mirror of https://github.com/coqui-ai/TTS.git
75 lines
1.9 KiB
Python
75 lines
1.9 KiB
Python
import os
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from TTS.config.shared_configs import BaseAudioConfig
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from TTS.trainer import Trainer, TrainingArgs
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from TTS.tts.configs import BaseDatasetConfig, Tacotron2Config
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from TTS.tts.datasets import load_tts_samples
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from TTS.tts.models.tacotron2 import Tacotron2
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from TTS.utils.audio import AudioProcessor
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# from TTS.tts.datasets.tokenizer import Tokenizer
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output_path = os.path.dirname(os.path.abspath(__file__))
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# init configs
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dataset_config = BaseDatasetConfig(
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name="ljspeech", meta_file_train="metadata.csv", path=os.path.join(output_path, "../LJSpeech-1.1/")
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)
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audio_config = BaseAudioConfig(
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sample_rate=22050,
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do_trim_silence=True,
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trim_db=60.0,
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signal_norm=False,
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mel_fmin=0.0,
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mel_fmax=8000,
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spec_gain=1.0,
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log_func="np.log",
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ref_level_db=20,
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preemphasis=0.0,
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)
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config = Tacotron2Config( # This is the config that is saved for the future use
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audio=audio_config,
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batch_size=64,
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eval_batch_size=16,
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num_loader_workers=4,
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num_eval_loader_workers=4,
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run_eval=True,
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test_delay_epochs=-1,
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r=6,
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gradual_training=[[0, 6, 64], [10000, 4, 32], [50000, 3, 32], [100000, 2, 32]],
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double_decoder_consistency=True,
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epochs=1000,
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text_cleaner="phoneme_cleaners",
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use_phonemes=True,
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phoneme_language="en-us",
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phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
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print_step=25,
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print_eval=True,
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mixed_precision=False,
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output_path=output_path,
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datasets=[dataset_config],
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)
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# init audio processor
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ap = AudioProcessor(**config.audio.to_dict())
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# load training samples
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train_samples, eval_samples = load_tts_samples(dataset_config, eval_split=True)
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# init model
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model = Tacotron2(config)
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# init the trainer and 🚀
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trainer = Trainer(
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TrainingArgs(),
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config,
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output_path,
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model=model,
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train_samples=train_samples,
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eval_samples=eval_samples,
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training_assets={"audio_processor": ap},
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)
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trainer.fit()
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