---
language:
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- divakaivan/glaswegian_audio
metrics:
- wer
model-index:
- name: Glaswegian_Whisper
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Glaswegian audio
      type: divakaivan/glaswegian_audio
      config: default
      split: train
      args: default
    metrics:
    - name: Wer
      type: wer
      value: 40.5394016013485
---

Fine-tuned using [this notebook](https://colab.research.google.com/drive/1CCRr0rXts18cios1zaIZv7SxhCub-gu-?usp=sharing)

<!-- 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. -->

# Glaswegian_Whisper

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the Glaswegian audio dataset.
It achieves the following results on the evaluation set:
- Loss: 1.4788
- Wer: 40.5394

## 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: 1e-05
- train_batch_size: 16
- 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: 500
- training_steps: 4000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 0.0084        | 16.3934 | 1000 | 1.2802          | 38.5588 |
| 0.0019        | 32.7869 | 2000 | 1.4141          | 39.0223 |
| 0.0002        | 49.1803 | 3000 | 1.4553          | 40.3287 |
| 0.0001        | 65.5738 | 4000 | 1.4788          | 40.5394 |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1