metadata
license: apache-2.0
base_model: ntu-spml/distilhubert
tags:
- generated_from_trainer
datasets:
- audiofolder
metrics:
- accuracy
model-index:
- name: heartbeat-detection
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: audiofolder
type: audiofolder
config: default
split: train[:90]
args: default
metrics:
- name: Accuracy
type: accuracy
value: 1
heartbeat-detection
This model is a fine-tuned version of ntu-spml/distilhubert on the audiofolder dataset. It achieves the following results on the evaluation set:
- Loss: 0.6877
- Accuracy: 1.0
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: 1e-06
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.01
- num_epochs: 100
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.5999 | 1.0 | 8 | 1.5962 | 0.2222 |
| 1.5827 | 2.0 | 16 | 1.5749 | 0.8519 |
| 1.5617 | 3.0 | 24 | 1.5546 | 0.9630 |
| 1.541 | 4.0 | 32 | 1.5346 | 1.0 |
| 1.5215 | 5.0 | 40 | 1.5153 | 1.0 |
| 1.5014 | 6.0 | 48 | 1.4962 | 1.0 |
| 1.4817 | 7.0 | 56 | 1.4774 | 1.0 |
| 1.4622 | 8.0 | 64 | 1.4588 | 1.0 |
| 1.4444 | 9.0 | 72 | 1.4404 | 1.0 |
| 1.4244 | 10.0 | 80 | 1.4221 | 1.0 |
| 1.4068 | 11.0 | 88 | 1.4041 | 1.0 |
| 1.389 | 12.0 | 96 | 1.3862 | 1.0 |
| 1.3706 | 13.0 | 104 | 1.3685 | 1.0 |
| 1.3526 | 14.0 | 112 | 1.3510 | 1.0 |
| 1.3348 | 15.0 | 120 | 1.3337 | 1.0 |
| 1.3176 | 16.0 | 128 | 1.3165 | 1.0 |
| 1.3003 | 17.0 | 136 | 1.2997 | 1.0 |
| 1.283 | 18.0 | 144 | 1.2829 | 1.0 |
| 1.2667 | 19.0 | 152 | 1.2664 | 1.0 |
| 1.2508 | 20.0 | 160 | 1.2501 | 1.0 |
| 1.2345 | 21.0 | 168 | 1.2341 | 1.0 |
| 1.2189 | 22.0 | 176 | 1.2183 | 1.0 |
| 1.2052 | 23.0 | 184 | 1.2029 | 1.0 |
| 1.1882 | 24.0 | 192 | 1.1876 | 1.0 |
| 1.1734 | 25.0 | 200 | 1.1725 | 1.0 |
| 1.159 | 26.0 | 208 | 1.1579 | 1.0 |
| 1.1449 | 27.0 | 216 | 1.1436 | 1.0 |
| 1.1302 | 28.0 | 224 | 1.1295 | 1.0 |
| 1.1177 | 29.0 | 232 | 1.1156 | 1.0 |
| 1.1032 | 30.0 | 240 | 1.1021 | 1.0 |
| 1.0902 | 31.0 | 248 | 1.0887 | 1.0 |
| 1.0776 | 32.0 | 256 | 1.0758 | 1.0 |
| 1.0656 | 33.0 | 264 | 1.0628 | 1.0 |
| 1.0519 | 34.0 | 272 | 1.0502 | 1.0 |
| 1.0412 | 35.0 | 280 | 1.0378 | 1.0 |
| 1.0277 | 36.0 | 288 | 1.0255 | 1.0 |
| 1.0178 | 37.0 | 296 | 1.0135 | 1.0 |
| 1.0067 | 38.0 | 304 | 1.0021 | 1.0 |
| 0.9948 | 39.0 | 312 | 0.9907 | 1.0 |
| 0.9852 | 40.0 | 320 | 0.9799 | 1.0 |
| 0.973 | 41.0 | 328 | 0.9690 | 1.0 |
| 0.9635 | 42.0 | 336 | 0.9586 | 1.0 |
