--- license: apache-2.0 tags: - generated_from_trainer datasets: - common_voice_8_0 metrics: - wer model-index: - name: wav2vec2-large-xls-r-2b-frisian-cv-8 results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_8_0 type: common_voice_8_0 config: fy-NL split: validation args: fy-NL metrics: - name: Wer type: wer value: 0.040494215112126836 - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_8_0 type: common_voice_8_0 config: fy-NL split: test args: fy-NL metrics: - name: Wer type: wer value: 0.04223876282699812 --- # wav2vec2-large-xls-r-2b-frisian-cv-8 This model is a fine-tuned version of [facebook/wav2vec2-xls-r-2b](https://huggingface.co/facebook/wav2vec2-xls-r-2b) on the common_voice_8_0 dataset. It achieves the following results on the evaluation set: - Loss: 0.0465 - Wer: 0.0405 And on the test set: - Wer: 0.0422 ## Model description This model has been developed for my Master's thesis in "Voice Technology" at Rijksuniversiteit Groningen - Campus Fryslân. It corresponds to experiment 7 where I use as training set all validated data (~ 50 hours) except the test and evaluation sets (~ 4.5 hours each). The number of training hours adds up to 41 hours of Frisian speech. This varies from experiment 2 because I fine-tune on the 2B parameters version of XLS-R. ## Intended uses & limitations The intended use is for recognizing Frisian speech. Limitations include no LM rescoring and using version 8.0 of Common Voice instead of 13.0. ## Training and evaluation data The evaluation split used is the one available in the Common Voice 8.0 Frisian subset. The train split corresponds to all of the validated data except for the recordings found in the evaluation and test splits. ## Training procedure The script used for training this model can be found in this GitHub repository: [link](https://github.com/greenw0lf/MSc-VT-Thesis/). ### Training hyperparameters The following hyperparameters were used during training: - learning_rate: 3e-05 - train_batch_size: 16 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.98) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_ratio: 0.1 - num_epochs: 20 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 6.3316 | 0.21 | 400 | 2.9773 | 1.0 | | 2.7465 | 0.43 | 800 | 1.2564 | 0.9352 | | 1.4576 | 0.64 | 1200 | 0.6275 | 0.5809 | | 1.2245 | 0.86 | 1600 | 0.4438 | 0.4244 | | 0.9928 | 1.07 | 2000 | 0.3058 | 0.3247 | | 0.8768 | 1.29 | 2400 | 0.2656 | 0.2618 | | 0.8686 | 1.5 | 2800 | 0.2155 | 0.2289 | | 0.8325 | 1.72 | 3200 | 0.1924 | 0.2016 | | 0.8495 | 1.93 | 3600 | 0.1748 | 0.1853 | | 0.7069 | 2.14 | 4000 | 0.1792 | 0.1682 | | 0.7381 | 2.36 | 4400 | 0.1540 | 0.1524 | | 0.6648 | 2.57 | 4800 | 0.1397 | 0.1477 | | 0.7471 | 2.79 | 5200 | 0.1372 | 0.1389 | | 0.7219 | 3.0 | 5600 | 0.1296 | 0.1308 | | 0.5894 | 3.22 | 6000 | 0.1167 | 0.1287 | | 0.585 | 3.43 | 6400 | 0.1194 | 0.1264 | | 0.5486 | 3.65 | 6800 | 0.1159 | 0.1248 | | 0.5001 | 3.86 | 7200 | 0.1107 | 0.1160 | | 0.4838 | 4.08 | 7600 | 0.1079 | 0.1212 | | 0.4213 | 4.29 | 8000 | 0.1065 | 0.1145 | | 0.4493 | 4.5 | 8400 | 0.0998 | 0.1098 | | 0.4003 | 4.72 | 8800 | 0.0975 | 0.1027 | | 0.4034 | 4.93 | 9200 | 0.0947 | 0.1023 | | 0.3699 | 5.15 | 9600 | 