metadata
library_name: transformers
license: bsd-3-clause
base_model:
- MIT/ast-finetuned-speech-commands-v2
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- precision
- recall
- f1
model-index:
- name: ast-mlcommons-speech-commands
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: audiofolder
type: audiofolder
config: default
split: validation
args: default
metrics:
- name: Precision
type: precision
value: 0.9743628199079283
- name: Recall
type: recall
value: 0.9743424814179531
- name: F1
type: f1
value: 0.9743165983480835
ast-mlcommons-speech-commands
This model is a fine-tuned version of MIT/ast-finetuned-speech-commands-v2 on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.1346
- Precision: 0.9744
- Recall: 0.9743
- F1: 0.9743
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: 32
- eval_batch_size: 32
- seed: 42
- optimizer: Use 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: 5
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|---|---|---|---|---|---|---|
| 0.0799 | 1.0 | 3496 | 0.1498 | 0.9596 | 0.9573 | 0.9577 |
| 0.0624 | 2.0 | 6992 | 0.1141 | 0.9689 | 0.9687 | 0.9685 |
| 0.0091 | 3.0 | 10488 | 0.1285 | 0.9713 | 0.9713 | 0.9711 |
| 0.0384 | 4.0 | 13984 | 0.1237 | 0.9743 | 0.9743 | 0.9742 |
| 0.0019 | 5.0 | 17480 | 0.1346 | 0.9744 | 0.9743 | 0.9743 |
Framework versions
- Transformers 4.51.3
- Pytorch 2.7.0+cu128
- Datasets 3.6.0
- Tokenizers 0.21.1