Ecosyste.ms: Awesome

An open API service indexing awesome lists of open source software.

Awesome Lists | Featured Topics | Projects

https://github.com/cywang97/StreamingTransformer


https://github.com/cywang97/StreamingTransformer

Last synced: 24 days ago
JSON representation

Awesome Lists containing this project

README

        

# Streaming Transformer
**This repo contains the streaming Transformer of our work ``On the Comparison of Popular End-to-End Models for Large Scale Speech Recognition``, which is based on ESPnet0.6.0. The streaming Transformer includes a streaming encoder, either chunk-based or look-ahead based, and a trigger-attention based decoder.**

We will release following models and show reproducible results on Librispeech

* Streaming_transformer-chunk32 with ESPnet Conv2d Encoder. (https://drive.google.com/file/d/1LSBY_vK50Jxvw_GeiYrPwRtJ0DsKU6zL/view?usp=sharing)

* Streaming_transformer-chunk32 with VGG Encoder. (https://drive.google.com/file/d/12P6TsxtOCxrHezqgtk0USjSKBsYHIe7K/view?usp=sharing)

* Streaming_transformer-lookahead with ESPnet Conv2d Encoder. (https://drive.google.com/file/d/1YJQaofzsk9_KsL2W9Zb42sGLRRIKRs9X/view?usp=sharing)

* Streaming_transformer-lookahead with VGG Encoder. (https://drive.google.com/file/d/1LO_0pPxU5XJffqJMgtx4W4IL-Aih5m0M/view?usp=sharing)

## Results on Librispeech (beam=10)
| Model | test-clean | test-other |latency |size |
| -------- | -----: | :----: |:----: |:----: |
| streaming_transformer-chunk32-conv2d | 2.8 | 7.5 | 640ms | 78M |
| streaming_transformer-chunk32-vgg | 2.8 | 7.0| 640ms | 78M |
| streaming_transformer-lookahead2-conv2d | 3.0 | 8.6| 1230ms | 78M |
| streaming_transformer-lookahead2-vgg | 2.8 | 7.5 | 1230ms | 78M |

## Installation
Our installation follow the installation process of ESPnet
### Step 1. setting of the environment
CUDAROOT=/path/to/cuda

export PATH=$CUDAROOT/bin:$PATH
export LD_LIBRARY_PATH=$CUDAROOT/lib64:$LD_LIBRARY_PATH
export CFLAGS="-I$CUDAROOT/include $CFLAGS"
export CUDA_HOME=$CUDAROOT
export CUDA_PATH=$CUDAROOT`
### Step 2. installation including Kaldi
cd tools
make -j 10

## Build a streaming Transformer model
### Step 1. Data Prepare
cd egs/librispeech/asr1
./run.sh
By default. the processed data will stored in the current directory. You can change the path by editing the scripts.
### Step 2. Viterbi decoding
To train a TA based streaming Transformer, the alignments between CTC paths and transcriptions are required. In our work, we apply Viterbi decoding using the offline Transformer model.

cd egs/librispeech/asr1
./viterbi_decode.sh /path/to/model

### Step 3. Train a streaming Transformer
Here, we train a chunk-based streaming Transformer which is initialized with an offline Transformer provided by ESPnet. Set `enc-init` in `conf/train_streaming_transformer.yaml` to the path of your offline model.

cd egs/librispeech/asr1
./train.sh

If you want to train a look-ahead based streaming Transformer, set `chunk` to False and change the `left-window, right-window, dec-left-window, dec-right-window` arguments. The training log is written in `exp/streaming_transformer/train.log`. You can monitor the output through `tail -f exp/streaming_transformer/train.log`

### Step 4. Decoding
Execute the following script with to decoding on test_clean and test_other sets

./decode.sh num_of_gpu job_per_gpu

### Offline Transformer Reference
Regarding the offline Transformer model, Please visit [here](https://github.com/MarkWuNLP/SemanticMask)