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wav2vec2-xls-r-300m-Fleurs_AMMI_AFRIVOICE_LRSC-ln-100hrs-v2

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wav2vec2
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metadata
library_name: transformers
license: apache-2.0
base_model: facebook/wav2vec2-xls-r-300m
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: wav2vec2-xls-r-300m-Fleurs_AMMI_AFRIVOICE_LRSC-ln-100hrs-v2
    results: []

wav2vec2-xls-r-300m-Fleurs_AMMI_AFRIVOICE_LRSC-ln-100hrs-v2

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3710
  • Wer: 0.1939
  • Cer: 0.0632

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: 1e-05
  • train_batch_size: 4
  • eval_batch_size: 2
  • seed: 42
  • optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
  • lr_scheduler_type: linear
  • num_epochs: 100
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
3.0206 1.0 5760 0.6687 0.4122 0.1239
0.8506 2.0 11520 0.4709 0.3106 0.0945
0.7012 3.0 17280 0.3870 0.3028 0.0920
0.6373 4.0 23040 0.3576 0.2734 0.0826
0.598 5.0 28800 0.3538 0.2662 0.0814
0.5728 6.0 34560 0.3209 0.2538 0.0781
0.5507 7.0 40320 0.3151 0.2467 0.0751
0.5327 8.0 46080 0.3041 0.2341 0.0727
0.517 9.0 51840 0.3150 0.2384 0.0736
0.5056 10.0 57600 0.3031 0.2215 0.0697
0.4968 11.0 63360 0.2960 0.2184 0.0684
0.4829 12.0 69120 0.3008 0.2166 0.0679
0.4736 13.0 74880 0.2885 0.2153 0.0667
0.4628 14.0 80640 0.3040 0.2144 0.0669
0.4556 15.0 86400 0.2832 0.2049 0.0638
0.4435 16.0 92160 0.3059 0.2063 0.0646
0.4358 17.0 97920 0.2956 0.2079 0.0658
0.4244 18.0 103680 0.2960 0.2019 0.0628
0.4137 19.0 109440 0.2882 0.2066 0.0655
0.4054 20.0 115200 0.2945 0.2033 0.0658
0.3997 21.0 120960 0.2998 0.2061 0.0657
0.3897 22.0 126720 0.2866 0.2025 0.0669
0.3779 23.0 132480 0.3029 0.2013 0.0685
0.3686 24.0 138240 0.3168 0.1965 0.0632
0.3619 25.0 144000 0.3174 0.1972 0.0648
0.3549 26.0 149760 0.3099 0.1994 0.0656
0.3483 27.0 155520 0.3107 0.2015 0.0694
0.3401 28.0 161280 0.3254 0.1934 0.0626
0.3319 29.0 167040 0.3291 0.1990 0.0663
0.3268 30.0 172800 0.3275 0.1941 0.0656
0.3209 31.0 178560 0.3213 0.2037 0.0700
0.3157 32.0 184320 0.3189 0.1994 0.0675
0.3103 33.0 190080 0.3433 0.1952 0.0618
0.3063 34.0 195840 0.3200 0.1934 0.0629
0.3009 35.0 201600 0.3270 0.1940 0.0655
0.2966 36.0 207360 0.3395 0.1926 0.0628
0.2921 37.0 213120 0.3619 0.1893 0.0612
0.2893 38.0 218880 0.3535 0.1923 0.0642
0.285 39.0 224640 0.3570 0.1942 0.0649
0.2796 40.0 230400 0.3634 0.1890 0.0612
0.2759 41.0 236160 0.3369 0.1995 0.0669
0.2743 42.0 241920 0.3605 0.1945 0.0655
0.27 43.0 247680 0.3838 0.1902 0.0627
0.2676 44.0 253440 0.3750 0.1895 0.0628
0.2643 45.0 259200 0.3672 0.1917 0.0637
0.2603 46.0 264960 0.3717 0.1922 0.0641
0.2555 47.0 270720 0.3710 0.1939 0.0632

Framework versions

  • Transformers 4.46.1
  • Pytorch 2.1.0+cu118
  • Datasets 3.1.0
  • Tokenizers 0.20.1