---
license: apache-2.0
base_model: openai/whisper-medium
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-medium-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-medium-ATCOSIM

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.0993
- Wer: 4.5786

## 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: 8
- eval_batch_size: 4
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 12500
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|
| 0.0448        | 1.0460  | 1000  | 0.0953          | 6.5544 |
| 0.0192        | 2.0921  | 2000  | 0.0926          | 4.5786 |
| 0.0098        | 3.1381  | 3000  | 0.1084          | 5.3088 |
| 0.0079        | 4.1841  | 4000  | 0.0894          | 4.9609 |
| 0.0051        | 5.2301  | 5000  | 0.0971          | 4.5658 |
| 0.0027        | 6.2762  | 6000  | 0.0946          | 4.2050 |
| 0.0023        | 7.3222  | 7000  | 0.1061          | 4.8406 |
| 0.001         | 8.3682  | 8000  | 0.1013          | 4.6302 |
| 0.0006        | 9.4142  | 9000  | 0.1041          | 4.8535 |
| 0.0004        | 10.4603 | 10000 | 0.1057          | 4.8063 |
| 0.0001        | 11.5063 | 11000 | 0.1033          | 4.6731 |
| 0.0002        | 12.5523 | 12000 | 0.0993          | 4.5786 |


### Framework versions

- Transformers 4.43.3
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1