import glob import json import os import shutil from trainer import get_last_checkpoint from tests import get_device_id, get_tests_output_path, run_cli from TTS.tts.configs.delightful_tts_config import DelightfulTtsAudioConfig, DelightfulTTSConfig from TTS.tts.models.delightful_tts import DelightfulTtsArgs, VocoderConfig config_path = os.path.join(get_tests_output_path(), "test_model_config.json") output_path = os.path.join(get_tests_output_path(), "train_outputs") audio_config = DelightfulTtsAudioConfig() model_args = DelightfulTtsArgs(use_speaker_embedding=False) vocoder_config = VocoderConfig() config = DelightfulTTSConfig( model_args=model_args, audio=audio_config, vocoder=vocoder_config, batch_size=2, eval_batch_size=8, compute_f0=True, run_eval=True, test_delay_epochs=-1, text_cleaner="english_cleaners", use_phonemes=True, phoneme_language="en-us", phoneme_cache_path="tests/data/ljspeech/phoneme_cache/", f0_cache_path="tests/data/ljspeech/f0_cache_delightful/", ## delightful f0 cache is incompatible with other models epochs=1, print_step=1, print_eval=True, binary_align_loss_alpha=0.0, use_attn_priors=False, test_sentences=[ ["Be a voice, not an echo.", "ljspeech"], ], output_path=output_path, num_speakers=4, use_speaker_embedding=True, ) # active multispeaker d-vec mode config.model_args.use_speaker_embedding = True config.model_args.use_d_vector_file = False config.model_args.d_vector_file = None config.model_args.d_vector_dim = 256 config.save_json(config_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.dataset_name 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.datasets.0.meta_file_attn_mask tests/data/ljspeech/metadata_attn_mask.txt " "--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") speaker_id = "ljspeech" # 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.' --speaker_idx {speaker_id} --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) shutil.rmtree("tests/data/ljspeech/f0_cache_delightful/")