[init] init repo

README.md

@@ -1,3 +1,143 @@
 ---
+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: 6.89
 ---
+
+# **ChunkFormer-Large-En-Libri-960h: Pretrained ChunkFormer-Large on 960 hours of LibriSpeech dataset**
+
+[![License: CC BY-NC 4.0](https://img.shields.io/badge/License-CC%20BY--NC%204.0-lightgrey.svg)](https://creativecommons.org/licenses/by-nc/4.0/)
+[![GitHub](https://img.shields.io/badge/GitHub-ChunkFormer-blue)](https://github.com/khanld/chunkformer)
+[![Paper](https://img.shields.io/badge/Paper-ICASSP%202025-green)](paper.pdf)
+
+---
+## 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)
+
+---
+<a name = "description" ></a>
+## 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.
+
+---
+<a name = "implementation" ></a>
+## Documentation and Implementation
+The [Documentation]() and [Implementation](https://github.com/khanld/chunkformer) of ChunkFormer are publicly available.
+
+---
+<a name = "benchmark" ></a>
+## 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       | 6.89       | 4.79  |
+|  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  |
+
+
+
+---
+<a name = "usage" ></a>
+## 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 \
+    --max_duration 14400 \ #in second, default is 1800
+    --chunk_size 64 \
+    --left_context_size 128 \
+    --right_context_size 128
+```
+**Advanced Usage** can be found [HERE](https://github.com/khanld/chunkformer/tree/main?tab=readme-ov-file#usage)
+
+
+---
+<a name = "citation" ></a>
+## 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}
+}
+```
+
+---
+<a name = "contact"></a>
+## Contact
+- [email protected]
+- [![GitHub](https://img.shields.io/badge/github-%23121011.svg?style=for-the-badge&logo=github&logoColor=white)](https://github.com/khanld)
+- [![LinkedIn](https://img.shields.io/badge/linkedin-%230077B5.svg?style=for-the-badge&logo=linkedin&logoColor=white)](https://www.linkedin.com/in/khanhld257/)
+
+
+
+

config.yaml

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+encoder: chunkformer
+is_json_cmvn: true
+cmvn_file: chunkformer-large-en/global_cmvn
+input_dim: 80
+output_dim: 4990
+
+encoder_conf:
+    output_size: 512    # dimension of attention
+    attention_heads: 8
+    linear_units: 2048  # the number of units of position-wise feed forward
+    num_blocks: 17      # the number of encoder blocks
+    dropout_rate: 0.1
+    positional_dropout_rate: 0.1
+    attention_dropout_rate: 0.1
+    input_layer: 'depthwise' # encoder input type, you can chose conv2d, conv2d6 and conv2d8
+    normalize_before: true
+    cnn_module_kernel: 15
+    use_cnn_module: true
+    activation_type: 'swish'
+    pos_enc_layer_type: 'stream_rel_pos'
+    selfattention_layer_type: 'stream_rel_selfattn'
+    causal: false
+    use_dynamic_chunk: false
+    use_limited_chunk: false
+    use_context_hint_chunk: false
+    right_context_probs: [0.75]
+    right_context_sizes: [128, 128, 128]
+    limited_decoding_chunk_sizes: [64, 128, 256]
+    limited_left_chunk_sizes: [128, 256, 128]
+    cnn_module_norm: 'layer_norm' # using nn.LayerNorm makes model converge faster
+    use_dynamic_left_chunk: false
+    use_dynamic_conv: true
+    freeze_subsampling_layer: false

dataset.tsv

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+		
+data	Estimated hour	Link
+AILAB-VNUHCM/vivos	15	https://huggingface.co/datasets/AILAB-VNUHCM/vivos
+doof-ferb/vlsp2020_vinai_100h	100	https://huggingface.co/datasets/doof-ferb/vlsp2020_vinai_100h
+doof-ferb/fpt_fosd	100	https://huggingface.co/datasets/doof-ferb/fpt_fosd
+doof-ferb/infore1_25hours	25	https://huggingface.co/datasets/doof-ferb/infore1_25hours
+linhtran92/viet_bud500	500	https://huggingface.co/datasets/linhtran92/viet_bud500
+doof-ferb/LSVSC	100	https://huggingface.co/datasets/doof-ferb/LSVSC
+doof-ferb/vais1000	2	https://huggingface.co/datasets/doof-ferb/vais1000
+doof-ferb/VietMed_labeled	3	https://huggingface.co/datasets/doof-ferb/VietMed_labeled
+NhutP/VSV-1100	1100	https://huggingface.co/datasets/NhutP/VSV-1100
+doof-ferb/Speech-MASSIVE_vie	1	https://huggingface.co/datasets/doof-ferb/Speech-MASSIVE_vie
+doof-ferb/BibleMMS_vie	1	https://huggingface.co/datasets/doof-ferb/BibleMMS_vie
+capleaf/viVoice	1000	https://huggingface.co/datasets/capleaf/viVoice
+linhtran92/viet_youtube_asr_corpus_v2	100	https://huggingface.co/datasets/linhtran92/viet_youtube_asr_corpus_v2

global_cmvn

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pytorch_model.bin

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