metadata
base_model: openai/whisper-base
datasets:
- fleurs
language:
- tr
license: apache-2.0
metrics:
- wer
tags:
- hf-asr-leaderboard
- generated_from_trainer
model-index:
- name: Whisper Base Turkish 8000 - Chee Li
results:
- task:
type: automatic-speech-recognition
name: Automatic Speech Recognition
dataset:
name: Google Fleurs
type: fleurs
config: tr_tr
split: None
args: 'config: tr split: test'
metrics:
- type: wer
value: 25.847853142501553
name: Wer
Whisper Base Turkish 8000 - Chee Li
This model is a fine-tuned version of openai/whisper-base on the Google Fleurs dataset. It achieves the following results on the evaluation set:
- Loss: 0.5649
- Wer: 25.8479
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: 850
- training_steps: 8000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.1634 | 5.5866 | 1000 | 0.4092 | 24.8833 |
| 0.0075 | 11.1732 | 2000 | 0.4509 | 24.2066 |
| 0.0024 | 16.7598 | 3000 | 0.4874 | 24.1910 |
| 0.0012 | 22.3464 | 4000 | 0.5125 | 24.3777 |
| 0.0008 | 27.9330 | 5000 | 0.5305 | 24.5644 |
| 0.0005 | 33.5196 | 6000 | 0.5473 | 24.8289 |
| 0.0004 | 39.1061 | 7000 | 0.5592 | 24.9922 |
| 0.0003 | 44.6927 | 8000 | 0.5649 | 25.8479 |
Framework versions
- Transformers 4.43.4
- Pytorch 2.3.1+cu121
- Datasets 2.20.0
- Tokenizers 0.19.1