[doc] add ctc weight for attention rescoring & substitute batchnorm in convolution module with layernorm #19
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whiteshirt0429
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robin1001
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wenet/transformer/asr_model.py
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| @@ -417,6 +419,9 @@ def attention_rescoring(self, | |||
| for j, w in enumerate(hyp): | |||
| score += decoder_out[i][j][w] | |||
| score += decoder_out[i][len(hyp)][self.eos] | |||
| # add ctc score | |||
| score += log_add([cur_hyps[i][1][0], cur_hyps[i][1][1] | |||
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put log_add in _ctc_prefix_beam_search
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| @@ -56,7 +56,7 @@ def __init__(self, | |||
| groups=channels, | |||
| bias=bias, | |||
| ) | |||
| self.norm = nn.BatchNorm1d(channels) | |||
| self.norm = nn.LayerNorm(channels) | |||
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modify your commit message to cover this change or change it in a pull request.
robin1001
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wenet/transformer/asr_model.py
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| @@ -332,7 +332,7 @@ def _ctc_prefix_beam_search( | |||
| reverse=True) | |||
| cur_hyps = next_hyps[:beam_size] | |||
| hyps = [x[0] for x in cur_hyps] | |||
| return hyps, encoder_out | |||
| return hyps, encoder_out, cur_hyps | |||
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hyps shares the same info with cur_hyps, it's better we return hyps with it's corresponding total score here. The return is like List[(hyp, score)], encoder_out.
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[network] substitute batchnorm in convolution with layernorm
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…in convolution with layernorm(#19) [network] substitute batchnorm in convolution with layernorm
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