Rename converted to wavs for TTS training format. Add script to repeat metadata.csv column for ljspeech format.
Paul Pham
3 years ago
parent
e61dccf52e
commit
733afa7f45
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import pyjson5
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import os
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CUR_DIR = os.getcwd()
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with open("./example-config.json") as input_f:
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with open("./config.json", "w") as output_f:
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lines = input_f.readlines()
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data = ''.join(lines)
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obj = pyjson5.loads(data)
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audio = obj["audio"]
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audio["stats_path"] = os.path.join(CUR_DIR, 'scale_stats.npy' )
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#output_path = obj['output_path']
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obj["output_path"] = os.path.join(CUR_DIR, 'models', 'LJSpeech')
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#phoneme_path = obj['phoneme_cache_path']
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obj["phoneme_cache_path"] = os.path.join(CUR_DIR, 'models', 'phoneme_cache')
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obj["datasets"][0]["path"] = os.path.join(CUR_DIR)
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output_f.write(pyjson5.dumps(obj))
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import os
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CUR_DIR = os.getcwd()
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with open("./example-metadata.csv") as input_f:
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with open("./metadata.csv", "w") as output_f:
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lines = input_f.readlines()
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write_lines = []
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for line in lines:
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cols = line.split('|')
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cols.append(cols[1]) # repeat col[1] to be col[2] for the ljspeech format
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write_lines.append('|'.join(cols))
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output_f.write('\n'.join(write_lines))
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TTS
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pyjson5
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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.shared_configs import BaseDatasetConfig
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from TTS.tts.configs.tacotron2_config import Tacotron2Config
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from TTS.tts.datasets import load_tts_samples
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from TTS.tts.models.tacotron2 import Tacotron2
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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.tts.datasets.tokenizer import Tokenizer
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output_path = "."
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# init configs
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dataset_config = BaseDatasetConfig(
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formatter="mailabs",
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dataset_name="ljspeech", meta_file_train="metadata.csv",
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path=output_path # os.path.join(output_path, "/content/LJSpeech-1.1")
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)
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print(str(dataset_config))
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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 = Tacotron2Config( # This is the config that is saved for the future use
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audio=audio_config,
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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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run_eval=True,
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test_delay_epochs=-1,
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ga_alpha=0.0,
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decoder_loss_alpha=0.25,
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postnet_loss_alpha=0.25,
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postnet_diff_spec_alpha=0,
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decoder_diff_spec_alpha=0,
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decoder_ssim_alpha=0,
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postnet_ssim_alpha=0,
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r=2,
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attention_type="dynamic_convolution",
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double_decoder_consistency=False,
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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=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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)
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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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# 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 = Tacotron2(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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