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wav2vec2-xls-r-300m-gn-cv8

Automatic Speech Recognition
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PyTorch
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wav2vec2
Generated from Trainer
robust-speech-event
hf-asr-leaderboard
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wav2vec2-xls-r-300m-gn-cv8 / README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
  - gn
  - robust-speech-event
datasets:
  - common_voice
model-index:
  - name: wav2vec2-xls-r-300m-gn-cv8
    results: []

wav2vec2-xls-r-300m-gn-cv8

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

  • Loss: 0.9392
  • Wer: 0.7033

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: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 16
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 100
  • training_steps: 5000
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
20.0601 5.54 100 5.1622 1.0
3.7052 11.11 200 3.2869 1.0
3.3275 16.65 300 3.2162 1.0
3.2984 22.22 400 3.1638 1.0
3.1111 27.76 500 2.5541 1.0
2.238 33.32 600 1.2198 0.9616
1.5284 38.86 700 0.9571 0.8593
1.2735 44.43 800 0.8719 0.8363
1.1269 49.97 900 0.8334 0.7954
1.0427 55.54 1000 0.7700 0.7749
1.0152 61.11 1100 0.7747 0.7877
0.943 66.65 1200 0.7151 0.7442
0.9132 72.22 1300 0.7224 0.7289
0.8397 77.76 1400 0.7354 0.7059
0.8577 83.32 1500 0.7285 0.7263
0.7931 88.86 1600 0.7863 0.7084
0.7995 94.43 1700 0.7562 0.6880
0.799 99.97 1800 0.7905 0.7059
0.7373 105.54 1900 0.7791 0.7161
0.749 111.11 2000 0.8125 0.7161
0.6925 116.65 2100 0.7722 0.6905
0.7034 122.22 2200 0.8989 0.7136
0.6745 127.76 2300 0.8270 0.6982
0.6837 133.32 2400 0.8569 0.7161
0.6689 138.86 2500 0.8339 0.6982
0.6471 144.43 2600 0.8441 0.7110
0.615 149.97 2700 0.9038 0.7212
0.6477 155.54 2800 0.9089 0.7059
0.6047 161.11 2900 0.9149 0.7059
0.5613 166.65 3000 0.8582 0.7263
0.6017 172.22 3100 0.8787 0.7084
0.5546 177.76 3200 0.8753 0.6957
0.5747 183.32 3300 0.9167 0.7212
0.5535 188.86 3400 0.8448 0.6905
0.5331 194.43 3500 0.8644 0.7161
0.5428 199.97 3600 0.8730 0.7033
0.5219 205.54 3700 0.9047 0.6982
0.5158 211.11 3800 0.8706 0.7033
0.5107 216.65 3900 0.9139 0.7084
0.4903 222.22 4000 0.9456 0.7315
0.4772 227.76 4100 0.9475 0.7161
0.4713 233.32 4200 0.9237 0.7059
0.4743 238.86 4300 0.9305 0.6957
0.4705 244.43 4400 0.9561 0.7110
0.4908 249.97 4500 0.9389 0.7084
0.4717 255.54 4600 0.9234 0.6982
0.4462 261.11 4700 0.9323 0.6957
0.4556 266.65 4800 0.9432 0.7033
0.4691 272.22 4900 0.9389 0.7059
0.4601 277.76 5000 0.9392 0.7033

Framework versions

  • Transformers 4.16.0
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.1
  • Tokenizers 0.11.0