metadata
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_6_1
metrics:
- wer
model-index:
- name: Whisper Small Frisian 10h
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 6.1
type: mozilla-foundation/common_voice_6_1
args: 'config: frisian, split: test'
metrics:
- name: Wer
type: wer
value: 22.427374799500978
Whisper Small Frisian 10h
This model is a fine-tuned version of openai/whisper-small on the Common Voice 6.1 dataset. It achieves the following results on the evaluation set:
- Loss: 0.3402
- Wer: 22.4274
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: 8
- 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: 50
- training_steps: 1500
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.0548 | 0.1070 | 100 | 1.0200 | 52.0620 |
| 0.6944 | 0.2139 | 200 | 0.7126 | 39.6222 |
| 0.6024 | 0.3209 | 300 | 0.6052 | 36.0791 |
| 0.4697 | 0.4278 | 400 | 0.5303 | 32.5040 |
| 0.4222 | 0.5348 | 500 | 0.4780 | 30.0766 |
| 0.4075 | 0.6417 | 600 | 0.4458 | 28.4691 |
| 0.374 | 0.7487 | 700 | 0.4151 | 26.9292 |
| 0.3381 | 0.8556 | 800 | 0.3949 | 25.4678 |
| 0.3235 | 0.9626 | 900 | 0.3764 | 24.8904 |
| 0.1861 | 1.0695 | 1000 | 0.3643 | 23.5716 |
| 0.1554 | 1.1765 | 1100 | 0.3608 | 23.4183 |
| 0.1639 | 1.2834 | 1200 | 0.3511 | 23.0298 |
| 0.1453 | 1.3904 | 1300 | 0.3449 | 22.6591 |
| 0.1531 | 1.4973 | 1400 | 0.3419 | 22.4452 |
| 0.1299 | 1.6043 | 1500 | 0.3402 | 22.4274 |
Framework versions
- Transformers 4.40.1
- Pytorch 2.3.0+cu121
- Datasets 2.19.0
- Tokenizers 0.19.1