ASR project

Installation guide

1. Install Python packages

pip install -r ./requirements.txt

2. Download checkpoint and default config

mkdir default_test_model
gdown --id 1FMoIxP_rQA4gXQ9395FZupQ4juKOu7LZ -O default_test_model/checkpoint.pth  # checkpoint
gdown --id 1-VJb5kP2Pa7IL59WkQ38pLJkQLrxn6Bx -O default_test_model/config.json  # default config

3. Necessary resources are downloadable. If a class requires some external material, it downloads it. The list of such classes:

• BackgroundNoise from hw_asr/augmentations/wave_augmentations downloads noise for augmentations in line 22
• CTCBPETextEncoder from hw_asr/text_encoder downloads pretrained BPE model in line 19
• CTCCharTextEncoder from hw_asr/text_encoder downloads pretrained KenLM and a vocab for shallow fusion in line 42 and line 68

About

This repository includes implementations of:

1. LSTM
2. QuartzNet [1]
3. Deep Speech 2 [2]

There are also implementations of multiple decoding strategies and vocabularies. Language model is KenLM.

References

1. Samuel Kriman et al. QuartzNet: Deep Automatic Speech Recognition with 1D Time-Channel Separable Convolutions. 2019
2. Dario Amodei et al. Deep Speech 2: End-to-End Speech Recognition in English and Mandarin. 2015