From d503d7a80f7d7c3bf6525f08f2fd318b659bf1b6 Mon Sep 17 00:00:00 2001 From: Edresson Casanova Date: Mon, 23 Oct 2023 14:46:32 -0300 Subject: [PATCH] Remove accidentally commited recipe --- .../train_gpt_xtts.py | 178 ------------------ 1 file changed, 178 deletions(-) delete mode 100644 recipes/ljspeech/xtts_v1/run/training/GPT_XTTS_LJSpeech_FT-October-23-2023_02+08PM-de1d521c/train_gpt_xtts.py diff --git a/recipes/ljspeech/xtts_v1/run/training/GPT_XTTS_LJSpeech_FT-October-23-2023_02+08PM-de1d521c/train_gpt_xtts.py b/recipes/ljspeech/xtts_v1/run/training/GPT_XTTS_LJSpeech_FT-October-23-2023_02+08PM-de1d521c/train_gpt_xtts.py deleted file mode 100644 index 94f3975c2f..0000000000 --- a/recipes/ljspeech/xtts_v1/run/training/GPT_XTTS_LJSpeech_FT-October-23-2023_02+08PM-de1d521c/train_gpt_xtts.py +++ /dev/null @@ -1,178 +0,0 @@ -import os - -from trainer import Trainer, TrainerArgs - -from TTS.config.shared_configs import BaseDatasetConfig -from TTS.tts.datasets import load_tts_samples -from TTS.tts.layers.xtts.trainer.gpt_trainer import GPTArgs, GPTTrainer, GPTTrainerConfig, XttsAudioConfig -from TTS.utils.manage import ModelManager - - -# Logging parameters -RUN_NAME = "GPT_XTTS_LJSpeech_FT" -PROJECT_NAME = "XTTS_trainer" -DASHBOARD_LOGGER = "tensorboard" -LOGGER_URI = None - -# Set here the path that the checkpoints will be saved. Default: ./run/training/ -OUT_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "run", "training") - -# Training Parameters -OPTIMIZER_WD_ONLY_ON_WEIGHTS = True # for multi-gpu training please make it False -START_WITH_EVAL = True # if True it will star with evaluation -BATCH_SIZE = 3 # set here the batch size -GRAD_ACUMM_STEPS = 84 # set here the grad accumulation steps -# Note: we recommend that BATCH_SIZE * GRAD_ACUMM_STEPS need to be at least 252 for more efficient training. You can increase/decrease BATCH_SIZE but then set GRAD_ACUMM_STEPS accordingly. - -# Define here the dataset that you want to use for the fine-tuning on. -config_dataset = BaseDatasetConfig( - formatter="ljspeech", - dataset_name="ljspeech", - path="/raid/datasets/LJSpeech-1.1_24khz/", - meta_file_train="/raid/datasets/LJSpeech-1.1_24khz/metadata.csv", - language="en", -) - -# Add here the configs of the datasets -DATASETS_CONFIG_LIST = [config_dataset] - -# Define the path where XTTS v1.1.1 files will be downloaded -CHECKPOINTS_OUT_PATH = os.path.join(OUT_PATH, "XTTS_v1.1_original_model_files/") -os.makedirs(CHECKPOINTS_OUT_PATH, exist_ok=True) - - -# DVAE files -DVAE_CHECKPOINT_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/v1.1.1/dvae.pth" -MEL_NORM_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/v1.1.1/mel_stats.pth" - -# Set the path to the downloaded files -DVAE_CHECKPOINT = os.path.join(CHECKPOINTS_OUT_PATH, DVAE_CHECKPOINT_LINK.split("/")[-1]) -MEL_NORM_FILE = os.path.join(CHECKPOINTS_OUT_PATH, MEL_NORM_LINK.split("/")[-1]) - -# download DVAE files if needed -if not os.path.isfile(DVAE_CHECKPOINT) or not os.path.isfile(MEL_NORM_FILE): - print(" > Downloading DVAE files!") - ModelManager._download_model_files([MEL_NORM_LINK, DVAE_CHECKPOINT_LINK], CHECKPOINTS_OUT_PATH, progress_bar=True) - - -# Download XTTS v1.1 checkpoint if needed -TOKENIZER_FILE_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/v1.1.1/vocab.json" -XTTS_CHECKPOINT_LINK = "https://coqui.gateway.scarf.sh/hf-coqui/XTTS-v1/v1.1.1/model.pth" - -# XTTS transfer learning parameters: You we need to provide the paths of XTTS model checkpoint that you want to do the fine tuning. -TOKENIZER_FILE = os.path.join(CHECKPOINTS_OUT_PATH, TOKENIZER_FILE_LINK.split("/")[-1]) # vocab.json file -XTTS_CHECKPOINT = os.path.join(CHECKPOINTS_OUT_PATH, XTTS_CHECKPOINT_LINK.split("/")[-1]) # model.pth