---
base_model: openai/whisper-large-v3
datasets:
- google/fleurs
language:
- pl
license: apache-2.0
metrics:
- wer
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: Whisper Large V3 pl Fleurs Aug 2 - Chee Li
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Google Fleurs
      type: google/fleurs
      config: pl_pl
      split: None
      args: 'config: pl split: test'
    metrics:
    - type: wer
      value: 402.6139222812413
      name: Wer
---

<!-- This model card has been generated automatically according to the information the Trainer had access to. You
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# Whisper Large V3 pl Fleurs Aug 2 - Chee Li

This model is a fine-tuned version of [openai/whisper-large-v3](https://huggingface.co/openai/whisper-large-v3) on the Google Fleurs dataset.
It achieves the following results on the evaluation set:
- Loss: 0.1295
- Wer: 402.6139

## 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: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 750
- training_steps: 6000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer      |
|:-------------:|:------:|:----:|:---------------:|:--------:|
| 0.0563        | 1.2579 | 1000 | 0.1102          | 448.8748 |
| 0.0144        | 2.5157 | 2000 | 0.1207          | 354.0117 |
| 0.0035        | 3.7736 | 3000 | 0.1205          | 514.6701 |
| 0.0009        | 5.0314 | 4000 | 0.1263          | 391.4104 |
| 0.0003        | 6.2893 | 5000 | 0.1280          | 385.1901 |
| 0.0001        | 7.5472 | 6000 | 0.1295          | 402.6139 |


### Framework versions

- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1