--- language: ko license: apache-2.0 tags: - automatic-speech-recognition - generated_from_trainer - hf-asr-leaderboard - robust-speech-event datasets: - kresnik/zeroth_korean base_model: facebook/wav2vec2-xls-r-1b model-index: - name: Wav2Vec2 XLS-R 1B Korean results: - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Robust Speech Event - Dev Data type: speech-recognition-community-v2/dev_data args: ko metrics: - type: wer value: 82.07 name: Test WER - type: cer value: 42.12 name: Test CER - task: type: automatic-speech-recognition name: Automatic Speech Recognition dataset: name: Robust Speech Event - Test Data type: speech-recognition-community-v2/eval_data args: ko metrics: - type: wer value: 82.09 name: Test WER --- # This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the KRESNIK/ZEROTH_KOREAN - CLEAN dataset. It achieves the following results on the evaluation set: - Loss: 0.0639 - Wer: 0.0449 ## 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: 7.5e-05 - train_batch_size: 8 - eval_batch_size: 8 - seed: 42 - gradient_accumulation_steps: 4 - total_train_batch_size: 32 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 2000 - num_epochs: 50.0 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 4.603 | 0.72 | 500 | 4.6572 | 0.9985 | | 2.6314 | 1.44 | 1000 | 2.0424 | 0.9256 | | 2.2708 | 2.16 | 1500 | 0.9889 | 0.6989 | | 2.1769 | 2.88 | 2000 | 0.8366 | 0.6312 | | 2.1142 | 3.6 | 2500 | 0.7555 | 0.5998 | | 2.0084 | 4.32 | 3000 | 0.7144 | 0.6003 | | 1.9272 | 5.04 | 3500 | 0.6311 | 0.5461 | | 1.8687 | 5.75 | 4000 | 0.6252 | 0.5430 | | 1.8186 | 6.47 | 4500 | 0.5491 | 0.4988 | | 1.7364 | 7.19 | 5000 | 0.5463 | 0.4959 | | 1.6809 | 7.91 | 5500 | 0.4724 | 0.4484 | | 1.641 | 8.63 | 6000 | 0.4679 | 0.4461 | | 1.572 | 9.35 | 6500 | 0.4387 | 0.4236 | | 1.5256 | 10.07 | 7000 | 0.3970 | 0.4003 | | 1.5044 | 10.79 | 7500 | 0.3690 | 0.3893 | | 1.4563 | 11.51 | 8000 | 0.3752 | 0.3875 | | 1.394 | 12.23 | 8500 | 0.3386 | 0.3567 | | 1.3641 | 12.95 | 9000 | 0.3290 | 0.3467 | | 1.2878 | 13.67 | 9500 | 0.2893 | 0.3135 | | 1.2602 | 14.39 | 10000 | 0.2723 | 0.3029 | | 1.2302 | 15.11 | 10500 | 0.2603 | 0.2989 | | 1.1865 | 15.83 | 11000 | 0.2440 | 0.2794 | | 1.1491 | 16.55 | 11500 | 0.2500 | 0.2788 | | 1.093 | 17.27 | 12000 | 0.2279 | 0.2629 | | 1.0367 | 17.98 | 12500 | 0.2076 | 0.2443 | | 0.9954 | 18.7 | 13000 | 0.1844 | 0.2259 | | 0.99 | 19.42 | 13500 | 0.1794 | 0.2179 | | 0.9385 | 20.14 | 14000 | 0.1765 | 0.2122 | | 0.8952 | 20.86 | 14500 | 0.1706 | 0.1974 | | 0.8841 | 21.58 | 15000 | 0.1791 | 0.1969 | | 0.847 | 22.3 | 15500 | 0.1780 | 0.2060 | | 0.8669 | 23.02 | 16000 | 0.1608 | 0.1862 | | 0.8066 | 23.74 | 16500 | 0.1447 | 0.1626 | | 0.7908 | 24.46 | 17000 | 0.1457 | 0.1655 | | 0.7459 | 25.18 | 17500 | 0.1350 | 0.1445 | | 0.7218 | 25.9 | 18000 | 0.1276 | 0.1421 | | 0.703 | 26.62 | 18500 | 0.1177 | 0.1302 | | 0.685 | 27.34 | 19000 | 0.1147 | 0.1305 | | 0.6811 | 28.06 | 19500 | 0.1128 | 0.1244 | | 0.6444 | 28.78 | 20000 | 0.1120 | 0.1213 | | 0.6323 | 29.5 | 20500 | 0.1137 | 0.1166 | | 0.5998 | 30.22 | 21000 | 0.1051 | 0.1107 | | 0.5706 | 30.93 | 21500 | 0.1035 | 0.1037 | | 0.5555 | 31.65 | 22000 | 0.1031 | 0.0927 | | 0.5389 | 32.37 | 22500 | 0.0997 | 0.0900 | | 0.5201 | 33.09 | 23000 | 0.0920 | 0.0912 | | 0.5146 | 33.81 | 23500 | 0.0929 | 0.0947 | | 0.515 | 34.53 | 24000 | 0.1000 | 0.0953 | | 0.4743 | 35.25 | 24500 | 0.0922 | 0.0892 | | 0.4707 | 35.97 | 25000 | 0.0852 | 0.0808 | | 0.4456 | 36.69 | 25500 | 0.0855 | 0.0779 | | 0.443 | 37.41 | 26000 | 0.0843 | 0.0738 | | 0.4388 | 38.13 | 26500 | 0.0816 | 0.0699 | | 0.4162 | 38.85 | 27000 | 0.0752 | 0.0645 | | 0.3979 | 39.57 | 27500 | 0.0761 | 0.0621 | | 0.3889 | 40.29 | 28000 | 0.0771 | 0.0625 | | 0.3923 | 41.01 | 28500 | 0.0755 | 0.0598 | | 0.3693 | 41.73 | 29000 | 0.0730 | 0.0578 | | 0.3642 | 42.45 | 29500 | 0.0739 | 0.0598 | | 0.3532 | 43.17 | 30000 | 0.0712 | 0.0553 | | 0.3513 | 43.88 | 30500 | 0.0762 | 0.0516 | | 0.3349 | 44.6 | 31000 | 0.0731 | 0.0504 | | 0.3305 | 45.32 | 31500 | 0.0725 | 0.0507 | | 0.3285 | 46.04 | 32000 | 0.0709 | 0.0489 | | 0.3179 | 46.76 | 32500 | 0.0667 | 0.0467 | | 0.3158 | 47.48 | 33000 | 0.0653 | 0.0494 | | 0.3033 | 48.2 | 33500 | 0.0638 | 0.0456 | | 0.3023 | 48.92 | 34000 | 0.0644 | 0.0464 | | 0.2975 | 49.64 | 34500 | 0.0643 | 0.0455 | ### Framework versions - Transformers 4.17.0.dev0 - Pytorch 1.10.2+cu102 - Datasets 1.18.3.dev0 - Tokenizers 0.11.0