mirror of https://github.com/coqui-ai/TTS.git
80 lines
2.9 KiB
Python
80 lines
2.9 KiB
Python
import os
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# Trainer: Where the ✨️ happens.
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# TrainingArgs: Defines the set of arguments of the Trainer.
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from TTS.trainer import Trainer, TrainingArgs
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# GlowTTSConfig: all model related values for training, validating and testing.
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from TTS.tts.configs.glow_tts_config import GlowTTSConfig
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# BaseDatasetConfig: defines name, formatter and path of the dataset.
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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.utils.audio import AudioProcessor
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# we use the same path as this script as our training folder.
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output_path = os.path.dirname(os.path.abspath(__file__))
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# DEFINE DATASET CONFIG
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# Set LJSpeech as our target dataset and define its path.
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# You can also use a simple Dict to define the dataset and pass it to your custom formatter.
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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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# INITIALIZE THE TRAINING CONFIGURATION
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# Configure the model. Every config class inherits the BaseTTSConfig.
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config = GlowTTSConfig(
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batch_size=32,
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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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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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)
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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(**config.audio.to_dict())
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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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# INITIALIZE THE MODEL
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# Models take a config object and a speaker manager as input
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# Config defines the details of the model like the number of layers, the size of the embedding, etc.
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# Speaker manager is used by multi-speaker models.
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model = GlowTTS(config, speaker_manager=None)
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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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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}, # assets are objetcs used by the models but not class members.
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)
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# AND... 3,2,1... 🚀
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trainer.fit()
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