---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: HO_ASR-Model_KIIT2025
  results: []
---

<!-- 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. -->

# HO_ASR-Model_KIIT2025

This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.6844
- Wer: 0.5516

## 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: 32
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- 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: 250
- num_epochs: 50
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 2.9609        | 2.0   | 250  | 2.8620          | 0.9958 |
| 2.3792        | 4.0   | 500  | 1.5657          | 0.8976 |
| 0.8142        | 6.0   | 750  | 0.8104          | 0.7383 |
| 0.5211        | 8.0   | 1000 | 0.6461          | 0.6508 |
| 0.4145        | 10.0  | 1250 | 0.5793          | 0.6257 |
| 0.3562        | 12.0  | 1500 | 0.5991          | 0.6315 |
| 0.3135        | 14.0  | 1750 | 0.5680          | 0.6295 |
| 0.2694        | 16.0  | 2000 | 0.5731          | 0.6139 |
| 0.2333        | 18.0  | 2250 | 0.6170          | 0.6482 |
| 0.2061        | 20.0  | 2500 | 0.5771          | 0.5852 |
| 0.1823        | 22.0  | 2750 | 0.5820          | 0.5776 |
| 0.1628        | 24.0  | 3000 | 0.5853          | 0.5793 |
| 0.1434        | 26.0  | 3250 | 0.6188          | 0.5776 |
| 0.129         | 28.0  | 3500 | 0.6095          | 0.5644 |
| 0.1185        | 30.0  | 3750 | 0.6210          | 0.5753 |
| 0.1071        | 32.0  | 4000 | 0.6250          | 0.5680 |
| 0.097         | 34.0  | 4250 | 0.6207          | 0.5636 |
| 0.0901        | 36.0  | 4500 | 0.6477          | 0.5760 |
| 0.0833        | 38.0  | 4750 | 0.6510          | 0.5666 |
| 0.0758        | 40.0  | 5000 | 0.6519          | 0.5553 |
| 0.0693        | 42.0  | 5250 | 0.6641          | 0.5549 |
| 0.0637        | 44.0  | 5500 | 0.6648          | 0.5490 |
| 0.0591        | 46.0  | 5750 | 0.6809          | 0.5535 |
| 0.0585        | 48.0  | 6000 | 0.6786          | 0.5512 |
| 0.0555        | 50.0  | 6250 | 0.6844          | 0.5516 |


### Framework versions

- Transformers 4.28.0
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.13.3