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metadata
language:
  - sv-SE
license: apache-2.0
tags:
  - automatic-speech-recognition
  - mozilla-foundation/common_voice_7_0
  - generated_from_trainer
datasets:
  - common_voice
model-index:
  - name: ''
    results: []

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the MOZILLA-FOUNDATION/COMMON_VOICE_7_0 - SV-SE dataset. It achieves the following results on the evaluation set:

  • Loss: 0.3179
  • Wer: 0.2735

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: 8
  • eval_batch_size: 8
  • seed: 42
  • gradient_accumulation_steps: 4
  • 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: 2000
  • num_epochs: 50.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
3.3332 1.45 500 3.2920 1.0
2.9269 2.91 1000 2.9415 0.9966
2.0719 4.36 1500 1.1641 0.8508
1.7404 5.81 2000 0.7281 0.6846
1.5921 7.27 2500 0.5886 0.5147
1.4941 8.72 3000 0.5183 0.5063
1.4486 10.17 3500 0.4749 0.4676
1.3899 11.63 4000 0.4565 0.4432
1.3881 13.08 4500 0.4316 0.4228
1.3572 14.53 5000 0.4195 0.3834
1.3261 15.99 5500 0.3974 0.3607
1.2809 17.44 6000 0.3845 0.3467
1.2713 18.89 6500 0.3832 0.3450
1.257 20.35 7000 0.3779 0.3373
1.2298 21.8 7500 0.3744 0.3391
1.2173 23.26 8000 0.3745 0.3262
1.1966 24.71 8500 0.3680 0.3241
1.1925 26.16 9000 0.3605 0.3171
1.1692 27.61 9500 0.3512 0.3147
1.1704 29.07 10000 0.3532 0.3098
1.1595 30.52 10500 0.3425 0.3039
1.1433 31.97 11000 0.3568 0.3026
1.1295 33.43 11500 0.3461 0.2992
1.1131 34.88 12000 0.3349 0.2942
1.1015 36.34 12500 0.3378 0.2961
1.0835 37.79 13000 0.3282 0.2865
1.083 39.24 13500 0.3182 0.2826
1.0819 40.7 14000 0.3264 0.2850
1.072 42.15 14500 0.3279 0.2817
1.0456 43.6 15000 0.3234 0.2793
1.0581 45.06 15500 0.3220 0.2779
1.0406 46.51 16000 0.3208 0.2762
1.0422 47.96 16500 0.3184 0.2752
1.0099 49.42 17000 0.3181 0.2735

Framework versions

  • Transformers 4.16.0.dev0
  • Pytorch 1.10.1+cu102
  • Datasets 1.17.1.dev0
  • Tokenizers 0.11.0