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wav2vec2-xls-r-300m-gn-cv8-3

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: 0.9517
  • Wer: 0.8542

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
19.9125 5.54 100 5.4279 1.0
3.8031 11.11 200 3.3070 1.0
3.3783 16.65 300 3.2450 1.0
3.3472 22.22 400 3.2424 1.0
3.2714 27.76 500 3.1100 1.0
3.2367 33.32 600 3.1091 1.0
3.1968 38.86 700 3.1013 1.0
3.2004 44.43 800 3.1173 1.0
3.1656 49.97 900 3.0682 1.0
3.1563 55.54 1000 3.0457 1.0
3.1356 61.11 1100 3.0139 1.0
3.086 66.65 1200 2.8108 1.0
2.954 72.22 1300 2.3238 1.0
2.6125 77.76 1400 1.6461 1.0
2.3296 83.32 1500 1.2834 0.9744
2.1345 88.86 1600 1.1091 0.9693
2.0346 94.43 1700 1.0273 0.9233
1.9611 99.97 1800 0.9642 0.9182
1.9066 105.54 1900 0.9590 0.9105
1.8178 111.11 2000 0.9679 0.9028
1.7799 116.65 2100 0.9007 0.8619
1.7726 122.22 2200 0.9689 0.8951
1.7389 127.76 2300 0.8876 0.8593
1.7151 133.32 2400 0.8716 0.8542
1.6842 138.86 2500 0.9536 0.8772
1.6449 144.43 2600 0.9296 0.8542
1.5978 149.97 2700 0.8895 0.8440
1.6515 155.54 2800 0.9162 0.8568
1.6586 161.11 2900 0.9039 0.8568
1.5966 166.65 3000 0.8627 0.8542
1.5695 172.22 3100 0.9549 0.8824
1.5699 177.76 3200 0.9332 0.8517
1.5297 183.32 3300 0.9163 0.8338
1.5367 188.86 3400 0.8822 0.8312
1.5586 194.43 3500 0.9217 0.8363
1.5429 199.97 3600 0.9564 0.8568
1.5273 205.54 3700 0.9508 0.8542
1.5043 211.11 3800 0.9374 0.8542
1.4724 216.65 3900 0.9622 0.8619
1.4794 222.22 4000 0.9550 0.8363
1.4843 227.76 4100 0.9577 0.8465
1.4781 233.32 4200 0.9543 0.8440
1.4507 238.86 4300 0.9553 0.8491
1.4997 244.43 4400 0.9728 0.8491
1.4371 249.97 4500 0.9543 0.8670
1.4825 255.54 4600 0.9636 0.8619
1.4187 261.11 4700 0.9609 0.8440
1.4363 266.65 4800 0.9567 0.8593
1.4463 272.22 4900 0.9581 0.8542
1.4117 277.76 5000 0.9517 0.8542

Framework versions

  • Transformers 4.16.0
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.1
  • Tokenizers 0.11.0
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Dataset used to train lgris/wav2vec2-xls-r-300m-gn-cv8-3

Evaluation results