---
license: apache-2.0
language:
- lus
base_model: facebook/wav2vec2-xls-r-1b
tags:
- mizo
- audio
- automatic-speech-recognition
- lus
datasets:
- generator
metrics:
- wer
model-index:
- name: wav2vec2-xls-r-1b-mizo-lus-v25.3
  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: 0.1520826294705343
---

<!-- 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-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1267
- Wer: 0.1435
## 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: 50
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.2954        | 2.18  | 300  | 0.3737          | 0.4528 |
| 0.6507        | 4.35  | 600  | 0.1903          | 0.2866 |
| 0.492         | 6.53  | 900  | 0.1740          | 0.2419 |
| 0.4302        | 8.7   | 1200 | 0.1503          | 0.2189 |
| 0.3512        | 10.88 | 1500 | 0.1344          | 0.1884 |
| 0.2963        | 13.06 | 1800 | 0.1264          | 0.2071 |
| 0.2536        | 15.23 | 2100 | 0.1250          | 0.1868 |
| 0.2075        | 17.41 | 2400 | 0.1217          | 0.1599 |
| 0.1775        | 19.58 | 2700 | 0.1121          | 0.1602 |
| 0.151         | 21.76 | 3000 | 0.1204          | 0.1601 |
| 0.1253        | 23.93 | 3300 | 0.1211          | 0.1435 |
| 0.1073        | 26.11 | 3600 | 0.1267          | 0.1521 |


### Framework versions

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