mirror of https://github.com/coqui-ai/TTS.git
92 lines
3.2 KiB
Python
92 lines
3.2 KiB
Python
import os
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from trainer import Trainer, TrainerArgs
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from TTS.config.shared_configs import BaseAudioConfig
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from TTS.tts.configs.glow_tts_config import GlowTTSConfig
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from TTS.tts.configs.shared_configs import BaseDatasetConfig
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from TTS.tts.datasets import load_tts_samples
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from TTS.tts.models.glow_tts import GlowTTS
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from TTS.tts.utils.speakers import SpeakerManager
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from TTS.tts.utils.text.tokenizer import TTSTokenizer
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from TTS.utils.audio import AudioProcessor
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# set experiment paths
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output_path = os.path.dirname(os.path.abspath(__file__))
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dataset_path = os.path.join(output_path, "../VCTK/")
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# download the dataset if not downloaded
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if not os.path.exists(dataset_path):
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from TTS.utils.downloaders import download_vctk
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download_vctk(dataset_path)
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# define dataset config
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dataset_config = BaseDatasetConfig(name="vctk", meta_file_train="", path=dataset_path)
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# define audio config
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# ❗ resample the dataset externally using `TTS/bin/resample.py` and set `resample=False` for faster training
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audio_config = BaseAudioConfig(sample_rate=22050, resample=True, do_trim_silence=True, trim_db=23.0)
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# define model config
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config = GlowTTSConfig(
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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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precompute_num_workers=4,
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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="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=False,
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mixed_precision=True,
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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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min_text_len=0,
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max_text_len=500,
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min_audio_len=0,
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max_audio_len=500000,
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)
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# INITIALIZE THE AUDIO PROCESSOR
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# Audio processor is used for feature extraction and audio I/O.
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# It mainly serves to the dataloader and the training loggers.
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ap = AudioProcessor.init_from_config(config)
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# INITIALIZE THE TOKENIZER
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# Tokenizer is used to convert text to sequences of token IDs.
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# If characters are not defined in the config, default characters are passed to the config
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tokenizer, config = TTSTokenizer.init_from_config(config)
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# LOAD DATA SAMPLES
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# Each sample is a list of ```[text, audio_file_path, speaker_name]```
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# You can define your custom sample loader returning the list of samples.
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# Or define your custom formatter and pass it to the `load_tts_samples`.
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# Check `TTS.tts.datasets.load_tts_samples` for more details.
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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.num_speakers = speaker_manager.num_speakers
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# init model
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model = GlowTTS(config, ap, tokenizer, speaker_manager=speaker_manager)
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# INITIALIZE THE TRAINER
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# Trainer provides a generic API to train all the 🐸TTS models with all its perks like mixed-precision training,
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# distributed training, etc.
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trainer = Trainer(
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TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples
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)
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# AND... 3,2,1... 🚀
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trainer.fit()
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