mirror of https://github.com/coqui-ai/TTS.git
85 lines
2.9 KiB
Python
85 lines
2.9 KiB
Python
import os
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from trainer import Trainer, TrainerArgs
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from TTS.tts.configs.align_tts_config import AlignTTSConfig
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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.align_tts import AlignTTS
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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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from TTS.utils.downloaders import download_thorsten_de
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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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formatter="thorsten", meta_file_train="metadata.csv", path=os.path.join(output_path, "../thorsten-de/")
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)
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# download dataset if not already present
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if not os.path.exists(dataset_config.path):
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print("Downloading dataset")
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download_thorsten_de(os.path.split(os.path.abspath(dataset_config.path))[0])
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config = AlignTTSConfig(
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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=False,
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phoneme_language="de",
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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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test_sentences=[
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"Es hat mich viel Zeit gekostet ein Stimme zu entwickeln, jetzt wo ich sie habe werde ich nicht mehr schweigen.",
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"Sei eine Stimme, kein Echo.",
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"Es tut mir Leid David. Das kann ich leider nicht machen.",
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"Dieser Kuchen ist großartig. Er ist so lecker und feucht.",
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"Vor dem 22. November 1963.",
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],
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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.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(
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dataset_config,
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eval_split=True,
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eval_split_max_size=config.eval_split_max_size,
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eval_split_size=config.eval_split_size,
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)
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# init model
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model = AlignTTS(config, ap, tokenizer)
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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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