--- license: apache-2.0 base_model: facebook/wav2vec2-large-xlsr-53 tags: - automatic-speech-recognition - DewiBrynJones/banc-trawsgrifiadau-bangor-clean-with-ccv - generated_from_trainer metrics: - wer model-index: - name: wav2vec2-xlsr-53-ft-btb-ccv-enc-cy results: [] --- # wav2vec2-xlsr-53-ft-btb-ccv-enc-cy This model is a fine-tuned version of [facebook/wav2vec2-large-xlsr-53](https://huggingface.co/facebook/wav2vec2-large-xlsr-53) on the DEWIBRYNJONES/BANC-TRAWSGRIFIADAU-BANGOR-CLEAN-WITH-CCV - DEFAULT dataset. It achieves the following results on the evaluation set: - Loss: 0.4095 - Wer: 0.3271 ## 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: 8 - seed: 42 - optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08 - lr_scheduler_type: linear - lr_scheduler_warmup_steps: 500 - training_steps: 10000 - mixed_precision_training: Native AMP ### Training results | Training Loss | Epoch | Step | Validation Loss | Wer | |:-------------:|:------:|:-----:|:---------------:|:------:| | No log | 0.0194 | 100 | 3.5475 | 1.0 | | No log | 0.0387 | 200 | 3.0259 | 1.0 | | No log | 0.0581 | 300 | 3.0887 | 1.0 | | No log | 0.0774 | 400 | 2.3822 | 0.9972 | | 4.0938 | 0.0968 | 500 | 1.4547 | 0.9020 | | 4.0938 | 0.1161 | 600 | 1.2603 | 0.8510 | | 4.0938 | 0.1355 | 700 | 1.0940 | 0.7655 | | 4.0938 | 0.1549 | 800 | 1.0705 | 0.7602 | | 4.0938 | 0.1742 | 900 | 0.9356 | 0.6973 | | 1.0597 | 0.1936 | 1000 | 0.9104 | 0.6766 | | 1.0597 | 0.2129 | 1100 | 0.8879 | 0.6570 | | 1.0597 | 0.2323 | 1200 | 0.8595 | 0.6612 | | 1.0597 | 0.2516 | 1300 | 0.8352 | 0.6075 | | 1.0597 | 0.2710 | 1400 | 0.7912 | 0.6033 | | 0.8484 | 0.2904 | 1500 | 0.7862 | 0.6067 | | 0.8484 | 0.3097 | 1600 | 0.7790 | 0.6009 | | 0.8484 | 0.3291 | 1700 | 0.7678 | 0.5629 | | 0.8484 | 0.3484 | 1800 | 0.7515 | 0.5799 | | 0.8484 | 0.3678 | 1900 | 0.7424 | 0.5859 | | 0.764 | 0.3871 | 2000 | 0.7130 | 0.5521 | | 0.764 | 0.4065 | 2100 | 0.7114 | 0.5408 | | 0.764 | 0.4259 | 2200 | 0.7229 | 0.5577 | | 0.764 | 0.4452 | 2300 | 0.6773 | 0.5160 | | 0.764 | 0.4646 | 2400 | 0.6784 | 0.5178 | | 0.6868 | 0.4839 | 2500 | 0.6720 | 0.5262 | | 0.6868 | 0.5033 | 2600 | 0.6804 | 0.5337 | | 0.6868 | 0.5226 | 2700 | 0.6599 | 0.5024 | | 0.6868 | 0.5420 | 2800 | 0.6287 | 0.4902 | | 0.6868 | 0.5614 | 2900 | 0.6304 | 0.4947 | | 0.6761 | 0.5807 | 3000 | 0.6258 | 0.4851 | | 0.6761 | 0.6001 | 3100 | 0.6311 | 0.4990 | | 0.6761 | 0.6194 | 3200 | 0.6172 | 0.4901 | | 0.6761 | 0.6388 | 3300 | 0.6187 | 0.4666 | | 0.6761 | 0.6581 | 3400 | 0.6045 | 0.4725 | | 0.6462 | 0.6775 | 3500 | 0.5950 | 0.4717 | | 0.6462 | 0.6969 | 3600 | 0.5903 | 0.4602 | | 0.6462 | 0.7162 | 3700 | 0.5865 | 0.4727 | | 0.6462 | 0.7356 | 3800 | 0.5820 | 0.4590 | | 0.6462 | 0.7549 | 3900 | 0.6026 | 0.4830 | | 0.6193 | 0.7743 | 4000 | 0.5807 | 0.4496 | | 0.6193 | 0.7937 | 4100 | 0.5621 | 0.4486 | | 0.6193 | 0.8130 | 4200 | 0.5730 | 0.4593 | | 0.6193 | 0.8324 | 4300 | 0.5592 | 0.4374 | | 0.6193 | 0.8517 | 4400 | 0.5621 | 0.4239 | | 0.59 | 0.8711 | 4500 | 0.5458 | 0.4304 | | 0.59 | 0.8904 | 4600 | 0.5406 | 0.4271 | | 0.59 | 0.9098 | 4700 | 0.5269 | 0.4132 | | 0.59 | 0.9292 | 4800 | 0.5362 | 0.4215 | | 0.59 | 0.9485 | 4900 | 0.5226 | 0.4163 | | 0.5636 | 0.9679 | 5000 | 0.5297 | 0.4148 | | 0.5636 | 0.9872 | 5100 | 0.5226 | 0.4136 | | 0.5636 | 1.0066 | 5200 | 0.5239 | 0.4054 | | 0.5636 | 1.0259 | 5300 | 0.5383 | 0.4058 | | 0.5636 | 1.0453 | 5400 | 0.5125 | 0.4067 | | 0.4924 | 1.0647 | 5500 | 0.5029 | 0.3953 | | 0.4924 | 1.0840 | 5600 | 0.5054 | 0.3932 | | 0.4924 | 1.1034 | 5700 | 0.4969 | 0.3894 | | 0.4924 | 1.1227 | 5800 | 0.4935 | 0.3851 | | 0.4924 | 1.1421 | 5900 | 0.4977 | 0.3817 | | 0.4602 | 1.1614 | 6000 | 0.4863 | 0.3874 | | 0.4602 | 1.1808 | 6100 | 0.4906 | 0.3777 | | 0.4602 | 1.2002 | 6200 | 0.4891 | 0.3764 | | 0.4602 | 1.2195 | 6300 | 0.4881 | 0.3801 | | 0.4602 | 1.2389 | 6400 | 0.4814 | 0.3727 | | 0.4407 | 1.2582 | 6500 | 0.4714 | 0.3772 | | 0.4407 | 1.2776 | 6600 | 0.4739 | 0.3706 | | 0.4407 | 1.2969 | 6700 | 0.4692 | 0.3714 | | 0.4407 | 1.3163 | 6800 | 0.4673 | 0.3728 | | 0.4407 | 1.3357 | 6900 | 0.4610 | 0.3678 | | 0.4284 | 1.3550 | 7000 | 0.4730 | 0.3653 | | 0.4284 | 1.3744 | 7100 | 0.4606 | 0.3640 | | 0.4284 | 1.3937 | 7200 | 0.4572 | 0.3620 | | 0.4284 | 1.4131 | 7300 | 0.4575 | 0.3630 | | 0.4284 | 1.4324 | 7400 | 0.4578 | 0.3590 | | 0.4299 | 1.4518 | 7500 | 0.4477 | 0.3569 | | 0.4299 | 1.4712 | 7600 | 0.4442 | 0.3552 | | 0.4299 | 1.4905 | 7700 | 0.4420 | 0.3546 | | 0.4299 | 1.5099 | 7800 | 0.4437 | 0.3483 | | 0.4299 | 1.5292 | 7900 | 0.4373 | 0.3486 | | 0.408 | 1.5486 | 8000 | 0.4336 | 0.3464 | | 0.408 | 1.5679 | 8100 | 0.4348 | 0.3448 | | 0.408 | 1.5873 | 8200 | 0.4276 | 0.3418 | | 0.408 | 1.6067 | 8300 | 0.4294 | 0.3399 | | 0.408 | 1.6260 | 8400 | 0.4272 | 0.3388 | | 0.3964 | 1.6454 | 8500 | 0.4311 | 0.3409 | | 0.3964 | 1.6647 | 8600 | 0.4260 | 0.3381 | | 0.3964 | 1.6841 | 8700 | 0.4260 | 0.3371 | | 0.3964 | 1.7034 | 8800 | 0.4260 | 0.3364 | | 0.3964 | 1.7228 | 8900 | 0.4215 | 0.3351 | | 0.3866 | 1.7422 | 9000 | 0.4234 | 0.3330 | | 0.3866 | 1.7615 | 9100 | 0.4210 | 0.3319 | | 0.3866 | 1.7809 | 9200 | 0.4156 | 0.3301 | | 0.3866 | 1.8002 | 9300 | 0.4158 | 0.3303 | | 0.3866 | 1.8196 | 9400 | 0.4155 | 0.3294 | | 0.37 | 1.8389 | 9500 | 0.4137 | 0.3292 | | 0.37 | 1.8583 | 9600 | 0.4120 | 0.3284 | | 0.37 | 1.8777 | 9700 | 0.4109 | 0.3301 | | 0.37 | 1.8970 | 9800 | 0.4100 | 0.3279 | | 0.37 | 1.9164 | 9900 | 0.4095 | 0.3267 | | 0.371 | 1.9357 | 10000 | 0.4095 | 0.3271 | ### Framework versions - Transformers 4.41.2 - Pytorch 2.3.1+cu121 - Datasets 2.20.0 - Tokenizers 0.19.1