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ai-light-dance_drums_ft_pretrain_wav2vec2-base

Automatic Speech Recognition
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wav2vec2
gary109/AI_Light_Dance
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metadata
tags:
  - generated_from_trainer
datasets:
  - ai_light_dance
model-index:
  - name: ai-light-dance_drums_ft_pretrain_wav2vec2-base
    results: []

ai-light-dance_drums_ft_pretrain_wav2vec2-base

This model is a fine-tuned version of gary109/ai-light-dance_drums_pretrain_wav2vec2-base on the ai_light_dance dataset. It achieves the following results on the evaluation set:

  • Loss: 2.4280
  • Wer: 0.7546

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: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 64
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 20
  • num_epochs: 200.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
No log 0.9 8 129.7304 1.0826
58.4765 1.9 16 92.1020 1.3475
37.6409 2.9 24 23.8801 1.0
6.3535 3.9 32 13.2330 1.0
5.0319 4.9 40 8.3613 1.0
5.0319 5.9 48 5.5184 1.0
4.7733 6.9 56 4.1720 1.0
3.8911 7.9 64 3.9397 0.9994
3.6 8.9 72 4.0742 0.9994
3.1586 9.9 80 3.8396 0.9997
3.1586 10.9 88 3.8821 0.9994
3.3812 11.9 96 3.9755 0.9979
3.0088 12.9 104 4.5299 0.9970
2.7633 13.9 112 4.1392 0.9964
2.6359 14.9 120 3.8528 0.9985
2.6359 15.9 128 3.4156 0.9973
3.1964 16.9 136 3.3741 0.9967
2.4962 17.9 144 3.6132 0.9922
2.4907 18.9 152 3.5363 0.9958
2.413 19.9 160 3.6351 0.9928
2.413 20.9 168 3.6113 0.9925
2.5555 21.9 176 3.7120 0.9904
2.2909 22.9 184 3.4334 0.9913
2.298 23.9 192 3.5458 0.9913
2.2366 24.9 200 3.7105 0.9898
2.2366 25.9 208 4.2298 0.9865
2.3428 26.9 216 3.3207 0.9889
2.2519 27.9 224 3.1548 0.9895
2.0429 28.9 232 3.7694 0.9877
2.1886 29.9 240 3.5341 0.9877
2.1886 30.9 248 3.1388 0.9850
2.2182 31.9 256 2.9390 0.9817
1.9479 32.9 264 3.0060 0.9826
1.9703 33.9 272 3.2571 0.9787
1.9385 34.9 280 3.1086 0.9776
1.9385 35.9 288 2.8231 0.9656
2.0297 36.9 296 2.8961 0.9668
1.8406 37.9 304 2.8829 0.9671
1.8707 38.9 312 3.1194 0.9584
1.7798 39.9 320 3.0686 0.9599
1.7798 40.9 328 2.7280 0.9527
1.9163 41.9 336 2.6321 0.9476
1.7248 42.9 344 2.6813 0.9413
1.7602 43.9 352 2.7252 0.9419
1.7357 44.9 360 3.0335 0.9398
1.7357 45.9 368 2.8732 0.9345
1.7997 46.9 376 2.7709 0.9353
1.6268 47.9 384 2.7681 0.9279
1.6527 48.9 392 2.8259 0.9303
1.5715 49.9 400 2.8841 0.9342
1.5715 50.9 408 2.7944 0.9222
1.6903 51.9 416 3.1597 0.9204
1.5722 52.9 424 2.5595 0.9213
1.539 53.9 432 2.8160 0.9117
1.538 54.9 440 2.5656 0.9168
1.538 55.9 448 2.9077 0.9075
1.624 56.9 456 2.8725 0.8985
1.5052 57.9 464 2.6217 0.8902
1.4367 58.9 472 2.5043 0.8976
1.4814 59.9 480 3.0905 0.8949
1.4814 60.9 488 2.7213 0.8991
1.5696 61.9 496 2.6882 0.8913
1.4408 62.9 504 2.7322 0.8737
1.4065 63.9 512 2.7024 0.8827
1.3989 64.9 520 2.6809 0.8806
1.3989 65.9 528 2.6340 0.8866
1.5102 66.9 536 2.8651 0.8851
1.4158 67.9 544 2.7928 0.8758
1.3322 68.9 552 2.7549 0.8800
1.4226 69.9 560 2.6618 0.8776
1.4226 70.9 568 2.6937 0.8650
1.4735 71.9 576 2.6285 0.8668
1.338 72.9 584 2.5628 0.8668
1.335 73.9 592 2.4783 0.8608
1.3433 74.9 600 2.6549 0.8605
1.3433 75.9 608 2.3851 0.8536
1.4341 76.9 616 2.6057 0.8641
1.3036 77.9 624 2.4144 0.8614
1.2617 78.9 632 2.5002 0.8596
1.308 79.9 640 2.4929 0.8575
1.308 80.9 648 2.6993 0.8462
1.3877 81.9 656 2.5874 0.8509
1.2553 82.9 664 2.6430 0.8536
1.211 83.9 672 3.0369 0.8497
1.2647 84.9 680 2.7012 0.8438
1.2647 85.9 688 2.5129 0.8581
1.3168 86.9 696 2.5123 0.8453
1.1997 87.9 704 2.4592 0.8503
1.1866 88.9 712 2.6306 0.8420
1.2396 89.9 720 2.4730 0.8459
