---
language:
- en
license: apache-2.0
tags:
- generated_from_trainer
base_model: openai/whisper-small
datasets:
- Jzuluaga/atcosim_corpus
metrics:
- wer
model-index:
- name: Whisper Base ATCOSIM
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: atcosim_corpus
      type: Jzuluaga/atcosim_corpus
      args: 'config: en, split: test'
    metrics:
    - type: wer
      value: 4.169242999735006
      name: Wer
---

<!-- 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 Base ATCOSIM

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the atcosim_corpus dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0557
- Wer: 4.1692

## 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: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 1.6572        | 0.2092 | 100  | 1.2694          | 71.0096 |
| 0.3373        | 0.4184 | 200  | 0.3225          | 16.4296 |
| 0.147         | 0.6276 | 300  | 0.1653          | 9.2130  |
| 0.0975        | 0.8368 | 400  | 0.1107          | 6.3334  |
| 0.0544        | 1.0460 | 500  | 0.0892          | 6.2583  |
| 0.0344        | 1.2552 | 600  | 0.0760          | 5.4103  |
| 0.0517        | 1.4644 | 700  | 0.0663          | 5.0393  |
| 0.0396        | 1.6736 | 800  | 0.0607          | 4.3106  |
| 0.0297        | 1.8828 | 900  | 0.0566          | 4.2708  |
| 0.0133        | 2.0921 | 1000 | 0.0557          | 4.1692  |


### Framework versions

- Transformers 4.42.0.dev0
- Pytorch 2.3.0+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1