RNN-Transducer

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

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 bin/train.py -config config/aishell.yaml

Eval

python bin/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

Acknowledge

Thanks to warp-transducer and ctc-decoder.

ctc decoder

alpha表示语言模型分数的占比（不匹配语料0.2，匹配语料1） beta表示每增加一个字的奖励，越大字越多（一般取2，字数比较合适）