README.md

---
language:
- pl
license: apache-2.0
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
- google/fleurs
metrics:
- wer
model-index:
- name: Whisper Medium 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: 8.71
      name: WER
    - type: wer_without_norm
      value: 22.0
      name: WER unnormalized
    - type: cer
      value: 2.41
      name: CER
    - type: mer
      value: 8.65
      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: 11.99
      name: WER
    - type: wer_without_norm
      value: 30.9
      name: WER unnormalized
    - type: cer
      value: 6.54
      name: CER
    - type: mer
      value: 11.68
      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: 10.89
      name: WER
    - type: wer_without_norm
      value: 30.7
      name: WER unnormalized
    - type: cer
      value: 4.04
      name: CER
    - type: mer
      value: 10.8
      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 Medium PL

This model is a fine-tuned version of [openai/whisper-medium](https://huggingface.co/openai/whisper-medium) on the Common Voice 11.0 and the FLEURS datasets.
It achieves the following results on the evaluation set:
- Loss: 0.3947
- Wer: 8.6872

## 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: 4
- eval_batch_size: 1
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 8000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer     |
|:-------------:|:-----:|:----:|:---------------:|:-------:|
| 0.0805        | 0.48  | 500  | 0.2556          | 10.4888 |
| 0.0685        | 0.96  | 1000 | 0.2462          | 10.7608 |
| 0.0356        | 1.45  | 1500 | 0.2561          | 9.6728  |
| 0.0337        | 1.93  | 2000 | 0.2327          | 9.6459  |
| 0.017         | 2.41  | 2500 | 0.2444          | 9.9464  |
| 0.0179        | 2.9   | 3000 | 0.2554          | 9.6476  |
| 0.0056        | 3.38  | 3500 | 0.3001          | 9.3638  |
| 0.007         | 3.86  | 4000 | 0.2809          | 9.2245  |
| 0.0033        | 4.34  | 4500 | 0.3235          | 9.3437  |
| 0.0024        | 4.83  | 5000 | 0.3148          | 9.0633  |
| 0.0008        | 5.31  | 5500 | 0.3416          | 9.0112  |
| 0.0011        | 5.79  | 6000 | 0.3876          | 9.1858  |
| 0.0004        | 6.27  | 6500 | 0.3745          | 8.7292  |
| 0.0003        | 6.76  | 7000 | 0.3704          | 9.0314  |
| 0.0003        | 7.24  | 7500 | 0.3929          | 8.6553  |
| 0.0002        | 7.72  | 8000 | 0.3947          | 8.6872  |


### Framework versions

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