1.2396 90.9 728 2.6146 0.8488
1.3184 91.9 736 2.6204 0.8423
1.1704 92.9 744 2.8896 0.8441
1.1436 93.9 752 2.9971 0.8390
1.1716 94.9 760 2.7293 0.8479
1.1716 95.9 768 2.9620 0.8426
1.2487 96.9 776 2.6880 0.8333
1.118 97.9 784 2.6754 0.8321
1.186 98.9 792 2.6925 0.8342
1.1373 99.9 800 2.9207 0.8339
1.1373 100.9 808 2.8559 0.8354
1.2086 101.9 816 2.9774 0.8336
1.1227 102.9 824 3.0108 0.8192
1.1446 103.9 832 2.8997 0.8270
1.1142 104.9 840 2.6626 0.8306
1.1142 105.9 848 2.7737 0.8195
1.1665 106.9 856 2.5447 0.8186
1.1 107.9 864 2.4472 0.8312
1.0674 108.9 872 2.4062 0.8225
1.0556 109.9 880 2.4098 0.8246
1.0556 110.9 888 2.3447 0.8255
1.1834 111.9 896 2.5571 0.8066
1.0533 112.9 904 2.5983 0.8150
1.101 113.9 912 2.6911 0.7950
1.0633 114.9 920 2.5733 0.8078
1.0633 115.9 928 2.5813 0.8192
1.1512 116.9 936 2.6237 0.8135
1.0317 117.9 944 2.5566 0.8031
1.0117 118.9 952 2.5485 0.8016
1.0556 119.9 960 2.4271 0.7980
1.0556 120.9 968 2.6579 0.7941
1.1204 121.9 976 2.6150 0.7944
1.0378 122.9 984 2.5499 0.8025
1.0213 123.9 992 2.7273 0.7938
1.0247 124.9 1000 2.7522 0.7863
1.0247 125.9 1008 2.9163 0.7971
1.0939 126.9 1016 2.6221 0.7896
1.0399 127.9 1024 2.9418 0.8004
1.0233 128.9 1032 2.7558 0.7857
0.9702 129.9 1040 2.5745 0.7905
0.9702 130.9 1048 2.6720 0.7899
1.0676 131.9 1056 2.6901 0.8022
1.0044 132.9 1064 2.6594 0.7773
1.0276 133.9 1072 2.4739 0.7932
0.949 134.9 1080 2.5398 0.7755
0.949 135.9 1088 2.6267 0.7797
1.0508 136.9 1096 2.4829 0.7722
0.9937 137.9 1104 2.4289 0.7776
0.9677 138.9 1112 2.5845 0.7815
1.0115 139.9 1120 2.7132 0.7704
1.0115 140.9 1128 2.4297 0.7836
1.049 141.9 1136 2.3430 0.7830
0.9412 142.9 1144 2.6202 0.7698
0.9647 143.9 1152 2.5072 0.7710
0.9839 144.9 1160 2.3640 0.7752
0.9839 145.9 1168 2.3263 0.7803
1.0245 146.9 1176 2.4205 0.7683
0.9537 147.9 1184 2.3593 0.7833
0.9787 148.9 1192 2.5319 0.7740
0.9443 149.9 1200 2.4923 0.7734
0.9443 150.9 1208 2.3936 0.7725
1.0125 151.9 1216 2.4754 0.7614
0.943 152.9 1224 2.4341 0.7701
0.9482 153.9 1232 2.4232 0.7698
0.8958 154.9 1240 2.4942 0.7516
0.8958 155.9 1248 2.5161 0.7680
1.0073 156.9 1256 2.5339 0.7698
0.9784 157.9 1264 2.4987 0.7561
0.904 158.9 1272 2.4729 0.7561
0.9352 159.9 1280 2.4668 0.7591
0.9352 160.9 1288 2.4547 0.7558
1.0036 161.9 1296 2.6065 0.7647
0.9437 162.9 1304 2.5466 0.7641
0.8998 163.9 1312 2.5044 0.7629
0.9195 164.9 1320 2.4214 0.7632
0.9195 165.9 1328 2.3591 0.7609
0.9795 166.9 1336 2.4736 0.7543
0.9041 167.9 1344 2.4043 0.7597
0.9111 168.9 1352 2.4594 0.7417
0.8902 169.9 1360 2.4252 0.7492
0.8902 170.9 1368 2.4007 0.7543
0.9956 171.9 1376 2.3503 0.7606
0.8645 172.9 1384 2.3733 0.7471
0.8989 173.9 1392 2.3426 0.7546
0.8961 174.9 1400 2.4074 0.7522
0.8961 175.9 1408 2.4200 0.7546
0.9481 176.9 1416 2.3616 0.7504
0.9347 177.9 1424 2.3545 0.7540
0.9105 178.9 1432 2.3841 0.7540
0.8936 179.9 1440 2.4328 0.7531
0.8936 180.9 1448 2.4596 0.7474
0.9511 181.9 1456 2.4178 0.7510
0.8743 182.9 1464 2.4075 0.7513
0.8905 183.9 1472 2.3900 0.7525
0.8968 184.9 1480 2.4383 0.7543
0.8968 185.9 1488 2.4401 0.7519
0.9459 186.9 1496 2.4344 0.7498
0.9273 187.9 1504 2.4179 0.7450
0.9523 188.9 1512 2.4077 0.7525
0.8903 189.9 1520 2.4482 0.7522
0.8903 190.9 1528 2.4507 0.7573
0.9759 191.9 1536 2.4391 0.7564
0.887 192.9 1544 2.4516 0.7522
0.8796 193.9 1552 2.4404 0.7543
0.861 194.9 1560 2.4268 0.7519
0.861 195.9 1568 2.4319 0.7507
0.9349 196.9 1576 2.4230 0.7540
0.9154 197.9 1584 2.4296 0.7558
0.8695 198.9 1592 2.4308 0.7558
0.8754 199.9 1600 2.4280 0.7546

Framework versions

  • Transformers 4.24.0.dev0
  • Pytorch 1.12.1+cu113
  • Datasets 2.6.1
  • Tokenizers 0.13.1