TTS/recipes/blizzard2013/tacotron2-Capacitron/train_capacitron_t2.py

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()