gentaiscool / end2end-asr-pytorch

End-to-End Automatic Speech Recognition on PyTorch

End-to-End Speech Recognition on Pytorch

Transformer-based Speech Recognition Model

If you use any source codes included in this toolkit in your work, please cite the following paper.

• Winata, G. I., Madotto, A., Wu, C. S., & Fung, P. (2019). Code-Switched Language Models Using Neural Based Synthetic Data from Parallel Sentences. In Proceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL) (pp. 271-280).
• Winata, G. I., Cahyawijaya, S., Lin, Z., Liu, Z., & Fung, P. (2019). Lightweight and Efficient End-to-End Speech Recognition Using Low-Rank Transformer. arXiv preprint arXiv:1910.13923. (Accepted by ICASSP 2020)
• Zhou, S., Dong, L., Xu, S., & Xu, B. (2018). Syllable-Based Sequence-to-Sequence Speech Recognition with the Transformer in Mandarin Chinese. Proc. Interspeech 2018, 791-795.

Highlights

• supports batch parallelization on multi-GPU
• supports multiple dataset training and evaluation

Requirements

• Python 3.5 or later
• Install Pytorch 1.4 (https://pytorch.org/)
• Install torchaudio (https://github.com/pytorch/audio)
• run ❱❱❱ bash requirement.sh

Results

AiShell-1

Decoding strategy	CER
Greedy	14.5%
Beam-search (beam width=8)	13.5%

Data

AiShell-1 (Chinese)

To preprocess the data. You need to download the data from https://www.openslr.org/33/. I will add a script to automate the process.

❱❱❱ python data/aishell.py

Librispeech (English)

To automatically download the data

❱❱❱ python data/librispeech.py

Training

usage: train.py [-h] [--train-manifest-list] [--valid-manifest-list] [--test-manifest-list] [--cuda] [--verbose] [--batch-size] [--labels-path] [--lr] [--name] [--save-folder] [--save-every] [--feat_extractor] [--emb_trg_sharing] [--shuffle] [--sample_rate] [--label-smoothing] [--window-size] [--window-stride] [--window] [--epochs]  [--src-max-len] [--tgt-max-len] [--warmup] [--momentum] [--lr-anneal] [--num-layers] [--num-heads] [--dim-model] [--dim-key] [--dim-value] [--dim-input] [--dim-inner] [--dim-emb] [--shuffle]

Parameters

- feat_extractor: "emb_cnn" or "vgg_cnn" as the feature extractor, or set "" for none
    - emb_cnn: add 4-layer 2D CNN
    - vgg_cnn: add 6-layer 2D CNN
- cuda: train on GPU
- shuffle: randomly shuffle every batch

Example

❱❱❱ python train.py --train-manifest-list data/manifests/aishell_train_manifest.csv --valid-manifest-list data/manifests/aishell_dev_manifest.csv --test-manifest-list data/manifests/aishell_test_manifest.csv --cuda --batch-size 12 --labels-path data/labels/aishell_labels.json --lr 1e-4 --name aishell_drop0.1_cnn_batch12_4_vgg_layer4 --save-folder save/ --save-every 5 --feat_extractor vgg_cnn --dropout 0.1 --num-layers 4 --num-heads 8 --dim-model 512 --dim-key 64 --dim-value 64 --dim-input 161 --dim-inner 2048 --dim-emb 512 --shuffle --min-lr 1e-6 --k-lr 1

Use python train.py --help for more parameters and options.

Results

AiShell-1 Loss Curve

Multi-GPU Training

usage: train.py [--parallel] [--device-ids]

Parameters

- parallel: split batches to GPUs (the number of batch has to be divisible by the number of GPUs)
- device-ids: GPU ids

Example

❱❱❱ CUDA_VISIBLE_DEVICES=0,1 python train.py --train-manifest-list data/manifests/aishell_train_manifest.csv --valid-manifest-list data/manifests/aishell_dev_manifest.csv --test-manifest-list data/manifests/aishell_test_manifest.csv --cuda --batch-size 12 --labels-path data/labels/aishell_labels.json --lr 1e-4 --name aishell_drop0.1_cnn_batch12_4_vgg_layer4 --save-folder save/ --save-every 5 --feat_extractor vgg_cnn --dropout 0.1 --num-layers 4 --num-heads 8 --dim-model 512 --dim-key 64 --dim-value 64 --dim-input 161 --dim-inner 2048 --dim-emb 512 --shuffle --min-lr 1e-6 --k-lr 1 --parallel --device-ids 0 1

Test

usage: test.py [-h] [--test-manifest] [--cuda] [--verbose] [--continue_from]

Parameters

- cuda: test on GPU
- continue_from: path to the trained model

Example

❱❱❱ python test.py --test-manifest-list libri_test_clean_manifest.csv --cuda --continue_from save/model

Use python multi_train.py --help for more parameters and options.

Custom Dataset

Manifest file

To use your own dataset, you must create a CSV manifest file using the following format:

/path/to/audio.wav,/path/to/text.txt
/path/to/audio2.wav,/path/to/text2.txt
...

Each line contains the path to the audio file and transcript file separated by a comma.

Label file

You need to specify all characters in the corpus by using the following JSON format:

[ 
  "_",
  "'",
  "A",
  ...,
  "Z",
  " "
]

Bug Report

Feel free to create an issue