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This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_9_0 - HI dataset. It achieves the following results on the evaluation set:

  • Loss: 0.5164
  • Wer: 0.3349
  • Cer: 0.1082

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: 7.5e-05
  • train_batch_size: 64
  • eval_batch_size: 64
  • seed: 42
  • gradient_accumulation_steps: 2
  • total_train_batch_size: 128
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_ratio: 0.1
  • training_steps: 9815
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
3.9471 8.16 400 3.7109 1.0 1.0
3.274 16.32 800 3.1582 0.9917 0.9573
1.5889 24.48 1200 0.7763 0.6030 0.1990
1.3647 32.65 1600 0.6051 0.5135 0.1687
1.2532 40.81 2000 0.5423 0.4712 0.1539
1.1905 48.97 2400 0.5180 0.4532 0.1490
1.1193 57.14 2800 0.4906 0.4248 0.1393
1.0584 65.3 3200 0.4854 0.4069 0.1332
1.0095 73.46 3600 0.4780 0.3926 0.1287
0.9759 81.63 4000 0.4666 0.3925 0.1269
0.9593 89.79 4400 0.4808 0.3830 0.1247
0.909 97.95 4800 0.4798 0.3765 0.1212
0.8788 106.12 5200 0.4906 0.3608 0.1162
0.8471 114.28 5600 0.4759 0.3604 0.1166
0.8116 122.44 6000 0.5080 0.3627 0.1176
0.7881 130.61 6400 0.4868 0.3489 0.1135
0.766 138.77 6800 0.4955 0.3492 0.1136
0.7333 146.93 7200 0.5019 0.3461 0.1125
0.709 155.1 7600 0.5084 0.3468 0.1117
0.6911 163.26 8000 0.5144 0.3412 0.1106
0.6683 171.42 8400 0.5219 0.3409 0.1117
0.659 179.59 8800 0.5230 0.3376 0.1096
0.6475 187.75 9200 0.5229 0.3398 0.1097
0.6419 195.91 9600 0.5200 0.3337 0.1084

Framework versions

  • Transformers 4.19.0.dev0
  • Pytorch 1.11.0+cu102
  • Datasets 2.1.1.dev0
  • Tokenizers 0.12.1
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Dataset used to train anuragshas/wav2vec2-xls-r-300m-hi-cv9-with-lm

Evaluation results