTS_MODEL.config.repetition_penalty,
        top_k=XTTS_MODEL.config.top_k,
        top_p=XTTS_MODEL.config.top_p,
    )

    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as fp:
        out["wav"] = torch.tensor(out["wav"]).unsqueeze(0)
        out_path = fp.name
        torchaudio.save(out_path, out["wav"], 24000)

    return "Speech generated !", out_path, speaker_audio_file




# define a logger to redirect 
class Logger:
    def __init__(self, filename="log.out"):
        self.log_file = filename
        self.terminal = sys.stdout
        self.log = open(self.log_file, "w")

    def write(self, message):
        self.terminal.write(message)
        self.log.write(message)

    def flush(self):
        self.terminal.flush()
        self.log.flush()

    def isatty(self):
        return False

# redirect stdout and stderr to a file
sys.stdout = Logger()
sys.stderr = sys.stdout


# logging.basicConfig(stream=sys.stdout, level=logging.INFO)
import logging
logging.basicConfig(
    level=logging.INFO,
    format="%(asctime)s [%(levelname)s] %(message)s",
    handlers=[
        logging.StreamHandler(sys.stdout)
    ]
)

def read_logs():
    sys.stdout.flush()
    with open(sys.stdout.log_file, "r") as f:
        return f.read()


if __name__ == "__main__":

    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: /tmp/xtts_ft/",
        default="/tmp/xtts_ft/",
    )

    parser.add_argument(
        "--num_epochs",
        type=int,
        help="Number of epochs to train. Default: 10",
        default=10,
    )
    parser.add_argument(
        "--batch_size",
        type=int,
        help="Batch size. Default: 4",
        default=4,
    )
    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()

    language_names = {
    "en": "English",
    "es": "Spanish",
    "fr": "French",
    "de": "German",
    "it": "Italian",
    "pt": "Portuguese",
    "pl": "Polish",
    "tr": "Turkish",
    "ru": "Russian",
    "nl": "Dutch",
    "cs": "Czech",
    "ar": "Arabic",
    "zh": "Chinese",
    "hu": "Hungarian",
    "ko": "Korean",
    "ja": "Japanese",
}

    with gr.Blocks() as demo:
        with gr.Tab("Inference"):
            with gr.Row():
                with gr.Column() as col1:
                    xtts_checkpoint = gr.Textbox(
                        label="XTTS checkpoint path:",
                        value="/content/Model/best_model.pth",
                    )
                    xtts_config = gr.Textbox(
                        label="XTTS config path:",
                        value="/content/Model/config.json",
                    )

                    xtts_vocab = gr.Textbox(
                        label="XTTS vocab path:",
                        value="/content/Model/vocab.json",
                    )
                    progress_load = gr.Label(
                        label="Progress:"
                    )
                    load_btn = gr.Button(value="Load Fine-tuned XTTS model")

                with gr.Column() as col2:
                    speaker_reference_audio = gr.File(
                        file_count="multiple",
                        label="Speaker reference audio (Supported formats: wav, mp3, and flac)",
                    )
                    tts_language = gr.Dropdown(
                        label="Language",
                        value="en",
                        choices=list(zip(language_names.values(), language_names.keys()))
                    )
                    tts_text = gr.Textbox(
                        label="Input Text.",
                        value="This model sounds really good and above all, it's reasonably fast.",
                    )
                    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.")

            
            load_btn.click(
                fn=load_model,
                inputs=[
                    xtts_checkpoint,
                    xtts_config,
                    xtts_vocab
                ],
                outputs=[progress_load],
            )

            tts_btn.click(
                fn=run_tts,
                inputs=[
                    tts_language,
                    tts_text,
                    speaker_reference_audio,
                ],
                outputs=[progress_gen, tts_output_audio, reference_audio],
            )

    demo.launch(
        share=True,
        debug=False,
        server_port=args.port,
        server_name="0.0.0.0"
    )