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---
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tags:
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- generated_from_trainer
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datasets:
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- ai_light_dance
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model-index:
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- name: ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-13k_onset-drums_fold_2
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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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# ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-13k_onset-drums_fold_2
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This model is a fine-tuned version of [gary109/ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-13k_onset-drums_fold_1](https://huggingface.co/gary109/ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-13k_onset-drums_fold_1) on the ai_light_dance dataset.
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It achieves the following results on the evaluation set:
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- Loss: 0.4720
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- Wer: 0.1388
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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: 0.0003
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- train_batch_size: 4
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- eval_batch_size: 4
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- seed: 42
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- gradient_accumulation_steps: 4
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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: 100
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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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| 0.3238 | 0.99 | 69 | 0.4581 | 0.2081 |
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| 0.275 | 1.99 | 138 | 0.6494 | 0.3343 |
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| 0.2965 | 2.99 | 207 | 0.6193 | 0.2275 |
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| 0.3406 | 3.99 | 276 | 0.6934 | 0.2615 |
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| 0.3906 | 4.99 | 345 | 0.6265 | 0.1835 |
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| 0.4643 | 5.99 | 414 | 0.5879 | 0.1899 |
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| 0.4652 | 6.99 | 483 | 0.4961 | 0.1604 |
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| 0.4512 | 7.99 | 552 | 0.5712 | 0.2801 |
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| 0.5321 | 8.99 | 621 | 0.6898 | 0.2936 |
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| 0.64 | 9.99 | 690 | 0.5916 | 0.2648 |
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| 0.2959 | 10.99 | 759 | 0.5574 | 0.1745 |
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| 0.2053 | 11.99 | 828 | 0.5216 | 0.2009 |
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| 0.2433 | 12.99 | 897 | 0.4738 | 0.1643 |
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| 0.2036 | 13.99 | 966 | 0.5063 | 0.1651 |
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| 0.2654 | 14.99 | 1035 | 0.4904 | 0.1511 |
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| 0.3641 | 15.99 | 1104 | 0.4660 | 0.1669 |
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| 0.373 | 16.99 | 1173 | 0.5133 | 0.2106 |
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| 0.4715 | 17.99 | 1242 | 0.5313 | 0.1912 |
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| 0.4893 | 18.99 | 1311 | 0.5152 | 0.1712 |
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| 0.4875 | 19.99 | 1380 | 0.5482 | 0.1718 |
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| 0.1971 | 20.99 | 1449 | 0.4566 | 0.1449 |
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| 0.1286 | 21.99 | 1518 | 0.4515 | 0.1478 |
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| 0.1472 | 22.99 | 1587 | 0.5059 | 0.1418 |
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| 0.1917 | 23.99 | 1656 | 0.5583 | 0.1457 |
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| 0.2874 | 24.99 | 1725 | 0.5195 | 0.1503 |
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| 0.2252 | 25.99 | 1794 | 0.4409 | 0.1506 |
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| 0.3142 | 26.99 | 1863 | 0.4180 | 0.1433 |
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| 0.385 | 27.99 | 1932 | 0.4708 | 0.1367 |
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| 0.4296 | 28.99 | 2001 | 0.4740 | 0.1506 |
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| 0.4404 | 29.99 | 2070 | 0.4652 | 0.1646 |
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| 0.2466 | 30.99 | 2139 | 0.5013 | 0.1528 |
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| 0.1017 | 31.99 | 2208 | 0.4578 | 0.1552 |
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| 0.1383 | 32.99 | 2277 | 0.5026 | 0.1419 |
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| 0.1719 | 33.99 | 2346 | 0.4651 | 0.1442 |
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| 0.1808 | 34.99 | 2415 | 0.4499 | 0.1412 |
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| 0.2429 | 35.99 | 2484 | 0.4523 | 0.1472 |
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| 0.2651 | 36.99 | 2553 | 0.4544 | 0.1397 |
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| 0.2748 | 37.99 | 2622 | 0.4181 | 0.1386 |
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| 0.4171 | 38.99 | 2691 | 0.4385 | 0.1334 |
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| 0.4119 | 39.99 | 2760 | 0.4568 | 0.1504 |
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| 0.1453 | 40.99 | 2829 | 0.4425 | 0.1431 |
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| 0.105 | 41.99 | 2898 | 0.4367 | 0.1353 |
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| 0.1205 | 42.99 | 2967 | 0.4418 | 0.1340 |
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| 0.2039 | 43.99 | 3036 | 0.4586 | 0.1379 |
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| 98 |
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| 0.1773 | 44.99 | 3105 | 0.4686 | 0.1391 |
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| 0.2186 | 45.99 | 3174 | 0.4975 | 0.1446 |
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| 0.2358 | 46.99 | 3243 | 0.4886 | 0.1448 |
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| 0.3525 | 47.99 | 3312 | 0.4706 | 0.1398 |
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| 0.3713 | 48.99 | 3381 | 0.4713 | 0.1388 |
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| 0.3543 | 49.99 | 3450 | 0.4720 | 0.1388 |
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### Framework versions
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- Transformers 4.24.0.dev0
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- Pytorch 1.12.1+cu113
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- Datasets 2.6.1
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- Tokenizers 0.13.1
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