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wav2vec2-large-xlsr-Mongolian-cv17-base

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metadata
license: apache-2.0
base_model: facebook/wav2vec2-large-xlsr-53
tags:
  - generated_from_trainer
datasets:
  - common_voice_17_0
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xlsr-Mongolian-cv17-base
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_17_0
          type: common_voice_17_0
          config: mn
          split: validation
          args: mn
        metrics:
          - name: Wer
            type: wer
            value: 0.6570458404074703

wav2vec2-large-xlsr-Mongolian-cv17-base

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1550
  • Wer: 0.6570

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
  • train_batch_size: 32
  • eval_batch_size: 8
  • seed: 42
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 80
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
No log 0.5882 20 12.9741 1.0
No log 1.1765 40 12.7936 1.0002
No log 1.7647 60 12.3703 1.0
No log 2.3529 80 5.6880 1.0
No log 2.9412 100 3.6853 1.0
No log 3.5294 120 3.3076 1.0
No log 4.1176 140 3.2023 1.0
No log 4.7059 160 3.1422 1.0
No log 5.2941 180 3.1331 1.0
No log 5.8824 200 3.1183 1.0
No log 6.4706 220 3.1175 1.0
No log 7.0588 240 3.1132 1.0
No log 7.6471 260 3.1111 1.0
No log 8.2353 280 3.1101 1.0
No log 8.8235 300 3.1135 1.0
No log 9.4118 320 3.1039 1.0
No log 10.0 340 3.0961 1.0
No log 10.5882 360 3.0809 1.0
No log 11.1765 380 3.0651 1.0
4.9312 11.7647 400 3.0478 1.0
4.9312 12.3529 420 3.0584 1.0
4.9312 12.9412 440 3.0064 1.0
4.9312 13.5294 460 2.8224 1.0
4.9312 14.1176 480 2.5811 1.0
4.9312 14.7059 500 2.1769 1.0032
4.9312 15.2941 520 1.7646 1.0742
4.9312 15.8824 540 1.4124 1.0159
4.9312 16.4706 560 1.2848 0.9538
4.9312 17.0588 580 1.2267 0.9808
4.9312 17.6471 600 1.1108 0.9423
4.9312 18.2353 620 1.1815 0.9678
4.9312 18.8235 640 1.0553 0.8896
4.9312 19.4118 660 1.0977 0.8884
4.9312 20.0 680 0.9775 0.8532
4.9312 20.5882 700 0.9972 0.8340
4.9312 21.1765 720 1.0438 0.8009
4.9312 21.7647 740 0.9990 0.7850
4.9312 22.3529 760 0.9693 0.7595
4.9312 22.9412 780 1.0659 0.7699
1.3568 23.5294 800 0.9913 0.7610
1.3568 24.1176 820 1.0340 0.7547
1.3568 24.7059 840 1.0347 0.7337
1.3568 25.2941 860 1.0703 0.7437
1.3568 25.8824 880 1.0441 0.7350
1.3568 26.4706 900 1.0683 0.7261
1.3568 27.0588 920 1.0231 0.7296
1.3568 27.6471 940 1.0517 0.7291
1.3568 28.2353 960 1.1089 0.7417
1.3568 28.8235 980 1.0957 0.7223
1.3568 29.4118 1000 1.1120 0.7258
1.3568 30.0 1020 1.0992 0.7396
1.3568 30.5882 1040 1.1502 0.7190
1.3568 31.1765 1060 1.0743 0.7225
1.3568 31.7647 1080 1.0548 0.7178
1.3568 32.3529 1100 1.0534 0.7104
1.3568 32.9412 1120 1.0752 0.7083
1.3568 33.5294 1140 1.1574 0.7160
