metadata
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
Whisper Large V3 Turbo SQ
This model is a fine-tuned version of 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