---
language:
- pl
tags:
- whisper-event
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
- google/fleurs
metrics:
- wer
base_model: bardsai/whisper-large-v2-pl
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: 7.280175959972464
      name: WER
    - type: wer
      value: 7.31
      name: WER
    - type: wer_without_norm
      value: 20.18
      name: WER unnormalized
    - type: cer
      value: 2.08
      name: CER
    - type: mer
      value: 7.27
      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: 9.61
      name: WER
    - type: wer_without_norm
      value: 30.33
      name: WER unnormalized
    - type: cer
      value: 5.5
      name: CER
    - type: mer
      value: 9.45
      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: 8.68
      name: WER
    - type: wer_without_norm
      value: 29.33
      name: WER unnormalized
    - type: cer
      value: 3.63
      name: CER
    - type: mer
      value: 8.62
      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 [bardsai/whisper-large-v2-pl](https://huggingface.co/bardsai/whisper-large-v2-pl) on the Common Voice 11.0 and the FLEURS datasets.
It achieves the following results on the evaluation set:
- Loss: 0.3684
- Wer: 7.2802

## 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: 2100
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch | Step | Validation Loss | Wer    |
|:-------------:|:-----:|:----:|:---------------:|:------:|
| 0.0047        | 1.35  | 700  | 0.3428          | 8.5562 |
| 0.0011        | 2.7   | 1400 | 0.3605          | 7.5505 |
| 0.0003        | 4.05  | 2100 | 0.3684          | 7.2802 |


### Framework versions

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