---
language:
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- openai/whisper-small
metrics:
- wer
model-index:
- name: Whisper Small fine tuned with comms
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: BrainHack ASR
      type: openai/whisper-small
    metrics:
    - name: Wer
      type: wer
      value: 0.004561529393675362
---

<!-- 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 fine tuned with comms

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the BrainHack ASR dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0818
- Wer: 0.0046

## 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: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer    |
|:-------------:|:-------:|:----:|:---------------:|:------:|
| 0.0022        | 6.4935  | 1000 | 0.0641          | 0.0057 |
| 0.0009        | 12.9870 | 2000 | 0.0705          | 0.0050 |
| 0.0           | 19.4805 | 3000 | 0.0766          | 0.0045 |
| 0.0           | 25.9740 | 4000 | 0.0805          | 0.0046 |
| 0.0           | 32.4675 | 5000 | 0.0818          | 0.0046 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1