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Add greedy_search_batch
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@ -659,6 +659,8 @@ def greedy_search_batch(
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context_size = model.decoder.context_size
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batch_size_list = packed_encoder_out.batch_sizes.tolist()
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unsorted_indices = packed_encoder_out.unsorted_indices.tolist()
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N = encoder_out.size(0)
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assert torch.all(encoder_out_lens > 0), encoder_out_lens
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assert N == batch_size_list[0], (N, batch_size_list)
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@ -675,7 +677,9 @@ def greedy_search_batch(
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dtype=torch.int64,
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) # (N, context_size)
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decoder_out = model.decoder(decoder_input, need_pad=False)
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k = torch.zeros(N, 1, device=device, dtype=torch.int64)
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decoder_out = model.decoder(decoder_input, k, need_pad=False)
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decoder_out = model.joiner.decoder_proj(decoder_out)
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# decoder_out: (N, 1, decoder_out_dim)
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@ -709,18 +713,32 @@ def greedy_search_batch(
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if emitted:
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# update decoder output
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decoder_input = [h[-context_size:] for h in hyps[:batch_size]]
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c = torch.tensor(
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[h[-context_size - 1 :] for h in hyps[:batch_size]],
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device=device,
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)
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k[:, 0] = torch.sum(
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(
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c[:, -context_size - 1 : -1]
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== c[:, -1].expand_as(c[:, -context_size - 1 : -1])
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).int(),
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dim=1,
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keepdim=True,
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)
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decoder_input = torch.tensor(
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decoder_input,
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device=device,
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dtype=torch.int64,
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)
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decoder_out = model.decoder(decoder_input, need_pad=False)
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decoder_out = model.decoder(decoder_input, k, need_pad=False)
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decoder_out = model.joiner.decoder_proj(decoder_out)
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sorted_ans = [h[context_size:] for h in hyps]
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ans = []
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ans_timestamps = []
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unsorted_indices = packed_encoder_out.unsorted_indices.tolist()
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for i in range(N):
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ans.append(sorted_ans[unsorted_indices[i]])
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ans_timestamps.append(timestamps[unsorted_indices[i]])
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