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xtreme_s_xlsr_300m_fleurs_asr_western_european

Automatic Speech Recognition
Transformers
PyTorch
TensorBoard
Allar
wav2vec2
fleurs-asr
google/xtreme_s
Generated from Trainer
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xtreme_s_xlsr_300m_fleurs_asr_western_european / README.md
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metadata
license: apache-2.0
tags:
  - generated_from_trainer
model-index:
  - name: xtreme_s_xlsr_300m_fleurs_asr_western_european_nomask
    results: []

xtreme_s_xlsr_300m_fleurs_asr_western_european_nomask

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: 1.2572
  • Wer: 0.6278
  • Cer: 0.2394

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: 8
  • eval_batch_size: 1
  • seed: 42
  • distributed_type: multi-GPU
  • num_devices: 8
  • total_train_batch_size: 64
  • total_eval_batch_size: 8
  • optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
  • lr_scheduler_type: linear
  • lr_scheduler_warmup_steps: 1000
  • num_epochs: 20.0
  • mixed_precision_training: Native AMP

Training results

Training Loss Epoch Step Validation Loss Wer Cer
3.1411 0.49 500 3.1673 1.0 1.0
0.6397 0.97 1000 0.9039 0.7171 0.2862
0.4033 1.46 1500 0.8914 0.6862 0.2763
0.3473 1.94 2000 0.8017 0.6505 0.2536
0.3143 2.43 2500 0.8568 0.6566 0.2627
0.3004 2.91 3000 0.8898 0.6640 0.2686
0.282 3.4 3500 0.8489 0.6637 0.2571
0.2489 3.88 4000 0.8955 0.6744 0.2691
0.1706 4.37 4500 0.9190 0.6788 0.2688
0.3336 4.85 5000 0.8915 0.6594 0.2572
0.1426 5.34 5500 0.9501 0.6784 0.2686
0.2301 5.83 6000 1.0217 0.6719 0.2735
0.1325 6.31 6500 0.9578 0.6691 0.2655
0.1145 6.8 7000 0.9129 0.6680 0.2593
0.1202 7.28 7500 0.9646 0.6749 0.2619
0.143 7.77 8000 0.9200 0.6554 0.2554
0.1012 8.25 8500 0.9553 0.6787 0.2628
0.1018 8.74 9000 0.9455 0.6445 0.2511
0.1148 9.22 9500 1.0206 0.6725 0.2629
0.0794 9.71 10000 0.9305 0.6547 0.2526
0.2891 10.19 10500 1.0424 0.6709 0.2570
0.1665 10.68 11000 0.9760 0.6596 0.2507
0.1956 11.17 11500 0.9549 0.6340 0.2440
0.0828 11.65 12000 0.9598 0.6403 0.2460
0.059 12.14 12500 0.9972 0.6574 0.2531
0.0505 12.62 13000 0.9836 0.6534 0.2525
0.0336 13.11 13500 1.0619 0.6564 0.2519
0.0435 13.59 14000 1.0844 0.6480 0.2543
0.0216 14.08 14500 1.1084 0.6512 0.2521
0.0265 14.56 15000 1.1152 0.6607 0.2563
0.0975 15.05 15500 1.1060 0.6456 0.2471
0.1396 15.53 16000 1.1100 0.6337 0.2418
0.0701 16.02 16500 1.1731 0.6309 0.2415
0.1171 16.5 17000 1.1302 0.6315 0.2396
0.0778 16.99 17500 1.1485 0.6379 0.2447
0.0642 17.48 18000 1.2009 0.6400 0.2464
0.0322 17.96 18500 1.2028 0.6357 0.2425
0.031 18.45 19000 1.2381 0.6285 0.2416
0.0579 18.93 19500 1.2299 0.6265 0.2409
0.0628 19.42 20000 1.2582 0.6277 0.2395
0.074 19.9 20500 1.2572 0.6278 0.2394

Framework versions

  • Transformers 4.18.0.dev0
  • Pytorch 1.10.1+cu111
  • Datasets 1.18.4.dev0
  • Tokenizers 0.11.6