metadata
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
- generated_from_trainer
datasets:
- arrow
metrics:
- accuracy
model-index:
- name: wav2vec2-xlrs-finetuning
results:
- task:
name: Audio Classification
type: audio-classification
dataset:
name: arrow
type: arrow
config: default
split: train
args: default
metrics:
- name: Accuracy
type: accuracy
value: 0.2802721088435374
wav2vec2-xlrs-finetuning
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the arrow dataset. It achieves the following results on the evaluation set:
- Loss: 2.6930
- Accuracy: 0.2803
- F1 Score: 0.1655
- Mse: 1.9782
- Mae: 1.0558
- Mae^m: 1.5570
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: 0.0003
- train_batch_size: 9
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 18
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Accuracy | F1 Score | Mse | Mae | Mae^m |
|---|---|---|---|---|---|---|---|---|
| 1.7525 | 0.6116 | 100 | 1.7058 | 0.3497 | 0.0648 | 3.0952 | 1.2231 | 2.75 |
| 1.6944 | 1.2232 | 200 | 1.6854 | 0.3497 | 0.0648 | 3.0952 | 1.2231 | 2.75 |
| 1.7037 | 1.8349 | 300 | 1.7210 | 0.2367 | 0.0479 | 2.0190 | 1.1075 | 2.25 |
| 1.7667 | 2.4465 | 400 | 1.6868 | 0.3646 | 0.0927 | 2.6707 | 1.1224 | 2.5934 |
| 1.6859 | 3.0581 | 500 | 1.6833 | 0.3497 | 0.0648 | 3.0952 | 1.2231 | 2.75 |
| 1.6991 | 3.6697 | 600 | 1.6827 | 0.3497 | 0.0648 | 3.0952 | 1.2231 | 2.75 |
| 1.6404 | 4.2813 | 700 | 1.7078 | 0.3497 | 0.0648 | 3.0952 | 1.2231 | 2.75 |
| 1.7088 | 4.8930 | 800 | 1.6962 | 0.3497 | 0.0648 | 3.0952 | 1.2231 | 2.75 |
| 1.751 | 5.5046 | 900 | 1.6863 | 0.3497 | 0.0648 | 3.0952 | 1.2231 | 2.75 |
| 1.7813 | 6.1162 | 1000 | 1.6966 | 0.3497 | 0.0648 | 3.0952 | 1.2231 | 2.75 |
| 1.712 | 6.7278 | 1100 | 1.6783 | 0.3497 | 0.0648 | 3.0952 | 1.2231 | 2.75 |
| 1.6503 | 7.3394 | 1200 | 1.6816 | 0.3497 | 0.0648 | 3.0952 | 1.2231 | 2.75 |
| 1.5967 | 7.9511 | 1300 | 1.6588 | 0.3497 | 0.0648 | 3.0952 | 1.2231 | 2.75 |
| 1.6538 | 8.5627 | 1400 | 1.6229 | 0.3497 | 0.0648 | 3.0952 | 1.2231 | 2.75 |
| 1.654 | 9.1743 | 1500 | 1.6118 | 0.3633 | 0.0919 | 2.5347 | 1.1061 | 2.2236 |
| 1.5913 | 9.7859 | 1600 | 1.5914 | 0.3660 | 0.1215 | 2.1537 | 1.0218 | 2.1284 |
| 1.5896 | 10.3976 | 1700 | 1.6029 | 0.3578 | 0.1333 | 1.9102 | 0.9796 | 1.9420 |
| 1.5274 | 11.0092 | 1800 | 1.6201 | 0.3769 | 0.1124 | 2.3429 | 1.0558 | 2.1678 |
| 1.4581 | 11.6208 | 1900 | 1.5739 | 0.3687 | 0.1036 | 2.3918 | 1.0748 | 2.2286 |
