| File was renamed from funasr/models_transducer/decoder/stateless_decoder.py |
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| | | import torch |
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| | | from typeguard import check_argument_types |
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| | | from funasr.models_transducer.beam_search_transducer import Hypothesis |
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| | | from funasr.models_transducer.decoder.abs_decoder import AbsDecoder |
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| | | from funasr.modules.beam_search.beam_search_transducer import Hypothesis |
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| | | from funasr.models.rnnt_decoder.abs_decoder import AbsDecoder |
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| | | from funasr.models.specaug.specaug import SpecAug |
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| | | class StatelessDecoder(AbsDecoder): |
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| | | embed_size: int = 256, |
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| | | embed_dropout_rate: float = 0.0, |
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| | | embed_pad: int = 0, |
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| | | use_embed_mask: bool = False, |
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| | | ) -> None: |
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| | | """Construct a StatelessDecoder object.""" |
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| | | super().__init__() |
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| | | self.device = next(self.parameters()).device |
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| | | self.score_cache = {} |
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| | | self.use_embed_mask = use_embed_mask |
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| | | if self.use_embed_mask: |
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| | | self._embed_mask = SpecAug( |
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| | | time_mask_width_range=3, |
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| | | num_time_mask=1, |
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| | | apply_freq_mask=False, |
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| | | apply_time_warp=False |
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| | | ) |
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| | | def forward( |
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| | | """ |
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| | | dec_embed = self.embed_dropout_rate(self.embed(labels)) |
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| | | if self.use_embed_mask and self.training: |
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| | | dec_embed = self._embed_mask(dec_embed, label_lens)[0] |
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| | | return dec_embed |
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| | | |
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| | | def score( |
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