0.0927 | 0.1006 | | 0.3748 | 5.36 | 10000 | 0.0955 | 0.0994 | | 0.3681 | 5.58 | 10400 | 0.0923 | 0.0952 | | 0.3416 | 5.79 | 10800 | 0.0902 | 0.0968 | | 0.3594 | 6.01 | 11200 | 0.0848 | 0.0935 | | 0.3303 | 6.22 | 11600 | 0.0889 | 0.0921 | | 0.3205 | 6.43 | 12000 | 0.0843 | 0.0893 | | 0.3267 | 6.65 | 12400 | 0.0884 | 0.0882 | | 0.33 | 6.86 | 12800 | 0.0859 | 0.0936 | | 0.3023 | 7.08 | 13200 | 0.0830 | 0.0851 | | 0.3057 | 7.29 | 13600 | 0.0826 | 0.0860 | | 0.3007 | 7.51 | 14000 | 0.0841 | 0.0836 | | 0.2981 | 7.72 | 14400 | 0.0790 | 0.0817 | | 0.282 | 7.94 | 14800 | 0.0761 | 0.0779 | | 0.2758 | 8.15 | 15200 | 0.0767 | 0.0776 | | 0.275 | 8.36 | 15600 | 0.0788 | 0.0781 | | 0.283 | 8.58 | 16000 | 0.0728 | 0.0775 | | 0.2684 | 8.79 | 16400 | 0.0722 | 0.0742 | | 0.2701 | 9.01 | 16800 | 0.0742 | 0.0720 | | 0.248 | 9.22 | 17200 | 0.0711 | 0.0729 | | 0.2467 | 9.44 | 17600 | 0.0698 | 0.0711 | | 0.2588 | 9.65 | 18000 | 0.0688 | 0.0710 | | 0.2566 | 9.87 | 18400 | 0.0699 | 0.0708 | | 0.2425 | 10.08 | 18800 | 0.0699 | 0.0683 | | 0.2292 | 10.29 | 19200 | 0.0697 | 0.0662 | | 0.2317 | 10.51 | 19600 | 0.0670 | 0.0663 | | 0.2381 | 10.72 | 20000 | 0.0649 | 0.0648 | | 0.2281 | 10.94 | 20400 | 0.0619 | 0.0621 | | 0.2329 | 11.15 | 20800 | 0.0648 | 0.0627 | | 0.2197 | 11.37 | 21200 | 0.0630 | 0.0632 | | 0.2406 | 11.58 | 21600 | 0.0611 | 0.0609 | | 0.2221 | 11.8 | 22000 | 0.0621 | 0.0601 | | 0.2316 | 12.01 | 22400 | 0.0637 | 0.0596 | | 0.202 | 12.23 | 22800 | 0.0622 | 0.0592 | | 0.2071 | 12.44 | 23200 | 0.0603 | 0.0589 | | 0.2119 | 12.65 | 23600 | 0.0589 | 0.0581 | | 0.2072 | 12.87 | 24000 | 0.0586 | 0.0588 | | 0.1948 | 13.08 | 24400 | 0.0576 | 0.0562 | | 0.1967 | 13.3 | 24800 | 0.0573 | 0.0543 | | 0.1981 | 13.51 | 25200 | 0.0582 | 0.0567 | | 0.1869 | 13.73 | 25600 | 0.0550 | 0.0533 | | 0.1929 | 13.94 | 26000 | 0.0530 | 0.0540 | | 0.1837 | 14.16 | 26400 | 0.0550 | 0.0519 | | 0.1823 | 14.37 | 26800 | 0.0535 | 0.0521 | | 0.1756 | 14.58 | 27200 | 0.0552 | 0.0515 | | 0.1769 | 14.8 | 27600 | 0.0553 | 0.0502 | | 0.1769 | 15.01 | 28000 | 0.0516 | 0.0493 | | 0.1781 | 15.23 | 28400 | 0.0519 | 0.0485 | | 0.1763 | 15.44 | 28800 | 0.0511 | 0.0482 | | 0.1705 | 15.66 | 29200 | 0.0513 | 0.0471 | | 0.1696 | 15.87 | 29600 | 0.0484 | 0.0467 | | 0.1668 | 16.09 | 30000 | 0.0492 | 0.0464 | | 0.1635 | 16.3 | 30400 | 0.0492 | 0.0470 | | 0.1597 | 16.51 | 30800 | 0.0505 | 0.0471 | | 0.152 | 16.73 | 31200 | 0.0495 | 0.0471 | | 0.1589 | 16.94 | 31600 | 0.0478 | 0.0456 | | 0.1586 | 17.16 | 32000 | 0.0490 | 0.0441 | | 0.1516 | 17.37 | 32400 | 0.0482 | 0.0448 | | 0.1506 | 17.59 | 32800 | 0.0485 | 0.0439 | | 0.1513 | 17.8 | 33200 | 0.0485 | 0.0439 | | 0.1545 | 18.02 | 33600 | 0.0479 | 0.0432 | | 0.1472 | 18.23 | 34000 | 0.0479 | 0.0428 | | 0.148 | 18.45 | 34400 | 0.0475 | 0.0424 | | 0.1446 | 18.66 | 34800 | 0.0477 | 0.0420 | | 0.1413 | 18.87 | 35200 | 0.0466 | 0.0416 | | 0.1398 | 19.09 | 35600 | 0.0477 | 0.0407 | | 0.1431 | 19.3 | 36000 | 0.0466 | 0.0406 | | 0.1437 | 19.52 | 36400 | 0.0467 | 0.0401 | | 0.1393 | 19.73 | 36800 | 0.0468 | 0.0404 | | 0.1416 | 19.95 | 37200 | 0.0465 | 0.0405 | ### Framework versions - Transformers 4.28.1 - Pytorch 2.0.0+cu117 - Datasets 2.11.0 - Tokenizers 0.13.3