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b25c234c51
* Minor fix to conformer-mmi * Minor fixes * Fix decode.py * add training files * train with ctc warmup * add pruned_transducer_stateless7_mmi * add zipformer_mmi/mmi_decode.py, using HP as decoding graph * add mmi_decode.py * remove pruned_transducer_stateless7_mmi * rename zipformer_mmi/train_with_ctc.py as zipformer_mmi/train.py * remove unused method * rename mmi_decode.py * add export.py pretrained.py jit_pretrained.py ... * add RESULTS.md * add CI test * add docs * add README.md Co-authored-by: pkufool <wkang.pku@gmail.com>
58 lines
1.7 KiB
Python
Executable File
58 lines
1.7 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 ./zipformer_mmi/test_model.py
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"""
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import torch
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from train import get_ctc_model, get_params
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def test_model():
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params = get_params()
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params.vocab_size = 500
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params.num_encoder_layers = "2,4,3,2,4"
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# params.feedforward_dims = "1024,1024,1536,1536,1024"
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params.feedforward_dims = "1024,1024,2048,2048,1024"
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params.nhead = "8,8,8,8,8"
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params.encoder_dims = "384,384,384,384,384"
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params.attention_dims = "192,192,192,192,192"
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params.encoder_unmasked_dims = "256,256,256,256,256"
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params.zipformer_downsampling_factors = "1,2,4,8,2"
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params.cnn_module_kernels = "31,31,31,31,31"
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model = get_ctc_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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features = torch.randn(2, 100, 80)
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feature_lengths = torch.full((2,), 100)
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model(x=features, x_lens=feature_lengths)
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def main():
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test_model()
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if __name__ == "__main__":
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main()
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