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w2v-bert-2.0-Fleurs_AMMI_AFRIVOICE_LRSC-ln-20hrs-v2

Automatic Speech Recognition
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wav2vec2-bert
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metadata
library_name: transformers
license: mit
base_model: facebook/w2v-bert-2.0
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: w2v-bert-2.0-Fleurs_AMMI_AFRIVOICE_LRSC-ln-20hrs-v2
    results: []

w2v-bert-2.0-Fleurs_AMMI_AFRIVOICE_LRSC-ln-20hrs-v2

This model is a fine-tuned version of facebook/w2v-bert-2.0 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7994
  • Wer: 0.2042
  • Cer: 0.0647

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: 8
  • eval_batch_size: 4
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • 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: 80
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
2.2265 1.0 356 0.4952 0.3304 0.1008
0.5303 2.0 712 0.3928 0.2891 0.0862
0.4278 3.0 1068 0.3568 0.2574 0.0769
0.3675 4.0 1424 0.3322 0.2385 0.0735
0.3271 5.0 1780 0.3118 0.2411 0.0776
0.2848 6.0 2136 0.3208 0.2092 0.0659
0.2556 7.0 2492 0.3289 0.2090 0.0658
0.2265 8.0 2848 0.3280 0.2324 0.0812
0.2043 9.0 3204 0.3279 0.2206 0.0700
0.1865 10.0 3560 0.3551 0.2132 0.0676
0.1658 11.0 3916 0.3360 0.2108 0.0697
0.1521 12.0 4272 0.3539 0.2347 0.0785
0.1408 13.0 4628 0.3642 0.2124 0.0708
0.128 14.0 4984 0.3877 0.2141 0.0689
0.1159 15.0 5340 0.4239 0.1999 0.0635
0.1073 16.0 5696 0.4337 0.2142 0.0700
0.0975 17.0 6052 0.4401 0.2022 0.0653
0.0899 18.0 6408 0.4767 0.2101 0.0662
0.0857 19.0 6764 0.4927 0.2041 0.0677
0.0783 20.0 7120 0.5006 0.2091 0.0677
0.0714 21.0 7476 0.5030 0.2140 0.0709
0.0667 22.0 7832 0.5110 0.2144 0.0691
0.0665 23.0 8188 0.5129 0.2136 0.0669
0.0586 24.0 8544 0.5906 0.1980 0.0632
0.0527 25.0 8900 0.6139 0.2019 0.0631
0.0471 26.0 9256 0.5897 0.2127 0.0677
0.0425 27.0 9612 0.6123 0.2037 0.0655
0.0374 28.0 9968 0.5954 0.2154 0.0697
0.035 29.0 10324 0.6249 0.2017 0.0634
0.0311 30.0 10680 0.6401 0.2056 0.0649
0.0274 31.0 11036 0.6688 0.2105 0.0668
0.0243 32.0 11392 0.6783 0.2122 0.0661
0.0223 33.0 11748 0.6974 0.2062 0.0652
0.0188 34.0 12104 0.7393 0.2069 0.0662
0.0177 35.0 12460 0.7598 0.2065 0.0645
0.0168 36.0 12816 0.7584 0.2058 0.0640
0.0137 37.0 13172 0.8009 0.2052 0.0645
0.0122 38.0 13528 0.7671 0.2045 0.0644
0.0105 39.0 13884 0.7994 0.2042 0.0647

Framework versions

  • Transformers 4.46.3
  • Pytorch 2.1.0+cu118
  • Datasets 3.1.0
  • Tokenizers 0.20.3