ChunkFormer-Large-En-Libri-960h: Pretrained ChunkFormer-Large on 960 hours of LibriSpeech dataset

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Table of contents

1. Model Description
2. Documentation and Implementation
3. Benchmark Results
4. Usage
5. Citation
6. Contact

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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.

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Documentation and Implementation

The Documentation and Implementation of ChunkFormer are publicly available.

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Benchmark Results

We evaluate the models using Word Error Rate (WER). To ensure a fair comparison, all models are trained exclusively with the 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

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Quick Usage

To use the ChunkFormer model for English Automatic Speech Recognition, follow these steps:

1. Download the ChunkFormer Repository

git clone https://github.com/khanld/chunkformer.git
cd chunkformer
pip install -r requirements.txt   

2. Download the Model Checkpoint from Hugging Face

pip install huggingface_hub
huggingface-cli download khanhld/chunkformer-large-en-libri-960h --local-dir "./chunkformer-large-en-libri-960h"

or

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

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

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Citation

If you use this work in your research, please cite:

@INPROCEEDINGS{10888640,
  author={Le, Khanh and Ho, Tuan Vu and Tran, Dung and Chau, Duc Thanh},
  booktitle={ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)}, 
  title={ChunkFormer: Masked Chunking Conformer For Long-Form Speech Transcription}, 
  year={2025},
  volume={},
  number={},
  pages={1-5},
  keywords={Scalability;Memory management;Graphics processing units;Signal processing;Performance gain;Hardware;Resource management;Speech processing;Standards;Context modeling;chunkformer;masked batch;long-form transcription},
  doi={10.1109/ICASSP49660.2025.10888640}}
}

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Contact

• [email protected]
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