---
language: en
datasets:
- librispeech
metrics:
- wer
pipeline_tag: automatic-speech-recognition
tags:
- transcription
- audio
- speech
- chunkformer
- asr
- automatic-speech-recognition
- long-form transcription
- librispeech
license: cc-by-nc-4.0
model-index:
- name: ChunkFormer-Large-En-Libri-960h
results:
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: test-clean
type: librispeech
args: en
metrics:
- name: Test WER
type: wer
value: 2.69
- task:
name: Speech Recognition
type: automatic-speech-recognition
dataset:
name: test-other
type: librispeech
args: en
metrics:
- name: Test WER
type: wer
value: 6.91
---
# **ChunkFormer-Large-En-Libri-960h: Pretrained ChunkFormer-Large on 960 hours of LibriSpeech dataset**
[](https://creativecommons.org/licenses/by-nc/4.0/)
[](https://github.com/khanld/chunkformer)
[](https://arxiv.org/abs/2502.14673)
[](#description)
**!!!ATTENTION: Input audio must be MONO (1 channel) at 16,000 sample rate**
---
## Table of contents
1. [Model Description](#description)
2. [Documentation and Implementation](#implementation)
3. [Benchmark Results](#benchmark)
4. [Usage](#usage)
6. [Citation](#citation)
7. [Contact](#contact)
---
## Model Description
**ChunkFormer-Large-En-Libri-960h** is an English Automatic Speech Recognition (ASR) model based on the **ChunkFormer** architecture, introduced at **ICASSP 2025**. The model has been fine-tuned on 960 hours of LibriSpeech, a widely-used dataset for ASR research.
---
## Documentation and Implementation
The [Documentation]() and [Implementation](https://github.com/khanld/chunkformer) of ChunkFormer are publicly available.
---
## Benchmark Results
We evaluate the models using **Word Error Rate (WER)**. To ensure a fair comparison, all models are trained exclusively with the [**WENET**](https://github.com/wenet-e2e/wenet) framework.
| STT | Model | Test-Clean | Test-Other | Avg. |
|-----|-----------------------|------------|------------|------ |
| 1 | **ChunkFormer** | 2.69 | 6.91 | 4.80 |
| 2 | **Efficient Conformer** | 2.71 | 6.95 | 4.83 |
| 3 | **Conformer** | 2.77 | 6.93 | 4.85 |
| 4 | **Squeezeformer** | 2.87 | 7.16 | 5.02 |
---
## Quick Usage
To use the ChunkFormer model for English Automatic Speech Recognition, follow these steps:
1. **Download the ChunkFormer Repository**
```bash
git clone https://github.com/khanld/chunkformer.git
cd chunkformer
pip install -r requirements.txt
```
2. **Download the Model Checkpoint from Hugging Face**
```bash
pip install huggingface_hub
huggingface-cli download khanhld/chunkformer-large-en-libri-960h --local-dir "./chunkformer-large-en-libri-960h"
```
or
```bash
git lfs install
git clone https://huggingface.co/khanhld/chunkformer-large-en-libri-960h
```
This will download the model checkpoint to the checkpoints folder inside your chunkformer directory.
3. **Run the model**
```bash
python decode.py \
--model_checkpoint path/to/local/chunkformer-large-en-libri-960h \
--long_form_audio path/to/audio.wav \
--total_batch_duration 14400 \ #in second, default is 1800
--chunk_size 64 \
--left_context_size 128 \
--right_context_size 128
```
Example Output:
```
[00:00:01.200] - [00:00:02.400]: this is a transcription example
[00:00:02.500] - [00:00:03.700]: testing the long-form audio
```
**Advanced Usage** can be found [HERE](https://github.com/khanld/chunkformer/tree/main?tab=readme-ov-file#usage)
---
## Citation
If you use this work in your research, please cite:
```bibtex
@inproceedings{chunkformer,
title={ChunkFormer: Masked Chunking Conformer For Long-Form Speech Transcription},
author={Khanh Le, Tuan Vu Ho, Dung Tran and Duc Thanh Chau},
booktitle={ICASSP},
year={2025}
}
```
---
## Contact
- khanhld218@gmail.com
- [](https://github.com/khanld)
- [](https://www.linkedin.com/in/khanhld257/)