CoolFace
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NbAiLab/wav2vec2-1b-npsc-nst-bokmaal

sourceHugging Faceapache-2.0updated 2y agoView on Hugging Face
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Model Card

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wav2vec2-1b-npsc-nst-bokmaal

This model is a fine-tuned version of facebook/wav2vec2-xls-r-1b on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.0636
  • Wer: 0.0345

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: 2e-05
  • trainbatchsize: 12
  • evalbatchsize: 12
  • seed: 42
  • gradientaccumulationsteps: 2
  • totaltrainbatch_size: 24
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lrschedulertype: linear
  • lrschedulerwarmup_steps: 2000
  • num_epochs: 40.0
  • mixedprecisiontraining: Native AMP

Training results

Training LossEpochStepValidation LossWer
2.92890.045002.79711.0
0.76510.0810000.57660.5701
0.5180.1215000.36600.3564
0.54810.1620000.27300.2557
0.42860.225000.22820.2385
0.45430.2430000.18520.1843
0.40640.2835000.15740.1544
0.33080.3240000.15580.1404
0.30050.3645000.14170.1349
0.28980.450000.13040.1301
0.27390.4455000.13160.1258
0.24290.4860000.12220.1221
0.24050.5265000.12240.1147
0.26810.5670000.11620.1119
0.23820.675000.12510.1338
0.21930.6480000.11210.1087
0.22350.6885000.10580.1059
0.21240.7290000.11060.1060
0.20760.7695000.10230.1003
0.20820.8100000.09850.1037
0.22720.84105000.09900.1019
0.19030.88110000.09820.0978
0.20440.92115000.09660.0991
0.19760.96120000.09250.0958
0.19661.0125000.09240.0929
0.19591.04130000.08880.0918
0.17271.08135000.09010.0943
0.18531.12140000.09340.0890
0.17911.16145000.09350.0914
0.18721.2150000.08510.0883
0.18211.24155000.08570.0873
0.17561.28160000.08840.0890
0.16661.32165000.08710.0849
0.17081.36170000.08370.0863
0.16531.4175000.08310.0849
0.17341.44180000.08080.0850
0.16431.48185000.08140.0835
0.16351.52190000.08100.0816
0.16111.56195000.08270.0829
0.16331.6200000.07970.0820
0.16261.64205000.07890.0820
0.16181.68210000.07660.0817
0.16341.72215000.07830.0832
0.17611.76220000.07870.0817
0.15181.8225000.07750.0817
0.14921.84230000.07850.0802
0.16521.88235000.07590.0783
0.15451.92240000.07580.0788
0.15481.96245000.07740.0816
0.16412.0250000.07340.0780
0.15062.04255000.07180.0748
0.14742.08260000.07480.0746
0.1372.12265000.07360.0744
0.14832.16270000.07440.0763
0.14722.2275000.07280.0736
0.1432.24280000.07080.0767
0.14672.28285000.07210.0737
0.12862.32290000.07010.0748
0.14072.36295000.06950.0740
0.13772.4300000.06740.0725
0.13442.44305000.06960.0711
0.13372.48310000.06860.0733
0.13842.52315000.06860.0710
0.13552.56320000.06670.0721
0.13342.6325000.06650.0712
0.14012.64330000.06940.0719
0.13682.68335000.06890.0692
0.12592.72340000.06690.0701
0.13542.76345000.06720.0691
0.13192.8350000.07070.0702
0.14082.84355000.06500.0685
0.13552.88360000.06710.0696
0.12522.92365000.06550.0671
0.1552.96370000.06620.0679
0.12663.0375000.06540.0669
0.11833.04380000.06550.0664
0.12133.08385000.06680.0665
0.10993.12390000.06620.0660
0.11963.16395000.06520.0657
0.12533.2400000.06740.0655
0.11723.24405000.06560.0654
0.12073.28410000.06400.0660
0.12283.32415000.06680.0658
0.12033.36420000.06400.0642
0.12843.4425000.06500.0664
0.10583.44430000.06180.0648
0.12363.48435000.06380.0649
0.11493.52440000.06360.0653
0.12033.56445000.06330.0644
0.1143.6450000.06240.0635
0.12013.64455000.06080.0638
0.1213.68460000.06100.0631
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0.09564.24530000.06190.0615
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0.09974.4550000.05900.0605
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0.14.68585000.05940.0596
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0.10534.8600000.05750.0588
0.09844.84605000.05750.0583
0.09954.88610000.05970.0591
0.09554.92615000.05600.0590
0.11864.96620000.05910.0577
0.09765.0625000.05730.0585
0.10495.04630000.05780.0572
0.09535.08635000.05870.0574
0.09765.12640000.05670.0589
0.10195.16645000.05830.0582
0.09865.2650000.05770.0571
0.09855.24655000.05720.0577
0.1035.28660000.05810.0593
0.1015.32665000.05750.0579
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0.0966.12765000.05580.0549
0.09716.16770000.05690.0564
0.09176.2775000.05690.0552
0.08966.24780000.05680.0550
0.08866.28785000.05500.0550
0.09176.32790000.05540.0562
0.08396.36795000.05510.0570
0.08566.4800000.05330.0545
0.09396.44805000.05640.0545
0.08686.48810000.05560.0557
0.08826.52815000.05470.0544
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0.08666.6825000.05340.0555
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0.10336.68835000.05390.0551
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0.09016.84855000.05330.0544
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0.0977.0875000.05200.0557
0.08357.04880000.05420.0538
0.0847.08885000.05450.0543
0.09837.12890000.05280.0557
0.097.16895000.05420.0540
0.08797.2900000.05590.0533
0.08187.24905000.05460.0529
0.08497.28910000.05350.0533
0.07777.32915000.05480.0544
0.08877.36920000.05450.0533
0.08867.4925000.05450.0527
0.07527.44930000.05520.0531
0.08197.48935000.05250.0532
0.07537.52940000.05220.0515
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0.023738.424800000.06310.0347
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0.019338.54810000.06290.0348
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0.014538.584820000.06350.0348
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0.021439.674955000.06350.0345
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0.020339.994995000.06360.0345

Framework versions

  • Transformers 4.18.0
  • Pytorch 1.11.0+cu113
  • Datasets 2.1.0
  • Tokenizers 0.12.1