mirror of
https://github.com/k2-fsa/icefall.git
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5b6699a835
* Disable weight decay. * Remove input feature batchnorm.. * Replace BatchNorm in the Conformer model with LayerNorm. * Use tanh in the joint network. * Remove sos ID. * Reduce the number of decoder layers from 4 to 2. * Minor fixes. * Fix typos.
60 lines
1.5 KiB
Python
Executable File
60 lines
1.5 KiB
Python
Executable File
#!/usr/bin/env python3
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# 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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"""
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To run this file, do:
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cd icefall/egs/librispeech/ASR
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python ./transducer/test_transformer.py
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"""
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import torch
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from transformer import Transformer
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def test_transformer():
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output_dim = 1024
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transformer = Transformer(
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num_features=80,
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output_dim=output_dim,
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subsampling_factor=4,
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d_model=512,
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nhead=8,
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dim_feedforward=2048,
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num_encoder_layers=12,
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)
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N = 3
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T = 100
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C = 80
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x = torch.randn(N, T, C)
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x_lens = torch.tensor([50, 100, 80])
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logits, logit_lens = transformer(x, x_lens)
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expected_T = ((T - 1) // 2 - 1) // 2
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assert logits.shape == (N, expected_T, output_dim)
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assert logit_lens.max().item() == expected_T
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print(logits.shape)
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print(logit_lens)
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def main():
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test_transformer()
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if __name__ == "__main__":
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main()
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