---
library_name: transformers
language:
- sq
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
datasets:
- Kushtrim/audioshqip
metrics:
- wer
model-index:
- name: Whisper Large v3 Turbo Shqip
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Audio Shqip 115 orë
      type: Kushtrim/audioshqip
      args: 'config: sq, split: test'
    metrics:
    - type: wer
      value: 22.006858788533318
      name: Wer
---

<!-- 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 Shqip

This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the Audio Shqip 115 orë dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3322
- Wer: 22.0069

## 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: 4
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 4
- total_train_batch_size: 16
- 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     |
|:-------------:|:------:|:-----:|:---------------:|:-------:|
| 0.5211        | 0.2738 | 500   | 0.5221          | 36.9257 |
| 0.4152        | 0.5476 | 1000  | 0.4144          | 31.1469 |
| 0.3847        | 0.8215 | 1500  | 0.3747          | 28.2953 |
| 0.2703        | 1.0953 | 2000  | 0.3536          | 26.4348 |
| 0.2471        | 1.3691 | 2500  | 0.3419          | 25.5897 |
| 0.2691        | 1.6429 | 3000  | 0.3293          | 24.5533 |
| 0.2426        | 1.9168 | 3500  | 0.3202          | 24.5742 |
| 0.1993        | 2.1906 | 4000  | 0.3178          | 23.5548 |
| 0.204         | 2.4644 | 4500  | 0.3124          | 23.6609 |
| 0.2           | 2.7382 | 5000  | 0.3098          | 23.5131 |
| 0.1298        | 3.0120 | 5500  | 0.3101          | 22.5753 |
| 0.1213        | 3.2859 | 6000  | 0.3145          | 23.0129 |
| 0.1343        | 3.5597 | 6500  | 0.3105          | 22.6511 |
| 0.1341        | 3.8335 | 7000  | 0.3076          | 22.3479 |
| 0.0895        | 4.1073 | 7500  | 0.3210          | 22.3593 |
| 0.0883        | 4.3812 | 8000  | 0.3223          | 22.4786 |
| 0.0892        | 4.6550 | 8500  | 0.3182          | 22.1073 |
| 0.0937        | 4.9288 | 9000  | 0.3179          | 21.9008 |
| 0.0608        | 5.2026 | 9500  | 0.3326          | 22.0466 |
| 0.0482        | 5.4765 | 10000 | 0.3322          | 22.0069 |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.20.3