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gary109/ai-light-dance_drums_ft_pretrain_wav2vec2-base-new_onset-idmt-mdb-enst-2

sourceHugging Faceupdated 4y agoView on Hugging Face
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Model Card

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ai-light-dancedrumsftpretrainwav2vec2-base-new_onset-idmt-mdb-enst-2

This model is a fine-tuned version of gary109/ai-light-dance_drums_ft_pretrain_wav2vec2-base-new_onset-idmt-mdb-2 on the GARY109/AILIGHTDANCE - ONSET-IDMT-MDB-ENST-2 dataset. It achieves the following results on the evaluation set:

  • —Loss: 0.6379
  • —Wer: 0.3792

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: 0.0003
  • —trainbatchsize: 4
  • —evalbatchsize: 4
  • —seed: 42
  • —gradientaccumulationsteps: 4
  • —totaltrainbatch_size: 16
  • —optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • —lrschedulertype: linear
  • —lrschedulerwarmup_steps: 30
  • —num_epochs: 100.0
  • —mixedprecisiontraining: Native AMP

Training results

Training LossEpochStepValidation LossWer
1.42260.99352.04350.4154
0.87441.99701.71930.4382
0.94742.991051.78530.4374
0.83163.991401.28270.4306
0.83364.991751.06760.4040
0.73455.992101.53640.4264
0.66666.992451.42840.4585
0.66777.992800.94750.4003
0.67798.993151.11720.4209
0.65039.993500.89990.3834
0.615910.993851.15010.4386
0.683111.994201.08600.3825
0.595912.994550.94100.4045
0.715413.994901.04630.3821
0.609414.995250.85980.3965
0.692915.995600.94940.3931
0.762716.995950.80600.3948
0.60117.996300.98900.3965
0.54618.996650.80590.3990
0.522219.997000.63790.3792
0.580220.997350.69950.3661
0.573121.997700.84050.3606
0.546222.998050.66670.3965
0.605723.998400.83960.3762
0.532324.998750.90540.3952
0.68325.999100.68980.4062
0.52526.999450.72450.3884
0.488527.999800.80760.4049
0.465328.9910150.81000.3838
0.482729.9910500.72470.3863
0.483930.9910850.70090.3817
0.498231.9911200.76370.3914
0.610532.9911550.73430.3914
0.493633.9911900.73900.3762
0.467434.9912250.67240.3581
0.467735.9912600.67300.3488
0.51636.9912950.69560.3728
0.450737.9913300.64830.3615
0.420738.9913650.77180.3484
0.480339.9914000.83160.3775
0.394640.9914350.83220.3568
0.41141.9914700.99330.3707
0.440542.9915050.87890.3943
0.512443.9915400.90300.3707
0.595944.9915750.78090.3948
0.384145.9916100.77160.3965
0.397546.9916450.70640.3931
1.498347.9916803.28660.3627
0.396248.9917150.64860.3648
0.442249.9917500.84500.3779
0.419850.9917850.76280.3564
0.357751.9918200.75530.3678
0.442552.9918550.75660.3716
0.349253.9918900.77100.3631
0.373154.9919250.77370.3627
0.386855.9919600.70210.3572
0.331156.9919950.66030.3518
0.399357.9920300.66640.3581
0.422658.9920650.68130.3551
0.414359.9921000.65670.3568
0.362360.9921350.65680.3454
0.322861.9921700.73260.3568
0.320462.9922050.72770.3640
0.37763.9922400.71450.3585
0.348764.9922750.69430.3505
0.34365.9923100.74610.3395
0.325166.9923450.74420.3564
0.313567.9923800.73310.3530
0.38168.9924150.73060.3513
0.331969.9924500.84950.3484
0.355270.9924850.75460.3551
0.329271.9925200.74830.3450
0.304172.9925550.73050.3522
0.360673.9925900.73580.3484
0.362974.9926250.77090.3446
0.340975.9926600.75680.3585
0.331576.9926950.74660.3475
0.293477.9927300.73510.3496
0.336678.9927650.80140.3484
0.317679.9928000.80140.3420
0.331980.9928350.79960.3437
0.296781.9928700.81560.3412
0.313782.9929050.80250.3361
0.313383.9929400.77840.3416
0.313484.9929750.78940.3336
0.321685.9930100.83310.3395
0.36586.9930450.79800.3353
0.296287.9930800.79650.3404
0.312688.9931150.74700.3420
0.284389.9931500.77880.3404
0.296790.9931850.79020.3374
0.317191.9932200.80220.3404
0.306992.9932550.79990.3345
0.357193.9932900.78960.3404
0.280594.9933250.78310.3391
0.309995.9933600.79090.3366
0.286896.9933950.79180.3395
0.262697.9934300.77660.3429
0.263498.9934650.77700.3437
0.28899.9935000.77620.3429

Framework versions

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