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xtreme_s_xlsr_300m_fleurs_asr_western_european

This model is a fine-tuned version of facebook/wav2vec2-xls-r-300m on the GOOGLE/XTREME_S - FLEURS.ALL dataset. It achieves the following results on the evaluation set:

  • Cer: 0.2484
  • Cer Ast Es: 0.1598
  • Cer Bs Ba: 0.1749
  • Cer Ca Es: 0.1655
  • Cer Cy Gb: 0.2280
  • Cer Da Dk: 0.3616
  • Cer De De: 0.1287
  • Cer El Gr: 0.6020
  • Cer En Us: 0.1938
  • Cer Es 419: 0.1288
  • Cer Fi Fi: 0.2050
  • Cer Fr Fr: 0.1811
  • Cer Ga Ie: 0.4474
  • Cer Gl Es: 0.1324
  • Cer Hr Hr: 0.1555
  • Cer Hu Hu: 0.3911
  • Cer Is Is: 0.4646
  • Cer It It: 0.1283
  • Cer Kea Cv: 0.1818
  • Cer Lb Lu: 0.2594
  • Cer Mt Mt: 0.3628
  • Cer Nb No: 0.2254
  • Cer Nl Nl: 0.1790
  • Cer Oci Fr: 0.2159
  • Cer Pt Br: 0.2275
  • Cer Sv Se: 0.3092
  • Loss: 1.3089
  • Loss Ast Es: 0.7715
  • Loss Bs Ba: 0.7378
  • Loss Ca Es: 0.7868
  • Loss Cy Gb: 1.1441
  • Loss Da Dk: 1.9130
  • Loss De De: 0.5391
  • Loss El Gr: 3.4904
  • Loss En Us: 0.9632
  • Loss Es 419: 0.6186
  • Loss Fi Fi: 0.8953
  • Loss Fr Fr: 0.9076
  • Loss Ga Ie: 3.0217
  • Loss Gl Es: 0.5788
  • Loss Hr Hr: 0.6462
  • Loss Hu Hu: 1.9029
  • Loss Is Is: 2.6551
  • Loss It It: 0.6052
  • Loss Kea Cv: 0.9107
  • Loss Lb Lu: 1.3705
  • Loss Mt Mt: 2.3651
  • Loss Nb No: 1.1518
  • Loss Nl Nl: 0.8490
  • Loss Oci Fr: 1.1421
  • Loss Pt Br: 1.1641
  • Loss Sv Se: 1.5910
  • Wer: 0.6451
  • Wer Ast Es: 0.4654
  • Wer Bs Ba: 0.5443
  • Wer Ca Es: 0.4979
  • Wer Cy Gb: 0.5962
  • Wer Da Dk: 0.8455
  • Wer De De: 0.4221
  • Wer El Gr: 0.9805
  • Wer En Us: 0.4556
  • Wer Es 419: 0.3928
  • Wer Fi Fi: 0.8116
  • Wer Fr Fr: 0.4690
  • Wer Ga Ie: 0.8519
  • Wer Gl Es: 0.4245
  • Wer Hr Hr: 0.4895
  • Wer Hu Hu: 0.9099
  • Wer Is Is: 0.9960
  • Wer It It: 0.4415
  • Wer Kea Cv: 0.5202
  • Wer Lb Lu: 0.7225
  • Wer Mt Mt: 1.0096
  • Wer Nb No: 0.6541
  • Wer Nl Nl: 0.5257
  • Wer Oci Fr: 0.5770
  • Wer Pt Br: 0.6685
  • Wer Sv Se: 0.8546
  • Predict Samples: 20043

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
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Dataset used to train anton-l/xtreme_s_xlsr_300m_fleurs_asr_western_european