Hugging Face's logo

callmesan
/
whisper-audio-abuse-feature

Audio Classification
Transformers
PyTorch
whisper
Generated from Trainer
Inference Endpoints
Model card Files Community
whisper-audio-abuse-feature / README.md
callmesan's picture
callmesan
End of training
0dc3ac6 verified
preview code
|
raw history blame contribute delete
No virus
4.52 kB
metadata
license: apache-2.0
base_model: parambharat/whisper-tiny-south-indic
tags:
  - generated_from_trainer
metrics:
  - accuracy
model-index:
  - name: whisper-audio-abuse-feature
    results: []

whisper-audio-abuse-feature

This model is a fine-tuned version of parambharat/whisper-tiny-south-indic on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.4947
  • Accuracy: 0.8174
  • Macro F1-score: 0.7845

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: 2e-05
  • train_batch_size: 32
  • eval_batch_size: 32
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 128
  • 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

Training results

Training Loss Epoch Step Validation Loss Accuracy Macro F1-score
7.8152 0.52 10 7.7606 0.0 0.0
7.6445 1.04 20 7.4368 0.6718 0.1348
7.1349 1.56 30 6.6932 0.6897 0.4082
6.2832 2.08 40 5.7185 0.6897 0.4082
5.3726 2.6 50 4.8531 0.6897 0.4082
4.4541 3.12 60 4.1519 0.6897 0.4082
3.9045 3.64 70 3.5417 0.6897 0.4082
3.3278 4.16 80 2.9984 0.6897 0.4082
2.7361 4.68 90 2.5167 0.6897 0.4082
2.3838 5.19 100 2.1039 0.6897 0.4082
1.9557 5.71 110 1.7366 0.6897 0.4082
1.5922 6.23 120 1.4170 0.6897 0.4082
1.3228 6.75 130 1.1497 0.7068 0.4669
1.0767 7.27 140 0.9322 0.7779 0.6610
0.8649 7.79 150 0.7814 0.7860 0.6800
0.7058 8.31 160 0.6608 0.8165 0.7453
0.6253 8.83 170 0.5688 0.8327 0.7936
0.5269 9.35 180 0.5137 0.8291 0.7896
0.5016 9.87 190 0.4862 0.8300 0.7747
0.4409 10.39 200 0.4776 0.8040 0.7796
0.3793 10.91 210 0.4511 0.8345 0.8048
0.3501 11.43 220 0.4491 0.8228 0.7922
0.3692 11.95 230 0.4254 0.8327 0.7982
0.3148 12.47 240 0.4452 0.8228 0.7901
0.3114 12.99 250 0.4543 0.8345 0.7905
0.2812 13.51 260 0.4398 0.8363 0.7992
0.2635 14.03 270 0.4607 0.8327 0.7960
0.2491 14.55 280 0.4818 0.8327 0.7864
0.2825 15.06 290 0.4616 0.8219 0.7920
0.2494 15.58 300 0.4784 0.8129 0.7897
0.2127 16.1 310 0.4669 0.8273 0.7862
0.2003 16.62 320 0.4760 0.8174 0.7796
0.1907 17.14 330 0.4845 0.8246 0.7943
0.177 17.66 340 0.4870 0.8219 0.7867
0.1877 18.18 350 0.4884 0.8201 0.7909
0.1511 18.7 360 0.4907 0.8228 0.7845
0.1826 19.22 370 0.4932 0.8165 0.7839
0.1419 19.74 380 0.4947 0.8174 0.7845

Framework versions

  • Transformers 4.33.0
  • Pytorch 2.0.0
  • Datasets 2.1.0
  • Tokenizers 0.13.3