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Automatic Speech Recognition
Transformers
Safetensors
wav2vec2
Generated from Trainer
Eval Results
Inference Endpoints
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metadata
tags:
  - generated_from_trainer
datasets:
  - common_voice_17_0
metrics:
  - wer
model-index:
  - name: MyDrive
    results:
      - task:
          name: Automatic Speech Recognition
          type: automatic-speech-recognition
        dataset:
          name: common_voice_17_0
          type: common_voice_17_0
          config: ar
          split: test[:10%]
          args: ar
        metrics:
          - name: Wer
            type: wer
            value: 0.5566888976069047

MyDrive

This model was trained from scratch on the common_voice_17_0 dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1679
  • Wer: 0.5567

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: 10
  • eval_batch_size: 10
  • seed: 42
  • gradient_accumulation_steps: 3
  • total_train_batch_size: 30
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 25
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
0.1174 0.4228 200 1.2291 0.6153
0.098 0.8457 400 1.2325 0.6275
0.1301 1.2685 600 1.1969 0.6128
0.1514 1.6913 800 1.2293 0.6489
0.1494 2.1142 1000 1.3062 0.6701
0.1382 2.5370 1200 1.2223 0.6261
0.1382 2.9598 1400 1.3116 0.6506
0.1239 3.3827 1600 1.1977 0.6189
0.1228 3.8055 1800 1.1852 0.6281
0.1117 4.2283 2000 1.3370 0.6495
0.1118 4.6512 2200 1.3265 0.6432
0.1101 5.0740 2400 1.3458 0.6310
0.1328 5.4968 2600 1.2545 0.6342
0.1384 5.9197 2800 1.2806 0.6265
0.1334 6.3425 3000 1.2484 0.6369
0.1383 6.7653 3200 1.2701 0.6479
0.1281 7.1882 3400 1.1926 0.6314
0.1232 7.6110 3600 1.2255 0.6187
0.0727 8.0338 3800 1.2398 0.6014
0.0749 8.4567 4000 1.2319 0.5957
0.0734 8.8795 4200 1.2247 0.5879
0.0684 9.3023 4400 1.3474 0.6136
0.073 9.7252 4600 1.2837 0.5936
0.0728 10.1480 4800 1.2477 0.5910
0.0718 10.5708 5000 1.2472 0.5867
0.0685 10.9937 5200 1.2693 0.5789
0.0649 11.4165 5400 1.2165 0.5787
0.0632 11.8393 5600 1.2447 0.5842
0.0625 12.2622 5800 1.3088 0.5806
0.061 12.6850 6000 1.3399 0.5924
0.0595 13.1078 6200 1.3049 0.5769
0.0608 13.5307 6400 1.2737 0.5734
0.0596 13.9535 6600 1.2288 0.5747
0.0565 14.3763 6800 1.2599 0.5677
0.0568 14.7992 7000 1.2705 0.5622
0.0538 15.2220 7200 1.3540 0.5838
0.0585 15.6448 7400 1.3334 0.5798
0.0548 16.0677 7600 1.3313 0.5724
0.0526 16.4905 7800 1.3299 0.5720
0.0577 16.9133 8000 1.3206 0.5830
0.0513 17.3362 8200 1.3500 0.5787
0.0506 17.7590 8400 1.3185 0.5698
0.0498 18.1818 8600 1.3656 0.5800
0.0515 18.6047 8800 1.3253 0.5669
0.05 19.0275 9000 1.3411 0.5810
0.048 19.4503 9200 1.3628 0.5730
0.049 19.8732 9400 1.3700 0.5730
0.0469 20.2960 9600 1.3646 0.5718
0.0474 20.7188 9800 1.4191 0.5787
0.0488 21.1416 10000 1.3450 0.5753
0.0466 21.5645 10200 1.2961 0.5612
0.0462 21.9873 10400 1.3379 0.5732
0.0479 22.4101 10600 1.3641 0.5755
0.0475 22.8330 10800 1.3316 0.5751
0.0461 23.2558 11000 1.4021 0.5779
0.0443 23.6786 11200 1.3808 0.5767
0.0448 24.1015 11400 1.4157 0.5779
0.1948 16.3609 11600 0.8630 0.5620
0.1658 16.6429 11800 0.9330 0.5692
0.1632 16.9248 12000 0.8790 0.5518
0.1373 17.2068 12200 0.9279 0.5455
0.1233 17.4887 12400 1.0114 0.5634
0.1223 17.7707 12600 1.0203 0.5638
0.1207 18.0526 12800 1.0660 0.5724
0.1009 18.3346 13000 1.0873 0.5667
0.106 18.6165 13200 1.1188 0.5667
0.0989 18.8985 13400 1.0954 0.5689
0.0981 19.1805 13600 1.1168 0.5636
0.0858 19.4638 13800 1.1655 0.5669
0.0851 19.7458 14000 1.1516 0.5596
0.0929 20.0277 14200 1.1067 0.5545
0.0816 20.3097 14400 1.1479 0.5608
0.0853 20.5916 14600 1.1574 0.5626
0.0823 20.8736 14800 1.1786 0.5655
0.0837 21.1555 15000 1.1809 0.5618
0.0806 21.4375 15200 1.1776 0.5547
0.0819 21.7195 15400 1.1668 0.5581
0.079 22.0014 15600 1.2081 0.5573
0.0739 22.2834 15800 1.2005 0.5565
0.0751 22.5653 16000 1.1868 0.5539
0.0777 22.8473 16200 1.1831 0.5569
0.0705 23.1292 16400 1.2246 0.5579
0.0704 23.4112 16600 1.2922 0.5614
0.0684 23.6931 16800 1.2495 0.5555
0.0714 23.9751 17000 1.2268 0.5539
0.0669 24.2570 17200 1.3074 0.5647
0.067 24.5390 17400 1.2619 0.5555
0.0664 24.8210 17600 1.2757 0.5587
0.1389 21.5232 17800 1.1468 0.5704
0.1246 21.7648 18000 1.1285 0.5577
0.1292 22.0064 18200 1.1010 0.5524
0.1115 22.2481 18400 1.1428 0.5563
0.1129 22.4897 18600 1.1834 0.5647
0.1178 22.7314 18800 1.1346 0.5522
0.1119 22.9730 19000 1.1957 0.5587
0.1031 23.2147 19200 1.1525 0.5457
0.1066 23.4563 19400 1.1926 0.5583
0.103 23.6979 19600 1.2014 0.5563
0.1016 23.9396 19800 1.2301 0.5583
0.1009 24.1812 20000 1.2208 0.5530
0.0953 24.4229 20200 1.2250 0.5587
0.0981 24.6645 20400 1.2353 0.5543
0.0973 24.9062 20600 1.2359 0.5571
0.11 24.4417 20800 1.2127 0.5620
0.1119 24.6766 21000 1.1876 0.5571
0.1053 24.9115 21200 1.1679 0.5567

Framework versions

  • Transformers 4.41.2
  • Pytorch 2.3.1+cu121
  • Datasets 2.21.0
  • Tokenizers 0.19.1