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wav2vec2-large-xls-r-300m-gn

Automatic Speech Recognition
Transformers
PyTorch
wav2vec2
Generated from Trainer
Inference Endpoints
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wav2vec2-large-xls-r-300m-gn / README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
metrics:
  - wer
model-index:
  - name: wav2vec2-large-xls-r-300m-gn
    results: []

wav2vec2-large-xls-r-300m-gn

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the None dataset. It achieves the following results on the evaluation set:

  • Loss: 1.1074
  • Wer: 0.8952

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: 0.0003
  • train_batch_size: 12
  • eval_batch_size: 12
  • seed: 42
  • gradient_accumulation_steps: 8
  • total_train_batch_size: 96
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 500
  • num_epochs: 15
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer
3.4445 0.45 400 0.6193 0.6606
0.4459 0.91 800 0.3260 0.3907
0.3307 1.36 1200 0.2739 0.3315
0.3049 1.81 1600 0.2565 0.3027
0.2688 2.27 2000 0.2526 0.2863
0.2589 2.72 2400 0.2426 0.2821
0.2608 3.17 2800 0.2513 0.2965
0.2384 3.62 3200 0.2555 0.3052
0.2504 4.08 3600 0.2462 0.2855
0.2193 4.53 4000 0.2367 0.2691
0.2177 4.99 4400 0.2313 0.2637
0.2029 5.44 4800 0.2344 0.2633
0.2032 5.89 5200 0.2248 0.2553
0.1921 6.35 5600 0.2286 0.2668
0.188 6.8 6000 0.2239 0.2550
0.1808 7.25 6400 0.2323 0.2546
0.1791 7.71 6800 0.2285 0.2500
0.1796 8.16 7200 0.2467 0.2653
0.3112 8.61 7600 0.3921 0.3988
0.3545 9.07 8000 0.3703 0.3951
0.2528 9.52 8400 0.2441 0.2764
0.1932 9.97 8800 0.2385 0.2659
0.1688 10.42 9200 0.2245 0.2413
0.1645 10.88 9600 0.2220 0.2396
0.1736 11.34 10000 0.2456 0.2421
0.3031 11.79 10400 0.4347 0.2643
0.5795 12.24 10800 0.6777 0.3291
0.7227 12.7 11200 0.6537 0.3372
0.8282 13.15 11600 1.0544 0.8223
0.9917 13.6 12000 1.0290 0.7793
0.9407 14.06 12400 1.0095 0.7090
1.1154 14.51 12800 1.1014 0.8454
1.1354 14.97 13200 1.1074 0.8952

Framework versions

  • Transformers 4.29.2
  • Pytorch 2.0.1+cu117
  • Datasets 2.12.0
  • Tokenizers 0.13.3