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1d44da845b
* Begin to add RNN-T training for librispeech. * Copy files from conformer_ctc. Will edit it. * Use conformer/transformer model as encoder. * Begin to add training script. * Add training code. * Remove long utterances to avoid OOM when a large max_duraiton is used. * Begin to add decoding script. * Add decoding script. * Minor fixes. * Add beam search. * Use LSTM layers for the encoder. Need more tunings. * Use stateless decoder. * Minor fixes to make it ready for merge. * Fix README. * Update RESULT.md to include RNN-T Conformer. * Minor fixes. * Fix tests. * Minor fixes. * Minor fixes. * Fix tests.
70 lines
1.8 KiB
Python
Executable File
70 lines
1.8 KiB
Python
Executable File
#!/usr/bin/env python3
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# Copyright 2021 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 ./transducer/test_decoder.py
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"""
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import torch
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from decoder import Decoder
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def test_decoder():
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vocab_size = 3
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blank_id = 0
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sos_id = 2
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embedding_dim = 128
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num_layers = 2
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hidden_dim = 6
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output_dim = 8
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N = 3
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U = 5
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decoder = Decoder(
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vocab_size=vocab_size,
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embedding_dim=embedding_dim,
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blank_id=blank_id,
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sos_id=sos_id,
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num_layers=num_layers,
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hidden_dim=hidden_dim,
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output_dim=output_dim,
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embedding_dropout=0.0,
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rnn_dropout=0.0,
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)
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x = torch.randint(1, vocab_size, (N, U))
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decoder_out, (h, c) = decoder(x)
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assert decoder_out.shape == (N, U, output_dim)
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assert h.shape == (num_layers, N, hidden_dim)
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assert c.shape == (num_layers, N, hidden_dim)
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decoder_out, (h, c) = decoder(x, (h, c))
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assert decoder_out.shape == (N, U, output_dim)
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assert h.shape == (num_layers, N, hidden_dim)
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assert c.shape == (num_layers, N, hidden_dim)
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def main():
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test_decoder()
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if __name__ == "__main__":
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main()
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