metadata
language:
- en
license: apache-2.0
base_model: openai/whisper-small
tags:
- generated_from_trainer
datasets:
- mozilla-foundation/common_voice_11_0
metrics:
- wer
model-index:
- name: Whisper Small Refined
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: Common Voice 11.0
type: mozilla-foundation/common_voice_11_0
args: 'config: en, split: test'
metrics:
- name: Wer
type: wer
value: 15.384615384615385
Whisper Small Refined - Seagate Lim
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: 0.8921
- Wer: 15.3846
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: 5e-08
- train_batch_size: 16
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 250
- training_steps: 4000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 0.0045 | 400.0 | 400 | 0.9209 | 30.7692 |
| 0.0008 | 800.0 | 800 | 0.8990 | 15.3846 |
| 0.0003 | 1200.0 | 1200 | 0.8957 | 15.3846 |
| 0.0002 | 1600.0 | 1600 | 0.8931 | 15.3846 |
| 0.0001 | 2000.0 | 2000 | 0.8927 | 15.3846 |
| 0.0001 | 2400.0 | 2400 | 0.8927 | 15.3846 |
| 0.0001 | 2800.0 | 2800 | 0.8919 | 15.3846 |
| 0.0001 | 3200.0 | 3200 | 0.8912 | 15.3846 |
| 0.0001 | 3600.0 | 3600 | 0.8918 | 15.3846 |
| 0.0001 | 4000.0 | 4000 | 0.8921 | 15.3846 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.3.0
- Datasets 2.19.1
- Tokenizers 0.19.1