---
base_model: openai/whisper-large-v3
library_name: transformers
license: apache-2.0
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: whisper-large-v3-genbed-m-model
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: genbed
      type: genbed
      config: en
      split: test
    metrics:
    - type: wer
      value: 37.19
      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-large-v3-genbed-m-model

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co./openai/whisper-large-v3) on an unknown dataset.
It achieves the following results on the evaluation set:
- Loss: 0.7479
- Wer: 36.9425

## 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: 1.75e-05
- train_batch_size: 4
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 8
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 30000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step | Validation Loss | Wer     |
|:-------------:|:-------:|:----:|:---------------:|:-------:|
| 1.4385        | 0.6596  | 250  | 0.7026          | 57.3435 |
| 0.578         | 1.3193  | 500  | 0.6312          | 47.4271 |
| 0.499         | 1.9789  | 750  | 0.5735          | 43.2676 |
| 0.2829        | 2.6385  | 1000 | 0.5949          | 41.0913 |
| 0.2304        | 3.2982  | 1250 | 0.6149          | 40.5660 |
| 0.1672        | 3.9578  | 1500 | 0.5645          | 38.5399 |
| 0.1019        | 4.6174  | 1750 | 0.6265          | 42.0026 |
| 0.0911        | 5.2770  | 2000 | 0.6534          | 38.5399 |
| 0.0713        | 5.9367  | 2250 | 0.6533          | 38.1754 |
| 0.0545        | 6.5963  | 2500 | 0.6577          | 37.7466 |
| 0.0497        | 7.2559  | 2750 | 0.6626          | 39.3117 |
| 0.0425        | 7.9156  | 3000 | 0.6901          | 37.2642 |
| 0.0374        | 8.5752  | 3250 | 0.6919          | 38.6256 |
| 0.0312        | 9.2348  | 3500 | 0.7093          | 37.2856 |
| 0.0302        | 9.8945  | 3750 | 0.7260          | 35.7740 |
| 0.0233        | 10.5541 | 4000 | 0.7181          | 36.5780 |
| 0.0262        | 11.2137 | 4250 | 0.7352          | 35.5703 |
| 0.0241        | 11.8734 | 4500 | 0.7340          | 36.4172 |
| 0.0198        | 12.5330 | 4750 | 0.7463          | 36.8461 |
| 0.0201        | 13.1926 | 5000 | 0.7479          | 36.9425 |


### Framework versions

- Transformers 4.45.0.dev0
- Pytorch 2.4.1+cu121
- Datasets 3.0.0
- Tokenizers 0.19.1