---
language:
- en
license: apache-2.0
base_model: openai/whisper-large
tags:
- generated_from_trainer
datasets:
- jlvdoorn/atco2-asr-atcosim
metrics:
- wer
model-index:
- name: Whisper Large - Whisper with atco2-asr-atcosim
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: 'This is a dataset constructed from two datasets: ATCO2-ASR and ATCOSIM.'
      type: jlvdoorn/atco2-asr-atcosim
      args: 'config: en, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 2.642174131857071
---

<!-- 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 Large - Whisper with atco2-asr-atcosim

This model is a fine-tuned version of [openai/whisper-large](https://huggingface.co/openai/whisper-large) on the This is a dataset constructed from two datasets: ATCO2-ASR and ATCOSIM. dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0715
- Wer: 2.6422

## 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    |
|:-------------:|:------:|:----:|:---------------:|:------:|
| 0.0547        | 1.9763 | 1000 | 0.0675          | 4.0346 |
| 0.0115        | 3.9526 | 2000 | 0.0690          | 2.8309 |
| 0.003         | 5.9289 | 3000 | 0.0682          | 2.6212 |
| 0.0003        | 7.9051 | 4000 | 0.0715          | 2.6422 |


### Framework versions

- Transformers 4.40.2
- Pytorch 2.2.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1