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wav2vec2-large-xls-r-300m-hi-cv8-b2

Automatic Speech Recognition
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wav2vec2
robust-speech-event
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metadata
language:
  - hi
license: apache-2.0
tags:
  - automatic-speech-recognition
  - robust-speech-event
datasets:
  - mozilla-foundation/common_voice_8_0
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xls-r-300m-hi-cv8-b2
    results:
      - task:
          type: automatic-speech-recognition
          name: Speech Recognition
        dataset:
          type: mozilla-foundation/common_voice_8_0
          name: Common Voice 7
          args: hi
        metrics:
          - type: wer
            value: []
            name: Test WER
          - name: Test CER
            type: cer
            value: []

wav2vec2-large-xls-r-300m-hi-cv8-b2

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_8_0 - HI dataset. It achieves the following results on the evaluation set:

  • Loss: 0.7322
  • Wer: 0.3469

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: 0.00025
  • 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: 700
  • num_epochs: 35
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
9.6226 1.04 200 3.8855 1.0
3.4678 2.07 400 3.4283 1.0
2.3668 3.11 600 1.0743 0.7175
0.7308 4.15 800 0.7663 0.5498
0.4985 5.18 1000 0.6957 0.5001
0.3817 6.22 1200 0.6932 0.4866
0.3281 7.25 1400 0.7034 0.4983
0.2752 8.29 1600 0.6588 0.4606
0.2475 9.33 1800 0.6514 0.4328
0.219 10.36 2000 0.6396 0.4176
0.2036 11.4 2200 0.6867 0.4162
0.1793 12.44 2400 0.6943 0.4196
0.1724 13.47 2600 0.6862 0.4260
0.1554 14.51 2800 0.7615 0.4222
0.151 15.54 3000 0.7058 0.4110
0.1335 16.58 3200 0.7172 0.3986
0.1326 17.62 3400 0.7182 0.3923
0.1225 18.65 3600 0.6995 0.3910
0.1146 19.69 3800 0.7075 0.3875
0.108 20.73 4000 0.7297 0.3858
0.1048 21.76 4200 0.7413 0.3850
0.0979 22.8 4400 0.7452 0.3793
0.0946 23.83 4600 0.7436 0.3759
0.0897 24.87 4800 0.7289 0.3754
0.0854 25.91 5000 0.7271 0.3667
0.0803 26.94 5200 0.7378 0.3656
0.0752 27.98 5400 0.7488 0.3680
0.0718 29.02 5600 0.7185 0.3619
0.0702 30.05 5800 0.7428 0.3554
0.0653 31.09 6000 0.7447 0.3559
0.0638 32.12 6200 0.7327 0.3523
0.058 33.16 6400 0.7339 0.3488
0.0594 34.2 6600 0.7322 0.3469

Framework versions

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