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

• pytorch >= 0.4
• warp-transducer

python train.py -config config/aishell.yaml

python eval.py -config config/aishell.yaml

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.

Thanks to warp-transducer.