--- license: apache-2.0 base_model: facebook/wav2vec2-xls-r-1b tags: - generated_from_trainer datasets: - common_voice_16_1 metrics: - wer model-index: - name: wav2vec-turkish-300m-xls results: - task: name: Automatic Speech Recognition type: automatic-speech-recognition dataset: name: common_voice_16_1 type: common_voice_16_1 config: tr split: test args: tr metrics: - name: Wer type: wer value: 0.4719196059396685 --- # wav2vec-turkish-300m-xls This model is a fine-tuned version of [facebook/wav2vec2-xls-r-1b](https://huggingface.co/facebook/wav2vec2-xls-r-1b) on the common_voice_16_1 dataset. It achieves the following results on the evaluation set: - Loss: 0.6040 - Wer: 0.4719 ## 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: 0.0005 - train_batch_size: 32 - eval_batch_size: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - num_epochs: 30 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:-----:|:-----:|:---------------:|:------:| | 2.2653 | 0.29 | 400 | 1.1675 | 0.9024 | | 1.0108 | 0.58 | 800 | 1.2504 | 0.9083 | | 0.9497 | 0.88 | 1200 | 1.2197 | 0.9001 | | 0.8842 | 1.17 | 1600 | 0.8955 | 0.8195 | | 0.8603 | 1.46 | 2000 | 0.8623 | 0.8267 | | 0.8271 | 1.75 | 2400 | 0.8324 | 0.7950 | | 0.8037 | 2.05 | 2800 | 0.7933 | 0.7751 | | 0.7488 | 2.34 | 3200 | 0.8006 | 0.7841 | | 0.7683 | 2.63 | 3600 | 0.8042 | 0.7925 | | 0.7554 | 2.92 | 4000 | 0.7955 | 0.7728 | | 0.7078 | 3.21 | 4400 | 0.8038 | 0.7781 | | 0.7161 | 3.51 | 4800 | 0.8470 | 0.7967 | | 0.7114 | 3.8 | 5200 | 0.8424 | 0.8074 | | 0.696 | 4.09 | 5600 | 0.7482 | 0.7617 | | 0.6581 | 4.38 | 6000 | 0.7177 | 0.7427 | | 0.6448 | 4.67 | 6400 | 0.7201 | 0.7292 | | 0.6343 | 4.97 | 6800 | 0.6995 | 0.7265 | | 0.6072 | 5.26 | 7200 | 0.7234 | 0.7549 | | 0.598 | 5.55 | 7600 | 0.6919 | 0.7215 | | 0.5938 | 5.84 | 8000 | 0.7364 | 0.7310 | | 0.5641 | 6.14 | 8400 | 0.7075 | 0.7074 | | 0.5557 | 6.43 | 8800 | 0.6785 | 0.7069 | | 0.5537 | 6.72 | 9200 | 0.6434 | 0.7019 | | 0.5475 | 7.01 | 9600 | 0.6415 | 0.6797 | | 0.5053 | 7.3 | 10000 | 0.6402 | 0.6751 | | 0.518 | 7.6 | 10400 | 0.6214 | 0.6618 | | 0.5041 | 7.89 | 10800 | 0.6156 | 0.6607 | | 0.4853 | 8.18 | 11200 | 0.6600 | 0.6879 | | 0.4738 | 8.47 | 11600 | 0.6359 | 0.6659 | | 0.4766 | 8.77 | 12000 | 0.6674 | 0.6980 | | 0.4822 | 9.06 | 12400 | 0.6189 | 0.6545 | | 0.4421 | 9.35 | 12800 | 0.6090 | 0.6434 | | 0.4562 | 9.64 | 13200 | 0.6099 | 0.6383 | | 0.4559 | 9.93 | 13600 | 0.6009 | 0.6511 | | 0.4456 | 10.23 | 14000 | 0.6090 | 0.6409 | | 0.4277 | 10.52 | 14400 | 0.5940 | 0.6374 | | 0.4228 | 10.81 | 14800 | 0.5992 | 0.6402 | | 0.4139 | 11.1 | 15200 | 0.6287 | 0.6344 | | 0.3918 | 11.4 | 15600 | 0.6134 | 0.6326 | | 0.3957 | 11.69 | 16000 | 0.5952 | 0.6275 | | 0.4022 | 11.98 | 16400 | 0.5957 | 0.6350 | | 0.3733 | 12.27 | 16800 | 0.5466 | 0.6010 | | 