---
library_name: transformers
language:
- en
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
datasets:
- suhaibmasood/med-audio-3
metrics:
- wer
model-index:
- name: Whisper Small en-Harpreet Singh
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: med-audio-3
      type: suhaibmasood/med-audio-3
      args: 'config: en, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 12.560386473429952
---

<!-- 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 Small en-Harpreet Singh

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the med-audio-3 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0384
- Wer: 12.5604

## 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: 1
- eval_batch_size: 2
- seed: 42
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- training_steps: 500

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.0221        | 0.3268 | 100  | 0.0383          | 13.1643 |
| 0.0339        | 0.6536 | 200  | 0.0373          | 13.0435 |
| 0.0265        | 0.9804 | 300  | 0.0382          | 12.9227 |
| 0.0048        | 1.3072 | 400  | 0.0388          | 13.0435 |
| 0.0088        | 1.6340 | 500  | 0.0384          | 12.5604 |


### Framework versions

- Transformers 4.46.3
- Pytorch 2.5.1+cu121
- Datasets 3.1.0
- Tokenizers 0.20.3