metadata
license: apache-2.0
base_model: openai/whisper-base.en
tags:
- generated_from_trainer
datasets:
- speech_commands
metrics:
- accuracy
model-index:
- name: whisper-base.en-speech-commands-h
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: speech_commands
type: speech_commands
config: v0.02
split: None
args: v0.02
metrics:
- name: Accuracy
type: accuracy
value: 0.7922661870503597
whisper-base.en-speech-commands-h
This model is a fine-tuned version of openai/whisper-base.en on the speech_commands dataset. It achieves the following results on the evaluation set:
- Loss: 1.3313
- Accuracy: 0.7923
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: 0.0005
- train_batch_size: 96
- eval_batch_size: 96
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.3859 | 1.0 | 412 | 1.3474 | 0.7707 |
| 0.2732 | 2.0 | 824 | 1.2471 | 0.7599 |
| 0.2373 | 3.0 | 1236 | 1.2114 | 0.7729 |
| 0.1694 | 4.0 | 1648 | 1.1600 | 0.7914 |
| 0.1495 | 5.0 | 2060 | 1.1535 | 0.7914 |
| 0.1931 | 6.0 | 2472 | 1.1446 | 0.7860 |
| 0.1329 | 7.0 | 2884 | 1.3313 | 0.7923 |
| 0.0731 | 8.0 | 3296 | 1.2812 | 0.7860 |
| 0.0702 | 9.0 | 3708 | 1.2134 | 0.7873 |
| 0.0828 | 10.0 | 4120 | 1.6292 | 0.7887 |
| 0.08 | 11.0 | 4532 | 1.4677 | 0.7797 |
| 0.0481 | 12.0 | 4944 | 1.3770 | 0.7909 |
Framework versions
- Transformers 4.43.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1