mirror of
https://github.com/k2-fsa/icefall.git
synced 2025-08-09 18:12:19 +00:00
6e609c67a2
* support streaming in conformer * Add more documents * support streaming on pruned_transducer_stateless2; add delay penalty; fixes for decode states * Minor fixes * streaming for pruned_transducer_stateless4 * Fix conv cache error, support async streaming decoding * Fix style * Fix style * Fix style * Add torch.jit.export * mask the initial cache * Cutting off invalid frames of encoder_embed output * fix relative positional encoding in streaming decoding for compution saving * Minor fixes * Minor fixes * Minor fixes * Minor fixes * Minor fixes * Fix jit export for torch 1.6 * Minor fixes for streaming decoding * Minor fixes on decode stream * move model parameters to train.py * make states in forward streaming optional * update pretrain to support streaming model * update results.md * update tensorboard and pre-models * fix typo * Fix tests * remove unused arguments * add streaming decoding ci * Minor fix * Minor fix * disable right context by default
77 lines
2.1 KiB
Python
Executable File
77 lines
2.1 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_stateless/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.dynamic_chunk_training = False
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params.short_chunk_size = 25
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params.num_left_chunks = 4
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params.causal_convolution = 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 test_model_streaming():
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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.dynamic_chunk_training = True
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params.short_chunk_size = 25
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params.num_left_chunks = 4
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params.causal_convolution = True
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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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test_model_streaming()
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if __name__ == "__main__":
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main()
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