---
license: apache-2.0
base_model: NbAiLab/nb-whisper-medium-verbatim
tags:
- generated_from_trainer
metrics:
- wer
model-index:
- name: nb-whisper-medium-karelian-CodeSwitching
  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. -->

# nb-whisper-medium-karelian-CodeSwitching

This model is a fine-tuned version of [NbAiLab/nb-whisper-medium-verbatim](https://huggingface.co/NbAiLab/nb-whisper-medium-verbatim) on the None dataset.
It achieves the following results on the evaluation set:
- Loss: 0.5439
- Wer: 0.2585
- Cer: 0.0714

## 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: 16
- seed: 42
- distributed_type: multi-GPU
- gradient_accumulation_steps: 2
- 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: 10000
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch   | Step  | Validation Loss | Wer    | Cer    |
|:-------------:|:-------:|:-----:|:---------------:|:------:|:------:|
| 0.2467        | 1.1351  | 500   | 0.5664          | 0.3488 | 0.0895 |
| 0.0718        | 2.2701  | 1000  | 0.5562          | 0.3166 | 0.0819 |
| 0.0513        | 3.4052  | 1500  | 0.5366          | 0.2997 | 0.0798 |
| 0.0377        | 4.5403  | 2000  | 0.5430          | 0.2815 | 0.0730 |
| 0.0339        | 5.6754  | 2500  | 0.5444          | 0.2906 | 0.0755 |
| 0.0263        | 6.8104  | 3000  | 0.5439          | 0.2757 | 0.0735 |
| 0.0182        | 7.9455  | 3500  | 0.5474          | 0.2754 | 0.0741 |
| 0.0141        | 9.0806  | 4000  | 0.5625          | 0.2808 | 0.0758 |
| 0.0117        | 10.2157 | 4500  | 0.5537          | 0.2662 | 0.0716 |
| 0.0122        | 11.3507 | 5000  | 0.5610          | 0.2703 | 0.0726 |
| 0.0118        | 12.4858 | 5500  | 0.5557          | 0.2686 | 0.0720 |
| 0.0075        | 13.6209 | 6000  | 0.5522          | 0.2673 | 0.0711 |
| 0.0069        | 14.7560 | 6500  | 0.5576          | 0.2764 | 0.0745 |
| 0.0072        | 15.8910 | 7000  | 0.5562          | 0.2676 | 0.0705 |
| 0.0085        | 17.0261 | 7500  | 0.5474          | 0.2713 | 0.0868 |
| 0.0041        | 18.1612 | 8000  | 0.5493          | 0.2639 | 0.0716 |
| 0.0041        | 19.2963 | 8500  | 0.5493          | 0.2612 | 0.0712 |
| 0.0041        | 20.4313 | 9000  | 0.5449          | 0.2554 | 0.0699 |
| 0.004         | 21.5664 | 9500  | 0.5444          | 0.2591 | 0.0708 |
| 0.0028        | 22.7015 | 10000 | 0.5439          | 0.2585 | 0.0714 |


### Framework versions

- Transformers 4.41.2
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1