---
base_model: openai/whisper-large-v3-turbo
datasets:
- Kushtrim/common_voice_19_sq
language:
- sq
library_name: transformers
license: mit
metrics:
- wer
tags:
- generated_from_trainer
model-index:
- name: Whisper Large V3 Turbo SQ
  results:
  - task:
      type: automatic-speech-recognition
      name: Automatic Speech Recognition
    dataset:
      name: Common Voice 19.0
      type: Kushtrim/common_voice_19_sq
      args: 'config: sq, split: test'
    metrics:
    - type: wer
      value: 22.451899358658114
      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 Turbo SQ

This model is a fine-tuned version of [openai/whisper-large-v3-turbo](https://huggingface.co/openai/whisper-large-v3-turbo) on the Common Voice 19.0 dataset.
It achieves the following results on the evaluation set:
- Loss: 0.3040
- Wer: 22.4519

## 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
- num_epochs: 4
- mixed_precision_training: Native AMP

### Training results

| Training Loss | Epoch  | Step | Validation Loss | Wer     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.5578        | 0.3163 | 250  | 0.6037          | 43.1130 |
| 0.4707        | 0.6325 | 500  | 0.5295          | 40.3256 |
| 0.4221        | 0.9488 | 750  | 0.4559          | 35.2985 |
| 0.2853        | 1.2650 | 1000 | 0.4205          | 33.3103 |
| 0.2685        | 1.5813 | 1250 | 0.3798          | 30.7844 |
| 0.256         | 1.8975 | 1500 | 0.3552          | 28.5890 |
| 0.18          | 2.2138 | 1750 | 0.3480          | 27.5728 |
| 0.2158        | 2.5300 | 2000 | 0.3349          | 27.2521 |
| 0.1396        | 2.8463 | 2250 | 0.3182          | 24.2526 |
| 0.1123        | 3.1626 | 2500 | 0.3175          | 23.5520 |
| 0.124         | 3.4788 | 2750 | 0.3100          | 23.4090 |
| 0.0908        | 3.7951 | 3000 | 0.3040          | 22.4519 |


### Framework versions

- Transformers 4.45.2
- Pytorch 2.4.1+cu121
- Datasets 3.0.1
- Tokenizers 0.20.1