1.3568 34.1176 1160 1.1471 0.7190
1.3568 34.7059 1180 1.1077 0.7093
0.2559 35.2941 1200 1.0737 0.7004
0.2559 35.8824 1220 1.0822 0.6905
0.2559 36.4706 1240 1.0836 0.6889
0.2559 37.0588 1260 1.1399 0.6975
0.2559 37.6471 1280 1.0981 0.6880
0.2559 38.2353 1300 1.0887 0.6938
0.2559 38.8235 1320 1.0870 0.7112
0.2559 39.4118 1340 1.1324 0.6978
0.2559 40.0 1360 1.1170 0.6834
0.2559 40.5882 1380 1.1032 0.6761
0.2559 41.1765 1400 1.1361 0.7035
0.2559 41.7647 1420 1.0855 0.6965
0.2559 42.3529 1440 1.1320 0.6933
0.2559 42.9412 1460 1.1194 0.6849
0.2559 43.5294 1480 1.0870 0.6912
0.2559 44.1176 1500 1.1434 0.6785
0.2559 44.7059 1520 1.1434 0.6926
0.2559 45.2941 1540 1.1703 0.6839
0.2559 45.8824 1560 1.1275 0.6762
0.2559 46.4706 1580 1.1511 0.6840
0.1626 47.0588 1600 1.1336 0.6771
0.1626 47.6471 1620 1.1421 0.6785
0.1626 48.2353 1640 1.1084 0.6831
0.1626 48.8235 1660 1.1682 0.6831
0.1626 49.4118 1680 1.1349 0.6763
0.1626 50.0 1700 1.1561 0.6793
0.1626 50.5882 1720 1.1117 0.6660
0.1626 51.1765 1740 1.1875 0.6834
0.1626 51.7647 1760 1.1453 0.6782
0.1626 52.3529 1780 1.1040 0.6744
0.1626 52.9412 1800 1.1213 0.6711
0.1626 53.5294 1820 1.1454 0.6689
0.1626 54.1176 1840 1.1659 0.6706
0.1626 54.7059 1860 1.1616 0.6823
0.1626 55.2941 1880 1.2440 0.6817
0.1626 55.8824 1900 1.1472 0.6753
0.1626 56.4706 1920 1.1588 0.6691
0.1626 57.0588 1940 1.1590 0.6731
0.1626 57.6471 1960 1.1649 0.6712
0.1626 58.2353 1980 1.1990 0.6680
0.123 58.8235 2000 1.1282 0.6681
0.123 59.4118 2020 1.1609 0.6686
0.123 60.0 2040 1.1722 0.6703
0.123 60.5882 2060 1.1538 0.6739
0.123 61.1765 2080 1.1679 0.6727
0.123 61.7647 2100 1.1747 0.6687
0.123 62.3529 2120 1.1716 0.6691
0.123 62.9412 2140 1.1785 0.6655
0.123 63.5294 2160 1.1485 0.6658
0.123 64.1176 2180 1.1578 0.6626
0.123 64.7059 2200 1.1694 0.6648
0.123 65.2941 2220 1.1711 0.6677
0.123 65.8824 2240 1.1581 0.6624
0.123 66.4706 2260 1.1650 0.6723
0.123 67.0588 2280 1.1789 0.6637
0.123 67.6471 2300 1.1705 0.6624
0.123 68.2353 2320 1.1071 0.6615
0.123 68.8235 2340 1.1300 0.6654
0.123 69.4118 2360 1.1616 0.6672
0.123 70.0 2380 1.1671 0.6568
0.0991 70.5882 2400 1.1493 0.6587
0.0991 71.1765 2420 1.1476 0.6575
0.0991 71.7647 2440 1.1691 0.6582
0.0991 72.3529 2460 1.1867 0.6609
0.0991 72.9412 2480 1.1427 0.6519
0.0991 73.5294 2500 1.1635 0.6558
0.0991 74.1176 2520 1.1503 0.6553
0.0991 74.7059 2540 1.1487 0.6562
0.0991 75.2941 2560 1.1552 0.6576
0.0991 75.8824 2580 1.1638 0.6586
0.0991 76.4706 2600 1.1601 0.6566
0.0991 77.0588 2620 1.1603 0.6558
0.0991 77.6471 2640 1.1564 0.6547
0.0991 78.2353 2660 1.1560 0.6556
0.0991 78.8235 2680 1.1550 0.6564
0.0991 79.4118 2700 1.1550 0.6567
0.0991 80.0 2720 1.1550 0.6570

Framework versions

  • Transformers 4.42.4
  • Pytorch 2.3.1+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1