---
base_model: openai/whisper-medium
datasets:
- AT_ENT
language:
- aeb
library_name: transformers
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: Whisper medium AT
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: AT_ENT
      type: AT_ENT
      args: 'config: aeb, split: test'
    metrics:
    - type: wer
      value: 66.82967723906664
      name: Wer
---

<!-- 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_ENT dataset.
It achieves the following results on the evaluation set:
- Loss: 1.2100
- Wer: 66.8297

## 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: 32
- 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   | 111  | 1.0527          | 63.9374 |
| No log        | 2.0   | 222  | 1.0961          | 65.8796 |
| No log        | 3.0   | 333  | 1.1626          | 67.5283 |
| No log        | 4.0   | 444  | 1.2100          | 66.8297 |


### Framework versions

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