metadata
base_model: openai/whisper-large-v3
datasets:
- google/fleurs
language:
- tr
license: apache-2.0
metrics:
- wer
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: Whisper Large V3 tr Fleurs - Chee Li
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Google Fleurs
type: google/fleurs
config: tr_tr
split: None
args: 'config: tr split: test'
metrics:
- type: wer
value: 649.9222153080274
name: Wer
Whisper Large V3 tr Fleurs - Chee Li
This model is a fine-tuned version of openai/whisper-large-v3 on the Google Fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 0.1432
- Wer: 649.9222
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: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0466 | 2.7933 | 500 | 0.1060 | 147.9932 |
| 0.006 | 5.5866 | 1000 | 0.1208 | 481.1605 |
| 0.0017 | 8.3799 | 1500 | 0.1291 | 602.0769 |
| 0.0012 | 11.1732 | 2000 | 0.1288 | 627.3647 |
| 0.0002 | 13.9665 | 2500 | 0.1382 | 641.4203 |
| 0.0001 | 16.7598 | 3000 | 0.1411 | 647.7520 |
| 0.0001 | 19.5531 | 3500 | 0.1426 | 642.9294 |
| 0.0001 | 22.3464 | 4000 | 0.1432 | 649.9222 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1