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# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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"""Test decoders."""
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import unittest
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E2E/Streaming Transformer/Conformer ASR (#578)
* add cmvn and label smoothing loss layer
* add layer for transformer
* add glu and conformer conv
* add torch compatiable hack, mask funcs
* not hack size since it exists
* add test; attention
* add attention, common utils, hack paddle
* add audio utils
* conformer batch padding mask bug fix #223
* fix typo, python infer fix rnn mem opt name error and batchnorm1d, will be available at 2.0.2
* fix ci
* fix ci
* add encoder
* refactor egs
* add decoder
* refactor ctc, add ctc align, refactor ckpt, add warmup lr scheduler, cmvn utils
* refactor docs
* add fix
* fix readme
* fix bugs, refactor collator, add pad_sequence, fix ckpt bugs
* fix docstring
* refactor data feed order
* add u2 model
* refactor cmvn, test
* add utils
* add u2 config
* fix bugs
* fix bugs
* fix autograd maybe has problem when using inplace operation
* refactor data, build vocab; add format data
* fix text featurizer
* refactor build vocab
* add fbank, refactor feature of speech
* refactor audio feat
* refactor data preprare
* refactor data
* model init from config
* add u2 bins
* flake8
* can train
* fix bugs, add coverage, add scripts
* test can run
* fix data
* speed perturb with sox
* add spec aug
* fix for train
* fix train logitc
* fix logger
* log valid loss, time dataset process
* using np for speed perturb, remove some debug log of grad clip
* fix logger
* fix build vocab
* fix logger name
* using module logger as default
* fix
* fix install
* reorder imports
* fix board logger
* fix logger
* kaldi fbank and mfcc
* fix cmvn and print prarams
* fix add_eos_sos and cmvn
* fix cmvn compute
* fix logger and cmvn
* fix subsampling, label smoothing loss, remove useless
* add notebook test
* fix log
* fix tb logger
* multi gpu valid
* fix log
* fix log
* fix config
* fix compute cmvn, need paddle 2.1
* add cmvn notebook
* fix layer tools
* fix compute cmvn
* add rtf
* fix decoding
* fix layer tools
* fix log, add avg script
* more avg and test info
* fix dataset pickle problem; using 2.1 paddle; num_workers can > 0; ckpt save in exp dir;fix setup.sh;
* add vimrc
* refactor tiny script, add transformer and stream conf
* spm demo; librisppech scripts and confs
* fix log
* add librispeech scripts
* refactor data pipe; fix conf; fix u2 default params
* fix bugs
* refactor aishell scripts
* fix test
* fix cmvn
* fix s0 scripts
* fix ds2 scripts and bugs
* fix dev & test dataset filter
* fix dataset filter
* filter dev
* fix ckpt path
* filter test, since librispeech will cause OOM, but all test wer will be worse, since mismatch train with test
* add comment
* add syllable doc
* fix ds2 configs
* add doc
* add pypinyin tools
* fix decoder using blank_id=0
* mmseg with pybind11
* format code
4 years ago
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Support paddle 2.x (#538)
* 2.x model
* model test pass
* fix data
* fix soundfile with flac support
* one thread dataloader test pass
* export feasture size
add trainer and utils
add setup model and dataloader
update travis using Bionic dist
* add venv; test under venv
* fix unittest; train and valid
* add train and config
* add config and train script
* fix ctc cuda memcopy error
* fix imports
* fix train valid log
* fix dataset batch shuffle shift start from 1
fix rank_zero_only decreator error
close tensorboard when train over
add decoding config and code
* test process can run
* test with decoding
* test and infer with decoding
* fix infer
* fix ctc loss
lr schedule
sortagrad
logger
* aishell egs
* refactor train
add aishell egs
* fix dataset batch shuffle and add batch sampler log
print model parameter
* fix model and ctc
* sequence_mask make all inputs zeros, which cause grad be zero, this is a bug of LessThanOp
add grad clip by global norm
add model train test notebook
* ctc loss
remove run prefix
using ord value as text id
* using unk when training
compute_loss need text ids
ord id using in test mode, which compute wer/cer
* fix tester
* add lr_deacy
refactor code
* fix tools
* fix ci
add tune
fix gru model bugs
add dataset and model test
* fix decoding
* refactor repo
fix decoding
* fix musan and rir dataset
* refactor io, loss, conv, rnn, gradclip, model, utils
* fix ci and import
* refactor model
add export jit model
* add deploy bin and test it
* rm uselss egs
* add layer tools
* refactor socket server
new model from pretrain
* remve useless
* fix instability loss and grad nan or inf for librispeech training
* fix sampler
* fix libri train.sh
* fix doc
* add license on cpp
* fix doc
* fix libri script
* fix install
* clip 5 wer 7.39, clip 400 wer 7.54, 1.8 clip 400 baseline 7.49
4 years ago
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from deepspeech.decoders import decoders_deprecated as decoder
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class TestDecoders(unittest.TestCase):
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def setUp(self):
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self.vocab_list = ["\'", ' ', 'a', 'b', 'c', 'd']
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self.beam_size = 20
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self.probs_seq1 = [[
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0.06390443, 0.21124858, 0.27323887, 0.06870235, 0.0361254,
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0.18184413, 0.16493624
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], [
