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wav2vec2-common_voice_13_0-eo-3

Automatic Speech Recognition
Transformers
PyTorch
Esperanto
wav2vec2
mozilla-foundation/common_voice_13_0
Generated from Trainer
Inference Endpoints
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wav2vec2-common_voice_13_0-eo-3 / README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: wav2vec2-common_voice_13_0-eo-3
    results: []

wav2vec2-common_voice_13_0-eo-3

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 0.2191
  • Cer: 0.0208
  • Wer: 0.0686

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: 5e-06
  • train_batch_size: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • total_train_batch_size: 32
  • 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 Cer Validation Loss Wer
2.6416 2.13 1000 0.1541 0.8599 0.6449
0.2633 4.27 2000 0.0335 0.1897 0.1431
0.1739 6.4 3000 0.0289 0.1732 0.1145
0.1378 8.53 4000 0.0276 0.1729 0.1066
0.1172 10.67 5000 0.0268 0.1773 0.1019
0.1049 12.8 6000 0.0255 0.1701 0.0937
0.0951 14.93 7000 0.0253 0.1718 0.0933
0.0851 17.07 8000 0.0239 0.1787 0.0834
0.0809 19.2 9000 0.0235 0.1802 0.0835
0.0756 21.33 10000 0.0239 0.1784 0.0855
0.0708 23.47 11000 0.0235 0.1748 0.0824
0.0657 25.6 12000 0.0228 0.1830 0.0796
0.0605 27.73 13000 0.0230 0.1896 0.0798
0.0583 29.87 14000 0.0224 0.1889 0.0778
0.0608 32.0 15000 0.0223 0.1849 0.0757
0.0556 34.13 16000 0.0223 0.1872 0.0767
0.0534 36.27 17000 0.0221 0.1893 0.0751
0.0523 38.4 18000 0.0218 0.1925 0.0729
0.0494 40.53 19000 0.0221 0.1957 0.0745
0.0475 42.67 20000 0.0217 0.1961 0.0740
0.048 44.8 21000 0.0214 0.1957 0.0714
0.0459 46.93 22000 0.0215 0.1968 0.0717
0.0435 49.07 23000 0.0217 0.2008 0.0717
0.0428 51.2 24000 0.0212 0.1991 0.0696
0.0418 53.33 25000 0.0215 0.2034 0.0714
0.0404 55.47 26000 0.0210 0.2014 0.0684
0.0394 57.6 27000 0.0210 0.2050 0.0681
0.0399 59.73 28000 0.0211 0.2039 0.0700
0.0389 61.87 29000 0.0214 0.2091 0.0694
0.038 64.0 30000 0.0210 0.2100 0.0702
0.0361 66.13 31000 0.0215 0.2119 0.0703
0.0359 68.27 32000 0.0213 0.2108 0.0714
0.0354 70.4 33000 0.0211 0.2120 0.0699
0.0364 72.53 34000 0.0211 0.2128 0.0688
0.0361 74.67 35000 0.0212 0.2134 0.0694
0.0332 76.8 36000 0.0210 0.2176 0.0698
0.0341 78.93 37000 0.0208 0.2170 0.0688
0.032 81.07 38000 0.0209 0.2157 0.0686
0.0318 83.33 39000 0.0209 0.2166 0.0685
0.0325 85.47 40000 0.2172 0.0209 0.0687
0.0316 87.6 41000 0.2181 0.0208 0.0678
0.0302 89.73 42000 0.2171 0.0208 0.0679
0.0318 91.87 43000 0.2179 0.0211 0.0702
0.0314 94.0 44000 0.2186 0.0208 0.0690
0.0309 96.13 45000 0.2193 0.0210 0.0696
0.031 98.27 46000 0.2191 0.0208 0.0686

Framework versions

  • Transformers 4.29.1
  • Pytorch 2.0.1+cu118
  • Datasets 2.12.0
  • Tokenizers 0.13.3