mirror of https://github.com/coqui-ai/TTS.git
81 lines
2.2 KiB
Python
81 lines
2.2 KiB
Python
import os
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from TTS.config import BaseAudioConfig, BaseDatasetConfig
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from TTS.trainer import Trainer, TrainingArgs
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from TTS.tts.configs.speedy_speech_config import SpeedySpeechConfig
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from TTS.tts.datasets import load_tts_samples
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from TTS.tts.models.forward_tts import ForwardTTS
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from TTS.tts.utils.speakers import SpeakerManager
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from TTS.utils.audio import AudioProcessor
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output_path = os.path.dirname(os.path.abspath(__file__))
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dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=os.path.join(output_path, "../VCTK/"))
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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=23.0,
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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 = SpeedySpeechConfig(
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run_name="fast_pitch_ljspeech",
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audio=audio_config,
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batch_size=32,
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eval_batch_size=16,
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num_loader_workers=8,
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num_eval_loader_workers=4,
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compute_input_seq_cache=True,
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compute_f0=True,
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f0_cache_path=os.path.join(output_path, "f0_cache"),
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run_eval=True,
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test_delay_epochs=-1,
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epochs=1000,
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text_cleaner="english_cleaners",
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use_phonemes=True,
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use_espeak_phonemes=False,
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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=50,
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print_eval=False,
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mixed_precision=False,
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sort_by_audio_len=True,
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max_seq_len=500000,
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output_path=output_path,
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datasets=[dataset_config],
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use_speaker_embedding=True,
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)
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# init audio processor
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ap = AudioProcessor(**config.audio)
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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 speaker manager for multi-speaker training
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# it maps speaker-id to speaker-name in the model and data-loader
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speaker_manager = SpeakerManager()
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speaker_manager.set_speaker_ids_from_data(train_samples + eval_samples)
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config.model_args.num_speakers = speaker_manager.num_speakers
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# init model
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model = ForwardTTS(config, speaker_manager)
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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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