---
library_name: transformers
language:
- sq
license: mit
base_model: openai/whisper-large-v3-turbo
tags:
- generated_from_trainer
datasets:
- Kushtrim/common_voice_19_sq
metrics:
- wer
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: 23.96274909042358
      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 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: 0.3161
- Wer: 23.9627

## 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     |
|:-------------:|:------:|:----:|:---------------:|:-------:|
| 0.5057        | 0.5112 | 500  | 0.5311          | 39.0968 |
| 0.3303        | 1.0225 | 1000 | 0.4321          | 34.5439 |
| 0.3165        | 1.5337 | 1500 | 0.3782          | 31.1893 |
| 0.1799        | 2.0450 | 2000 | 0.3470          | 27.7212 |
| 0.1945        | 2.5562 | 2500 | 0.3320          | 26.4628 |
| 0.1277        | 3.0675 | 3000 | 0.3235          | 24.8606 |
| 0.1502        | 3.5787 | 3500 | 0.3161          | 23.9627 |


### Framework versions

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