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c4ee2bc0af
* a copy of stateless4 as base * distillation with hubert * fix typo * example usage * usage * Update egs/librispeech/ASR/pruned_transducer_stateless6/hubert_xlarge.py Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com> * fix comment * add results of 100hours * Update egs/librispeech/ASR/pruned_transducer_stateless6/hubert_xlarge.py Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com> * Update egs/librispeech/ASR/pruned_transducer_stateless6/hubert_xlarge.py Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com> * check fairseq and quantization * a short intro to distillation framework * Update egs/librispeech/ASR/pruned_transducer_stateless6/hubert_xlarge.py Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com> * add intro of statless6 in README * fix type error of dst_manifest_dir * Update egs/librispeech/ASR/pruned_transducer_stateless6/hubert_xlarge.py Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com> * make export.py call stateless6/train.py instead of stateless2/train.py * update results by stateless6 * adjust results format * fix typo Co-authored-by: Fangjun Kuang <csukuangfj@gmail.com>
52 lines
1.4 KiB
Python
Executable File
52 lines
1.4 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_stateless6/test_model.py
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"""
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import torch
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from train import get_params, get_transducer_model
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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.blank_id = 0
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params.context_size = 2
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params.unk_id = 2
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params.enable_distiallation = False
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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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model.__class__.forward = torch.jit.ignore(model.__class__.forward)
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torch.jit.script(model)
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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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