Create README.md
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README.md
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---
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{}
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---
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**This is the dataset for training MSA-ASR model**
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# MSA-ASR
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Multilingual Speaker-Attributed Automatic Speech Recognition
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### Demo
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<video src="https://huggingface.co/nguyenvulebinh/MSA-ASR/resolve/main/demo_sa-asr.mp4" width="640" height="480" controls></video>
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### Introduction
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This repository provides an implementation of a Speaker-Attributed Automatic Speech Recognition model. The model performs both multilingual speech recognition and speaker embedding extraction, enabling speaker differentiation.
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Model architecture
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### Setup
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```
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git clone [email protected]:nguyenvulebinh/MSA-ASR.git
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cd MSA-ASR
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conda create -n MSA-ASR python=3.10
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conda activate MSA-ASR
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pip install -r requirements.txt
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```
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Test script:
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```
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python infer.py
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```
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### Training Dataset
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*From ASR to SA-ASR dataset:*
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- Segment ASR data into single-speaker turns.
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- Match turns into group which may come from the same speaker by using speaker embedding cosine similarity.
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- Pick a few groups, each group a few turns.
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- Concatenate turns in random order.
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*In total:*
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- 15.5M turns
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- 14k audio hours
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- English only
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Dataset is openly available in [HF Dataset](https://huggingface.co/datasets/nguyenvulebinh/spk-attribute)
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*Example*
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Audio
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<audio controls>
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<source src="https://huggingface.co/nguyenvulebinh/MSA-ASR/resolve/main/sample_augment.wav" type="audio/wav">
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Your browser does not support the audio element.
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</audio>
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Label:
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```code
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spk_1 A 0.00 1.58 »spk_1
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spk_1 A 0.00 1.58 Pacifica
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spk_1 A 1.58 0.68 continues
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spk_1 A 2.27 0.52 today
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spk_1 A 2.79 0.24 to
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spk_1 A 3.03 0.20 be
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spk_1 A 3.23 0.14 a
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spk_1 A 3.37 0.54 listener
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spk_1 A 3.91 0.80 supported
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spk_1 A 4.71 0.70 network
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spk_1 A 5.42 0.38 of
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spk_2 A 5.80 0.12 »spk_2
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spk_2 A 5.80 0.12 At
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spk_2 A 5.92 0.42 home,
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spk_2 A 6.34 0.18 an
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spk_2 A 6.52 0.38 Aed
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spk_2 A 6.90 0.26 is
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spk_2 A 7.16 0.18 an
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spk_2 A 7.34 0.56 automated
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spk_2 A 7.90 0.60 external
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spk_2 A 8.50 0.90 defibrillator.
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spk_2 A 9.40 0.40 It's
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spk_2 A 9.81 0.08 the
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spk_2 A 9.89 0.36 device
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spk_2 A 10.25 0.08 you
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spk_2 A 10.33 0.16 use
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spk_2 A 10.49 0.12 when
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spk_2 A 10.61 0.10 your
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spk_2 A 10.73 0.16 heart
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spk_2 A 10.89 0.18 goes
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spk_2 A 11.07 0.12 into
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spk_2 A 11.19 0.38 cardiac
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spk_2 A 11.57 0.38 arrest
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spk_2 A 11.95 0.18 to
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spk_2 A 12.13 0.36 shock
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spk_2 A 12.49 0.14 it
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spk_2 A 12.63 0.28 back
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spk_2 A 12.91 0.22 into
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spk_2 A 13.13 0.06 a
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spk_2 A 13.19 0.32 normal
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spk_2 A 13.51 0.88 rhythm.
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spk_1 A 14.40 1.38 »spk_1
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spk_1 A 14.40 1.38 stations.
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```
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### Citation
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```bibtex
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@INPROCEEDINGS{10889116,
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author={Nguyen, Thai-Binh and Waibel, Alexander},
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booktitle={ICASSP 2025 - 2025 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
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title={MSA-ASR: Efficient Multilingual Speaker Attribution with frozen ASR Models},
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year={2025},
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volume={},
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number={},
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pages={1-5},
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keywords={Training;Adaptation models;Limiting;Predictive models;Data models;Robustness;Multilingual;Data mining;Speech processing;Standards;speaker-attributed;asr;multilingual},
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doi={10.1109/ICASSP49660.2025.10889116}}
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@INPROCEEDINGS{10446589,
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author={Nguyen, Thai-Binh and Waibel, Alexander},
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booktitle={ICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)},
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title={Synthetic Conversations Improve Multi-Talker ASR},
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year={2024},
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volume={},
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number={},
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pages={10461-10465},
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keywords={Systematics;Error analysis;Knowledge based systems;Oral communication;Signal processing;Data models;Acoustics;multi-talker;asr;synthetic conversation},
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doi={10.1109/ICASSP48485.2024.10446589}}
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```
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### License
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CC-BY-NC 4.0
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### Contact
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Contributions are welcome; feel free to create a PR or email me:
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```
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[Binh Nguyen](nguyenvulebinh[at]gmail.com)
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```
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