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66 lines
2.1 KiB
Python
66 lines
2.1 KiB
Python
#!/usr/bin/env python3
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# Copyright 2023 Xiaomi Corp. (authors: Daniel Povey)
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#
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# See ../../../../LICENSE for clarification regarding multiple authors
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import torch
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from torch import nn, Tensor
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from chunk_decoder import ChunkDecoder
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from zipformer import Zipformer2
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class Zipformer2LM(nn.Module):
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def __init__(self,
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encoder_embed: nn.Module,
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encoder: Zipformer2,
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decoder: ChunkDecoder):
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super().__init__()
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self.encoder_embed = encoder_embed
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self.encoder = encoder # does subsampling
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self.decoder = decoder
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def forward(self,
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labels: Tensor):
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"""
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Compute array of log-probs
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Args:
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labels: a Tensor containing the labels (in the range 0..num_symbols-1), of shape (batch_size, seq_len).
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Returns:
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a Tensor containing the log-probs for each label, of shape (batch_size, seq_len).
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"""
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(batch_size, seq_len) = labels.shape
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chunk_size = self.decoder.chunk_size
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labels_shifted = labels.t() # (time, batch)
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labels_shifted = torch.cat((torch.zeros_like(labels_shifted[:chunk_size]),
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labels_shifted[:-chunk_size]),
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dim=0)
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x = self.encoder_embed(labels_shifted)
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x_lens = torch.full((batch_size,), seq_len,
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dtype=torch.long, device=labels.device)
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# x_lens is after subsampling. Actually we don't need it.
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(x, x_lens) = self.encoder(x, x_lens)
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logprobs = self.decoder(labels, x)
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return logprobs
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