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.overflow_config import OverflowConfig 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 = OverflowConfig( 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)