metadata
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- common_voice_11_0
metrics:
- wer
model-index:
- name: whisper-small-en-nonnative
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_11_0
type: common_voice_11_0
config: en
split: test
args: en
metrics:
- name: Wer
type: wer
value: 40.75993091537133
whisper-small-en-nonnative
This model is a fine-tuned version of openai/whisper-small on the common_voice_11_0 dataset. It achieves the following results on the evaluation set:
- Loss: 0.4024
- Wer: 40.7599
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: 10000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.2992 | 0.2 | 1000 | 0.4468 | 29.3898 |
| 0.2918 | 0.39 | 2000 | 0.4240 | 27.1733 |
| 0.3078 | 0.59 | 3000 | 0.4173 | 28.4974 |
| 0.2711 | 0.79 | 4000 | 0.4027 | 24.5538 |
| 0.2813 | 0.98 | 5000 | 0.4029 | 28.6413 |
| 0.1416 | 1.18 | 6000 | 0.4078 | 25.9931 |
| 0.1399 | 1.38 | 7000 | 0.4078 | 28.8140 |
| 0.1478 | 1.57 | 8000 | 0.4070 | 31.3759 |
| 0.1479 | 1.77 | 9000 | 0.4033 | 33.5636 |
| 0.1266 | 1.97 | 10000 | 0.4024 | 40.7599 |
Framework versions
- Transformers 4.37.2
- Pytorch 2.2.0+cu121
- Datasets 2.16.1
- Tokenizers 0.15.1