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--- |
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license: apache-2.0 |
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tags: |
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- generated_from_trainer |
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model-index: |
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- name: wav2vec2-xlsr-1B-NPSC-NN |
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results: [] |
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--- |
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<!-- This model card has been generated automatically according to the information the Trainer had access to. You |
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should probably proofread and complete it, then remove this comment. --> |
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# wav2vec2-xlsr-1B-NPSC-NN |
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This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the None dataset. |
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It achieves the following results on the evaluation set: |
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- Loss: 0.4567 |
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- Wer: 0.1533 |
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## Model description |
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More information needed |
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## Intended uses & limitations |
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More information needed |
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## Training and evaluation data |
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More information needed |
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## Training procedure |
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### Training hyperparameters |
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The following hyperparameters were used during training: |
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- learning_rate: 6e-05 |
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- train_batch_size: 8 |
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- eval_batch_size: 8 |
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- seed: 42 |
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- gradient_accumulation_steps: 2 |
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- total_train_batch_size: 16 |
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- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 |
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- lr_scheduler_type: linear |
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- lr_scheduler_warmup_steps: 2000 |
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- num_epochs: 50.0 |
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- mixed_precision_training: Native AMP |
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### Training results |
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| Training Loss | Epoch | Step | Validation Loss | Wer | |
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|:-------------:|:-----:|:-----:|:---------------:|:------:| |
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| 1.6894 | 1.08 | 500 | 1.2423 | 0.8619 | |
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| 0.7543 | 2.15 | 1000 | 0.5956 | 0.3817 | |
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| 0.5481 | 3.23 | 1500 | 0.5043 | 0.3246 | |
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| 0.4661 | 4.3 | 2000 | 0.4813 | 0.2793 | |
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| 0.3901 | 5.38 | 2500 | 0.4371 | 0.2592 | |
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| 0.3512 | 6.45 | 3000 | 0.4216 | 0.2458 | |
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| 0.3016 | 7.53 | 3500 | 0.3814 | 0.2257 | |
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| 0.278 | 8.6 | 4000 | 0.4151 | 0.2145 | |
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| 0.2435 | 9.68 | 4500 | 0.4816 | 0.2130 | |
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| 0.2122 | 10.75 | 5000 | 0.4489 | 0.2137 | |
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| 0.1949 | 11.83 | 5500 | 0.3978 | 0.2063 | |
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| 0.1929 | 12.9 | 6000 | 0.3823 | 0.2026 | |
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| 0.1757 | 13.98 | 6500 | 0.3409 | 0.1965 | |
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| 0.1771 | 15.05 | 7000 | 0.3844 | 0.1936 | |
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| 0.1452 | 16.13 | 7500 | 0.3749 | 0.1900 | |
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| 0.1341 | 17.2 | 8000 | 0.4407 | 0.2026 | |
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| 0.13 | 18.28 | 8500 | 0.4253 | 0.1883 | |
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| 0.1183 | 19.35 | 9000 | 0.4311 | 0.1880 | |
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| 0.118 | 20.43 | 9500 | 0.4431 | 0.1882 | |
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| 0.1123 | 21.51 | 10000 | 0.4753 | 0.1820 | |
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| 0.1037 | 22.58 | 10500 | 0.4087 | 0.1834 | |
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| 0.1066 | 23.66 | 11000 | 0.4151 | 0.1845 | |
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| 0.0977 | 24.73 | 11500 | 0.4367 | 0.1783 | |
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| 0.0968 | 25.81 | 12000 | 0.4237 | 0.1756 | |
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| 0.0835 | 26.88 | 12500 | 0.4729 | 0.1781 | |
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| 0.0919 | 27.96 | 13000 | 0.4153 | 0.1701 | |
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| 0.0677 | 29.03 | 13500 | 0.4317 | 0.1693 | |
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| 0.0726 | 30.11 | 14000 | 0.4380 | 0.1736 | |
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| 0.066 | 31.18 | 14500 | 0.4384 | 0.1681 | |
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| 0.0713 | 32.26 | 15000 | 0.4215 | 0.1629 | |
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| 0.0605 | 33.33 | 15500 | 0.4574 | 0.1714 | |
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| 0.0632 | 34.41 | 16000 | 0.4343 | 0.1642 | |
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| 0.0567 | 35.48 | 16500 | 0.4231 | 0.1601 | |
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| 0.0556 | 36.56 | 17000 | 0.4404 | 0.1667 | |
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| 0.0426 | 37.63 | 17500 | 0.4459 | 0.1625 | |
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| 0.0445 | 38.71 | 18000 | 0.4484 | 0.1629 | |
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| 0.0463 | 39.78 | 18500 | 0.4508 | 0.1596 | |
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| 0.0448 | 40.86 | 19000 | 0.4395 | 0.1605 | |
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| 0.0434 | 41.94 | 19500 | 0.4490 | 0.1607 | |
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| 0.0347 | 43.01 | 20000 | 0.4772 | 0.1582 | |
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| 0.0332 | 44.09 | 20500 | 0.4729 | 0.1582 | |
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| 0.037 | 45.16 | 21000 | 0.4559 | 0.1573 | |
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| 0.0328 | 46.24 | 21500 | 0.4664 | 0.1560 | |
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| 0.0366 | 47.31 | 22000 | 0.4543 | 0.1543 | |
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| 0.0377 | 48.39 | 22500 | 0.4507 | 0.1560 | |
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| 0.0331 | 49.46 | 23000 | 0.4567 | 0.1533 | |
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### Framework versions |
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- Transformers 4.17.0.dev0 |
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- Pytorch 1.10.1+cu102 |
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- Datasets 1.18.2.dev0 |
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- Tokenizers 0.11.0 |
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