def main(cfg):
logging.info(f'Hydra config: {cfg.pretty()}')
trainer = pl.Trainer(**cfg.trainer)
log_dir = exp_manager(trainer, cfg.get("exp_manager", None))
speaker_model = EncDecSpeakerLabelModel(cfg=cfg.model, trainer=trainer)
trainer.fit(speaker_model)
model_path = os.path.join(log_dir, '..', 'spkr.nemo')
speaker_model.save_to(model_path)
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
trainer = pl.Trainer(gpus=gpu)
if speaker_model.prepare_test(trainer):
trainer.test(speaker_model)
def main(cfg):
logging.info(f'Hydra config: {OmegaConf.to_yaml(cfg)}')
trainer = pl.Trainer(**cfg.trainer)
log_dir = exp_manager(trainer, cfg.get("exp_manager", None))
speaker_model = EncDecSpeakerLabelModel(cfg=cfg.model, trainer=trainer)
trainer.fit(speaker_model)
if not trainer.fast_dev_run:
model_path = os.path.join(log_dir, '..', 'spkr.nemo')
speaker_model.save_to(model_path)
if hasattr(cfg.model,
'test_ds') and cfg.model.test_ds.manifest_filepath is not None:
trainer = pl.Trainer(devices=1, accelerator=cfg.trainer.accelerator)
if speaker_model.prepare_test(trainer):
trainer.test(speaker_model)
def main(cfg):
# add paths to manifests to config
cfg.model.train_ds.manifest_filepath = '/Users/xujinghua/speaker-verification-with-NeMo/data/train.json'
cfg.model.validation_ds.manifest_filepath = '/Users/xujinghua/speaker-verification-with-NeMo/data/train.json'
# an4 test files have a different set of speakers
# cfg.model.test_ds.manifest_filepath = '/Users/xujinghua/NeMo/data/an4/wav/an4_clstk/dev.json'
cfg.model.decoder.num_classes = 74
os.environ["OMP_NUM_THREADS"] = '1'
# tutorial default setting: flags
# modify some trainer configs for this demo
# Checks if we have GPU available and uses it
cuda = 1 if torch.cuda.is_available() else 0
cfg.trainer.gpus = cuda
# Reduces maximum number of epochs to 5 for quick demonstration
cfg.trainer.max_epochs = 5
# Remove distributed training flags
cfg.trainer.accelerator = None
logging.info(f'Hydra config: {cfg.pretty()}')
trainer = pl.Trainer(**cfg.trainer)
log_dir = exp_manager(trainer, cfg.get("exp_manager", None))
speaker_model = EncDecSpeakerLabelModel(cfg=cfg.model, trainer=trainer)
trainer.fit(speaker_model)
if not trainer.fast_dev_run:
model_path = os.path.join(log_dir, '..', 'spkr.nemo')
speaker_model.save_to(model_path)
# no need for testing
'''