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: XLSR-300M-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: 0.07699635320946434
          - name: Test (Bokmål) CER
            type: cer
            value: 0.0284288464829

XLSR-300M-bokmaal

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the NBAILAB/NPSC - 16K_MP3_BOKMAAL dataset. It achieves the following results on the evaluation set:

• Loss: 0.1635
• Wer: 0.1005

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: 0.0001
• 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: 2000
• num_epochs: 15.0
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Wer
3.0307	0.32	500	3.0026	1.0
2.7865	0.64	1000	2.4849	0.9926
0.7522	0.95	1500	0.4567	0.3594
0.5703	1.27	2000	0.3440	0.2586
0.4762	1.59	2500	0.2925	0.2178
0.4585	1.91	3000	0.2442	0.1981
0.4013	2.23	3500	0.2495	0.1818
0.449	2.54	4000	0.2152	0.1808
0.355	2.86	4500	0.2179	0.1670
0.3142	3.18	5000	0.1953	0.1542
0.3242	3.5	5500	0.2103	0.1526
0.3016	3.82	6000	0.1911	0.1477
0.2713	4.13	6500	0.1836	0.1422
0.2807	4.45	7000	0.1924	0.1447
0.2929	4.77	7500	0.1848	0.1402
0.2595	5.09	8000	0.1783	0.1330
0.2289	5.41	8500	0.1901	0.1313
0.2567	5.72	9000	0.1784	0.1298
0.2401	6.04	9500	0.1956	0.1298
0.2098	6.36	10000	0.1748	0.1277
0.2246	6.68	10500	0.1777	0.1254
0.2197	7.0	11000	0.1703	0.1222
0.2122	7.32	11500	0.1917	0.1221
0.2746	7.63	12000	0.1769	0.1215
0.2148	7.95	12500	0.1736	0.1193
0.1915	8.27	13000	0.1814	0.1161
0.2462	8.59	13500	0.1748	0.1166
0.1872	8.91	14000	0.1769	0.1133
0.1886	9.22	14500	0.1852	0.1143
0.1789	9.54	15000	0.1696	0.1126
0.1692	9.86	15500	0.1817	0.1122
0.1765	10.18	16000	0.1769	0.1093
0.1699	10.5	16500	0.1604	0.1084
0.1591	10.81	17000	0.1777	0.1080
0.1499	11.13	17500	0.1645	0.1074
0.163	11.45	18000	0.1704	0.1065
0.1597	11.77	18500	0.1576	0.1064
0.1484	12.09	19000	0.1637	0.1041
0.1464	12.4	19500	0.1631	0.1047
0.156	12.72	20000	0.1686	0.1029
0.1625	13.04	20500	0.1648	0.1023
0.1395	13.36	21000	0.1688	0.1027
0.1387	13.68	21500	0.1670	0.1013
0.1434	13.99	22000	0.1677	0.1017
0.1442	14.31	22500	0.1688	0.1008
0.1439	14.63	23000	0.1647	0.1004
0.137	14.95	23500	0.1636	0.1006

Framework versions

• Transformers 4.17.0.dev0
• Pytorch 1.10.1+cu102
• Datasets 1.18.2.dev0
• Tokenizers 0.11.0