| 1.4 | 12.2324 | 2000 | 1.6100 | 0.3442 | 0.1365 | 2.0395 | 1.0190 | 1.6647 |
| 1.5475 | 12.8440 | 2100 | 1.5741 | 0.3524 | 0.1431 | 1.6735 | 0.9388 | 1.7980 |
| 1.4724 | 13.4557 | 2200 | 1.5782 | 0.3660 | 0.1596 | 1.7537 | 0.9347 | 1.6576 |
| 1.3411 | 14.0673 | 2300 | 1.5875 | 0.3810 | 0.1273 | 2.0503 | 0.9891 | 1.8447 |
| 1.4884 | 14.6789 | 2400 | 1.5822 | 0.3537 | 0.1388 | 1.9293 | 0.9878 | 1.6427 |
| 1.4326 | 15.2905 | 2500 | 1.5996 | 0.3456 | 0.1613 | 1.7320 | 0.9565 | 1.5640 |
| 1.3412 | 15.9021 | 2600 | 1.6870 | 0.2952 | 0.1280 | 2.0231 | 1.0653 | 1.5219 |
| 1.3139 | 16.5138 | 2700 | 1.6911 | 0.3361 | 0.1439 | 1.7605 | 0.9660 | 1.6466 |
| 1.1558 | 17.1254 | 2800 | 1.6576 | 0.3469 | 0.1504 | 1.8340 | 0.9714 | 1.6081 |
| 1.3099 | 17.7370 | 2900 | 1.6184 | 0.3156 | 0.1694 | 1.7075 | 0.9701 | 1.5880 |
| 1.3763 | 18.3486 | 3000 | 1.7211 | 0.3333 | 0.1505 | 1.7578 | 0.9633 | 1.6180 |
| 1.1732 | 18.9602 | 3100 | 1.7625 | 0.3401 | 0.1580 | 1.8082 | 0.9728 | 1.6791 |
| 1.1137 | 19.5719 | 3200 | 1.8241 | 0.3252 | 0.1757 | 1.7946 | 0.9864 | 1.6387 |
| 1.128 | 20.1835 | 3300 | 1.8824 | 0.3156 | 0.1727 | 1.7823 | 0.9932 | 1.6233 |
| 1.0219 | 20.7951 | 3400 | 1.9237 | 0.3456 | 0.1630 | 1.7891 | 0.9619 | 1.5458 |
| 1.0466 | 21.4067 | 3500 | 1.9450 | 0.3238 | 0.1610 | 1.6993 | 0.9619 | 1.6316 |
| 1.1768 | 22.0183 | 3600 | 2.0642 | 0.3034 | 0.1720 | 1.9946 | 1.0503 | 1.4796 |
| 1.1656 | 22.6300 | 3700 | 2.2870 | 0.2680 | 0.1696 | 2.1741 | 1.1156 | 1.4731 |
| 0.8624 | 23.2416 | 3800 | 2.2612 | 0.3102 | 0.1749 | 1.8435 | 1.0 | 1.6763 |
| 1.0267 | 23.8532 | 3900 | 2.2753 | 0.2939 | 0.1647 | 2.0150 | 1.0599 | 1.5093 |
| 0.8059 | 24.4648 | 4000 | 2.2790 | 0.3197 | 0.1704 | 1.7714 | 0.9796 | 1.5151 |
| 0.8924 | 25.0765 | 4100 | 2.3183 | 0.2898 | 0.1755 | 1.9048 | 1.0313 | 1.5539 |
| 0.9581 | 25.6881 | 4200 | 2.3930 | 0.2803 | 0.1722 | 2.0082 | 1.0667 | 1.5516 |
| 0.7513 | 26.2997 | 4300 | 2.5025 | 0.2789 | 0.1631 | 2.0367 | 1.0735 | 1.5718 |
| 0.8617 | 26.9113 | 4400 | 2.5418 | 0.2966 | 0.1686 | 1.9959 | 1.0463 | 1.5909 |
| 0.7768 | 27.5229 | 4500 | 2.6084 | 0.2898 | 0.1718 | 1.9551 | 1.0463 | 1.5532 |
| 0.7316 | 28.1346 | 4600 | 2.6581 | 0.2844 | 0.1687 | 1.8803 | 1.0286 | 1.5414 |
| 0.7436 | 28.7462 | 4700 | 2.6899 | 0.2667 | 0.1611 | 2.0503 | 1.0871 | 1.5357 |
| 0.8487 | 29.3578 | 4800 | 2.6930 | 0.2803 | 0.1655 | 1.9782 | 1.0558 | 1.5570 |
Framework versions
- Transformers 4.44.2
- Pytorch 2.4.1+cu121
- Datasets 2.21.0
- Tokenizers 0.19.1