---
library_name: peft
language:
- pl
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- base_model:adapter:openai/whisper-large-v3-turbo
- lora
- transformers
metrics:
- wer
model-index:
- name: whisper-large-v3-turbo-med-pl-lora-r16-decoder-only-lr5e-05-ep3-whisper_bigos_all_fair
  results: []
---

<!-- 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-v3-turbo-med-pl-lora-r16-decoder-only-lr5e-05-ep3-whisper_bigos_all_fair

This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 1.6459
- Model Preparation Time: 0.0278
- Wer: 13.6291
- Cer: 4.1103
- Content Wer: 8.0298

## 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: 5e-05
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 3
- mixed_precision_training: Native AMP
- label_smoothing_factor: 0.1

### Training results

| Training Loss | Epoch | Step  | Validation Loss | Model Preparation Time | Wer     | Cer    | Content Wer |
|:-------------:|:-----:|:-----:|:---------------:|:----------------------:|:-------:|:------:|:-----------:|
| 1.6421        | 1.0   | 6133  | 1.6639          | 0.0278                 | 14.0938 | 4.2114 | 8.4618      |
| 1.6295        | 2.0   | 12266 | 1.6513          | 0.0278                 | 13.8319 | 4.1643 | 8.1738      |
| 1.6268        | 3.0   | 18399 | 1.6459          | 0.0278                 | 13.6291 | 4.1103 | 8.0298      |


### Framework versions

- PEFT 0.18.1
- Transformers 4.57.6
- Pytorch 2.8.0+cu128
- Datasets 4.5.0
- Tokenizers 0.22.2