mirror of
https://github.com/k2-fsa/icefall.git
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92 lines
2.6 KiB
Python
Executable File
92 lines
2.6 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 ./lstm_transducer_stateless/test_model.py
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"""
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import os
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from pathlib import Path
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import torch
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from export import (
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export_decoder_model_jit_trace,
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export_encoder_model_jit_trace,
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export_joiner_model_jit_trace,
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)
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from lstm import stack_states, unstack_states
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from scaling_converter import convert_scaled_to_non_scaled
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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.encoder_dim = 512
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params.rnn_hidden_size = 1024
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params.num_encoder_layers = 12
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params.aux_layer_period = 0
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params.exp_dir = Path("exp_test_model")
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model = get_transducer_model(params)
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model.eval()
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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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convert_scaled_to_non_scaled(model, inplace=True)
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params.exp_dir.mkdir(exist_ok=True)
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encoder_filename = params.exp_dir / "encoder_jit_trace.pt"
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export_encoder_model_jit_trace(model.encoder, encoder_filename)
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decoder_filename = params.exp_dir / "decoder_jit_trace.pt"
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export_decoder_model_jit_trace(model.decoder, decoder_filename)
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joiner_filename = params.exp_dir / "joiner_jit_trace.pt"
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export_joiner_model_jit_trace(model.joiner, joiner_filename)
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print("The model has been successfully exported using jit.trace.")
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def test_states_stack_and_unstack():
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layer, batch, hidden, cell = 12, 100, 512, 1024
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states = (
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torch.randn(layer, batch, hidden),
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torch.randn(layer, batch, cell),
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)
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states2 = stack_states(unstack_states(states))
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assert torch.allclose(states[0], states2[0])
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assert torch.allclose(states[1], states2[1])
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def main():
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test_model()
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test_states_stack_and_unstack()
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if __name__ == "__main__":
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main()
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