---
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- DTU54DL/common-accent
metrics:
- wer
- precision
- recall
- f1
model-index:
- name: Whisper Base CA
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Accent
      type: DTU54DL/common-accent
    metrics:
    - name: Wer
      type: wer
      value: 0.26376410965215386
    - name: Precision
      type: precision
      value: 0.8083025813102722
    - name: Recall
      type: recall
      value: 0.8232867121696472
    - name: F1
      type: f1
      value: 0.8149744272232056
---

<!-- 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 Base CA

This model is a fine-tuned version of [openai/whisper-base](https://huggingface.co/openai/whisper-base) on the Common Accent dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7230
- Wer Ortho: 30.5998
- Wer: 0.2638
- Cer: 0.1320
- Precision: 0.8083
- Recall: 0.8233
- F1: 0.8150

## 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: 16
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: constant_with_warmup
- lr_scheduler_warmup_steps: 50
- training_steps: 500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer Ortho | Wer    | Cer    | Precision | Recall | F1     |
|:-------------:|:-----:|:----:|:---------------:|:---------:|:------:|:------:|:---------:|:------:|:------:|
| 0.1367        | 0.8   | 500  | 0.7230          | 30.5998   | 0.2638 | 0.1320 | 0.8083    | 0.8233 | 0.8150 |


### Framework versions

- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0