python pytorch语音识别_一个使用pytorch基于CTC的语音识别系统
Update:
Update to pytorch1.2 and python3.
CTC-based Automatic Speech Recogniton
This is a CTC-based speech recognition system with pytorch.
At present, the system only supports phoneme recognition.
You can also do it at word-level and may get a high error rate.
Another way is to decode with a lexcion and word-level language model using WFST which is not included in this system.
Data
English Corpus: Timit
Training set: 3696 sentences(exclude SA utterance)
Dev set: 400 sentences
Test set: 192 sentences
Chinese Corpus: 863 Corpus
Training set:
Speaker
UtterId
Utterances
M50, F50
A1-A521, AW1-AW129
650 sentences
M54, F54
B522-B1040,BW130-BW259
649 sentences
M60, F60
C1041-C1560 CW260-CW388
649 sentences
M64, F64
D1-D625
625 sentences
All
5146 sentences
Test set:
Speaker
UtterId
Utterances
M51, F51
A1-A100
100 sentences
M55, F55
B522-B521
100 sentences
M61, F61
C1041-C1140
100 sentences
M63, F63
D1-D100
100 sentences
All
800 sentences
Install
Install Pytorch
Install warp-ctc and bind it to pytorch.
Notice: If use python2, reinstall the pytorch with source code instead of pip. Use pytorch1.2 built-in CTC function(nn.CTCLoss) Now.
Install Kaldi. We use kaldi to extract mfcc and fbank.
Install pytorch torchaudio(This is needed when using waveform as input).
Install KenLM. Training n-gram Languange Model if needed. Use Irstlm in kaldi tools instead.
Install and start visdom
pip3 install visdom
python -m visdom.server
Install other python packages
pip install -r requirements.txt
Usage
Install all the packages according to the Install part.
Revise the top script run.sh.
Open the config file to revise the super-parameters about everything.
Run the top script with four conditions
bash run.sh data_prepare + AM training + LM training + testing
bash run.sh 1 AM training + LM training + testing
bash run.sh 2 LM training + testing
bash run.sh 3 testing
RNN LM training is not implemented yet. They are added to the todo-list.
Data Prepare
Extract 39dim mfcc and 40dim fbank feature from kaldi.
Use compute-cmvn-stats and apply-cmvn with training data to get the global mean and variance and normalize the feature.
Rewrite Dataset and dataLoader in torch.nn.dataset to prepare data for training. You can find them in the steps/dataloader.py.
Model
RNN + DNN + CTC RNN here can be replaced by nn.LSTM and nn.GRU
CNN + RNN + DNN + CTC
CNN is use to reduce the variety of spectrum which can be caused by the speaker and environment difference.
How to choose
Use add_cnn to choose one of two models. If add_cnn is True, then CNN+RNN+DNN+CTC will be chosen.
Training:
initial-lr = 0.001
decay = 0.5
wight-decay = 0.005
Adjust the learning rate if the dev loss is around a specific loss for ten times.
Times of adjusting learning rate is 8 which can be alter in steps/train_ctc.py(line367).
Optimizer is nn.optimizer.Adam with weigth decay 0.005
Decoder
Greedy decoder:
Take the max prob of outputs as the result and get the path.
Calculate the WER and CER by used the function of the class.
Beam decoder:
Implemented with python. Original Code
I fix it to support phoneme for batch decode.
Beamsearch can improve about 0.2% of phonome accuracy.
Phoneme-level language model is inserted to beam search decoder now.
ToDo
Combine with RNN-LM
Beam search with RNN-LM
The code in 863_corpus is a mess. Need arranged.