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	---
	tags:
	- automatic-speech-recognition
	- gary109/AI_Light_Dance
	- generated_from_trainer
	datasets:
	- ai_light_dance
	metrics:
	- wer
	model-index:
	- name: ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-v2
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-v2

	This model is a fine-tuned version of [gary109/ai-light-dance_drums_pretrain_wav2vec2-base-new](https://huggingface.co/gary109/ai-light-dance_drums_pretrain_wav2vec2-base-new) on the GARY109/AI_LIGHT_DANCE - ONSET-IDMT-SMT-DRUMS-V2+MDBDRUMS dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.5264
	- Wer: 0.3635

	## 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: 0.0004
	- train_batch_size: 4
	- eval_batch_size: 4
	- seed: 42
	- gradient_accumulation_steps: 2
	- total_train_batch_size: 8
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 10
	- num_epochs: 100.0
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|
	\| 4.6468 \| 0.98 \| 22 \| 3.2315 \| 1.0 \|
	\| 1.5745 \| 1.98 \| 44 \| 3.1603 \| 1.0 \|
	\| 1.465 \| 2.98 \| 66 \| 2.2551 \| 1.0 \|
	\| 1.3168 \| 3.98 \| 88 \| 1.8461 \| 1.0 \|
	\| 1.1359 \| 4.98 \| 110 \| 1.4874 \| 0.9797 \|
	\| 0.9769 \| 5.98 \| 132 \| 1.7359 \| 0.5495 \|
	\| 0.9019 \| 6.98 \| 154 \| 1.5833 \| 0.5268 \|
	\| 0.8057 \| 7.98 \| 176 \| 1.4892 \| 0.5304 \|
	\| 1.0845 \| 8.98 \| 198 \| 1.3939 \| 0.5197 \|
	\| 0.7562 \| 9.98 \| 220 \| 1.1238 \| 0.5447 \|
	\| 0.7259 \| 10.98 \| 242 \| 1.2936 \| 0.5006 \|
	\| 0.7318 \| 11.98 \| 264 \| 1.2763 \| 0.4660 \|
	\| 0.6452 \| 12.98 \| 286 \| 1.2947 \| 0.4779 \|
	\| 0.6353 \| 13.98 \| 308 \| 1.1925 \| 0.4517 \|
	\| 0.6463 \| 14.98 \| 330 \| 0.8667 \| 0.4100 \|
	\| 0.5381 \| 15.98 \| 352 \| 1.1243 \| 0.3909 \|
	\| 0.5637 \| 16.98 \| 374 \| 0.8683 \| 0.3754 \|
	\| 0.6149 \| 17.98 \| 396 \| 1.1040 \| 0.3731 \|
	\| 0.6138 \| 18.98 \| 418 \| 1.1068 \| 0.3850 \|
	\| 0.7381 \| 19.98 \| 440 \| 0.9203 \| 0.3623 \|
	\| 0.5064 \| 20.98 \| 462 \| 0.8806 \| 0.3540 \|
	\| 0.4731 \| 21.98 \| 484 \| 0.7259 \| 0.3623 \|
	\| 0.5232 \| 22.98 \| 506 \| 0.7935 \| 0.3516 \|
	\| 0.4689 \| 23.98 \| 528 \| 0.7771 \| 0.3540 \|
	\| 0.4902 \| 24.98 \| 550 \| 0.6897 \| 0.3909 \|
	\| 0.4079 \| 25.98 \| 572 \| 0.8030 \| 0.3552 \|
	\| 0.5045 \| 26.98 \| 594 \| 0.6778 \| 0.3790 \|
	\| 0.4373 \| 27.98 \| 616 \| 0.7456 \| 0.3695 \|
	\| 0.4366 \| 28.98 \| 638 \| 0.7009 \| 0.3433 \|
	\| 0.3944 \| 29.98 \| 660 \| 0.6841 \| 0.3468 \|
	\| 0.4206 \| 30.98 \| 682 \| 0.7093 \| 0.3373 \|
	\| 0.3949 \| 31.98 \| 704 \| 0.6901 \| 0.3576 \|
	\| 0.4416 \| 32.98 \| 726 \| 0.6762 \| 0.3397 \|
	\| 0.4248 \| 33.98 \| 748 \| 0.7196 \| 0.3540 \|
	\| 0.4214 \| 34.98 \| 770 \| 0.6669 \| 0.3254 \|
	\| 0.416 \| 35.98 \| 792 \| 0.6422 \| 0.3445 \|
	\| 0.3687 \| 36.98 \| 814 \| 0.6345 \| 0.3504 \|
	\| 0.4119 \| 37.98 \| 836 \| 0.6306 \| 0.3385 \|
	\| 0.359 \| 38.98 \| 858 \| 0.6538 \| 0.3576 \|
	\| 0.359 \| 39.98 \| 880 \| 0.6613 \| 0.3349 \|
	\| 0.3488 \| 40.98 \| 902 \| 0.5976 \| 0.3468 \|
