xtreme_s_w2v2_minds14.en-US
This model is a fine-tuned version of facebook/wav2vec2-large-lv60 on the GOOGLE/XTREME_S - MINDS14.EN-US dataset. It achieves the following results on the evaluation set:
- Loss: 0.5337
- F1: 0.9144
- Accuracy: 0.9113
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: 2
- eval_batch_size: 8
- seed: 42
- distributed_type: multi-GPU
- num_devices: 2
- gradient_accumulation_steps: 8
- total_train_batch_size: 32
- total_eval_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 100
- num_epochs: 150.0
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | F1 | Accuracy |
|---|---|---|---|---|---|
| 2.6482 | 3.95 | 20 | 2.6421 | 0.0242 | 0.0745 |
| 2.6292 | 7.95 | 40 | 2.6359 | 0.0108 | 0.0816 |
| 2.5993 | 11.95 | 60 | 2.6301 | 0.0167 | 0.0674 |
| 2.4023 | 15.95 | 80 | 2.5514 | 0.1105 | 0.1454 |
| 1.4015 | 19.95 | 100 | 1.6843 | 0.5599 | 0.5851 |
| 0.4379 | 23.95 | 120 | 0.8126 | 0.7921 | 0.7908 |
| 0.0642 | 27.95 | 140 | 0.7178 | 0.8158 | 0.8156 |
| 0.0376 | 31.95 | 160 | 0.7286 | 0.8473 | 0.8475 |
| 0.0185 | 35.95 | 180 | 0.6779 | 0.8719 | 0.8723 |
| 0.0752 | 39.95 | 200 | 0.7096 | 0.8578 | 0.8511 |
| 0.0266 | 43.95 | 220 | 0.7655 | 0.8596 | 0.8546 |
| 0.0078 | 47.95 | 240 | 0.7623 | 0.8563 | 0.8511 |
| 0.007 | 51.95 | 260 | 0.6620 | 0.8794 | 0.8759 |
| 0.0047 | 55.95 | 280 | 0.5936 | 0.9045 | 0.9007 |
| 0.0067 | 59.95 | 300 | 0.8279 | 0.8546 | 0.8617 |
| 0.0394 | 63.95 | 320 | 0.8766 | 0.8359 | 0.8227 |
| 0.0051 | 67.95 | 340 | 0.8097 | 0.8483 | 0.8475 |
| 0.0095 | 71.95 | 360 | 0.6095 | 0.9083 | 0.9078 |
| 0.0026 | 75.95 | 380 | 0.5286 | 0.8889 | 0.8865 |
| 0.0023 | 79.95 | 400 | 0.7218 | 0.8926 | 0.8936 |
| 0.0023 | 83.95 | 420 | 0.6551 | 0.8997 | 0.8972 |
| 0.0027 | 87.95 | 440 | 0.6664 | 0.8848 | 0.8794 |
| 0.0019 | 91.95 | 460 | 0.5344 | 0.9032 | 0.9043 |
| 0.002 | 95.95 | 480 | 0.5863 | 0.8983 | 0.9007 |
| 0.0015 | 99.95 | 500 | 0.5715 | 0.9047 | 0.9043 |
| 0.0016 | 103.95 | 520 | 0.5615 | 0.8956 | 0.8936 |
| 0.0014 | 107.95 | 540 | 0.6353 | 0.8965 | 0.8936 |
| 0.0014 | 111.95 | 560 | 0.5593 | 0.9041 | 0.9007 |
| 0.0013 | 115.95 | 580 | 0.6041 | 0.8977 | 0.8936 |
| 0.0013 | 119.95 | 600 | 0.5794 | 0.9026 | 0.9007 |
| 0.0012 | 123.95 | 620 | 0.6858 | 0.9003 | 0.8972 |
| 0.0013 | 127.95 | 640 | 0.6730 | 0.9002 | 0.8972 |
| 0.0013 | 131.95 | 660 | 0.5707 | 0.9146 | 0.9113 |
| 0.0012 | 135.95 | 680 | 0.5604 | 0.9153 | 0.9113 |
| 0.0019 | 139.95 | 700 | 0.5468 | 0.9114 | 0.9078 |
| 0.0015 | 143.95 | 720 | 0.5361 | 0.9144 | 0.9113 |
| 0.0012 | 147.95 | 740 | 0.5337 | 0.9144 | 0.9113 |
Framework versions
- Transformers 4.18.0
- Pytorch 1.11.0+cu113
- Datasets 2.1.0
- Tokenizers 0.12.1