metadata
library_name: transformers
license: apache-2.0
base_model: openai/whisper-tiny
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
model-index:
- name: whisper-tiny-tel-tam
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: Speech Commands
type: audiofolder
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.9772727272727273
whisper-tiny-tel-tam
This model is a fine-tuned version of openai/whisper-tiny on the Speech Commands dataset. It achieves the following results on the evaluation set:
- Loss: 0.1934
- Accuracy: 0.9773
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: 2
- eval_batch_size: 2
- seed: 42
- optimizer: Use OptimizerNames.ADAMW_TORCH with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 10
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 0.7009 | 1.0 | 175 | 0.9922 | 0.5227 |
| 0.024 | 2.0 | 350 | 0.2727 | 0.9091 |
| 0.4103 | 3.0 | 525 | 0.0223 | 1.0 |
| 0.0008 | 4.0 | 700 | 0.1908 | 0.9773 |
| 0.0005 | 5.0 | 875 | 0.1802 | 0.9773 |
| 0.0005 | 6.0 | 1050 | 0.1826 | 0.9773 |
| 0.0003 | 7.0 | 1225 | 0.1868 | 0.9773 |
| 0.0002 | 8.0 | 1400 | 0.1904 | 0.9773 |
| 0.0003 | 9.0 | 1575 | 0.1925 | 0.9773 |
| 0.0002 | 10.0 | 1750 | 0.1934 | 0.9773 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.5.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0