	\| 0.3543 \| 41.98 \| 924 \| 0.6327 \| 0.3433 \|
	\| 0.3647 \| 42.98 \| 946 \| 0.6208 \| 0.3600 \|
	\| 0.3529 \| 43.98 \| 968 \| 0.6008 \| 0.3492 \|
	\| 0.3691 \| 44.98 \| 990 \| 0.6065 \| 0.3492 \|
	\| 0.329 \| 45.98 \| 1012 \| 0.6288 \| 0.3373 \|
	\| 0.3357 \| 46.98 \| 1034 \| 0.5760 \| 0.3480 \|
	\| 0.3318 \| 47.98 \| 1056 \| 0.5637 \| 0.3564 \|
	\| 0.3181 \| 48.98 \| 1078 \| 0.5560 \| 0.3468 \|
	\| 0.3313 \| 49.98 \| 1100 \| 0.5905 \| 0.3337 \|
	\| 0.3059 \| 50.98 \| 1122 \| 0.5443 \| 0.3278 \|
	\| 0.3375 \| 51.98 \| 1144 \| 0.5695 \| 0.3576 \|
	\| 0.3191 \| 52.98 \| 1166 \| 0.5874 \| 0.3385 \|
	\| 0.3115 \| 53.98 \| 1188 \| 0.5264 \| 0.3635 \|
	\| 0.3044 \| 54.98 \| 1210 \| 0.5480 \| 0.3433 \|
	\| 0.3256 \| 55.98 \| 1232 \| 0.5677 \| 0.3385 \|
	\| 0.2938 \| 56.98 \| 1254 \| 0.5597 \| 0.3445 \|
	\| 0.2853 \| 57.98 \| 1276 \| 0.5942 \| 0.3373 \|
	\| 0.3348 \| 58.98 \| 1298 \| 0.5733 \| 0.3421 \|
	\| 0.3024 \| 59.98 \| 1320 \| 0.5604 \| 0.3433 \|
	\| 0.2655 \| 60.98 \| 1342 \| 0.5348 \| 0.3468 \|
	\| 0.3029 \| 61.98 \| 1364 \| 0.5752 \| 0.3206 \|
	\| 0.3435 \| 62.98 \| 1386 \| 0.5489 \| 0.3063 \|
	\| 0.3125 \| 63.98 \| 1408 \| 0.5736 \| 0.3075 \|
	\| 0.263 \| 64.98 \| 1430 \| 0.5505 \| 0.3206 \|
	\| 0.2665 \| 65.98 \| 1452 \| 0.5391 \| 0.3230 \|
	\| 0.299 \| 66.98 \| 1474 \| 0.5389 \| 0.3135 \|
	\| 0.2909 \| 67.98 \| 1496 \| 0.5841 \| 0.3099 \|
	\| 0.2988 \| 68.98 \| 1518 \| 0.5847 \| 0.3004 \|
	\| 0.2879 \| 69.98 \| 1540 \| 0.5941 \| 0.2968 \|
	\| 0.2802 \| 70.98 \| 1562 \| 0.6612 \| 0.2920 \|
	\| 0.2877 \| 71.98 \| 1584 \| 0.5641 \| 0.3051 \|
	\| 0.2727 \| 72.98 \| 1606 \| 0.6138 \| 0.3063 \|
	\| 0.2668 \| 73.98 \| 1628 \| 0.6087 \| 0.2920 \|
	\| 0.2675 \| 74.98 \| 1650 \| 0.5876 \| 0.2932 \|
	\| 0.264 \| 75.98 \| 1672 \| 0.6043 \| 0.2980 \|
	\| 0.2352 \| 76.98 \| 1694 \| 0.5829 \| 0.2932 \|
	\| 0.2494 \| 77.98 \| 1716 \| 0.5775 \| 0.3063 \|
	\| 0.2621 \| 78.98 \| 1738 \| 0.5676 \| 0.2956 \|
	\| 0.2788 \| 79.98 \| 1760 \| 0.5864 \| 0.2932 \|
	\| 0.2615 \| 80.98 \| 1782 \| 0.5754 \| 0.3015 \|
	\| 0.2542 \| 81.98 \| 1804 \| 0.5651 \| 0.3027 \|
	\| 0.2641 \| 82.98 \| 1826 \| 0.5731 \| 0.3004 \|
	\| 0.2532 \| 83.98 \| 1848 \| 0.5782 \| 0.2968 \|
	\| 0.2645 \| 84.98 \| 1870 \| 0.5718 \| 0.3039 \|
	\| 0.2296 \| 85.98 \| 1892 \| 0.5628 \| 0.3147 \|
	\| 0.2394 \| 86.98 \| 1914 \| 0.5920 \| 0.3027 \|
	\| 0.2636 \| 87.98 \| 1936 \| 0.6085 \| 0.2968 \|
	\| 0.2371 \| 88.98 \| 1958 \| 0.5809 \| 0.3075 \|
	\| 0.2364 \| 89.98 \| 1980 \| 0.5927 \| 0.3039 \|
	\| 0.2812 \| 90.98 \| 2002 \| 0.5713 \| 0.3123 \|
	\| 0.2141 \| 91.98 \| 2024 \| 0.5743 \| 0.3039 \|
	\| 0.2919 \| 92.98 \| 2046 \| 0.5837 \| 0.3063 \|
	\| 0.2288 \| 93.98 \| 2068 \| 0.5860 \| 0.3015 \|
	\| 0.2585 \| 94.98 \| 2090 \| 0.5776 \| 0.3147 \|
	\| 0.2529 \| 95.98 \| 2112 \| 0.5625 \| 0.3159 \|
	\| 0.2343 \| 96.98 \| 2134 \| 0.5700 \| 0.3087 \|
	\| 0.2567 \| 97.98 \| 2156 \| 0.5729 \| 0.3087 \|
	\| 0.2448 \| 98.98 \| 2178 \| 0.5728 \| 0.3111 \|
	\| 0.2501 \| 99.98 \| 2200 \| 0.5744 \| 0.3099 \|


