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| import glob | |
| import json | |
| import os | |
| import shutil | |
| import torch | |
| from trainer import get_last_checkpoint | |
| from tests import get_device_id, get_tests_output_path, run_cli | |
| from TTS.tts.configs.neuralhmm_tts_config import NeuralhmmTTSConfig | |
| config_path = os.path.join(get_tests_output_path(), "test_model_config.json") | |
| output_path = os.path.join(get_tests_output_path(), "train_outputs") | |
| parameter_path = os.path.join(get_tests_output_path(), "lj_parameters.pt") | |
| torch.save({"mean": -5.5138, "std": 2.0636, "init_transition_prob": 0.3212}, parameter_path) | |
| config = NeuralhmmTTSConfig( | |
| batch_size=3, | |
| eval_batch_size=3, | |
| num_loader_workers=0, | |
| num_eval_loader_workers=0, | |
| text_cleaner="phoneme_cleaners", | |
| use_phonemes=True, | |
| phoneme_language="en-us", | |
| phoneme_cache_path=os.path.join(get_tests_output_path(), "train_outputs/phoneme_cache/"), | |
| run_eval=True, | |
| test_delay_epochs=-1, | |
| mel_statistics_parameter_path=parameter_path, | |
| epochs=1, | |
| print_step=1, | |
| test_sentences=[ | |
| "Be a voice, not an echo.", | |
| ], | |
| print_eval=True, | |
| max_sampling_time=50, | |
| ) | |
| config.audio.do_trim_silence = True | |
| config.audio.trim_db = 60 | |
| config.save_json(config_path) | |
| # train the model for one epoch when mel parameters exists | |
| command_train = ( | |
| f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} " | |
| f"--coqpit.output_path {output_path} " | |
| "--coqpit.datasets.0.formatter ljspeech " | |
| "--coqpit.datasets.0.meta_file_train metadata.csv " | |
| "--coqpit.datasets.0.meta_file_val metadata.csv " | |
| "--coqpit.datasets.0.path tests/data/ljspeech " | |
| "--coqpit.test_delay_epochs 0 " | |
| ) | |
| run_cli(command_train) | |
| # train the model for one epoch when mel parameters have to be computed from the dataset | |
| if os.path.exists(parameter_path): | |
| os.remove(parameter_path) | |
| command_train = ( | |
| f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --config_path {config_path} " | |
| f"--coqpit.output_path {output_path} " | |
| "--coqpit.datasets.0.formatter ljspeech " | |
| "--coqpit.datasets.0.meta_file_train metadata.csv " | |
| "--coqpit.datasets.0.meta_file_val metadata.csv " | |
| "--coqpit.datasets.0.path tests/data/ljspeech " | |
| "--coqpit.test_delay_epochs 0 " | |
| ) | |
| run_cli(command_train) | |
| # Find latest folder | |
| continue_path = max(glob.glob(os.path.join(output_path, "*/")), key=os.path.getmtime) | |
| # Inference using TTS API | |
| continue_config_path = os.path.join(continue_path, "config.json") | |
| continue_restore_path, _ = get_last_checkpoint(continue_path) | |
| out_wav_path = os.path.join(get_tests_output_path(), "output.wav") | |
| # Check integrity of the config | |
| with open(continue_config_path, "r", encoding="utf-8") as f: | |
| config_loaded = json.load(f) | |
| assert config_loaded["characters"] is not None | |
| assert config_loaded["output_path"] in continue_path | |
| assert config_loaded["test_delay_epochs"] == 0 | |
| # Load the model and run inference | |
| inference_command = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' tts --text 'This is an example.' --config_path {continue_config_path} --model_path {continue_restore_path} --out_path {out_wav_path}" | |
| run_cli(inference_command) | |
| # restore the model and continue training for one more epoch | |
| command_train = f"CUDA_VISIBLE_DEVICES='{get_device_id()}' python TTS/bin/train_tts.py --continue_path {continue_path} " | |
| run_cli(command_train) | |
| shutil.rmtree(continue_path) | |