---
language:
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- Jzuluaga/atcosim_corpus
metrics:
- wer
model-index:
- name: Whisper small - Whisper with atcosim
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: 'This is a dataset constructed from two datasets: ATCOSIM.'
      type: Jzuluaga/atcosim_corpus
      args: 'config: en, split: test small split [full]'
    metrics:
    - name: Wer
      type: wer
      value: 1.4177192827488738
---

<!-- 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 - Whisper with atcosim

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the This is a dataset constructed from two datasets: ATCOSIM. dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0385
- Wer: 1.4177

## 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: 50
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.0349        | 0.2092 | 100  | 0.0974          | 6.4040 |
| 0.0493        | 0.4184 | 200  | 0.0664          | 3.2329 |
| 0.0464        | 0.6276 | 300  | 0.0519          | 2.8708 |
| 0.0394        | 0.8368 | 400  | 0.0474          | 2.3055 |
| 0.0177        | 1.0460 | 500  | 0.0429          | 1.7004 |
| 0.0054        | 1.2552 | 600  | 0.0416          | 1.5458 |
| 0.0182        | 1.4644 | 700  | 0.0411          | 1.5193 |
| 0.008         | 1.6736 | 800  | 0.0400          | 1.4663 |
| 0.0055        | 1.8828 | 900  | 0.0387          | 1.4619 |
| 0.0053        | 2.0921 | 1000 | 0.0385          | 1.4177 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.0+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1