Python EncDecCTCModel.maybe_init_from_pretrained_checkpoint 예제들

예제 #1

파일: speech_to_text_ctc.py 프로젝트: quuhua911/NeMo

def main(cfg):
    logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')

    trainer = pl.Trainer(**cfg.trainer)
    exp_manager(trainer, cfg.get("exp_manager", None))
    asr_model = EncDecCTCModel(cfg=cfg.model, trainer=trainer)

    # Initialize the weights of the model from another model, if provided via config
    asr_model.maybe_init_from_pretrained_checkpoint(cfg)

    trainer.fit(asr_model)

    if hasattr(cfg.model,
               'test_ds') and cfg.model.test_ds.manifest_filepath is not None:
        if asr_model.prepare_test(trainer):
            trainer.test(asr_model)

예제 #2

파일: speech_to_text.py 프로젝트: blisc/NeMo

def main(cfg):
    logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')

    trainer = pl.Trainer(**cfg.trainer)
    exp_manager(trainer, cfg.get("exp_manager", None))
    asr_model = EncDecCTCModel(cfg=cfg.model, trainer=trainer)

    # Initialize the weights of the model from another model, if provided via config
    asr_model.maybe_init_from_pretrained_checkpoint(cfg)

    trainer.fit(asr_model)

    if hasattr(cfg.model,
               'test_ds') and cfg.model.test_ds.manifest_filepath is not None:
        gpu = 1 if cfg.trainer.gpus != 0 else 0
        test_trainer = pl.Trainer(
            gpus=gpu,
            precision=trainer.precision,
            amp_level=trainer.accelerator_connector.amp_level,
            amp_backend=cfg.trainer.get("amp_backend", "native"),
        )
        if asr_model.prepare_test(test_trainer):
            test_trainer.test(asr_model)