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[NeurIPS'22] Squeezeformer: An Efficient Transformer for Automatic Speech Recognition
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# Squeezeformer: An Efficient Transformer for Automatic Speech Recognition
We provide testing codes for Squeezeformer, along with the pre-trained checkpoints.
Check out our [paper](https://arxiv.org/pdf/2206.00888.pdf) for more details.
Squeezeformer is now supported at NVIDIA's [NeMo](https://docs.nvidia.com/deeplearning/nemo/user-guide/docs/en/stable/starthere/intro.html#:~:text=NVIDIA%20NeMo%2C%20part%20of%20the,%2DSpeech%20(TTS)%20models.) library as well, along with the training recipes and scripts. Please check out [link](https://github.com/NVIDIA/NeMo/tree/main/examples/asr/conf/squeezeformer).
## Install Squeezeformer
We recommend using Python version 3.8.
### 1. Install dependancies
We support Tensorflow version of 2.5. Run the following commands depending on your target device type.
* Running on CPUs: `pip install -e '.[tf2.5]'`
* Running on GPUs: `pip install -e '.[tf2.5-gpu]'`
### 2. Install CTC decoder
```bash
cd scripts
bash install_ctc_decoders.sh
```
## Prepare Dataset
### 1. Download Librispeech
[Librispeech](https://ieeexplore.ieee.org/document/7178964) is a widely-used ASR benchmark that consists of 960hr speech corpus with text transcriptions.
The dataset consists of 3 training sets (`train-clean-100`, `train-clean-360`, `train-other-500`),
2 development sets (`dev-clean`, `dev-other`), and 2 test sets (`test-clean`, `test-other`).
Download the datasets from this [link](http://www.openslr.org/12) and untar them.
If this is for testing purposes only, you can skip the training datasets to save disk space.
You should have flac files under `{dataset_path}/LibriSpeech`.
### 2. Create Manifest Files
Once you download the datasets, you should create a manifest file that links the file path to the audio input and its transcription.
We use a script from [TensorFlowASR](https://github.com/TensorSpeech/TensorFlowASR).
```bash
cd scripts
python create_librispeech_trans_all.py --data {dataset_path}/LibriSpeech --output {tsv_dir}
```
* The `dataset_path` is the directory that you untarred the datasets in the previous step.
* This script creates tsv files under `tsv_dir` that list the audio file path, duration, and the transcription.
* To skip processing the training datasets, use an additional argument `--mode test-only`.
If you have followed the instruction correctly, you should have the following files under `tsv_dir`.
* `dev_clean.tsv`, `dev_other.tsv`, `test_clean.tsv`, `test_other.tsv`
* `train_clean_100.tsv`, `train_clean_360.tsv`, `train_other_500.tsv` (if not `--mode test-only`)
* `train_other.tsv` that merges all training tsv files into one (if not `--mode test-only`)
## Testing Squeezeformer
### 1. Download Pre-trained Checkpoints
We provide pre-trained checkpoints for all variants of Squeezeformer.
| **Model** | **Checkpoint** | **test-clean** | **test-other** |
| :-----------------: | :---------------------------------------------------------------------------------------: | :------------: | :------------: |
| Squeezeformer-XS | [link](https://drive.google.com/file/d/1qSukKHz2ltBiWU-xHGmI-P9ziPJcLcSu/view?usp=sharing) | 3.74 | 9.09 |
| Squeezeformer-S | [link](https://drive.google.com/file/d/1PGao0AOe5aQXc-9eh2RDQZnZ4UcefcHB/view?usp=sharing) | 3.08 | 7.47 |
| Squeezeformer-SM | [link](https://drive.google.com/file/d/17cL1p0KJgT-EBu_-bg3bF7-Uh-pnf-8k/view?usp=sharing) | 2.79 | 6.89 |
| Squeezeformer-M | [link](https://drive.google.com/file/d/1fbaby-nOxHAGH0GqLoA0DIjFDPaOBl1d/view?usp=sharing) | 2.56 | 6.50 |
| Squeezeformer-ML | [link](https://drive.google.com/file/d/1-ZPtJjJUHrcbhPp03KioadenBtKpp-km/view?usp=sharing) | 2.61 | 6.05 |
| Squeezeformer-L | [link](httExcerpt of 5,818 characters
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