mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-08 09:32:20 +00:00
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.
87 lines
2.1 KiB
Python
Executable File
87 lines
2.1 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_transducer.py
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"""
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import k2
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import torch
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from conformer import Conformer
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from decoder import Decoder
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from joiner import Joiner
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from model import Transducer
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def test_transducer():
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# encoder params
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input_dim = 10
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output_dim = 20
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# decoder params
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vocab_size = 3
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blank_id = 0
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embedding_dim = 128
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num_layers = 2
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encoder = Conformer(
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num_features=input_dim,
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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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decoder = Decoder(
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vocab_size=vocab_size,
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embedding_dim=embedding_dim,
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blank_id=blank_id,
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num_layers=num_layers,
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hidden_dim=output_dim,
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output_dim=output_dim,
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embedding_dropout=0.0,
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rnn_dropout=0.0,
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)
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joiner = Joiner(output_dim, vocab_size)
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transducer = Transducer(encoder=encoder, decoder=decoder, joiner=joiner)
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y = k2.RaggedTensor([[1, 2, 1], [1, 1, 1, 2, 1]])
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N = y.dim0
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T = 50
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x = torch.rand(N, T, input_dim)
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x_lens = torch.randint(low=30, high=T, size=(N,), dtype=torch.int32)
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x_lens[0] = T
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loss = transducer(x, x_lens, y)
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print(loss)
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def main():
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test_transducer()
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if __name__ == "__main__":
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main()
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