---
library_name: transformers
language:
- hu
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: whisper-small-finetuned-hu
  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-small-finetuned-hu

This model is a fine-tuned version of [openai/whisper-small](https://huggingface.co/openai/whisper-small) on the custom dataset.
It achieves the following results on the evaluation set:
- Loss: 0.02658
- Wer: 0.08494

## 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: 2.5e-05
- train_batch_size: 32
- eval_batch_size: 32
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- total_train_batch_size: 64
- total_eval_batch_size: 64
- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 2
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step  | Validation Loss | Wer    |
|:-------------:|:------:|:-----:|:---------------:|:------:|
| 0.0559        | 0.0902 | 2000  | 0.0575          | 0.2634 |
| 0.0481        | 0.1804 | 4000  | 0.0488          | 0.1917 |
| 0.0415        | 0.2707 | 6000  | 0.0438          | 0.1329 |
| 0.0408        | 0.3609 | 8000  | 0.0408          | 0.1234 |
| 0.0393        | 0.4511 | 10000 | 0.0388          | 0.1173 |
| 0.0375        | 0.5413 | 12000 | 0.0372          | 0.1119 |
| 0.0342        | 0.6316 | 14000 | 0.0357          | 0.1101 |
| 0.0335        | 0.7218 | 16000 | 0.0349          | 0.1071 |
| 0.0323        | 0.8120 | 18000 | 0.0331          | 0.1037 |
| 0.0325        | 0.9022 | 20000 | 0.0326          | 0.1035 |
| 0.0305        | 0.9925 | 22000 | 0.0315          | 0.0974 |
| 0.02          | 1.0827 | 24000 | 0.0312          | 0.0992 |
| 0.0207        | 1.1729 | 26000 | 0.0310          | 0.0937 |
| 0.0203        | 1.2631 | 28000 | 0.0301          | 0.0941 |
| 0.0215        | 1.3534 | 30000 | 0.0296          | 0.0913 |
| 0.0199        | 1.4436 | 32000 | 0.0289          | 0.0911 |
| 0.0197        | 1.5338 | 34000 | 0.0285          | 0.0890 |
| 0.0187        | 1.6240 | 36000 | 0.0279          | 0.0887 |
| 0.0188        | 1.7143 | 38000 | 0.0276          | 0.0882 |
| 0.0186        | 1.8045 | 40000 | 0.0271          | 0.0856 |
| 0.0181        | 1.8947 | 42000 | 0.0266          | 0.0849 |
| 0.0176        | 1.9849 | 44000 | 0.0264          | 0.0863 |


### Framework versions

- Transformers 4.47.0
- Pytorch 2.5.1+cu118
- Datasets 3.1.0
- Tokenizers 0.21.0