---
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-Odia-large-xlsr53
  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. -->

# wav2vec2-Odia-large-xlsr53

This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.2083
- Wer: 0.1897
- Cer: 0.0476

## 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: 6
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 24
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 3000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    | Cer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 6.9058        | 2.3622  | 300  | 3.1227          | 1.0    | 0.8690 |
| 1.1432        | 4.7244  | 600  | 0.4002          | 0.4333 | 0.1134 |
| 0.2628        | 7.0866  | 900  | 0.3145          | 0.3314 | 0.0850 |
| 0.1368        | 9.4488  | 1200 | 0.2585          | 0.2716 | 0.0686 |
| 0.0865        | 11.8110 | 1500 | 0.2332          | 0.2524 | 0.0619 |
| 0.0596        | 14.1732 | 1800 | 0.2253          | 0.2196 | 0.0538 |
| 0.0445        | 16.5354 | 2100 | 0.2202          | 0.2100 | 0.0527 |
| 0.0324        | 18.8976 | 2400 | 0.2126          | 0.2001 | 0.0511 |
| 0.0264        | 21.2598 | 2700 | 0.2089          | 0.1966 | 0.0498 |
| 0.0211        | 23.6220 | 3000 | 0.2083          | 0.1897 | 0.0476 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 1.18.3
- Tokenizers 0.19.1