mirror of https://github.com/coqui-ai/TTS.git
69 lines
2.1 KiB
Python
69 lines
2.1 KiB
Python
import os
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from TTS.config import BaseAudioConfig, BaseDatasetConfig
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from TTS.trainer import Trainer, TrainingArgs, init_training
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from TTS.tts.configs import SpeedySpeechConfig
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from TTS.utils.manage import ModelManager
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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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name="ljspeech",
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meta_file_train="metadata.csv",
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# meta_file_attn_mask=os.path.join(output_path, "../LJSpeech-1.1/metadata_attn_mask.txt"),
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path=os.path.join(output_path, "../LJSpeech-1.1/"),
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)
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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=60.0,
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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="speedy_speech_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=4,
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num_eval_loader_workers=4,
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compute_input_seq_cache=True,
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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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)
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# compute alignments
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if not config.model_args.use_aligner:
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manager = ModelManager()
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model_path, config_path, _ = manager.download_model("tts_models/en/ljspeech/tacotron2-DCA")
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# TODO: make compute_attention python callable
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os.system(
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f"python TTS/bin/compute_attention_masks.py --model_path {model_path} --config_path {config_path} --dataset ljspeech --dataset_metafile metadata.csv --data_path ./recipes/ljspeech/LJSpeech-1.1/ --use_cuda true"
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)
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# train the model
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args, config, output_path, _, c_logger, tb_logger = init_training(TrainingArgs(), config)
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trainer = Trainer(args, config, output_path, c_logger, tb_logger)
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trainer.fit()
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