| --- |
| 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 |
|
|