| 0.953 | 43.0 | 344 | 0.9484 | 1.0 |
| 0.943 | 44.0 | 352 | 0.9384 | 1.0 |
| 0.935 | 45.0 | 360 | 0.9284 | 1.0 |
| 0.9249 | 46.0 | 368 | 0.9188 | 1.0 |
| 0.916 | 47.0 | 376 | 0.9092 | 1.0 |
| 0.9068 | 48.0 | 384 | 0.9001 | 1.0 |
| 0.8989 | 49.0 | 392 | 0.8912 | 1.0 |
| 0.8904 | 50.0 | 400 | 0.8826 | 1.0 |
| 0.8831 | 51.0 | 408 | 0.8741 | 1.0 |
| 0.8744 | 52.0 | 416 | 0.8659 | 1.0 |
| 0.8676 | 53.0 | 424 | 0.8580 | 1.0 |
| 0.8592 | 54.0 | 432 | 0.8501 | 1.0 |
| 0.8531 | 55.0 | 440 | 0.8427 | 1.0 |
| 0.8445 | 56.0 | 448 | 0.8352 | 1.0 |
| 0.8382 | 57.0 | 456 | 0.8281 | 1.0 |
| 0.8308 | 58.0 | 464 | 0.8212 | 1.0 |
| 0.8264 | 59.0 | 472 | 0.8145 | 1.0 |
| 0.819 | 60.0 | 480 | 0.8079 | 1.0 |
| 0.8132 | 61.0 | 488 | 0.8016 | 1.0 |
| 0.8078 | 62.0 | 496 | 0.7954 | 1.0 |
| 0.801 | 63.0 | 504 | 0.7895 | 1.0 |
| 0.7971 | 64.0 | 512 | 0.7838 | 1.0 |
| 0.791 | 65.0 | 520 | 0.7783 | 1.0 |
| 0.7861 | 66.0 | 528 | 0.7729 | 1.0 |
| 0.7801 | 67.0 | 536 | 0.7677 | 1.0 |
| 0.7756 | 68.0 | 544 | 0.7627 | 1.0 |
| 0.7719 | 69.0 | 552 | 0.7579 | 1.0 |
| 0.7671 | 70.0 | 560 | 0.7533 | 1.0 |
| 0.762 | 71.0 | 568 | 0.7488 | 1.0 |
| 0.7581 | 72.0 | 576 | 0.7446 | 1.0 |
| 0.7538 | 73.0 | 584 | 0.7405 | 1.0 |
| 0.7512 | 74.0 | 592 | 0.7365 | 1.0 |
| 0.7467 | 75.0 | 600 | 0.7327 | 1.0 |
| 0.7435 | 76.0 | 608 | 0.7291 | 1.0 |
| 0.7412 | 77.0 | 616 | 0.7256 | 1.0 |
| 0.7368 | 78.0 | 624 | 0.7223 | 1.0 |
| 0.7345 | 79.0 | 632 | 0.7192 | 1.0 |
| 0.7313 | 80.0 | 640 | 0.7162 | 1.0 |
| 0.7285 | 81.0 | 648 | 0.7134 | 1.0 |
| 0.7261 | 82.0 | 656 | 0.7107 | 1.0 |
| 0.724 | 83.0 | 664 | 0.7082 | 1.0 |
| 0.7221 | 84.0 | 672 | 0.7058 | 1.0 |
| 0.7189 | 85.0 | 680 | 0.7036 | 1.0 |
| 0.7171 | 86.0 | 688 | 0.7016 | 1.0 |
| 0.7148 | 87.0 | 696 | 0.6997 | 1.0 |
| 0.7131 | 88.0 | 704 | 0.6979 | 1.0 |
| 0.7116 | 89.0 | 712 | 0.6963 | 1.0 |
| 0.7107 | 90.0 | 720 | 0.6948 | 1.0 |
| 0.7089 | 91.0 | 728 | 0.6934 | 1.0 |
| 0.7077 | 92.0 | 736 | 0.6922 | 1.0 |
| 0.7069 | 93.0 | 744 | 0.6911 | 1.0 |
| 0.7071 | 94.0 | 752 | 0.6902 | 1.0 |
| 0.705 | 95.0 | 760 | 0.6895 | 1.0 |
| 0.7045 | 96.0 | 768 | 0.6888 | 1.0 |
| 0.7038 | 97.0 | 776 | 0.6883 | 1.0 |
| 0.7029 | 98.0 | 784 | 0.6880 | 1.0 |
| 0.7041 | 99.0 | 792 | 0.6878 | 1.0 |
| 0.7029 | 100.0 | 800 | 0.6877 | 1.0 |
Framework versions
- Transformers 4.39.3
- Pytorch 2.2.1+cu121
- Datasets 2.18.0
- Tokenizers 0.15.2