Hugging Face's logo

catactivationsound
/
distilhubert-finetuned-gtzan

Audio Classification
Transformers
PyTorch
TensorBoard
hubert
Generated from Trainer
Eval Results
Inference Endpoints
Model card Files Metrics Community
distilhubert-finetuned-gtzan / README.md
Cat Activation Sound
update model card README.md
e418bb6
preview code
|
raw
history blame contribute delete
3.51 kB
metadata
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
  - generated_from_trainer
datasets:
  - marsyas/gtzan
metrics:
  - accuracy
model-index:
  - name: ntu-spml/distilhubert-finetuned-gtzan
    results:
      - task:
          name: Audio Classification
          type: audio-classification
        dataset:
          name: GTZAN
          type: marsyas/gtzan
          config: all
          split: train
          args: all
        metrics:
          - name: Accuracy
            type: accuracy
            value: 0.87

ntu-spml/distilhubert-finetuned-gtzan

This model is a fine-tuned version of ntu-spml/distilhubert on the GTZAN dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7428
  • Accuracy: 0.87

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: 3.992986714871485e-05
  • train_batch_size: 16
  • eval_batch_size: 64
  • seed: 42
  • optimizer: Adam with betas=(0.9807885777224674,0.996064720140604) and epsilon=1e-08
  • lr_scheduler_type: cosine
  • lr_scheduler_warmup_steps: 509
  • num_epochs: 100

Training results

Training Loss Epoch Step Validation Loss Accuracy
No log 1.0 57 2.2832 0.3
No log 2.0 114 2.2273 0.28
No log 3.0 171 2.0861 0.46
No log 4.0 228 1.8473 0.5
No log 5.0 285 1.5146 0.6
No log 6.0 342 1.2140 0.69
No log 7.0 399 0.9856 0.74
No log 8.0 456 0.8056 0.79
1.6591 9.0 513 0.7135 0.8
1.6591 10.0 570 0.7642 0.75
1.6591 11.0 627 0.6344 0.79
1.6591 12.0 684 0.5982 0.83
1.6591 13.0 741 0.5369 0.86
1.6591 14.0 798 0.7501 0.79
1.6591 15.0 855 0.7493 0.78
1.6591 16.0 912 0.6891 0.83
1.6591 17.0 969 0.7492 0.8
0.2402 18.0 1026 0.6663 0.88
0.2402 19.0 1083 0.5750 0.89
0.2402 20.0 1140 0.8215 0.81
0.2402 21.0 1197 0.7435 0.79
0.2402 22.0 1254 0.8305 0.86
0.2402 23.0 1311 0.7636 0.83
0.2402 24.0 1368 0.9786 0.77
0.2402 25.0 1425 0.7082 0.88
0.2402 26.0 1482 0.7698 0.85
0.0206 27.0 1539 0.7360 0.87
0.0206 28.0 1596 0.8575 0.84
0.0206 29.0 1653 0.7428 0.87

Framework versions

  • Transformers 4.32.0.dev0
  • Pytorch 2.0.1
  • Datasets 2.13.2.dev1
  • Tokenizers 0.13.3