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1d44da845b
* Begin to add RNN-T training for librispeech. * Copy files from conformer_ctc. Will edit it. * Use conformer/transformer model as encoder. * Begin to add training script. * Add training code. * Remove long utterances to avoid OOM when a large max_duraiton is used. * Begin to add decoding script. * Add decoding script. * Minor fixes. * Add beam search. * Use LSTM layers for the encoder. Need more tunings. * Use stateless decoder. * Minor fixes to make it ready for merge. * Fix README. * Update RESULT.md to include RNN-T Conformer. * Minor fixes. * Fix tests. * Minor fixes. * Minor fixes. * Fix tests.
66 lines
2.0 KiB
Python
66 lines
2.0 KiB
Python
# Copyright 2021 Xiaomi Corp. (authors: Fangjun Kuang)
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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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import torch.nn as nn
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class Decoder(nn.Module):
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"""This class implements the stateless decoder from the following paper:
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RNN-transducer with stateless prediction network
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https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=9054419
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It removes the recurrent connection from the decoder, i.e., the prediction
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network.
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TODO: Implement https://arxiv.org/pdf/2109.07513.pdf
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"""
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def __init__(
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self,
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vocab_size: int,
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embedding_dim: int,
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blank_id: int,
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):
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"""
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Args:
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vocab_size:
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Number of tokens of the modeling unit including blank.
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embedding_dim:
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Dimension of the input embedding.
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blank_id:
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The ID of the blank symbol.
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"""
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super().__init__()
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self.embedding = nn.Embedding(
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num_embeddings=vocab_size,
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embedding_dim=embedding_dim,
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padding_idx=blank_id,
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)
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self.blank_id = blank_id
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def forward(self, y: torch.Tensor) -> torch.Tensor:
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"""
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Args:
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y:
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A 2-D tensor of shape (N, U) with blank prepended.
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Returns:
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Return a tensor of shape (N, U, embedding_dim).
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"""
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embeding_out = self.embedding(y)
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return embeding_out
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