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| import utils.commons.single_thread_env # NOQA | |
| import glob | |
| import subprocess | |
| from textgrid import TextGrid | |
| import os | |
| from utils.commons.hparams import hparams, set_hparams | |
| def train_mfa_align(mfa_outputs="mfa_outputs", | |
| mfa_inputs="mfa_inputs", | |
| model_name=None, pretrain_model_name=None, | |
| mfa_cmd='train'): | |
| CORPUS = hparams['processed_data_dir'].split("/")[-1] | |
| NUM_JOB = int(os.getenv('N_PROC', os.cpu_count())) | |
| env_vars = [f'CORPUS={CORPUS}', f'NUM_JOB={NUM_JOB}'] | |
| if mfa_outputs is not None: | |
| env_vars.append(f'MFA_OUTPUTS={mfa_outputs}') | |
| if mfa_inputs is not None: | |
| env_vars.append(f'MFA_INPUTS={mfa_inputs}') | |
| if model_name is not None: | |
| env_vars.append(f'MODEL_NAME={model_name}') | |
| if pretrain_model_name is not None: | |
| env_vars.append(f'PRETRAIN_MODEL_NAME={pretrain_model_name}') | |
| if mfa_cmd is not None: | |
| env_vars.append(f'MFA_CMD={mfa_cmd}') | |
| env_str = ' '.join(env_vars) | |
| print(f"| Run MFA for {CORPUS}. Env vars: {env_str}") | |
| subprocess.check_call(f'{env_str} bash mfa_usr/run_mfa_train_align.sh', shell=True) | |
| mfa_offset = hparams['preprocess_args']['mfa_offset'] | |
| if mfa_offset > 0: | |
| for tg_fn in glob.glob(f'{hparams["processed_data_dir"]}/{mfa_outputs}/*.TextGrid'): | |
| tg = TextGrid.fromFile(tg_fn) | |
| max_time = tg.maxTime | |
| for tier in tg.tiers: | |
| for interval in tier.intervals: | |
| interval.maxTime = min(interval.maxTime + mfa_offset, max_time) | |
| interval.minTime = min(interval.minTime + mfa_offset, max_time) | |
| tier.intervals[0].minTime = 0 | |
| tier.maxTime = min(tier.maxTime + mfa_offset, max_time) | |
| tg.write(tg_fn) | |
| TextGrid.fromFile(tg_fn) | |
| if __name__ == '__main__': | |
| set_hparams(print_hparams=False) | |
| train_mfa_align() | |