| | |
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| | | return x, tgt_mask, x_self_attn, x_src_attn |
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| | | |
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| | | |
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| | | class ContexutalBiasDecoder(nn.Module): |
|---|
| | | class ContextualBiasDecoder(nn.Module): |
|---|
| | | def __init__( |
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| | | self, |
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| | | size, |
|---|
| | |
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| | | normalize_before=True, |
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| | | ): |
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| | | """Construct an DecoderLayer object.""" |
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| | | super(ContexutalBiasDecoder, self).__init__() |
|---|
| | | super(ContextualBiasDecoder, self).__init__() |
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| | | self.size = size |
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| | | self.src_attn = src_attn |
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| | | if src_attn is not None: |
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| | |
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| | | |
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| | | class ContextualParaformerDecoder(ParaformerSANMDecoder): |
|---|
| | | """ |
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| | | author: Speech Lab, Alibaba Group, China |
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| | | Author: Speech Lab of DAMO Academy, Alibaba Group |
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| | | Paraformer: Fast and Accurate Parallel Transformer for Non-autoregressive End-to-End Speech Recognition |
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| | | https://arxiv.org/abs/2006.01713 |
|---|
| | | """ |
|---|
| | |
|---|
| | | ), |
|---|
| | | ) |
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| | | self.dropout = nn.Dropout(dropout_rate) |
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| | | self.bias_decoder = ContexutalBiasDecoder( |
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| | | self.bias_decoder = ContextualBiasDecoder( |
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| | | size=attention_dim, |
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| | | src_attn=MultiHeadedAttentionCrossAtt( |
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| | | attention_heads, attention_dim, src_attention_dropout_rate |
|---|
