def main(): """Run 🐸TTS trainer from terminal. This is also necessary to run DDP training by ```distribute.py```""" args, config, output_path, _, c_logger, dashboard_logger = init_training( sys.argv) trainer = Trainer(args, config, output_path, c_logger, dashboard_logger, cudnn_benchmark=False) trainer.fit()
def main(): try: args, config, output_path, _, c_logger, dashboard_logger = init_training( sys.argv) trainer = Trainer(args, config, output_path, c_logger, dashboard_logger) trainer.fit() except KeyboardInterrupt: remove_experiment_folder(output_path) try: sys.exit(0) except SystemExit: os._exit(0) # pylint: disable=protected-access except Exception: # pylint: disable=broad-except remove_experiment_folder(output_path) traceback.print_exc() sys.exit(1)
import os from TTS.trainer import Trainer, TrainingArgs, init_training from TTS.tts.configs import AlignTTSConfig, BaseDatasetConfig output_path = os.path.dirname(os.path.abspath(__file__)) dataset_config = BaseDatasetConfig( name="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/") ) config = AlignTTSConfig( batch_size=32, eval_batch_size=16, num_loader_workers=4, num_eval_loader_workers=4, run_eval=True, test_delay_epochs=-1, epochs=1000, text_cleaner="english_cleaners", use_phonemes=False, phoneme_language="en-us", phoneme_cache_path=os.path.join(output_path, "phoneme_cache"), print_step=25, print_eval=True, mixed_precision=False, output_path=output_path, datasets=[dataset_config], ) args, config, output_path, _, c_logger, dashboard_logger = init_training(TrainingArgs(), config) trainer = Trainer(args, config, output_path, c_logger, dashboard_logger) trainer.fit()
spec_gain=1.0, signal_norm=False, do_amp_to_db_linear=False, ) config = VitsConfig( audio=audio_config, run_name="vits_ljspeech", batch_size=48, eval_batch_size=16, batch_group_size=5, num_loader_workers=4, num_eval_loader_workers=4, run_eval=True, test_delay_epochs=-1, epochs=1000, text_cleaner="english_cleaners", use_phonemes=True, phoneme_language="en-us", phoneme_cache_path=os.path.join(output_path, "phoneme_cache"), compute_input_seq_cache=True, print_step=25, print_eval=True, mixed_precision=True, max_seq_len=500000, output_path=output_path, datasets=[dataset_config], ) args, config, output_path, _, c_logger, tb_logger = init_training(TrainingArgs(), config) trainer = Trainer(args, config, output_path, c_logger, tb_logger, cudnn_benchmark=True) trainer.fit()