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wav2vec2-demo-F01-2
This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset. It achieves the following results on the evaluation set:
- Loss: 1.6361
- Wer: 0.9025
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
Training results
Training Loss | Epoch | Step | Validation Loss | Wer |
---|---|---|---|---|
23.7408 | 0.81 | 500 | 3.3782 | 1.0 |
3.348 | 1.62 | 1000 | 2.9501 | 1.0 |
2.8539 | 2.44 | 1500 | 2.6975 | 1.0 |
2.311 | 3.25 | 2000 | 1.7770 | 1.3175 |
1.5102 | 4.06 | 2500 | 1.3481 | 1.3515 |
1.0616 | 4.87 | 3000 | 1.4306 | 1.2313 |
0.8493 | 5.68 | 3500 | 1.2261 | 1.1701 |
0.7058 | 6.49 | 4000 | 1.2132 | 1.1111 |
0.6129 | 7.31 | 4500 | 1.4230 | 1.1429 |
0.5513 | 8.12 | 5000 | 1.2003 | 1.0499 |
0.4957 | 8.93 | 5500 | 1.5534 | 1.1043 |
0.4456 | 9.74 | 6000 | 1.2315 | 1.0658 |
0.4101 | 10.55 | 6500 | 1.1621 | 1.0680 |
0.3776 | 11.36 | 7000 | 1.4302 | 1.0385 |
0.3318 | 12.18 | 7500 | 1.3488 | 0.9977 |
0.3189 | 12.99 | 8000 | 1.4050 | 1.0295 |
0.3103 | 13.8 | 8500 | 1.4535 | 1.0385 |
0.2791 | 14.61 | 9000 | 1.3318 | 1.0181 |
0.2681 | 15.42 | 9500 | 1.5199 | 0.9909 |
0.2352 | 16.23 | 10000 | 1.5019 | 1.0023 |
0.235 | 17.05 | 10500 | 1.7984 | 0.9955 |
0.2319 | 17.86 | 11000 | 1.3399 | 0.9705 |
0.221 | 18.67 | 11500 | 1.8316 | 0.9342 |
0.2154 | 19.48 | 12000 | 1.6837 | 0.9637 |
0.1911 | 20.29 | 12500 | 1.6999 | 0.9388 |
0.1754 | 21.1 | 13000 | 1.4801 | 0.9274 |
0.1776 | 21.92 | 13500 | 1.7954 | 0.9206 |
0.1616 | 22.73 | 14000 | 1.7891 | 0.9320 |
0.1579 | 23.54 | 14500 | 1.5692 | 0.9116 |
0.173 | 24.35 | 15000 | 1.4928 | 0.9048 |
0.1561 | 25.16 | 15500 | 1.6492 | 0.9116 |
0.1542 | 25.97 | 16000 | 1.7356 | 0.9048 |
0.131 | 26.79 | 16500 | 1.7785 | 0.9048 |
0.1295 | 27.6 | 17000 | 1.6532 | 0.9116 |
0.1374 | 28.41 | 17500 | 1.6760 | 0.9093 |
0.1186 | 29.22 | 18000 | 1.6361 | 0.9025 |
Framework versions
- Transformers 4.23.1
- Pytorch 1.12.1+cu113
- Datasets 1.18.3
- Tokenizers 0.13.2