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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
