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wav2vec2-large-xls-r-300m-grain

Automatic Speech Recognition
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wav2vec2
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metadata
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xls-r-300m-grain
    results: []

wav2vec2-large-xls-r-300m-grain

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the Grain gender-balanced dataset. It achieves the following results on the evaluation set:

  • Loss: 0.1510
  • Wer: 0.0762

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: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
4.1496 2.5 400 0.7656 0.8096
0.2914 5.0 800 0.3202 0.3544
0.1152 7.5 1200 0.2666 0.2894
0.0722 10.0 1600 0.2834 0.2458
0.0528 12.5 2000 0.2475 0.2159
0.0423 15.0 2400 0.2430 0.1971
0.0334 17.5 2800 0.2250 0.1925
0.0288 20.0 3200 0.2119 0.1779
0.0253 22.5 3600 0.2226 0.1711
0.0214 25.0 4000 0.2224 0.1685
0.0217 27.5 4400 0.2098 0.1516
0.0182 30.0 4800 0.2153 0.1716
0.0173 32.5 5200 0.1925 0.1451
0.0137 35.0 5600 0.2241 0.1469
0.0118 37.5 6000 0.2013 0.1515
0.0133 40.0 6400 0.1990 0.1332
0.0125 42.5 6800 0.2146 0.1502
0.0103 45.0 7200 0.2191 0.1317
0.0089 47.5 7600 0.1869 0.1246
0.0091 50.0 8000 0.1734 0.1251
0.008 52.5 8400 0.2008 0.1290
0.0071 55.0 8800 0.1828 0.1260
0.0064 57.5 9200 0.1689 0.1081
0.0061 60.0 9600 0.1676 0.1111
0.0051 62.5 10000 0.1707 0.1048
0.0056 65.0 10400 0.1741 0.1131
0.0046 67.5 10800 0.1836 0.1034
0.0036 70.0 11200 0.1655 0.0966
0.0037 72.5 11600 0.1734 0.1047
0.003 75.0 12000 0.1718 0.0975
0.0032 77.5 12400 0.1598 0.0986
0.0023 80.0 12800 0.1640 0.0966
0.0019 82.5 13200 0.1701 0.0862
0.0015 85.0 13600 0.1643 0.0854
0.0016 87.5 14000 0.1470 0.0823
0.0014 90.0 14400 0.1589 0.0838
0.0011 92.5 14800 0.1610 0.0834
0.0013 95.0 15200 0.1457 0.0788
0.001 97.5 15600 0.1537 0.0762
0.001 100.0 16000 0.1510 0.0762

Framework versions

  • Transformers 4.42.3
  • Pytorch 2.2.0+cu121
  • Datasets 2.20.0
  • Tokenizers 0.19.1