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wav2vec2-base-vios-v2
This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the vivos_dataset dataset. It achieves the following results on the evaluation set:
- Loss: 0.6056
- Wer: 0.2442
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: 8
- eval_batch_size: 8
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 16
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 500
- num_epochs: 20
- mixed_precision_training: Native AMP
Training results
| Training Loss | Epoch | Step | Validation Loss | Wer |
|---|---|---|---|---|
| 7.8344 | 0.69 | 500 | 3.5012 | 1.0 |
| 3.4505 | 1.37 | 1000 | 3.4081 | 1.0 |
| 2.1426 | 2.06 | 1500 | 0.8761 | 0.6241 |
| 0.8801 | 2.74 | 2000 | 0.5476 | 0.4241 |
| 0.6453 | 3.43 | 2500 | 0.4384 | 0.3495 |
| 0.5449 | 4.12 | 3000 | 0.4055 | 0.3160 |
| 0.4862 | 4.8 | 3500 | 0.3815 | 0.3002 |
| 0.4435 | 5.49 | 4000 | 0.3525 | 0.2776 |
| 0.4205 | 6.17 | 4500 | 0.3660 | 0.2725 |
| 0.3974 | 6.86 | 5000 | 0.3386 | 0.2565 |
| 0.3758 | 7.54 | 5500 | 0.3492 | 0.2607 |
| 0.3595 | 8.23 | 6000 | 0.3391 | 0.2441 |
| 0.3438 | 8.92 | 6500 | 0.3255 | 0.2354 |
| 0.3308 | 9.6 | 7000 | 0.3379 | 0.2422 |
| 0.3265 | 10.29 | 7500 | 0.3375 | 0.2349 |
| 0.311 | 10.97 | 8000 | 0.3356 | 0.2306 |
| 0.3071 | 11.66 | 8500 | 0.3286 | 0.2249 |
| 0.2941 | 12.35 | 9000 | 0.3176 | 0.2211 |
| 0.296 | 13.03 | 9500 | 0.3268 | 0.2257 |
| 0.2852 | 13.72 | 10000 | 0.3265 | 0.2196 |
| 0.3102 | 14.4 | 10500 | 0.3390 | 0.2209 |
| 0.2974 | 15.09 | 11000 | 0.3493 | 0.2199 |
| 0.3433 | 15.78 | 11500 | 0.3687 | 0.2199 |
| 0.3526 | 16.46 | 12000 | 0.3698 | 0.2170 |
| 0.36 | 17.15 | 12500 | 0.4110 | 0.2227 |
| 0.4322 | 17.83 | 13000 | 0.4830 | 0.2290 |
| 0.4973 | 18.52 | 13500 | 0.5280 | 0.2356 |
| 0.5701 | 19.2 | 14000 | 0.5990 | 0.2370 |
| 0.6014 | 19.89 | 14500 | 0.6056 | 0.2442 |
Framework versions
- Transformers 4.16.2
- Pytorch 1.11.0+cu113
- Datasets 1.18.3
- Tokenizers 0.12.1
