tain.py

import argparse
import os
import sys
import tempfile

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

    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,
    )
    
    # Add the new arguments
    parser.add_argument(
        "--lang",
        type=str,
        help="Dataset Language",
        default="en",
    )
    parser.add_argument(
        "--train_csv",
        type=str,
        help="Path to the train CSV file",
        required=True,
    )
    parser.add_argument(
        "--eval_csv",
        type=str,
        help="Path to the eval CSV file",
        required=True,
    )

    args = parser.parse_args()

    # ... (rest of your code)

    def train_model(language, train_csv, eval_csv, num_epochs, batch_size, grad_acumm, output_path, max_audio_length):
        clear_gpu_cache()
        if not train_csv or not eval_csv:
            return "You need to run the data processing step or manually set `Train CSV` and `Eval CSV` fields !", "", "", "", ""
        try:
            # convert seconds to waveform frames
            max_audio_length = int(max_audio_length * 22050)
            config_path, original_xtts_checkpoint, vocab_file, exp_path, speaker_wav = train_gpt(language, num_epochs, batch_size, grad_acumm, train_csv, eval_csv, output_path=output_path, max_audio_length=max_audio_length)
        except:
            traceback.print_exc()
            error = traceback.format_exc()
            return f"The training was interrupted due an error !! Please check the console to check the full error message! \n Error summary: {error}", "", "", "", ""

        # copy original files to avoid parameters changes issues
        os.system(f"cp {config_path} {exp_path}")
        os.system(f"cp {vocab_file} {exp_path}")

        ft_xtts_checkpoint = os.path.join(exp_path, "best_model.pth")
        print("Model training done!")
        clear_gpu_cache()
        return "Model training done!", config_path, vocab_file, ft_xtts_checkpoint, speaker_wav

    # ... (rest of your code)
    
    # The following section is the only part to be changed:
    # It now directly calls the train_model function instead of using Gradio
    
    if __name__ == "__main__":
        # ... (argparse setup)

        # Call the function directly
        train_model(
            args.lang,
            args.train_csv,
            args.eval_csv,
            args.num_epochs,
            args.batch_size,
            args.grad_acumm,
            args.out_path,
            args.max_audio_length,
        )