readme: ---
  language:
  - all
  license: apache-2.0
  tags:
  - fleurs-asr
  - google/xtreme_s
  - generated_from_trainer
  datasets:
  - google/xtreme_s
  model-index:
  - name: xtreme_s_xlsr_300m_fleurs_asr_western_european
    results: []
  ---
  
  <!-- This model card has been generated automatically according to the information the Trainer had access to. You
  should probably proofread and complete it, then remove this comment. -->
  
  # xtreme_s_xlsr_300m_fleurs_asr_western_european
  
  This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/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 | 1400