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0.03309247, 0.22866108, 0.24390638, 0.09699597, 0.31895462,
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0.0094893, 0.06890021
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], [
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0.218104, 0.19992557, 0.18245131, 0.08503348, 0.14903535,
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0.08424043, 0.08120984
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], [
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0.12094152, 0.19162472, 0.01473646, 0.28045061, 0.24246305,
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0.05206269, 0.09772094
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], [
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0.1333387, 0.00550838, 0.00301669, 0.21745861, 0.20803985,
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0.41317442, 0.01946335
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], [
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0.16468227, 0.1980699, 0.1906545, 0.18963251, 0.19860937,
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0.04377724, 0.01457421
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]]
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self.probs_seq2 = [[
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0.08034842, 0.22671944, 0.05799633, 0.36814645, 0.11307441,
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0.04468023, 0.10903471
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], [
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0.09742457, 0.12959763, 0.09435383, 0.21889204, 0.15113123,
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0.10219457, 0.20640612
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], [
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0.45033529, 0.09091417, 0.15333208, 0.07939558, 0.08649316,
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0.12298585, 0.01654384
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], [
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0.02512238, 0.22079203, 0.19664364, 0.11906379, 0.07816055,
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0.22538587, 0.13483174
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], [
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0.17928453, 0.06065261, 0.41153005, 0.1172041, 0.11880313,
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0.07113197, 0.04139363
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], [
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0.15882358, 0.1235788, 0.23376776, 0.20510435, 0.00279306,
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0.05294827, 0.22298418
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]]
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self.greedy_result = ["ac'bdc", "b'da"]
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self.beam_search_result = ['acdc', "b'a"]
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def test_greedy_decoder_1(self):
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bst_result = decoder.ctc_greedy_decoder(self.probs_seq1,
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self.vocab_list)
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self.assertEqual(bst_result, self.greedy_result[0])
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def test_greedy_decoder_2(self):
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bst_result = decoder.ctc_greedy_decoder(self.probs_seq2,
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self.vocab_list)
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self.assertEqual(bst_result, self.greedy_result[1])
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def test_beam_search_decoder_1(self):
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beam_result = decoder.ctc_beam_search_decoder(
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probs_seq=self.probs_seq1,
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beam_size=self.beam_size,
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vocabulary=self.vocab_list)
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self.assertEqual(beam_result[0][1], self.beam_search_result[0])
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def test_beam_search_decoder_2(self):
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beam_result = decoder.ctc_beam_search_decoder(
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probs_seq=self.probs_seq2,
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beam_size=self.beam_size,
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vocabulary=self.vocab_list)
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self.assertEqual(beam_result[0][1], self.beam_search_result[1])
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def test_beam_search_decoder_batch(self):
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beam_results = decoder.ctc_beam_search_decoder_batch(
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probs_split=[self.probs_seq1, self.probs_seq2],
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beam_size=self.beam_size,
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vocabulary=self.vocab_list,
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num_processes=24)
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self.assertEqual(beam_results[0][0][1], self.beam_search_result[0])
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self.assertEqual(beam_results[1][0][1], self.beam_search_result[1])
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if __name__ == '__main__':
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unittest.main()
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