README.md

metadata

license: apache-2.0
tags:
  - generated_from_trainer
  - automatic-speech-recognition
  - NbAiLab/NPSC
  - robust-speech-event
  - false
  - nb-NO
  - hf-asr-leaderboard
datasets:
  - NbAiLab/NPSC
language:
  - nb-NO
model-index:
  - name: wav2vec2-xls-r-300m-npsc-bokmaal
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: NPSC
          type: NbAiLab/NPSC
          args: 16K_mp3_bokmaal
        metrics:
          - name: Test (Bokmål) WER
            type: wer
            value: 0.07556265455560153
          - name: Test (Bokmål) CER
            type: cer
            value: 0.028191288775481386

wav2vec2-xls-r-300m-npsc-bokmaal

This model was trained from scratch on the None dataset. It achieves the following results on the evaluation set:

• Loss: 0.1663
• Wer: 0.0932

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: 5e-05
• train_batch_size: 16
• eval_batch_size: 16
• seed: 42
• gradient_accumulation_steps: 2
• total_train_batch_size: 32
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• lr_scheduler_warmup_steps: 500
• num_epochs: 15.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
0.0969	0.32	500	0.1773	0.1054
0.0929	0.64	1000	0.1672	0.1061
0.1018	0.97	1500	0.1770	0.1067
0.0871	1.29	2000	0.1832	0.1087
0.0908	1.61	2500	0.1830	0.1101
0.0975	1.93	3000	0.1848	0.1100
0.0936	2.26	3500	0.1853	0.1113
0.1025	2.58	4000	0.1958	0.1149
0.0989	2.9	4500	0.1776	0.1123
0.0946	3.22	5000	0.1825	0.1097
0.0859	3.55	5500	0.1864	0.1072
0.0867	3.87	6000	0.1886	0.1081
0.0783	4.19	6500	0.1883	0.1063
0.0804	4.51	7000	0.1831	0.1063
0.0797	4.84	7500	0.1884	0.1058
0.0705	5.16	8000	0.1802	0.1057
0.0795	5.48	8500	0.1854	0.1038
0.0711	5.8	9000	0.1766	0.1032
0.0973	6.13	9500	0.1663	0.1014
0.087	6.45	10000	0.1664	0.1014
0.0962	6.77	10500	0.1631	0.1009
0.0857	7.09	11000	0.1659	0.1002
0.0882	7.41	11500	0.1668	0.1007
0.0784	7.74	12000	0.1688	0.0996
0.0838	8.06	12500	0.1675	0.0984
0.0863	8.38	13000	0.1639	0.0979
0.0763	8.7	13500	0.1638	0.0980
0.0822	9.03	14000	0.1709	0.0972
0.0769	9.35	14500	0.1700	0.0965
0.0838	9.67	15000	0.1703	0.0974
0.0799	9.99	15500	0.1667	0.0957
0.0712	10.32	16000	0.1754	0.0960
0.0737	10.64	16500	0.1725	0.0968
0.0851	10.96	17000	0.1733	0.0958
0.076	11.28	17500	0.1682	0.0954
0.0712	11.61	18000	0.1713	0.0943
0.0745	11.93	18500	0.1662	0.0951
0.0864	12.25	19000	0.1692	0.0947
0.0937	12.57	19500	0.1624	0.0943
0.0915	12.89	20000	0.1678	0.0942
0.0926	13.22	20500	0.1641	0.0945
0.0912	13.54	21000	0.1665	0.0937
0.0917	13.86	21500	0.1648	0.0936
0.094	14.18	22000	0.1635	0.0935
0.0864	14.51	22500	0.1678	0.0934
0.0899	14.83	23000	0.1663	0.0932

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.2+cu113
• Datasets 1.18.4.dev0
• Tokenizers 0.11.0