RNN-Transducer

A Pytorch Implementation of Transducer Model for End-to-End Speech Recognition.

If you have any questions, please email to me! Email: zhengkun.tian@nlpr.ia.ac.cn

Environment

• pytorch >= 0.4
• warp-transducer

Preparation

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.

<blk> 0
<unk> 1
我 2
你 3
...

Train

python train.py -config config/aishell.yaml

Eval

python eval.py -config config/aishell.yaml

Experiments

The details of our RNN-Transducer are as follows.

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: 0.2

All 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

Acknowledge

Thanks to warp-transducer.