0.3749 | 12.56 | 17200 | 0.5566 | 0.6044 | | 0.3736 | 12.86 | 17600 | 0.5453 | 0.5994 | | 0.3701 | 13.15 | 18000 | 0.5846 | 0.6203 | | 0.359 | 13.44 | 18400 | 0.5880 | 0.5997 | | 0.3501 | 13.73 | 18800 | 0.5738 | 0.6153 | | 0.3403 | 14.02 | 19200 | 0.5766 | 0.5855 | | 0.3302 | 14.32 | 19600 | 0.5507 | 0.5954 | | 0.3295 | 14.61 | 20000 | 0.5467 | 0.5899 | | 0.3371 | 14.9 | 20400 | 0.5571 | 0.5907 | | 0.3253 | 15.19 | 20800 | 0.5266 | 0.5745 | | 0.3054 | 15.49 | 21200 | 0.5211 | 0.5681 | | 0.3044 | 15.78 | 21600 | 0.5409 | 0.5698 | | 0.2982 | 16.07 | 22000 | 0.5467 | 0.5827 | | 0.293 | 16.36 | 22400 | 0.5426 | 0.5706 | | 0.2901 | 16.65 | 22800 | 0.5404 | 0.5793 | | 0.2913 | 16.95 | 23200 | 0.5342 | 0.5647 | | 0.269 | 17.24 | 23600 | 0.5309 | 0.5623 | | 0.2803 | 17.53 | 24000 | 0.5300 | 0.5637 | | 0.2697 | 17.82 | 24400 | 0.5103 | 0.5539 | | 0.2727 | 18.12 | 24800 | 0.5414 | 0.5607 | | 0.2505 | 18.41 | 25200 | 0.5472 | 0.5508 | | 0.2554 | 18.7 | 25600 | 0.5260 | 0.5511 | | 0.2552 | 18.99 | 26000 | 0.5246 | 0.5389 | | 0.2333 | 19.28 | 26400 | 0.5392 | 0.5497 | | 0.2315 | 19.58 | 26800 | 0.5230 | 0.5395 | | 0.2366 | 19.87 | 27200 | 0.5303 | 0.5344 | | 0.2317 | 20.16 | 27600 | 0.5348 | 0.5350 | | 0.2229 | 20.45 | 28000 | 0.5138 | 0.5328 | | 0.2243 | 20.75 | 28400 | 0.5147 | 0.5235 | | 0.2169 | 21.04 | 28800 | 0.5494 | 0.5266 | | 0.2068 | 21.33 | 29200 | 0.5361 | 0.5266 | | 0.2073 | 21.62 | 29600 | 0.5660 | 0.5346 | | 0.2035 | 21.91 | 30000 | 0.5048 | 0.5196 | | 0.1954 | 22.21 | 30400 | 0.5498 | 0.5137 | | 0.1974 | 22.5 | 30800 | 0.5338 | 0.5157 | | 0.1914 | 22.79 | 31200 | 0.5311 | 0.5080 | | 0.1874 | 23.08 | 31600 | 0.5600 | 0.5020 | | 0.1792 | 23.37 | 32000 | 0.5428 | 0.5012 | | 0.182 | 23.67 | 32400 | 0.5237 | 0.5013 | | 0.1825 | 23.96 | 32800 | 0.5383 | 0.4999 | | 0.1723 | 24.25 | 33200 | 0.5690 | 0.5063 | | 0.1673 | 24.54 | 33600 | 0.5525 | 0.5030 | | 0.165 | 24.84 | 34000 | 0.5519 | 0.5001 | | 0.162 | 25.13 | 34400 | 0.5553 | 0.4966 | | 0.1597 | 25.42 | 34800 | 0.5614 | 0.4938 | | 0.1505 | 25.71 | 35200 | 0.5569 | 0.4932 | | 0.157 | 26.0 | 35600 | 0.5629 | 0.4931 | | 0.1468 | 26.3 | 36000 | 0.5808 | 0.4879 | | 0.1438 | 26.59 | 36400 | 0.5675 | 0.4871 | | 0.1462 | 26.88 | 36800 | 0.5568 | 0.4852 | | 0.138 | 27.17 | 37200 | 0.5995 | 0.4821 | | 0.1394 | 27.47 | 37600 | 0.5810 | 0.4798 | | 0.1363 | 27.76 | 38000 | 0.5776 | 0.4771 | | 0.1318 | 28.05 | 38400 | 0.5909 | 0.4763 | | 0.128 | 28.34 | 38800 | 0.5967 | 0.4782 | | 0.1304 | 28.63 | 39200 | 0.5866 | 0.4758 | | 0.1284 | 28.93 | 39600 | 0.5904 | 0.4747 | | 0.1207 | 29.22 | 40000 | 0.6023 | 0.4739 | | 0.1275 | 29.51 | 40400 | 0.6038 | 0.4733 | | 0.1241 | 29.8 | 40800 | 0.6040 | 0.4719 | ### Framework versions - Transformers 4.38.1 - Pytorch 2.2.0+cu121 - Datasets 2.17.0 - Tokenizers 0.15.2