--- language: - all license: apache-2.0 tags: - fleurs-asr - google/xtreme_s - 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](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: - Epoch Af Za: 20.0 - Epoch Am Et: 20.0 - Epoch Ar Eg: 20.0 - Epoch As In: 20.0 - Epoch Ast Es: 20.0 - Epoch Az Az: 20.0 - Epoch Be By: 20.0 - Epoch Bn In: 20.0 - Epoch Bs Ba: 20.0 - Epoch Ca Es: 20.0 - Epoch Ceb Ph: 20.0 - Epoch Cmn Hans Cn: 20.0 - Epoch Cs Cz: 20.0 - Epoch Cy Gb: 20.0 - Epoch Da Dk: 20.0 - Epoch De De: 20.0 - Epoch El Gr: 20.0 - Epoch En Us: 20.0 - Epoch Es 419: 20.0 - Epoch Et Ee: 20.0 - Epoch Fa Ir: 20.0 - Epoch Ff Sn: 20.0 - Epoch Fi Fi: 20.0 - Epoch Fil Ph: 20.0 - Epoch Fr Fr: 20.0 - Epoch Ga Ie: 20.0 - Epoch Gl Es: 20.0 - Epoch Gu In: 20.0 - Epoch Ha Ng: 20.0 - Epoch He Il: 20.0 - Epoch Hi In: 20.0 - Epoch Hr Hr: 20.0 - Epoch Hu Hu: 20.0 - Epoch Hy Am: 20.0 - Epoch Id Id: 20.0 - Epoch Ig Ng: 20.0 - Epoch Is Is: 20.0 - Epoch It It: 20.0 - Epoch Ja Jp: 20.0 - Epoch Jv Id: 20.0 - Epoch Ka Ge: 20.0 - Epoch Kam Ke: 20.0 - Epoch Kea Cv: 20.0 - Epoch Kk Kz: 20.0 - Epoch Km Kh: 20.0 - Epoch Kn In: 20.0 - Epoch Ko Kr: 20.0 - Epoch Ku Arab Iq: 20.0 - Epoch Ky Kg: 20.0 - Epoch Lb Lu: 20.0 - Epoch Lg Ug: 20.0 - Epoch Ln Cd: 20.0 - Epoch Lo La: 20.0 - Epoch Lt Lt: 20.0 - Epoch Luo Ke: 20.0 - Epoch Lv Lv: 20.0 - Epoch Mi Nz: 20.0 - Epoch Mk Mk: 20.0 - Epoch Ml In: 20.0 - Epoch Mn Mn: 20.0 - Epoch Mr In: 20.0 - Epoch Ms My: 20.0 - Epoch Mt Mt: 20.0 - Epoch My Mm: 20.0 - Epoch Nb No: 20.0 - Epoch Ne Np: 20.0 - Epoch Nl Nl: 20.0 - Epoch Nso Za: 20.0 - Epoch Ny Mw: 20.0 - Epoch Oci Fr: 20.0 - Epoch Om Et: 20.0 - Epoch Or In: 20.0 - Epoch Pa In: 20.0 - Epoch Pl Pl: 20.0 - Epoch Ps Af: 20.0 - Epoch Pt Br: 20.0 - Epoch Ro Ro: 20.0 - Epoch Ru Ru: 20.0 - Epoch Rup Bg: 20.0 - Epoch Sd Arab In: 20.0 - Epoch Sk Sk: 20.0 - Epoch Sl Si: 20.0 - Epoch Sn Zw: 20.0 - Epoch So So: 20.0 - Epoch Sr Rs: 20.0 - Epoch Sv Se: 20.0 - Epoch Sw Ke: 20.0 - Epoch Ta In: 20.0 - Epoch Te In: 20.0 - Epoch Tg Tj: 20.0 - Epoch Th Th: 20.0 - Epoch Tr Tr: 20.0 - Epoch Uk Ua: 20.0 - Epoch Umb Ao: 20.0 - Epoch Ur Pk: 20.0 - Epoch Uz Uz: 20.0 - Epoch Vi Vn: 20.0 - Epoch Wo Sn: 20.0 - Epoch Xh Za: 20.0 - Epoch Yo Ng: 20.0 - Epoch Yue Hant Hk: 20.0 - Epoch Zu Za: 20.0 - 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