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| | | from typing import Union |
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| | | |
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| | | import torch |
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| | | from typeguard import check_argument_types |
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| | | |
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| | | from funasr.models.e2e_asr_common import ErrorCalculator |
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| | | from funasr.modules.nets_utils import th_accuracy |
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| | |
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| | | postencoder: Optional[AbsPostEncoder] = None, |
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| | | encoder1_encoder2_joint_training: bool = True, |
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| | | ): |
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| | | assert check_argument_types() |
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| | | assert 0.0 <= ctc_weight <= 1.0, ctc_weight |
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| | | assert 0.0 <= interctc_weight < 1.0, interctc_weight |
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| | | |
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| | | self.enable_maas_finetune = enable_maas_finetune |
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| | | self.freeze_encoder2 = freeze_encoder2 |
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| | | self.encoder1_encoder2_joint_training = encoder1_encoder2_joint_training |
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| | | self.length_normalized_loss = length_normalized_loss |
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| | | |
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| | | def forward( |
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| | | self, |
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| | |
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| | | stats["loss2"] = torch.clone(loss2.detach()) |
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| | | stats["loss"] = torch.clone(loss.detach()) |
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| | | # force_gatherable: to-device and to-tensor if scalar for DataParallel |
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| | | if self.length_normalized_loss: |
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| | | batch_size = int((text_lengths + 1).sum()) |
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| | | loss, stats, weight = force_gatherable((loss, stats, batch_size), loss.device) |
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| | | return loss, stats, weight |
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| | | |
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