	### Framework versions

	- Transformers 4.25.0.dev0
	- Pytorch 1.8.1+cu111
	- Datasets 2.7.1.dev0
	- Tokenizers 0.13.2

	---
	tags:
	- automatic-speech-recognition
	- gary109/AI_Light_Dance
	- generated_from_trainer
	datasets:
	- ai_light_dance
	metrics:
	- wer
	model-index:
	- name: ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-v2
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# ai-light-dance_drums_ft_pretrain_wav2vec2-base-new-v2

	This model is a fine-tuned version of [gary109/ai-light-dance_drums_pretrain_wav2vec2-base-new](https://huggingface.co/gary109/ai-light-dance_drums_pretrain_wav2vec2-base-new) on the GARY109/AI_LIGHT_DANCE - ONSET-IDMT-SMT-DRUMS-V2+MDBDRUMS dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.5264
	- Wer: 0.3635

	## 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: 0.0004
	- train_batch_size: 4
	- eval_batch_size: 4
	- seed: 42
	- gradient_accumulation_steps: 2
	- total_train_batch_size: 8
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 10
	- num_epochs: 100.0
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \|
	\|:-------------:\|:-----:\|:----:\|:---------------:\|:------:\|
	\| 4.6468 \| 0.98 \| 22 \| 3.2315 \| 1.0 \|
	\| 1.5745 \| 1.98 \| 44 \| 3.1603 \| 1.0 \|
	\| 1.465 \| 2.98 \| 66 \| 2.2551 \| 1.0 \|
	\| 1.3168 \| 3.98 \| 88 \| 1.8461 \| 1.0 \|
	\| 1.1359 \| 4.98 \| 110 \| 1.4874 \| 0.9797 \|
	\| 0.9769 \| 5.98 \| 132 \| 1.7359 \| 0.5495 \|
	\| 0.9019 \| 6.98 \| 154 \| 1.5833 \| 0.5268 \|
	\| 0.8057 \| 7.98 \| 176 \| 1.4892 \| 0.5304 \|
	\| 1.0845 \| 8.98 \| 198 \| 1.3939 \| 0.5197 \|
	\| 0.7562 \| 9.98 \| 220 \| 1.1238 \| 0.5447 \|
	\| 0.7259 \| 10.98 \| 242 \| 1.2936 \| 0.5006 \|
	\| 0.7318 \| 11.98 \| 264 \| 1.2763 \| 0.4660 \|
	\| 0.6452 \| 12.98 \| 286 \| 1.2947 \| 0.4779 \|
	\| 0.6353 \| 13.98 \| 308 \| 1.1925 \| 0.4517 \|
	\| 0.6463 \| 14.98 \| 330 \| 0.8667 \| 0.4100 \|
	\| 0.5381 \| 15.98 \| 352 \| 1.1243 \| 0.3909 \|
	\| 0.5637 \| 16.98 \| 374 \| 0.8683 \| 0.3754 \|
	\| 0.6149 \| 17.98 \| 396 \| 1.1040 \| 0.3731 \|
	\| 0.6138 \| 18.98 \| 418 \| 1.1068 \| 0.3850 \|
