---
language:
- pl
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small PL
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Common Voice 11.0
      type: mozilla-foundation/common_voice_11_0
      config: pl
      split: test
      args: pl
    metrics:
    - type: wer
      value: 8.85
      name: WER
    - type: wer_without_norm
      value: 21.75
      name: WER unnormalized
    - type: cer
      value: 2.63
      name: CER
    - type: mer
      value: 8.76
      name: MER
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: facebook/voxpopuli
      type: facebook/voxpopuli
      config: pl
      split: test
    metrics:
    - type: wer
      value: 12.18
      name: WER
    - type: wer_without_norm
      value: 32.17
      name: WER unnormalized
    - type: cer
      value: 6.99
      name: CER
    - type: mer
      value: 11.84
      name: MER
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: google/fleurs
      type: google/fleurs
      config: pl_pl
      split: test
    metrics:
    - type: wer
      value: 12.77
      name: WER
    - type: wer_without_norm
      value: 32.37
      name: WER unnormalized
    - type: cer
      value: 5.87
      name: CER
    - type: mer
      value: 12.52
      name: MER
---

<!-- 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 [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3739
- Wer: 8.5898

## 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: 32
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 5000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0474        | 1.1   | 1000 | 0.2561          | 9.4612 |
| 0.0119        | 3.09  | 2000 | 0.2901          | 8.9726 |
| 0.0045        | 5.08  | 3000 | 0.3151          | 8.8870 |
| 0.0007        | 7.07  | 4000 | 0.4218          | 8.6032 |
| 0.0005        | 9.07  | 5000 | 0.3739          | 8.5898 |

### Evaluation results

When tested on diffrent polish ASR datasets (splits: test), this model achieves the following results:

| Dataset           | WER   | WER unnormalized | CER   | MER   |
|:-----------------:|:-----:|:----------------:|:-----:|:-----:|
|common_voice_11_0  | 8.85 | 21.75            | 2.63 | 8.76 | 

### Framework versions

- Transformers 4.26.0.dev0
- Pytorch 1.13.0+cu117
- Datasets 2.7.1.dev0
- Tokenizers 0.13.2