---
license: mit
pipeline_tag: automatic-speech-recognition
base_model: facebook/w2v-bert-2.0
tags:
- audio
- automatic-speech-recognition
datasets:
- andrewbawitlung/MiZonal-v2.0
metrics:
- wer
model-index:
- name: wav2vec2-bert-Mizo-Lus-v25.03.2
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: MiZonal v2.0
      type: andrewbawitlung/MiZonal-v2.0
      config: default
      split: train
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 0.1789709172259508
---
![Mizo Automatic Speech Recognition (ASR) Models v2.0](banner.jpg)

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# wav2vec2-bert-Mizo-Lus-v25.03.2

This model is a fine-tuned version of [facebook/w2v-bert-2.0](https://huggingface.co/facebook/w2v-bert-2.0) on the generator dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4014
- Wer: 0.1790

## Model description

More information needed

## Intended uses & limitations

More information needed

## Training and evaluation data

More information needed

## Training procedure

### 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: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1200
- num_epochs: 70
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.7975        | 2.62  | 1000  | 0.5053          | 0.4364 |
| 0.8518        | 5.24  | 2000  | 0.4891          | 0.4265 |
| 0.7219        | 7.86  | 3000  | 0.4057          | 0.3543 |
| 0.6005        | 10.48 | 4000  | 0.3664          | 0.3165 |
| 0.5312        | 13.11 | 5000  | 0.3695          | 0.2991 |
| 0.4785        | 15.73 | 6000  | 0.3264          | 0.2761 |
| 0.4022        | 18.35 | 7000  | 0.2972          | 0.2724 |
| 0.3761        | 20.97 | 8000  | 0.3167          | 0.2473 |
| 0.3299        | 23.59 | 9000  | 0.3106          | 0.2510 |
| 0.2788        | 26.21 | 10000 | 0.3112          | 0.2315 |
| 0.282         | 28.83 | 11000 | 0.2876          | 0.2277 |
| 0.2395        | 31.45 | 12000 | 0.3009          | 0.2179 |
| 0.1922        | 34.08 | 13000 | 0.3159          | 0.2093 |
| 0.1741        | 36.7  | 14000 | 0.2760          | 0.2135 |
| 0.1719        | 39.32 | 15000 | 0.3127          | 0.2221 |
| 0.1499        | 41.94 | 16000 | 0.3021          | 0.2098 |
| 0.1367        | 44.56 | 17000 | 0.3238          | 0.2032 |
| 0.1101        | 47.18 | 18000 | 0.3079          | 0.1894 |
| 0.1114        | 49.8  | 19000 | 0.3370          | 0.1851 |
| 0.0957        | 52.42 | 20000 | 0.3311          | 0.1936 |
| 0.084         | 55.05 | 21000 | 0.3571          | 0.1875 |
| 0.0668        | 57.67 | 22000 | 0.3858          | 0.1854 |
| 0.0665        | 60.29 | 23000 | 0.3940          | 0.1883 |
| 0.0553        | 62.91 | 24000 | 0.3668          | 0.1917 |
| 0.0558        | 65.53 | 25000 | 0.3928          | 0.1783 |
| 0.049         | 68.15 | 26000 | 0.4014          | 0.1790 |


### Framework versions

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