file - -# download XTTS v1.1 files if needed -if not os.path.isfile(TOKENIZER_FILE) or not os.path.isfile(XTTS_CHECKPOINT): - print(" > Downloading XTTS v1.1 files!") - ModelManager._download_model_files([TOKENIZER_FILE_LINK, XTTS_CHECKPOINT_LINK], CHECKPOINTS_OUT_PATH, progress_bar=True) - - -# Training sentences generations -SPEAKER_REFERENCE = ( - "./tests/data/ljspeech/wavs/LJ001-0002.wav" # speaker reference to be used in training test sentences -) -LANGUAGE = config_dataset.language - - -def main(): - # init args and config - model_args = GPTArgs( - max_conditioning_length=132300, # 6 secs - min_conditioning_length=66150, # 3 secs - debug_loading_failures=False, - max_wav_length=255995, # ~11.6 seconds - max_text_length=200, - mel_norm_file=MEL_NORM_FILE, - dvae_checkpoint=DVAE_CHECKPOINT, - # tokenizer_file="/raid/datasets/xtts_models/vocab.json", # vocab path of the model that you want to fine-tune - # xtts_checkpoint="https://huggingface.co/coqui/XTTS-v1/resolve/hifigan/model.pth", - xtts_checkpoint=XTTS_CHECKPOINT, # checkpoint path of the model that you want to fine-tune - tokenizer_file=TOKENIZER_FILE, - gpt_num_audio_tokens=8194, - gpt_start_audio_token=8192, - gpt_stop_audio_token=8193, - use_ne_hifigan=True, # if it is true it will keep the non-enhanced keys on the output checkpoint - ) - # define audio config - audio_config = XttsAudioConfig( - sample_rate=22050, dvae_sample_rate=22050, diffusion_sample_rate=24000, output_sample_rate=24000 - ) - # training parameters config - config = GPTTrainerConfig( - output_path=OUT_PATH, - model_args=model_args, - run_name=RUN_NAME, - project_name=PROJECT_NAME, - run_description=""" - GPT XTTS training - """, - dashboard_logger=DASHBOARD_LOGGER, - logger_uri=LOGGER_URI, - audio=audio_config, - batch_size=BATCH_SIZE, - batch_group_size=48, - eval_batch_size=BATCH_SIZE, - num_loader_workers=8, - eval_split_max_size=256, - print_step=50, - plot_step=100, - log_model_step=1000, - save_step=10000, - save_n_checkpoints=1, - save_checkpoints=True, - # target_loss="loss", - print_eval=False, - # Optimizer values like tortoise, pytorch implementation with modifications to not apply WD to non-weight parameters. - optimizer="AdamW", - optimizer_wd_only_on_weights=OPTIMIZER_WD_ONLY_ON_WEIGHTS, - optimizer_params={"betas": [0.9, 0.96], "eps": 1e-8, "weight_decay": 1e-2}, - lr=5e-06, # learning rate - lr_scheduler="MultiStepLR", - # it was adjusted accordly for the new step scheme - lr_scheduler_params={"milestones": [50000 * 18, 150000 * 18, 300000 * 18], "gamma": 0.5, "last_epoch": -1}, - test_sentences=[ - { - "text": "It took me quite a long time to develop a voice, and now that I have it I'm not going to be silent.", - "speaker_wav": SPEAKER_REFERENCE, - "language": LANGUAGE, - }, - { - "text": "This cake is great. It's so delicious and moist.", - "speaker_wav": SPEAKER_REFERENCE, - "language": LANGUAGE, - }, - ], - ) - - # init the model from config - model = GPTTrainer.init_from_config(config) - - # load training samples - train_samples, eval_samples = load_tts_samples( - DATASETS_CONFIG_LIST, - eval_split=True, - eval_split_max_size=config.eval_split_max_size, - eval_split_size=config.eval_split_size, - ) - - # init the trainer and 🚀 - trainer = Trainer( - TrainerArgs( - restore_path=None, # xtts checkpoint is restored via xtts_checkpoint key so no need of restore it using Trainer restore_path parameter - skip_train_epoch=False, - start_with_eval=START_WITH_EVAL, - grad_accum_steps=GRAD_ACUMM_STEPS, - ), - config, - output_path=OUT_PATH, - model=model, - train_samples=train_samples, - eval_samples=eval_samples, - ) - trainer.fit() - - -if __name__ == "__main__": - main()