---
license: apache-2.0
base_model: facebook/wav2vec2-base-960h
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: wav2vec2-base-Odia-large
  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-base-Odia-large

This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co./facebook/wav2vec2-base-960h) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3174
- Wer: 0.2448
- Cer: 0.0666

## 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    |
|:-------------:|:-------:|:----:|:---------------:|:------:|:------:|
| 6.2874        | 2.3622  | 300  | 3.3817          | 1.0    | 1.0    |
| 2.5772        | 4.7244  | 600  | 1.1573          | 0.7875 | 0.2934 |
| 0.8599        | 7.0866  | 900  | 0.6319          | 0.5302 | 0.1579 |
| 0.532         | 9.4488  | 1200 | 0.5208          | 0.4332 | 0.1278 |
| 0.374         | 11.8110 | 1500 | 0.4485          | 0.3917 | 0.1110 |
| 0.272         | 14.1732 | 1800 | 0.3939          | 0.3383 | 0.0928 |
| 0.2015        | 16.5354 | 2100 | 0.3646          | 0.3040 | 0.0824 |
| 0.152         | 18.8976 | 2400 | 0.3415          | 0.2700 | 0.0741 |
| 0.1146        | 21.2598 | 2700 | 0.3278          | 0.2584 | 0.0691 |
| 0.0939        | 23.6220 | 3000 | 0.3174          | 0.2448 | 0.0666 |


### Framework versions

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