Add vctk tacotron2 recipe

recipes/vctk/tacotron2/train_tacotron2.py

@@ -0,0 +1,87 @@
+import os
+
+from TTS.config.shared_configs import BaseAudioConfig
+from TTS.trainer import Trainer, TrainingArgs
+from TTS.tts.configs.shared_configs import BaseDatasetConfig
+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.speakers import SpeakerManager
+from TTS.utils.audio import AudioProcessor
+
+output_path = os.path.dirname(os.path.abspath(__file__))
+dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))
+
+audio_config = BaseAudioConfig(
+    sample_rate=22050,
+    resample=False,  # Resample to 22050 Hz. It slows down training. Use `TTS/bin/resample.py` to pre-resample and set this False for faster training.
+    do_trim_silence=True,
+    trim_db=23.0,
+    signal_norm=False,
+    mel_fmin=0.0,
+    mel_fmax=8000,
+    spec_gain=1.0,
+    log_func="np.log",
+    preemphasis=0.0,
+)
+
+config = Tacotron2Config(  # This is the config that is saved for the future use
+    audio=audio_config,
+    batch_size=32,
+    eval_batch_size=16,
+    num_loader_workers=4,
+    num_eval_loader_workers=4,
+    run_eval=True,
+    test_delay_epochs=-1,
+    r=2,
+    # gradual_training=[[0, 6, 48], [10000, 4, 32], [50000, 3, 32], [100000, 2, 32]],
+    double_decoder_consistency=False,
+    epochs=1000,
+    text_cleaner="phoneme_cleaners",
+    use_phonemes=True,
+    phoneme_language="en-us",
+    phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
+    print_step=150,
+    print_eval=False,
+    mixed_precision=True,
+    sort_by_audio_len=True,
+    min_seq_len=14800,
+    max_seq_len=22050 * 10,  # 44k is the original sampling rate before resampling, corresponds to 10 seconds of audio
+    output_path=output_path,
+    datasets=[dataset_config],
+    use_speaker_embedding=True,  # set this to enable multi-sepeaker training
+    decoder_ssim_alpha=0.0,  # disable ssim losses that causes NaN for some runs.
+    postnet_ssim_alpha=0.0,
+    postnet_diff_spec_alpha=0.0,
+    decoder_diff_spec_alpha=0.0,
+    attention_norm="softmax",
+    optimizer="Adam",
+    lr_scheduler=None,
+    lr=3e-5,
+)
+
+# init audio processor
+ap = AudioProcessor(**config.audio.to_dict())
+
+# load training samples
+train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
+
+# init speaker manager for multi-speaker training
+# it mainly handles speaker-id to speaker-name for the model and the data-loader
+speaker_manager = SpeakerManager()
+speaker_manager.set_speaker_ids_from_data(train_samples + eval_samples)
+
+# init model
+model = Tacotron2(config, speaker_manager)
+
+# init the trainer and 🚀
+trainer = Trainer(
+    TrainingArgs(),
+    config,
+    output_path,
+    model=model,
+    train_samples=train_samples,
+    eval_samples=eval_samples,
+    training_assets={"audio_processor": ap},
+)
+trainer.fit()