---
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 Large v2 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: 6.89
      name: WER
    - type: wer_without_norm
      value: 19.79
      name: WER unnormalized
    - type: cer
      value: 1.88
      name: CER
    - type: mer
      value: 6.84
      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: 9.26
      name: WER
    - type: wer_without_norm
      value: 30.25
      name: WER unnormalized
    - type: cer
      value: 5.32
      name: CER
    - type: mer
      value: 9.1
      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: 9.88
      name: WER
    - type: wer_without_norm
      value: 29.53
      name: WER unnormalized
    - type: cer
      value: 5.09
      name: CER
    - type: mer
      value: 9.73
      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 Large v2 PL

This model is a fine-tuned version of [openai/whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) on the Common Voice 11.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.4222
- Wer: 6.9125

## 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: 8
- eval_batch_size: 4
- seed: 42
- gradient_accumulation_steps: 8
- total_train_batch_size: 64
- 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    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.1144        | 1.93  | 500  | 0.2016          | 7.4749 |
| 0.0441        | 3.86  | 1000 | 0.2193          | 7.3154 |
| 0.0099        | 5.79  | 1500 | 0.2983          | 7.0804 |
| 0.0048        | 7.72  | 2000 | 0.3514          | 7.0988 |
| 0.0017        | 9.65  | 2500 | 0.3614          | 7.0485 |
| 0.0014        | 11.58 | 3000 | 0.3814          | 7.1240 |
| 0.001         | 13.51 | 3500 | 0.3773          | 6.9931 |
| 0.0005        | 15.44 | 4000 | 0.4085          | 6.9662 |
| 0.0004        | 17.37 | 4500 | 0.4195          | 6.9192 |
| 0.0004        | 19.3  | 5000 | 0.4222          | 6.9125 |


### Framework versions

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