---
library_name: transformers
language:
- ne
license: mit
base_model: kiranpantha/w2v-bert-2.0-nepali
tags:
- generated_from_trainer
datasets:
- kiranpantha/OpenSLR54-Balanced-Nepali
metrics:
- wer
model-index:
- name: Wave2Vec2-Bert2.0 - Kiran Pantha
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: OpenSLR54
      type: kiranpantha/OpenSLR54-Balanced-Nepali
      config: default
      split: test
      args: 'config: ne, split: train,test'
    metrics:
    - name: Wer
      type: wer
      value: 0.4301989457575242
---

<!-- 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. -->

# Wave2Vec2-Bert2.0 - Kiran Pantha

This model is a fine-tuned version of [kiranpantha/w2v-bert-2.0-nepali](https://huggingface.co/kiranpantha/w2v-bert-2.0-nepali) on the OpenSLR54 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4052
- Wer: 0.4302
- Cer: 0.1029

## 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: 5e-05
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|:------:|
| 0.7515        | 0.15  | 300  | 0.4814          | 0.4911 | 0.1183 |
| 0.6554        | 0.3   | 600  | 0.5699          | 0.5382 | 0.1385 |
| 0.6723        | 0.45  | 900  | 0.5463          | 0.5401 | 0.1395 |
| 0.6635        | 0.6   | 1200 | 0.5244          | 0.5043 | 0.1250 |
| 0.6132        | 0.75  | 1500 | 0.4725          | 0.4831 | 0.1184 |
| 0.5786        | 0.9   | 1800 | 0.4620          | 0.4702 | 0.1147 |
| 0.5639        | 1.05  | 2100 | 0.4810          | 0.4668 | 0.1140 |
| 0.4863        | 1.2   | 2400 | 0.4639          | 0.4766 | 0.1151 |
| 0.4784        | 1.35  | 2700 | 0.4527          | 0.4611 | 0.1108 |
| 0.456         | 1.5   | 3000 | 0.4229          | 0.4458 | 0.1089 |
| 0.4613        | 1.65  | 3300 | 0.4460          | 0.4478 | 0.1095 |
| 0.4506        | 1.8   | 3600 | 0.4166          | 0.4413 | 0.1047 |
| 0.4369        | 1.95  | 3900 | 0.4052          | 0.4302 | 0.1029 |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1