---
library_name: transformers
language:
- aeb
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- AT
metrics:
- wer
model-index:
- name: Whisper medium AT
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: AT
      type: AT
      args: 'config: aeb, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 65.98418372874012
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper medium AT

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the AT dataset.
It achieves the following results on the evaluation set:
- Loss: 0.9915
- Wer: 65.9842

## 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: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| No log        | 1.0   | 293  | 1.3198          | 74.6073 |
| 1.7949        | 2.0   | 586  | 1.0108          | 70.6316 |
| 1.7949        | 3.0   | 879  | 0.9583          | 65.9517 |
| 0.5076        | 4.0   | 1172 | 0.9915          | 65.9842 |


### Framework versions

- Transformers 4.45.1
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.0