---
license: apache-2.0
language:
- lus
base_model: facebook/wav2vec2-xls-r-300m
tags:
- mizo
- audio
- automatic-speech-recognition
- lus
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-300m-mizo-lus-v13
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: generator
      type: generator
      config: default
      split: train
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.11839374487185675
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
should probably proofread and complete it, then remove this comment. -->

# Mizo Automatic Speech Recognition

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the MiZonal v1.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0932
- Wer: 0.1184

## 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

### Training hyperparameters

The following hyperparameters were used during training:
- learning_rate: 0.0003
- train_batch_size: 8
- eval_batch_size: 8
- seed: 49
- gradient_accumulation_steps: 8
- 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        | 0.73  | 100  | 3.2655          | 1.0    |
| 4.2561        | 1.45  | 200  | 2.8818          | 1.0    |
| 4.2561        | 2.18  | 300  | 2.8428          | 1.0    |
| 2.8118        | 2.9   | 400  | 2.3670          | 0.9994 |
| 2.8118        | 3.63  | 500  | 0.8009          | 0.7144 |
| 1.4174        | 4.35  | 600  | 0.4873          | 0.5069 |
| 1.4174        | 5.08  | 700  | 0.3496          | 0.4169 |
| 0.754         | 5.8   | 800  | 0.2846          | 0.3422 |
| 0.754         | 6.53  | 900  | 0.2319          | 0.3116 |
| 0.5884        | 7.25  | 1000 | 0.2122          | 0.2833 |
| 0.5884        | 7.98  | 1100 | 0.1931          | 0.2655 |
| 0.4894        | 8.7   | 1200 | 0.1651          | 0.2221 |
| 0.4894        | 9.43  | 1300 | 0.1520          | 0.2100 |
| 0.4171        | 10.15 | 1400 | 0.1379          | 0.1925 |
| 0.4171        | 10.88 | 1500 | 0.1271          | 0.1793 |
| 0.3695        | 11.6  | 1600 | 0.1199          | 0.1763 |
| 0.3695        | 12.33 | 1700 | 0.1217          | 0.1712 |
| 0.3415        | 13.06 | 1800 | 0.1158          | 0.1640 |
| 0.3415        | 13.78 | 1900 | 0.1142          | 0.1605 |
| 0.3094        | 14.51 | 2000 | 0.1137          | 0.1530 |
| 0.3094        | 15.23 | 2100 | 0.1084          | 0.1454 |
| 0.2829        | 15.96 | 2200 | 0.1045          | 0.1464 |
| 0.2829        | 16.68 | 2300 | 0.1025          | 0.1416 |
| 0.2641        | 17.41 | 2400 | 0.0998          | 0.1374 |
| 0.2641        | 18.13 | 2500 | 0.0987          | 0.1461 |
| 0.2486        | 18.86 | 2600 | 0.0937          | 0.1332 |
| 0.2486        | 19.58 | 2700 | 0.0972          | 0.1337 |
| 0.2338        | 20.31 | 2800 | 0.0949          | 0.1322 |
| 0.2338        | 21.03 | 2900 | 0.0982          | 0.1313 |
| 0.2143        | 21.76 | 3000 | 0.0958          | 0.1311 |
| 0.2143        | 22.48 | 3100 | 0.0960          | 0.1252 |
| 0.2018        | 23.21 | 3200 | 0.0930          | 0.1251 |
| 0.2018        | 23.93 | 3300 | 0.0924          | 0.1243 |
| 0.1933        | 24.66 | 3400 | 0.0931          | 0.1225 |
| 0.1933        | 25.39 | 3500 | 0.0942          | 0.1197 |
| 0.1813        | 26.11 | 3600 | 0.0938          | 0.1208 |
| 0.1813        | 26.84 | 3700 | 0.0936          | 0.1199 |
| 0.1792        | 27.56 | 3800 | 0.0932          | 0.1184 |


### Framework versions

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