metadata
license: apache-2.0
base_model: facebook/wav2vec2-base
tags:
- generated_from_trainer
datasets:
- marsyas/gtzan
metrics:
- accuracy
model-index:
- name: wav2vec2-base-finetuned-organ
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: GTZAN
type: marsyas/gtzan
metrics:
- name: Accuracy
type: accuracy
value: 0.8181818181818182
wav2vec2-base-finetuned-organ
This model is a fine-tuned version of facebook/wav2vec2-base on the GTZAN dataset. It achieves the following results on the evaluation set:
- Loss: 1.0117
- Accuracy: 0.8182
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: 16
- eval_batch_size: 16
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_ratio: 0.1
- num_epochs: 50
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy |
|---|---|---|---|---|
| 1.0965 | 1.0 | 6 | 1.0843 | 0.4545 |
| 1.0989 | 2.0 | 12 | 1.0883 | 0.3636 |
| 1.0931 | 3.0 | 18 | 1.0914 | 0.5455 |
| 1.0702 | 4.0 | 24 | 1.0578 | 0.4545 |
| 0.9822 | 5.0 | 30 | 0.9994 | 0.7273 |
| 0.9139 | 6.0 | 36 | 0.9735 | 0.5455 |
| 0.8008 | 7.0 | 42 | 0.7004 | 0.9091 |
| 0.6798 | 8.0 | 48 | 0.7404 | 0.8182 |
| 0.5969 | 9.0 | 54 | 0.7192 | 0.7273 |
| 0.4976 | 10.0 | 60 | 0.4668 | 0.9091 |
| 0.436 | 11.0 | 66 | 0.7406 | 0.7273 |
| 0.5859 | 12.0 | 72 | 0.6139 | 0.7273 |
| 0.3788 | 13.0 | 78 | 0.6551 | 0.7273 |
| 0.3176 | 14.0 | 84 | 0.4746 | 0.9091 |
| 0.2892 | 15.0 | 90 | 0.8285 | 0.7273 |
| 0.2452 | 16.0 | 96 | 0.8523 | 0.7273 |
| 0.1464 | 17.0 | 102 | 0.9791 | 0.7273 |
| 0.4589 | 18.0 | 108 | 1.2469 | 0.6364 |
| 0.1641 | 19.0 | 114 | 1.1607 | 0.6364 |
| 0.1765 | 20.0 | 120 | 0.7318 | 0.8182 |
| 0.1553 | 21.0 | 126 | 1.1178 | 0.6364 |
| 0.2048 | 22.0 | 132 | 1.2835 | 0.6364 |
| 0.2477 | 23.0 | 138 | 0.7558 | 0.8182 |
| 0.2042 | 24.0 | 144 | 0.8053 | 0.8182 |
| 0.2242 | 25.0 | 150 | 1.1131 | 0.7273 |
| 0.2063 | 26.0 | 156 | 1.1455 | 0.7273 |
| 0.1148 | 27.0 | 162 | 1.1386 | 0.7273 |
| 0.0948 | 28.0 | 168 | 1.0196 | 0.7273 |
| 0.2296 | 29.0 | 174 | 1.2216 | 0.7273 |
| 0.1771 | 30.0 | 180 | 1.2645 | 0.7273 |
| 0.0749 | 31.0 | 186 | 1.3599 | 0.6364 |
| 0.0973 | 32.0 | 192 | 1.2880 | 0.7273 |
| 0.0231 | 33.0 | 198 | 0.9015 | 0.8182 |
| 0.1185 | 34.0 | 204 | 0.9180 | 0.8182 |
| 0.1645 | 35.0 | 210 | 1.3635 | 0.7273 |
| 0.0163 | 36.0 | 216 | 1.3961 | 0.7273 |
| 0.0743 | 37.0 | 222 | 1.3699 | 0.7273 |
| 0.0211 | 38.0 | 228 | 0.8085 | 0.8182 |
| 0.0713 | 39.0 | 234 | 0.8418 | 0.8182 |
| 0.0122 | 40.0 | 240 | 0.7659 | 0.8182 |
| 0.0116 | 41.0 | 246 | 0.9891 | 0.8182 |
| 0.0117 | 42.0 | 252 | 1.4963 | 0.7273 |
| 0.0738 | 43.0 | 258 | 1.4932 | 0.7273 |
| 0.0718 | 44.0 | 264 | 1.4665 | 0.7273 |
| 0.1334 | 45.0 | 270 | 0.9666 | 0.8182 |
| 0.0662 | 46.0 | 276 | 0.9798 | 0.8182 |
| 0.0973 | 47.0 | 282 | 0.9954 | 0.8182 |
| 0.0105 | 48.0 | 288 | 1.0073 | 0.8182 |
| 0.0092 | 49.0 | 294 | 1.0107 | 0.8182 |
| 0.0089 | 50.0 | 300 | 1.0117 | 0.8182 |
Framework versions
- Transformers 4.42.3
- Pytorch 2.3.1+cu121
- Datasets 2.19.1
- Tokenizers 0.19.1