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wav2vec2-large-xls-r-300m-hi-wx1

Automatic Speech Recognition
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
datasets:
  - common_voice
model-index:
  - name: wav2vec2-large-xls-r-300m-hi-wx1
    results: []

wav2vec2-large-xls-r-300m-hi-wx1

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: 0.6966
  • Wer: 0.3424

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: 0.00024
  • train_batch_size: 16
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 2
  • 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: 1500
  • num_epochs: 50
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
11.0193 1.36 200 5.6992 1.0
3.9285 2.72 400 3.4430 1.0
3.293 4.08 600 2.7707 1.0
1.2714 5.44 800 0.8479 0.6015
0.6469 6.8 1000 0.7087 0.5412
0.4982 8.16 1200 0.6655 0.5116
0.4131 9.52 1400 0.6422 0.4845
0.3653 10.88 1600 0.6452 0.4700
0.3177 12.24 1800 0.7519 0.5236
0.2771 13.6 2000 0.6238 0.4444
0.2424 14.96 2200 0.6317 0.4458
0.2207 16.33 2400 0.6634 0.4144
0.1997 17.68 2600 0.6469 0.4110
0.1843 19.05 2800 0.6958 0.4162
0.1705 20.41 3000 0.6658 0.3992
0.1535 21.77 3200 0.6829 0.4136
0.1492 23.13 3400 0.6628 0.4018
0.1383 24.49 3600 0.6603 0.4020
0.1334 25.85 3800 0.7079 0.3914
0.1215 27.21 4000 0.7016 0.3904
0.1211 28.57 4200 0.7232 0.3963
0.1125 29.93 4400 0.7258 0.3879
0.1074 31.29 4600 0.7476 0.3900
0.0965 32.65 4800 0.7120 0.3734
0.0916 34.01 5000 0.6694 0.3730
0.0865 35.37 5200 0.7181 0.3680
0.0822 36.73 5400 0.6698 0.3554
0.0757 38.09 5600 0.7035 0.3627
0.0723 39.45 5800 0.6832 0.3575
0.0746 40.81 6000 0.6942 0.3508
0.0667 42.18 6200 0.7075 0.3523
0.0638 43.54 6400 0.7009 0.3473
0.0585 44.89 6600 0.6887 0.3443
0.0562 46.26 6800 0.6888 0.3454
0.0534 47.62 7000 0.7048 0.3438
0.0481 48.98 7200 0.6966 0.3424

Framework versions

  • Transformers 4.16.2
  • Pytorch 1.10.0+cu111
  • Datasets 1.18.3
  • Tokenizers 0.11.0