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https://github.com/k2-fsa/icefall.git
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ca60ced213
* Fix typo for zipformer * Fix typo for pruned_transducer_stateless7 * Fix typo for pruned_transducer_stateless7_ctc * Fix typo for pruned_transducer_stateless7_ctc_bs * Fix typo for pruned_transducer_stateless7_streaming * Fix typo for pruned_transducer_stateless7_streaming_multi * Fix file permissions for pruned_transducer_stateless7_streaming_multi * Fix typo for pruned_transducer_stateless8 * Fix typo for pruned_transducer_stateless6 * Fix typo for pruned_transducer_stateless5 * Fix typo for pruned_transducer_stateless4 * Fix typo for pruned_transducer_stateless3
66 lines
1.8 KiB
Python
Executable File
66 lines
1.8 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2022 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 ./pruned_transducer_stateless5/test_model.py
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"""
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from train import get_params, get_transducer_model
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def test_model_1():
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params = get_params()
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params.vocab_size = 500
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params.blank_id = 0
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params.context_size = 2
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params.num_encoder_layers = 24
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params.dim_feedforward = 1536 # 384 * 4
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params.encoder_dim = 384
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model = get_transducer_model(params)
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num_param = sum([p.numel() for p in model.parameters()])
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print(f"Number of model parameters: {num_param}")
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# See Table 1 from https://arxiv.org/pdf/2005.08100.pdf
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def test_model_M():
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params = get_params()
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params.vocab_size = 500
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params.blank_id = 0
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params.context_size = 2
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params.num_encoder_layers = 18
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params.dim_feedforward = 1024
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params.encoder_dim = 256
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params.nhead = 4
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params.decoder_dim = 512
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params.joiner_dim = 512
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model = get_transducer_model(params)
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num_param = sum([p.numel() for p in model.parameters()])
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print(f"Number of model parameters: {num_param}")
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def main():
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# test_model_1()
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test_model_M()
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if __name__ == "__main__":
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main()
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