metadata
license: apache-2.0
base_model: openai/whisper-base
tags:
- generated_from_trainer
datasets:
- common_voice_17_0
metrics:
- wer
model-index:
- name: whisper-base-yoruba-colab-CV17.0
results:
- task:
name: Automatic Speech Recognition
type: automatic-speech-recognition
dataset:
name: common_voice_17_0
type: common_voice_17_0
config: yo
split: test
args: yo
metrics:
- name: Wer
type: wer
value: 0.7073728329205563
whisper-base-yoruba-colab-CV17.0
This model is a fine-tuned version of openai/whisper-base on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:
- Loss: 1.0478
- Wer: 0.7074
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-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_ratio: 0.15
- training_steps: 2000
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 1.7762 | 1.5385 | 200 | 0.9767 | 0.8377 |
| 0.5374 | 3.0769 | 400 | 0.8458 | 0.7728 |
| 0.2352 | 4.6154 | 600 | 0.8632 | 0.7407 |
| 0.1038 | 6.1538 | 800 | 0.9157 | 0.7078 |
| 0.0426 | 7.6923 | 1000 | 0.9531 | 0.7246 |
| 0.0201 | 9.2308 | 1200 | 0.9950 | 0.7219 |
| 0.0093 | 10.7692 | 1400 | 1.0347 | 0.6989 |
| 0.0037 | 12.3077 | 1600 | 1.0351 | 0.7063 |
| 0.0013 | 13.8462 | 1800 | 1.0429 | 0.7069 |
| 0.0008 | 15.3846 | 2000 | 1.0478 | 0.7074 |
Framework versions
- Transformers 4.42.4
- Pytorch 2.4.0+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1