---
language:
- eng
license: apache-2.0
base_model: openai/whisper-base.en
tags:
- generated_from_trainer
datasets:
- fyp
metrics:
- wer
model-index:
- name: Whisper Fine tuned Base
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Fyp Dataset
      type: fyp
      args: 'config: eng, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 15.01856226797165
---

<!-- 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. -->

# Whisper Fine tuned Base

This model is a fine-tuned version of [openai/whisper-base.en](https://huggingface.co/openai/whisper-base.en) on the Fyp Dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3550
- Wer: 15.0186

## 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: 5
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 2
- num_epochs: 3
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.3123        | 0.5   | 25   | 0.4652          | 20.1147 |
| 0.3282        | 1.0   | 50   | 0.3655          | 16.2673 |
| 0.0376        | 1.5   | 75   | 0.3693          | 15.4573 |
| 0.0468        | 2.0   | 100  | 0.3754          | 20.2497 |
| 0.0067        | 2.5   | 125  | 0.3585          | 15.3898 |
| 0.0098        | 3.0   | 150  | 0.3550          | 15.0186 |


### Framework versions

- Transformers 4.44.0
- Pytorch 2.3.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1