---
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: AsrTaskModel
  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. -->

[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/hamees-iitm.ac.in/huggingface/runs/vp52o41j)
# AsrTaskModel

This model is a fine-tuned version of [facebook/wav2vec2-base](https://huggingface.co/facebook/wav2vec2-base) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4220
- Wer: 0.2541

## 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: 0.0001
- 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: 1000
- num_epochs: 5
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 3.0261        | 0.5556 | 500  | 3.1243          | 0.9989 |
| 1.1139        | 1.1111 | 1000 | 0.9005          | 0.5382 |
| 0.8059        | 1.6667 | 1500 | 0.6447          | 0.3916 |
| 0.5712        | 2.2222 | 2000 | 0.5581          | 0.3395 |
| 0.5164        | 2.7778 | 2500 | 0.4805          | 0.2998 |
| 0.3958        | 3.3333 | 3000 | 0.4717          | 0.2820 |
| 0.4108        | 3.8889 | 3500 | 0.4494          | 0.2692 |
| 0.3403        | 4.4444 | 4000 | 0.4507          | 0.2588 |
| 0.3087        | 5.0    | 4500 | 0.4220          | 0.2541 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1