metadata

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

Glaswegian_Whisper

This model is a fine-tuned version of 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