A Pytorch Implementation of Transducer Model for End-to-End Speech Recognition
A Pytorch Implementation of Transducer Model for End-to-End Speech Recognition.
If you have any questions, please email to me! Email: [email protected]cn
We utilize Kaldi for data preparation. At least these files(text, feats.scp) should be included in the training/development/test set. If you apply cmvn, utt2spk and cmvn.scp are required. The format of these file is consistent with Kaidi. The format of vocab is as follows.
0 1 我 2 你 3 ...
python train.py -config config/aishell.yaml
python eval.py -config config/aishell.yaml
The details of our RNN-Transducer are as follows.
yaml model: enc: type: lstm hidden_size: 320 n_layers: 4 bidirectional: True dec: type: lstm hidden_size: 512 n_layers: 1 embedding_dim: 512 vocab_size: 4232 dropout: 0.2All experiments are conducted on AISHELL-1. During decoding, we use beam search with width of 5 for all the experiments. A character-level 5-gram language model from training text, is integrated into beam searching by shallow fusion.
| MODEL | DEV(CER) | TEST(CER) | |:---: | :---:|:---: | | RNNT+pretrain+LM | 10.13 | 11.82 |
Thanks to warp-transducer.