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| import os | |
| from trainer import Trainer, TrainerArgs | |
| from TTS.config.shared_configs import BaseAudioConfig | |
| from TTS.tts.configs.shared_configs import BaseDatasetConfig, CapacitronVAEConfig | |
| from TTS.tts.configs.tacotron2_config import Tacotron2Config | |
| from TTS.tts.datasets import load_tts_samples | |
| from TTS.tts.models.tacotron2 import Tacotron2 | |
| from TTS.tts.utils.text.tokenizer import TTSTokenizer | |
| from TTS.utils.audio import AudioProcessor | |
| output_path = os.path.dirname(os.path.abspath(__file__)) | |
| data_path = "/srv/data/blizzard2013/segmented" | |
| # Using LJSpeech like dataset processing for the blizzard dataset | |
| dataset_config = BaseDatasetConfig( | |
| formatter="ljspeech", | |
| meta_file_train="metadata.csv", | |
| path=data_path, | |
| ) | |
| audio_config = BaseAudioConfig( | |
| sample_rate=24000, | |
| do_trim_silence=True, | |
| trim_db=60.0, | |
| signal_norm=True, | |
| mel_fmin=80.0, | |
| mel_fmax=12000, | |
| spec_gain=25.0, | |
| log_func="np.log10", | |
| ref_level_db=20, | |
| preemphasis=0.0, | |
| min_level_db=-100, | |
| ) | |
| # Using the standard Capacitron config | |
| capacitron_config = CapacitronVAEConfig(capacitron_VAE_loss_alpha=1.0) | |
| config = Tacotron2Config( | |
| run_name="Blizzard-Capacitron-T2", | |
| audio=audio_config, | |
| capacitron_vae=capacitron_config, | |
| use_capacitron_vae=True, | |
| batch_size=246, # Tune this to your gpu | |
| max_audio_len=6 * 24000, # Tune this to your gpu | |
| min_audio_len=1 * 24000, | |
| eval_batch_size=16, | |
| num_loader_workers=12, | |
| num_eval_loader_workers=8, | |
| precompute_num_workers=24, | |
| run_eval=True, | |
| test_delay_epochs=5, | |
| r=2, | |
| optimizer="CapacitronOptimizer", | |
| optimizer_params={"RAdam": {"betas": [0.9, 0.998], "weight_decay": 1e-6}, "SGD": {"lr": 1e-5, "momentum": 0.9}}, | |
| attention_type="dynamic_convolution", | |
| grad_clip=0.0, # Important! We overwrite the standard grad_clip with capacitron_grad_clip | |
| double_decoder_consistency=False, | |
| epochs=1000, | |
| text_cleaner="phoneme_cleaners", | |
| use_phonemes=True, | |
| phoneme_language="en-us", | |
| phonemizer="espeak", | |
| phoneme_cache_path=os.path.join(data_path, "phoneme_cache"), | |
| stopnet_pos_weight=15, | |
| print_step=25, | |
| print_eval=True, | |
| mixed_precision=False, | |
| output_path=output_path, | |
| datasets=[dataset_config], | |
| lr=1e-3, | |
| lr_scheduler="StepwiseGradualLR", | |
| lr_scheduler_params={ | |
| "gradual_learning_rates": [ | |
| [0, 1e-3], | |
| [2e4, 5e-4], | |
| [4e4, 3e-4], | |
| [6e4, 1e-4], | |
| [8e4, 5e-5], | |
| ] | |
| }, | |
| scheduler_after_epoch=False, # scheduler doesn't work without this flag | |
| seq_len_norm=True, | |
| loss_masking=False, | |
| decoder_loss_alpha=1.0, | |
| postnet_loss_alpha=1.0, | |
| postnet_diff_spec_alpha=1.0, | |
| decoder_diff_spec_alpha=1.0, | |
| decoder_ssim_alpha=1.0, | |
| postnet_ssim_alpha=1.0, | |
| ) | |
| ap = AudioProcessor(**config.audio.to_dict()) | |
| tokenizer, config = TTSTokenizer.init_from_config(config) | |
| train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True) | |
| model = Tacotron2(config, ap, tokenizer, speaker_manager=None) | |
| trainer = Trainer( | |
| TrainerArgs(), | |
| config, | |
| output_path, | |
| model=model, | |
| train_samples=train_samples, | |
| eval_samples=eval_samples, | |
| training_assets={"audio_processor": ap}, | |
| ) | |
| trainer.fit() | |