mirror of
https://github.com/thorstenMueller/Thorsten-Voice.git
synced 2024-11-24 17:03:11 +01:00
95 lines
3.0 KiB
Python
95 lines
3.0 KiB
Python
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import os
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from trainer import Trainer, TrainerArgs
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from TTS.tts.configs.shared_configs import BaseDatasetConfig
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from TTS.tts.configs.vits_config import VitsConfig
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from TTS.tts.datasets import load_tts_samples
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from TTS.tts.models.vits import Vits, VitsAudioConfig
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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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def main():
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output_path = os.path.dirname(os.path.abspath(__file__))
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#output_path = "c:\\temp\tts"
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dataset_config = BaseDatasetConfig(
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formatter="ljspeech", meta_file_train="metadata_small.csv", path="C:\\Users\\ThorstenVoice\\TTS-Training\\ThorstenVoice-Dataset_2022.10"
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)
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audio_config = VitsAudioConfig(
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sample_rate=22050, win_length=1024, hop_length=256, num_mels=80, mel_fmin=0, mel_fmax=None
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)
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config = VitsConfig(
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audio=audio_config,
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run_name="vits_thorsten-voice",
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batch_size=4,
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eval_batch_size=4,
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batch_group_size=5,
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num_loader_workers=1,
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num_eval_loader_workers=1,
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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="de",
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phoneme_cache_path=os.path.join(output_path, "phoneme_cache"),
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compute_input_seq_cache=True,
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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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cudnn_benchmark=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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)
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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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# config is updated with the default characters if not defined in 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 = Vits(config, ap, tokenizer, speaker_manager=None)
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# init the trainer and 🚀
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trainer = Trainer(
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TrainerArgs(),
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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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)
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trainer.fit()
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print("Fertig!")
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from multiprocessing import Process, freeze_support
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if __name__ == '__main__':
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freeze_support() # needed for Windows
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main()
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