---
license: apache-2.0
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-large-v2-atco2-asr-atcosim
  results: []
---

<!-- 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-v2-atco2-asr-atcosim

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1063
- Wer: 5.5528

## 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
- distributed_type: multi-GPU
- num_devices: 4
- total_train_batch_size: 64
- total_eval_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 12644

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Wer    |
|:-------------:|:-----:|:-----:|:---------------:|:------:|
| 0.0503        | 1.97  | 250   | 0.0602          | 8.5346 |
| 0.0172        | 3.94  | 500   | 0.0602          | 4.1352 |
| 0.0084        | 5.91  | 750   | 0.0608          | 3.3803 |
| 0.0046        | 7.87  | 1000  | 0.0624          | 3.5523 |
| 0.0024        | 9.84  | 1250  | 0.0635          | 3.5774 |
| 0.0019        | 11.81 | 1500  | 0.0704          | 4.0933 |
| 0.0019        | 13.78 | 1750  | 0.0712          | 6.3832 |
| 0.0026        | 15.75 | 2000  | 0.0677          | 3.3635 |
| 0.0016        | 17.72 | 2250  | 0.0706          | 3.2000 |
| 0.0009        | 19.69 | 2500  | 0.0709          | 4.0597 |
| 0.0003        | 21.65 | 2750  | 0.0735          | 3.2922 |
| 0.0001        | 23.62 | 3000  | 0.0771          | 3.8836 |
| 0.0001        | 25.59 | 3250  | 0.0791          | 4.0178 |
| 0.0001        | 27.56 | 3500  | 0.0804          | 3.7913 |
| 0.0002        | 29.53 | 3750  | 0.0792          | 4.0597 |
| 0.0           | 31.5  | 4000  | 0.0831          | 4.1059 |
| 0.0           | 33.46 | 4250  | 0.0847          | 3.9507 |
| 0.0           | 35.43 | 4500  | 0.0859          | 4.1059 |
| 0.0           | 37.4  | 4750  | 0.0871          | 4.1688 |
| 0.0           | 39.37 | 5000  | 0.0883          | 4.2820 |
| 0.0           | 41.34 | 5250  | 0.0891          | 4.3449 |
| 0.0           | 43.31 | 5500  | 0.0898          | 4.5378 |
| 0.0           | 45.28 | 5750  | 0.0908          | 4.5546 |
| 0.0           | 47.24 | 6000  | 0.0915          | 4.7433 |
| 0.0           | 49.21 | 6250  | 0.0923          | 4.7643 |
| 0.0           | 51.18 | 6500  | 0.0933          | 4.8146 |
| 0.0           | 53.15 | 6750  | 0.0939          | 4.7140 |
| 0.0           | 55.12 | 7000  | 0.0947          | 4.7475 |
| 0.0           | 57.09 | 7250  | 0.0955          | 4.7266 |
| 0.0           | 59.06 | 7500  | 0.0962          | 4.8188 |
| 0.0           | 61.02 | 7750  | 0.0969          | 4.8775 |
| 0.0           | 62.99 | 8000  | 0.0976          | 5.0159 |
| 0.0           | 64.96 | 8250  | 0.0982          | 5.0872 |
| 0.0           | 66.93 | 8500  | 0.0989          | 5.1669 |
| 0.0           | 68.9  | 8750  | 0.0996          | 5.1208 |
| 0.0           | 70.87 | 9000  | 0.1002          | 5.1795 |
| 0.0           | 72.83 | 9250  | 0.1009          | 5.2969 |
| 0.0           | 74.8  | 9500  | 0.1014          | 5.2969 |
| 0.0           | 76.77 | 9750  | 0.1020          | 5.3892 |
| 0.0           | 78.74 | 10000 | 0.1027          | 5.4269 |
| 0.0           | 80.71 | 10250 | 0.1031          | 5.3431 |
| 0.0           | 82.68 | 10500 | 0.1038          | 5.4479 |
| 0.0           | 84.65 | 10750 | 0.1043          | 5.4940 |
| 0.0           | 86.61 | 11000 | 0.1047          | 5.4563 |
| 0.0           | 88.58 | 11250 | 0.1052          | 5.4857 |
| 0.0           | 90.55 | 11500 | 0.1055          | 5.4857 |
| 0.0           | 92.52 | 11750 | 0.1058          | 5.5024 |
| 0.0           | 94.49 | 12000 | 0.1060          | 5.5108 |
| 0.0           | 96.46 | 12250 | 0.1062          | 5.5150 |
| 0.0           | 98.43 | 12500 | 0.1063          | 5.5528 |


### Framework versions

- Transformers 4.30.0.dev0
- Pytorch 2.0.1+cu117
- Datasets 2.12.0
- Tokenizers 0.13.3