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---
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language: vie
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datasets:
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- legacy-datasets/common_voice
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- vlsp2020_vinai_100h
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- AILAB-VNUHCM/vivos
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- doof-ferb/vlsp2020_vinai_100h
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- doof-ferb/fpt_fosd
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- doof-ferb/infore1_25hours
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- linhtran92/viet_bud500
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- doof-ferb/LSVSC
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- doof-ferb/vais1000
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- doof-ferb/VietMed_labeled
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- NhutP/VSV-1100
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- doof-ferb/Speech-MASSIVE_vie
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- doof-ferb/BibleMMS_vie
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- capleaf/viVoice
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metrics:
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- wer
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pipeline_tag: automatic-speech-recognition
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tags:
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- transcription
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- audio
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- speech
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- chunkformer
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- asr
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- automatic-speech-recognition
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- long-form transcription
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license: cc-by-nc-4.0
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model-index:
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- name: ChunkFormer Large Vietnamese
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results:
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- task:
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name: Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: common-voice-vietnamese
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type: common_voice
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args: vi
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metrics:
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- name: Test WER
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type: wer
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value: x
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- task:
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name: Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: VIVOS
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type: vivos
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args: vi
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metrics:
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- name: Test WER
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type: wer
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value: x
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- task:
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name: Speech Recognition
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type: automatic-speech-recognition
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dataset:
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name: VLSP - Task 1
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type: vlsp
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args: vi
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metrics:
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- name: Test WER
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type: wer
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value: x
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---
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# **ChunkFormer-Large-Vie: Large-Scale Pretrained ChunkFormer for Vietnamese Automatic Speech Recognition**
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[](https://creativecommons.org/licenses/by-nc/4.0/)
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[](https://github.com/khanld/chunkformer)
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[](https://your-paper-link)
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---
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## Table of contents
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1. [Model Description](#description)
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2. [Documentation and Implementation](#implementation)
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3. [Benchmark Results](#benchmark)
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4. [Usage](#usage)
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6. [Citation](#citation)
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7. [Contact](#contact)
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---
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<a name = "description" ></a>
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## Model Description
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**ChunkFormer-Large-Vie** is a large-scale Vietnamese Automatic Speech Recognition (ASR) model based on the innovative **ChunkFormer** architecture, introduced at **ICASSP 2025**. The model has been fine-tuned on approximately **2000 hours** of public Vietnamese speech data sourced from diverse datasets. A list of datasets can be found [**HERE**](dataset.tsv).
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**!!! Please note that only the \[train-subset\] was used for tuning the model.**
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---
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<a name = "implementation" ></a>
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## Documentation and Implementation
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The [Documentation](#) and [Implementation](#) of ChunkFormer are publicly available.
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---
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<a name = "benchmark" ></a>
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## Benchmark Results
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| STT | Model | #Params | Vivos | Common Voice | VLSP - Task 1 | Avg. |
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|-----|--------------|--------|-------|--------------|---------------|------|
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| 1 | ChunkFormer | 110M | x | x | x | x |
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| 2 | [PhoWhisper](https://huggingface.co/vinai/PhoWhisper-large) | 1.55B | 4.67 | 8.14 | 13.75 | 8.85 |
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| 3 | [nguyenvulebinh/wav2vec2-base-vietnamese-250h](nguyenvulebinh/wav2vec2-base-vietnamese-250h) | 95M | 10.77 | 18.34 | 13.33 | 14.15 |
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| 4 | [khanhld/wav2vec2-base-vietnamese-160h](https://huggingface.co/khanhld/wav2vec2-base-vietnamese-160h) | 95M | 15.05 | 10.78 | x | x |
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---
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<a name = "usage" ></a>
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## Quick Usage
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To use the ChunkFormer model for Vietnamese Automatic Speech Recognition, follow these steps:
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1. **Download the ChunkFormer Repository**
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```bash
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git clone https://github.com/khanld/chunkformer.git
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cd chunkformer
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pip install -r requirements.txt
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```
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2. **Download the Model Checkpoint from Hugging Face**
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```bash
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git lfs install
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git clone https://huggingface.co/khanhld/chunkformer-large-vie
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```
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This will download the model checkpoint to the checkpoints folder inside your chunkformer directory.
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3. **Run the model**
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```bash
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python decode.py \
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--model_checkpoint path/to/chunkformer-large-vie \
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--long_form_audio path/to/long_audio.wav \
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--chunk_size 64 \
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--left_context_size 128 \
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--right_context_size 128
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```
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---
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<a name = "citation" ></a>
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## Citation
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If you use this work in your research, please cite:
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```bibtex
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@inproceedings{your_paper,
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title={ChunkFormer: Masked Chunking Conformer For Long-Form Speech Transcription},
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author={Khanh Le, Tuan Vu Ho, Dung Tran and Duc Thanh Chau},
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booktitle={ICASSP},
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year={2025}
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}
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```
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---
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<a name = "contact"></a>
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## Contact
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- [email protected]
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- [](https://github.com/khanld)
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- [](https://www.linkedin.com/in/khanhld257/)
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