---
language:
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- domain-asr
- generated_from_trainer
datasets:
- audiofolder
metrics:
- wer
model-index:
- name: Whisper
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: immunology dataset
      type: audiofolder
      config: default
      split: test
      args: 'config: en, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 9.337797619047619
---

<!-- 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

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the immunology dataset dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3058
- Wer: 9.3378

## 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0259        | 4.55  | 1000 | 0.2254          | 9.5610 |
| 0.0135        | 9.09  | 2000 | 0.2853          | 9.375  |
| 0.0022        | 13.64 | 3000 | 0.2989          | 9.375  |
| 0.0004        | 18.18 | 4000 | 0.3058          | 9.3378 |


### Framework versions

- Transformers 4.39.0.dev0
- Pytorch 2.1.0+cu121
- Datasets 2.17.1
- Tokenizers 0.15.2