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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

Mizo Automatic Speech Recognition

This model is a fine-tuned version of 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