	\| 0.7381 \| 19.98 \| 440 \| 0.9203 \| 0.3623 \|
	\| 0.5064 \| 20.98 \| 462 \| 0.8806 \| 0.3540 \|
	\| 0.4731 \| 21.98 \| 484 \| 0.7259 \| 0.3623 \|
	\| 0.5232 \| 22.98 \| 506 \| 0.7935 \| 0.3516 \|
	\| 0.4689 \| 23.98 \| 528 \| 0.7771 \| 0.3540 \|
	\| 0.4902 \| 24.98 \| 550 \| 0.6897 \| 0.3909 \|
	\| 0.4079 \| 25.98 \| 572 \| 0.8030 \| 0.3552 \|
	\| 0.5045 \| 26.98 \| 594 \| 0.6778 \| 0.3790 \|
	\| 0.4373 \| 27.98 \| 616 \| 0.7456 \| 0.3695 \|
	\| 0.4366 \| 28.98 \| 638 \| 0.7009 \| 0.3433 \|
	\| 0.3944 \| 29.98 \| 660 \| 0.6841 \| 0.3468 \|
	\| 0.4206 \| 30.98 \| 682 \| 0.7093 \| 0.3373 \|
	\| 0.3949 \| 31.98 \| 704 \| 0.6901 \| 0.3576 \|
	\| 0.4416 \| 32.98 \| 726 \| 0.6762 \| 0.3397 \|
	\| 0.4248 \| 33.98 \| 748 \| 0.7196 \| 0.3540 \|
	\| 0.4214 \| 34.98 \| 770 \| 0.6669 \| 0.3254 \|
	\| 0.416 \| 35.98 \| 792 \| 0.6422 \| 0.3445 \|
	\| 0.3687 \| 36.98 \| 814 \| 0.6345 \| 0.3504 \|
	\| 0.4119 \| 37.98 \| 836 \| 0.6306 \| 0.3385 \|
	\| 0.359 \| 38.98 \| 858 \| 0.6538 \| 0.3576 \|
	\| 0.359 \| 39.98 \| 880 \| 0.6613 \| 0.3349 \|
	\| 0.3488 \| 40.98 \| 902 \| 0.5976 \| 0.3468 \|
	\| 0.3543 \| 41.98 \| 924 \| 0.6327 \| 0.3433 \|
	\| 0.3647 \| 42.98 \| 946 \| 0.6208 \| 0.3600 \|
	\| 0.3529 \| 43.98 \| 968 \| 0.6008 \| 0.3492 \|
	\| 0.3691 \| 44.98 \| 990 \| 0.6065 \| 0.3492 \|
	\| 0.329 \| 45.98 \| 1012 \| 0.6288 \| 0.3373 \|
	\| 0.3357 \| 46.98 \| 1034 \| 0.5760 \| 0.3480 \|
	\| 0.3318 \| 47.98 \| 1056 \| 0.5637 \| 0.3564 \|
	\| 0.3181 \| 48.98 \| 1078 \| 0.5560 \| 0.3468 \|
	\| 0.3313 \| 49.98 \| 1100 \| 0.5905 \| 0.3337 \|
	\| 0.3059 \| 50.98 \| 1122 \| 0.5443 \| 0.3278 \|
	\| 0.3375 \| 51.98 \| 1144 \| 0.5695 \| 0.3576 \|
	\| 0.3191 \| 52.98 \| 1166 \| 0.5874 \| 0.3385 \|
	\| 0.3115 \| 53.98 \| 1188 \| 0.5264 \| 0.3635 \|
	\| 0.3044 \| 54.98 \| 1210 \| 0.5480 \| 0.3433 \|
	\| 0.3256 \| 55.98 \| 1232 \| 0.5677 \| 0.3385 \|
	\| 0.2938 \| 56.98 \| 1254 \| 0.5597 \| 0.3445 \|
	\| 0.2853 \| 57.98 \| 1276 \| 0.5942 \| 0.3373 \|
	\| 0.3348 \| 58.98 \| 1298 \| 0.5733 \| 0.3421 \|
	\| 0.3024 \| 59.98 \| 1320 \| 0.5604 \| 0.3433 \|
	\| 0.2655 \| 60.98 \| 1342 \| 0.5348 \| 0.3468 \|
