def __init__(self, cfg: DictConfig, trainer: Trainer = None):
self.world_size = 1
if trainer is not None:
self.world_size = trainer.num_nodes * trainer.num_devices
super().__init__(cfg=cfg, trainer=trainer)
self.preprocessor = EncDecSpeakerLabelModel.from_config_dict(cfg.preprocessor)
self.encoder = EncDecSpeakerLabelModel.from_config_dict(cfg.encoder)
self.decoder = EncDecSpeakerLabelModel.from_config_dict(cfg.decoder)
if 'angular' in cfg.decoder and cfg.decoder['angular']:
logging.info("loss is Angular Softmax")
scale = cfg.loss.scale
margin = cfg.loss.margin
self.loss = AngularSoftmaxLoss(scale=scale, margin=margin)
else:
logging.info("loss is Softmax-CrossEntropy")
self.loss = CELoss()
self.task = None
self._accuracy = TopKClassificationAccuracy(top_k=[1])
self.labels = None
if hasattr(self._cfg, 'spec_augment') and self._cfg.spec_augment is not None:
self.spec_augmentation = EncDecSpeakerLabelModel.from_config_dict(self._cfg.spec_augment)
else:
self.spec_augmentation = None
def __init__(self, cfg: DictConfig, trainer: Trainer = None):
super().__init__(cfg=cfg, trainer=trainer)
self.preprocessor = EncDecSpeakerLabelModel.from_config_dict(cfg.preprocessor)
self.encoder = EncDecSpeakerLabelModel.from_config_dict(cfg.encoder)
self.decoder = EncDecSpeakerLabelModel.from_config_dict(cfg.decoder)
if 'angular' in cfg.decoder and cfg.decoder['angular']:
logging.info("Training with Angular Softmax Loss")
scale = cfg.loss.scale
margin = cfg.loss.margin
self.loss = AngularSoftmaxLoss(scale=scale, margin=margin)
else:
logging.info("Training with Softmax-CrossEntropy loss")
self.loss = CELoss()
self.task = None
self._accuracy = TopKClassificationAccuracy(top_k=[1])
