---
library_name: transformers
language:
- pl
base_model: whisper-small-pl
tags:
- generated_from_trainer
datasets:
- leliw/common_voice_17_0_pl
metrics:
- wer
model-index:
- name: Whisper Small PL
  results:
  - task:
      name: Automatic Speech Recognition
      type: automatic-speech-recognition
    dataset:
      name: Common Voice 17.0
      type: leliw/common_voice_17_0_pl
      config: pl
      split: test
      args: 'config: pl, split: test'
    metrics:
    - name: Wer
      type: wer
      value: 120.26215182960132
---

<!-- 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 PL

This model is a fine-tuned version of [whisper-small-pl](https://huggingface.co/whisper-small-pl) on the Common Voice 17.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5061
- Wer: 120.2622

## 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: 50
- eval_batch_size: 8
- seed: 42
- 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: 100
- training_steps: 1000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer      |
|:-------------:|:-----:|:----:|:---------------:|:--------:|
| 0.0329        | 0.2   | 200  | 0.5055          | 105.0792 |
| 0.0276        | 0.4   | 400  | 0.4741          | 116.0295 |
| 0.0147        | 1.185 | 600  | 0.6035          | 97.4604  |
| 0.0091        | 1.385 | 800  | 0.6017          | 106.7723 |
| 0.0129        | 2.17  | 1000 | 0.5061          | 120.2622 |


### Framework versions

- Transformers 4.49.0
- Pytorch 2.6.0+cu124
- Datasets 3.3.1
- Tokenizers 0.21.0