	\| 0.3029 \| 61.98 \| 1364 \| 0.5752 \| 0.3206 \|
	\| 0.3435 \| 62.98 \| 1386 \| 0.5489 \| 0.3063 \|
	\| 0.3125 \| 63.98 \| 1408 \| 0.5736 \| 0.3075 \|
	\| 0.263 \| 64.98 \| 1430 \| 0.5505 \| 0.3206 \|
	\| 0.2665 \| 65.98 \| 1452 \| 0.5391 \| 0.3230 \|
	\| 0.299 \| 66.98 \| 1474 \| 0.5389 \| 0.3135 \|
	\| 0.2909 \| 67.98 \| 1496 \| 0.5841 \| 0.3099 \|
	\| 0.2988 \| 68.98 \| 1518 \| 0.5847 \| 0.3004 \|
	\| 0.2879 \| 69.98 \| 1540 \| 0.5941 \| 0.2968 \|
	\| 0.2802 \| 70.98 \| 1562 \| 0.6612 \| 0.2920 \|
	\| 0.2877 \| 71.98 \| 1584 \| 0.5641 \| 0.3051 \|
	\| 0.2727 \| 72.98 \| 1606 \| 0.6138 \| 0.3063 \|
	\| 0.2668 \| 73.98 \| 1628 \| 0.6087 \| 0.2920 \|
	\| 0.2675 \| 74.98 \| 1650 \| 0.5876 \| 0.2932 \|
	\| 0.264 \| 75.98 \| 1672 \| 0.6043 \| 0.2980 \|
	\| 0.2352 \| 76.98 \| 1694 \| 0.5829 \| 0.2932 \|
	\| 0.2494 \| 77.98 \| 1716 \| 0.5775 \| 0.3063 \|
	\| 0.2621 \| 78.98 \| 1738 \| 0.5676 \| 0.2956 \|
	\| 0.2788 \| 79.98 \| 1760 \| 0.5864 \| 0.2932 \|
	\| 0.2615 \| 80.98 \| 1782 \| 0.5754 \| 0.3015 \|
	\| 0.2542 \| 81.98 \| 1804 \| 0.5651 \| 0.3027 \|
	\| 0.2641 \| 82.98 \| 1826 \| 0.5731 \| 0.3004 \|
	\| 0.2532 \| 83.98 \| 1848 \| 0.5782 \| 0.2968 \|
	\| 0.2645 \| 84.98 \| 1870 \| 0.5718 \| 0.3039 \|
	\| 0.2296 \| 85.98 \| 1892 \| 0.5628 \| 0.3147 \|
	\| 0.2394 \| 86.98 \| 1914 \| 0.5920 \| 0.3027 \|
	\| 0.2636 \| 87.98 \| 1936 \| 0.6085 \| 0.2968 \|
	\| 0.2371 \| 88.98 \| 1958 \| 0.5809 \| 0.3075 \|
	\| 0.2364 \| 89.98 \| 1980 \| 0.5927 \| 0.3039 \|
	\| 0.2812 \| 90.98 \| 2002 \| 0.5713 \| 0.3123 \|
	\| 0.2141 \| 91.98 \| 2024 \| 0.5743 \| 0.3039 \|
	\| 0.2919 \| 92.98 \| 2046 \| 0.5837 \| 0.3063 \|
	\| 0.2288 \| 93.98 \| 2068 \| 0.5860 \| 0.3015 \|
	\| 0.2585 \| 94.98 \| 2090 \| 0.5776 \| 0.3147 \|
	\| 0.2529 \| 95.98 \| 2112 \| 0.5625 \| 0.3159 \|
	\| 0.2343 \| 96.98 \| 2134 \| 0.5700 \| 0.3087 \|
	\| 0.2567 \| 97.98 \| 2156 \| 0.5729 \| 0.3087 \|
	\| 0.2448 \| 98.98 \| 2178 \| 0.5728 \| 0.3111 \|
	\| 0.2501 \| 99.98 \| 2200 \| 0.5744 \| 0.3099 \|


	### Framework versions

	- Transformers 4.25.0.dev0
	- Pytorch 1.8.1+cu111
	- Datasets 2.7.1.dev0
	- Tokenizers 0.13.2