---
license: apache-2.0
pipeline_tag: automatic-speech-recognition
language:
- lus
base_model: facebook/wav2vec2-base
tags:
- mizo
- audio
- automatic-speech-recognition
- lus
datasets:
- andrewbawitlung/MiZonal-v1.0
metrics:
- wer
model-index:
- name: wav2vec2-base-mizo-lus-v25
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: MiZonal v1.0
      type: andrewbawitlung/MiZonal-v1.0
      config: default
      split: train
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.16593464935566388
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Mizo Automatic Speech Recognition

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on the MiZonal v1.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1614
- Wer: 0.1659

## Citation

**BibTeX entry and citation info:**

```
@article{10.1145/3746063,
author = {Bawitlung, Andrew and Dash, Sandeep Kumar and Pattanayak, Radha Mohan},
title = {Mizo Automatic Speech Recognition: Leveraging Wav2vec 2.0 and XLS-R for Enhanced Accuracy in Low-Resource Language Processing},
year = {2025},
issue_date = {July 2025},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
volume = {24},
number = {7},
issn = {2375-4699},
url = {https://doi.org/10.1145/3746063},
doi = {10.1145/3746063},
journal = {ACM Trans. Asian Low-Resour. Lang. Inf. Process.},
month = jul,
articleno = {72},
numpages = {15},
}
```

## Training and evaluation data

MiZonal v1.0.0

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 16
- eval_batch_size: 8
- seed: 49
- gradient_accumulation_steps: 4
- total_train_batch_size: 64
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 28
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| No log        | 0.72  | 100  | 2.9278          | 1.0    |
| 3.6344        | 1.45  | 200  | 2.8377          | 1.0    |
| 3.6344        | 2.17  | 300  | 2.0445          | 0.9965 |
| 2.1941        | 2.9   | 400  | 0.9115          | 0.7317 |
| 2.1941        | 3.62  | 500  | 0.6427          | 0.5815 |
| 1.0173        | 4.35  | 600  | 0.5384          | 0.5008 |
| 1.0173        | 5.07  | 700  | 0.4707          | 0.4641 |
| 0.7632        | 5.8   | 800  | 0.3804          | 0.4103 |
| 0.7632        | 6.52  | 900  | 0.3635          | 0.3750 |
| 0.6463        | 7.25  | 1000 | 0.3351          | 0.3670 |
| 0.6463        | 7.97  | 1100 | 0.2953          | 0.3336 |
| 0.5674        | 8.7   | 1200 | 0.2711          | 0.3065 |
| 0.5674        | 9.42  | 1300 | 0.2527          | 0.2877 |
| 0.4916        | 10.14 | 1400 | 0.2403          | 0.2823 |
| 0.4916        | 10.87 | 1500 | 0.2352          | 0.2717 |
| 0.442         | 11.59 | 1600 | 0.2312          | 0.2639 |
| 0.442         | 12.32 | 1700 | 0.2251          | 0.2517 |
| 0.4056        | 13.04 | 1800 | 0.1932          | 0.2275 |
| 0.4056        | 13.77 | 1900 | 0.2013          | 0.2294 |
| 0.3726        | 14.49 | 2000 | 0.1954          | 0.2226 |
| 0.3726        | 15.22 | 2100 | 0.1957          | 0.2175 |
| 0.3426        | 15.94 | 2200 | 0.2045          | 0.2107 |
| 0.3426        | 16.67 | 2300 | 0.2003          | 0.2127 |
| 0.3275        | 17.39 | 2400 | 0.1933          | 0.2023 |
| 0.3275        | 18.12 | 2500 | 0.1859          | 0.2006 |
| 0.3112        | 18.84 | 2600 | 0.1821          | 0.1909 |
| 0.3112        | 19.57 | 2700 | 0.1756          | 0.1888 |
| 0.293         | 20.29 | 2800 | 0.1761          | 0.1865 |
| 0.293         | 21.01 | 2900 | 0.1748          | 0.1990 |
| 0.2684        | 21.74 | 3000 | 0.1694          | 0.1788 |
| 0.2684        | 22.46 | 3100 | 0.1745          | 0.1778 |
| 0.2502        | 23.19 | 3200 | 0.1726          | 0.1739 |
| 0.2502        | 23.91 | 3300 | 0.1699          | 0.1708 |
| 0.2435        | 24.64 | 3400 | 0.1670          | 0.1701 |
| 0.2435        | 25.36 | 3500 | 0.1616          | 0.1714 |
| 0.2337        | 26.09 | 3600 | 0.1609          | 0.1689 |
| 0.2337        | 26.81 | 3700 | 0.1615          | 0.1680 |
| 0.2266        | 27.54 | 3800 | 0.1614          | 0.1659 |


### Framework versions

- Transformers 4.37.2
- Pytorch 2.3.1+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1