https://github.com/HawkAaron/mxnet-transducer

Fast parallel RNN-Transducer.
https://github.com/HawkAaron/mxnet-transducer

mxnet rnn-transducer sequence-transduction transducer

Last synced: 2 months ago
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Fast parallel RNN-Transducer.

• Host: GitHub
• URL: https://github.com/HawkAaron/mxnet-transducer
• Owner: HawkAaron
• License: mit
• Created: 2018-04-18T07:26:59.000Z (almost 7 years ago)
• Default Branch: master
• Last Pushed: 2019-11-01T14:02:17.000Z (about 5 years ago)
• Last Synced: 2024-08-01T22:41:47.806Z (6 months ago)
• Topics: mxnet, rnn-transducer, sequence-transduction, transducer
• Language: C++
• Size: 72.3 KB
• Stars: 10
• Watchers: 5
• Forks: 2
• Open Issues: 3
• Metadata Files:
  • Readme: README.md
  • License: LICENSE

Awesome Lists containing this project

• Awesome-MXNet - RNN-Transducer

README

        
# mxnet-transducer

A fast parallel implementation of RNN Transducer (Graves 2013 joint network), on both CPU and GPU for mxnet.

[GPU version is now available for Graves2012 add network.](https://github.com/HawkAaron/mxnet-transducer/tree/add_network)

## Install and Test

First get mxnet and the code:

``` bash

git clone --recursive https://github.com/apache/incubator-mxnet

git clone https://github.com/HawkAaron/mxnet-transducer

```

Copy all files into mxnet dir:

``` bash

cp -r mxnet-transducer/rnnt* incubator-mxnet/src/operator/contrib/

```

Then follow the installation instructions of mxnet:

```

https://mxnet.incubator.apache.org/install/index.html

```

Finally, add Python API into `/path/to/mxnet_root/mxnet/gluon/loss.py`:

```python

class RNNTLoss(Loss):

    def __init__(self, batch_first=True, blank_label=0, weight=None, **kwargs):

        batch_axis = 0 if batch_first else 2

        super(RNNTLoss, self).__init__(weight, batch_axis, **kwargs)

        self.batch_first = batch_first

        self.blank_label = blank_label

    def hybrid_forward(self, F, pred, label, pred_lengths, label_lengths):

        if not self.batch_first:

            pred = F.transpose(pred, (2, 0, 1, 3))

        loss = F.contrib.RNNTLoss(pred, label.astype('int32', False), 

                                    pred_lengths.astype('int32', False), 

                                    label_lengths.astype('int32', False), 

                                    blank_label=self.blank_label)

        return loss

```

From the repo test with:

```bash

python test/test.py 10 300 100 50 --mx

```

## Reference

* [Sequence Transduction with Recurrent Neural Networks](https://arxiv.org/abs/1211.3711)

* [SPEECH RECOGNITION WITH DEEP RECURRENT NEURAL NETWORKS](https://arxiv.org/pdf/1303.5778.pdf)

* [Baidu warp-ctc](https://github.com/baidu-research/warp-ctc)

* [warp-transducer](https://github.com/HawkAaron/warp-transducer)