metadata
license: apache-2.0
base_model: facebook/hubert-base-ls960
tags:
- generated_from_trainer
datasets:
- speech_commands
metrics:
- accuracy
model-index:
- name: hubert-base-ls960-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: 0.7594424460431655
hubert-base-ls960-speech-commands-h
This model is a fine-tuned version of facebook/hubert-base-ls960 on the speech_commands dataset. It achieves the following results on the evaluation set:
- Loss: 1.3148
- Accuracy: 0.7594
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: 48
- eval_batch_size: 48
- 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 |
|---|---|---|---|---|
| 2.743 | 1.0 | 824 | 3.4107 | 0.1781 |
| 2.3383 | 2.0 | 1648 | 3.4632 | 0.1862 |
| 2.2702 | 3.0 | 2472 | 3.5701 | 0.0787 |
| 2.3059 | 4.0 | 3296 | 3.5742 | 0.0971 |
| 2.2574 | 5.0 | 4120 | 3.5457 | 0.1493 |
| 2.0617 | 6.0 | 4944 | 2.8490 | 0.3453 |
| 2.0289 | 7.0 | 5768 | 2.7607 | 0.3215 |
| 1.7807 | 8.0 | 6592 | 2.5721 | 0.4681 |
| 1.8188 | 9.0 | 7416 | 2.5625 | 0.5301 |
| 1.3812 | 10.0 | 8240 | 2.4258 | 0.6942 |
| 1.3136 | 11.0 | 9064 | 2.2087 | 0.6884 |
| 1.2867 | 12.0 | 9888 | 1.8347 | 0.7221 |
| 1.1036 | 13.0 | 10712 | 1.6731 | 0.7383 |
| 0.9534 | 14.0 | 11536 | 1.8732 | 0.7307 |
| 0.9289 | 15.0 | 12360 | 1.5742 | 0.7415 |
| 1.0973 | 16.0 | 13184 | 1.3693 | 0.7365 |
| 0.989 | 17.0 | 14008 | 1.2718 | 0.7455 |
| 0.8876 | 18.0 | 14832 | 1.3148 | 0.7594 |
| 0.814 | 19.0 | 15656 | 1.2231 | 0.7558 |
| 0.9899 | 20.0 | 16480 | 1.2349 | 0.7522 |
Framework versions
- Transformers 4.43.3
- Pytorch 2.2.2+cu121
- Datasets 2.18.0
- Tokenizers 0.19.1