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-finetuned-en-alphabets
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: 0.9650943396226415
- name: Recall
type: recall
value: 0.9481132075471698
- name: F1
type: f1
value: 0.9476170056358736
ast-finetuned-en-alphabets
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: 0.1933
- Precision: 0.9651
- Recall: 0.9481
- F1: 0.9476
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: 64
- eval_batch_size: 64
- 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: 4
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Precision | Recall | F1 |
|---|---|---|---|---|---|---|
| 0.3705 | 1.0 | 113 | 0.2441 | 0.9566 | 0.9434 | 0.9432 |
| 0.1728 | 2.0 | 226 | 0.1617 | 0.9608 | 0.9481 | 0.9478 |
| 0.0321 | 3.0 | 339 | 0.1838 | 0.9651 | 0.9481 | 0.9476 |
| 0.011 | 4.0 | 452 | 0.1933 | 0.9651 | 0.9481 | 0.9476 |
Framework versions
- Transformers 4.47.1
- Pytorch 2.2.1+cu121
- Datasets 3.2.0
- Tokenizers 0.21.0