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remove if-branch at downsample pad in zipformer for onnx-export compatibility (#965)

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Peng He 2023-03-24 14:30:43 +08:00 committed by GitHub
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1 changed files with 6 additions and 7 deletions
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13
egs/librispeech/ASR/pruned_transducer_stateless7/zipformer.py
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@ -781,13 +781,12 @@ class AttentionDownsample(torch.nn.Module):
ds = self.downsample ds = self.downsample
d_seq_len = (seq_len + ds - 1) // ds d_seq_len = (seq_len + ds - 1) // ds
# Pad to an exact multiple of self.downsample # Pad to an exact multiple of self.downsample, could be 0 for onnx-export-compatibility
if seq_len != d_seq_len * ds: # right-pad src, repeating the last element.
# right-pad src, repeating the last element. pad = d_seq_len * ds - seq_len
pad = d_seq_len * ds - seq_len src_extra = src[src.shape[0] - 1 :].expand(pad, src.shape[1], src.shape[2])
src_extra = src[src.shape[0] - 1 :].expand(pad, src.shape[1], src.shape[2]) src = torch.cat((src, src_extra), dim=0)
src = torch.cat((src, src_extra), dim=0) assert src.shape[0] == d_seq_len * ds, (src.shape[0], d_seq_len, ds)
assert src.shape[0] == d_seq_len * ds, (src.shape[0], d_seq_len, ds)
src = src.reshape(d_seq_len, ds, batch_size, in_channels) src = src.reshape(d_seq_len, ds, batch_size, in_channels)
scores = (src * self.query).sum(dim=-1, keepdim=True) scores = (src * self.query).sum(dim=-1, keepdim=True)