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wav2vec2-large-robust-ft-timit
This model is a fine-tuned version of facebook/wav2vec2-large-robust on the None dataset. It achieves the following results on the evaluation set:
- Loss: 0.2768
- Wer: 0.2321
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: 0.0001
- train_batch_size: 8
- eval_batch_size: 8
- seed: 42
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 1000
- num_epochs: 30
- mixed_precision_training: Native AMP
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
6.6175 | 1.0 | 500 | 3.3025 | 1.0 |
3.0746 | 2.01 | 1000 | 2.9598 | 1.0 |
1.967 | 3.01 | 1500 | 0.6760 | 0.5607 |
0.7545 | 4.02 | 2000 | 0.4500 | 0.4567 |
0.5415 | 5.02 | 2500 | 0.3702 | 0.3882 |
0.4445 | 6.02 | 3000 | 0.3421 | 0.3584 |
0.3601 | 7.03 | 3500 | 0.2947 | 0.3096 |
0.3098 | 8.03 | 4000 | 0.2740 | 0.2894 |
0.2606 | 9.04 | 4500 | 0.2725 | 0.2787 |
0.238 | 10.04 | 5000 | 0.2549 | 0.2617 |
0.2142 | 11.04 | 5500 | 0.2485 | 0.2530 |
0.1787 | 12.05 | 6000 | 0.2683 | 0.2514 |
0.1652 | 13.05 | 6500 | 0.2559 | 0.2476 |
0.1569 | 14.06 | 7000 | 0.2777 | 0.2470 |
0.1443 | 15.06 | 7500 | 0.2661 | 0.2431 |
0.1335 | 16.06 | 8000 | 0.2717 | 0.2422 |
0.1291 | 17.07 | 8500 | 0.2672 | 0.2428 |
0.1192 | 18.07 | 9000 | 0.2684 | 0.2395 |
0.1144 | 19.08 | 9500 | 0.2770 | 0.2411 |
0.1052 | 20.08 | 10000 | 0.2831 | 0.2379 |
0.1004 | 21.08 | 10500 | 0.2847 | 0.2375 |
0.1053 | 22.09 | 11000 | 0.2851 | 0.2360 |
0.1005 | 23.09 | 11500 | 0.2807 | 0.2361 |
0.0904 | 24.1 | 12000 | 0.2764 | 0.2346 |
0.0876 | 25.1 | 12500 | 0.2774 | 0.2325 |
0.0883 | 26.1 | 13000 | 0.2768 | 0.2313 |
0.0848 | 27.11 | 13500 | 0.2840 | 0.2307 |
0.0822 | 28.11 | 14000 | 0.2812 | 0.2316 |
0.09 | 29.12 | 14500 | 0.2768 | 0.2321 |
Framework versions
- Transformers 4.19.2
- Pytorch 1.8.2+cu111
- Datasets 1.17.0
- Tokenizers 0.11.6