---
license: apache-2.0
pipeline_tag: automatic-speech-recognition
language:
- lus
base_model: facebook/wav2vec2-large
tags:
- mizo
- audio
- automatic-speech-recognition
- lus
datasets:
- andrewbawitlung/MiZonal-v1.0
metrics:
- wer
model-index:
- name: wav2vec2-large-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: 0.19373521888122014
---

<!-- 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-large](https://huggingface.co/facebook/wav2vec2-large) on the MiZonal v1.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1551
- Wer: 0.1937
## 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

### Training hyperparameters

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  | 2.9713          | 1.0    |
| 3.3962        | 1.45  | 200  | 2.5742          | 0.9999 |
| 3.3962        | 2.17  | 300  | 1.0420          | 0.8141 |
| 1.5969        | 2.9   | 400  | 0.7051          | 0.6338 |
| 1.5969        | 3.62  | 500  | 0.5743          | 0.5490 |
| 0.9636        | 4.35  | 600  | 0.5414          | 0.4995 |
| 0.9636        | 5.07  | 700  | 0.4623          | 0.4644 |
| 0.804         | 5.8   | 800  | 0.4226          | 0.4230 |
| 0.804         | 6.52  | 900  | 0.4549          | 0.3944 |
| 0.7332        | 7.25  | 1000 | 0.4360          | 0.3908 |
| 0.7332        | 7.97  | 1100 | 0.3846          | 0.4101 |
| 0.666         | 8.7   | 1200 | 0.3979          | 0.4136 |
| 0.666         | 9.42  | 1300 | 0.3188          | 0.3691 |
| 0.6051        | 10.14 | 1400 | 0.2872          | 0.3473 |
| 0.6051        | 10.87 | 1500 | 0.2632          | 0.3448 |
| 0.5467        | 11.59 | 1600 | 0.2454          | 0.3091 |
| 0.5467        | 12.32 | 1700 | 0.2387          | 0.3390 |
| 0.5073        | 13.04 | 1800 | 0.2727          | 0.2812 |
| 0.5073        | 13.77 | 1900 | 0.2328          | 0.3185 |
| 0.4611        | 14.49 | 2000 | 0.2480          | 0.2777 |
| 0.4611        | 15.22 | 2100 | 0.2246          | 0.2517 |
| 0.4237        | 15.94 | 2200 | 0.2243          | 0.2598 |
| 0.4237        | 16.67 | 2300 | 0.2122          | 0.2719 |
| 0.3901        | 17.39 | 2400 | 0.1983          | 0.2461 |
| 0.3901        | 18.12 | 2500 | 0.2150          | 0.2240 |
| 0.3664        | 18.84 | 2600 | 0.2058          | 0.2310 |
| 0.3664        | 19.57 | 2700 | 0.1860          | 0.2231 |
| 0.3352        | 20.29 | 2800 | 0.1691          | 0.2276 |
| 0.3352        | 21.01 | 2900 | 0.1934          | 0.2206 |
| 0.313         | 21.74 | 3000 | 0.1887          | 0.2182 |
| 0.313         | 22.46 | 3100 | 0.1758          | 0.2060 |
| 0.2854        | 23.19 | 3200 | 0.1811          | 0.2166 |
| 0.2854        | 23.91 | 3300 | 0.1667          | 0.2109 |
| 0.2666        | 24.64 | 3400 | 0.1658          | 0.2005 |
| 0.2666        | 25.36 | 3500 | 0.1621          | 0.1960 |
| 0.2604        | 26.09 | 3600 | 0.1599          | 0.2018 |
| 0.2604        | 26.81 | 3700 | 0.1527          | 0.1968 |
| 0.2452        | 27.54 | 3800 | 0.1551          | 0.1937 |


### Framework versions

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