import os from trainer import Trainer, TrainerArgs from TTS.config.shared_configs import BaseAudioConfig, BaseDatasetConfig from TTS.tts.configs.fastspeech2_config import Fastspeech2Config from TTS.tts.datasets import load_tts_samples from TTS.tts.models.forward_tts import ForwardTTS from TTS.tts.utils.text.tokenizer import TTSTokenizer from TTS.utils.audio import AudioProcessor from TTS.utils.manage import ModelManager output_path = os.path.dirname(os.path.abspath(__file__)) # init configs dataset_config = BaseDatasetConfig( formatter="ljspeech", meta_file_train="metadata.csv", # meta_file_attn_mask=os.path.join(output_path, "../LJSpeech-1.1/metadata_attn_mask.txt"), path=os.path.join(output_path, "../LJSpeech-1.1/"), ) audio_config = BaseAudioConfig( sample_rate=22050, do_trim_silence=True, trim_db=60.0, signal_norm=False, mel_fmin=0.0, mel_fmax=8000, spec_gain=1.0, log_func="np.log", ref_level_db=20, preemphasis=0.0, ) config = Fastspeech2Config( run_name="fastspeech2_ljspeech", audio=audio_config, batch_size=32, eval_batch_size=16, num_loader_workers=8, num_eval_loader_workers=4, compute_input_seq_cache=True, compute_f0=True, f0_cache_path=os.path.join(output_path, "f0_cache"), compute_energy=True, energy_cache_path=os.path.join(output_path, "energy_cache"), run_eval=True, test_delay_epochs=-1, epochs=1000, text_cleaner="english_cleaners", use_phonemes=True, phoneme_language="en-us", phoneme_cache_path=os.path.join(output_path, "phoneme_cache"), precompute_num_workers=4, print_step=50, print_eval=False, mixed_precision=False, max_seq_len=500000, output_path=output_path, datasets=[dataset_config], ) # compute alignments if not config.model_args.use_aligner: manager = ModelManager() model_path, config_path, _ = manager.download_model("tts_models/en/ljspeech/tacotron2-DCA") # TODO: make compute_attention python callable os.system( 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" ) # INITIALIZE THE AUDIO PROCESSOR # Audio processor is used for feature extraction and audio I/O. # It mainly serves to the dataloader and the training loggers. ap = AudioProcessor.init_from_config(config) # INITIALIZE THE TOKENIZER # Tokenizer is used to convert text to sequences of token IDs. # If characters are not defined in the config, default characters are passed to the config tokenizer, config = TTSTokenizer.init_from_config(config) # LOAD DATA SAMPLES # Each sample is a list of ```[text, audio_file_path, speaker_name]``` # You can define your custom sample loader returning the list of samples. # Or define your custom formatter and pass it to the `load_tts_samples`. # Check `TTS.tts.datasets.load_tts_samples` for more details. train_samples, eval_samples = load_tts_samples( dataset_config, eval_split=True, eval_split_max_size=config.eval_split_max_size, eval_split_size=config.eval_split_size, ) # init the model model = ForwardTTS(config, ap, tokenizer, speaker_manager=None) # init the trainer and 🚀 trainer = Trainer( TrainerArgs(), config, output_path, model=model, train_samples=train_samples, eval_samples=eval_samples ) trainer.fit()