End of training

c662b59 verified 27 days ago

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	---
	library_name: transformers
	license: apache-2.0
	base_model: facebook/wav2vec2-xls-r-300m
	tags:
	- generated_from_trainer
	metrics:
	- wer
	model-index:
	- name: wav2vec2-xls-r-300m-lg-CV-Fleurs-200hrs-v11
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# wav2vec2-xls-r-300m-lg-CV-Fleurs-200hrs-v11

	This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.5459
	- Wer: 0.2673
	- Cer: 0.0600

	## 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: 4
	- seed: 42
	- gradient_accumulation_steps: 2
	- total_train_batch_size: 16
	- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
	- lr_scheduler_type: linear
	- num_epochs: 80
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \|
	\|:-------------:\|:-------:\|:------:\|:---------------:\|:------:\|:------:\|
	\| 0.5847 \| 1.0 \| 9604 \| 0.3651 \| 0.4380 \| 0.1030 \|
	\| 0.3444 \| 2.0 \| 19208 \| 0.3452 \| 0.4003 \| 0.0948 \|
	\| 0.3045 \| 3.0 \| 28812 \| 0.3180 \| 0.3849 \| 0.0894 \|
	\| 0.2762 \| 4.0 \| 38416 \| 0.3008 \| 0.3744 \| 0.0859 \|
	\| 0.2549 \| 5.0 \| 48020 \| 0.3076 \| 0.3745 \| 0.0862 \|
	\| 0.2392 \| 6.0 \| 57624 \| 0.2865 \| 0.3569 \| 0.0814 \|
	\| 0.225 \| 7.0 \| 67228 \| 0.2839 \| 0.3475 \| 0.0799 \|
	\| 0.2137 \| 8.0 \| 76832 \| 0.2730 \| 0.3496 \| 0.0793 \|
	\| 0.2031 \| 9.0 \| 86436 \| 0.2895 \| 0.3487 \| 0.0803 \|
	\| 0.1922 \| 10.0 \| 96040 \| 0.2667 \| 0.3519 \| 0.0801 \|
	\| 0.184 \| 11.0 \| 105644 \| 0.2853 \| 0.3340 \| 0.0762 \|
	\| 0.1748 \| 12.0 \| 115248 \| 0.2582 \| 0.3318 \| 0.0756 \|
	\| 0.1666 \| 13.0 \| 124852 \| 0.2601 \| 0.3324 \| 0.0743 \|
	\| 0.1605 \| 14.0 \| 134456 \| 0.2627 \| 0.3282 \| 0.0752 \|
	\| 0.1532 \| 15.0 \| 144060 \| 0.2682 \| 0.3248 \| 0.0748 \|
	\| 0.1462 \| 16.0 \| 153664 \| 0.2782 \| 0.3255 \| 0.0745 \|
	\| 0.139 \| 17.0 \| 163268 \| 0.2798 \| 0.3255 \| 0.0737 \|
	\| 0.1328 \| 18.0 \| 172872 \| 0.2794 \| 0.3212 \| 0.0725 \|
	\| 0.1267 \| 19.0 \| 182476 \| 0.2855 \| 0.3158 \| 0.0723 \|
	\| 0.121 \| 20.0 \| 192080 \| 0.2801 \| 0.3108 \| 0.0702 \|
	\| 0.1151 \| 21.0 \| 201684 \| 0.2737 \| 0.3060 \| 0.0689 \|
	\| 0.1104 \| 22.0 \| 211288 \| 0.2774 \| 0.3211 \| 0.0727 \|
	\| 0.1052 \| 23.0 \| 220892 \| 0.2842 \| 0.3125 \| 0.0716 \|
	\| 0.1001 \| 24.0 \| 230496 \| 0.2951 \| 0.3120 \| 0.0698 \|
	\| 0.095 \| 25.0 \| 240100 \| 0.2926 \| 0.3101 \| 0.0703 \|
	\| 0.0912 \| 26.0 \| 249704 \| 0.2863 \| 0.3042 \| 0.0701 \|
	\| 0.0881 \| 27.0 \| 259308 \| 0.3057 \| 0.3106 \| 0.0701 \|
	\| 0.0838 \| 28.0 \| 268912 \| 0.3083 \| 0.3099 \| 0.0704 \|
	\| 0.0811 \| 29.0 \| 278516 \| 0.3272 \| 0.3107 \| 0.0701 \|
	\| 0.0779 \| 30.0 \| 288120 \| 0.3337 \| 0.3110 \| 0.0696 \|
	\| 0.0746 \| 31.0 \| 297724 \| 0.3389 \| 0.3006 \| 0.0679 \|
	\| 0.0716 \| 32.0 \| 307328 \| 0.3380 \| 0.3021 \| 0.0686 \|
	\| 0.0691 \| 33.0 \| 316932 \| 0.3334 \| 0.3036 \| 0.0676 \|
	\| 0.0667 \| 34.0 \| 326536 \| 0.3280 \| 0.3033 \| 0.0676 \|
	\| 0.0646 \| 35.0 \| 336140 \| 0.3451 \| 0.3076 \| 0.0682 \|
	\| 0.0623 \| 36.0 \| 345744 \| 0.3544 \| 0.3012 \| 0.0677 \|
	\| 0.0604 \| 37.0 \| 355348 \| 0.3688 \| 0.3036 \| 0.0686 \|
	\| 0.0581 \| 38.0 \| 364952 \| 0.3706 \| 0.3040 \| 0.0683 \|
	\| 0.0567 \| 39.0 \| 374556 \| 0.3936 \| 0.2999 \| 0.0679 \|
	\| 0.0552 \| 40.0 \| 384160 \| 0.3663 \| 0.3034 \| 0.0678 \|
	\| 0.0529 \| 41.0 \| 393764 \| 0.3894 \| 0.3009 \| 0.0681 \|
	\| 0.052 \| 42.0 \| 403368 \| 0.3807 \| 0.2945 \| 0.0669 \|
	\| 0.0498 \| 43.0 \| 412972 \| 0.3960 \| 0.2945 \| 0.0668 \|
	\| 0.0487 \| 44.0 \| 422576 \| 0.4331 \| 0.2949 \| 0.0677 \|
	\| 0.0471 \| 45.0 \| 432180 \| 0.4023 \| 0.2926 \| 0.0663 \|
	\| 0.0458 \| 46.0 \| 441784 \| 0.3923 \| 0.2919 \| 0.0660 \|
	\| 0.0447 \| 47.0 \| 451388 \| 0.4166 \| 0.2957 \| 0.0659 \|
	\| 0.0428 \| 48.0 \| 460992 \| 0.4066 \| 0.2932 \| 0.0656 \|
	\| 0.0417 \| 49.0 \| 470596 \| 0.4177 \| 0.2929 \| 0.0671 \|
	\| 0.0409 \| 50.0 \| 480200 \| 0.4262 \| 0.2909 \| 0.0656 \|
	\| 0.0391 \| 51.0 \| 489804 \| 0.4366 \| 0.2875 \| 0.0655 \|
	\| 0.0381 \| 52.0 \| 499408 \| 0.4492 \| 0.2916 \| 0.0647 \|
	\| 0.0374 \| 53.0 \| 509012 \| 0.4400 \| 0.2822 \| 0.0639 \|
	\| 0.0362 \| 54.0 \| 518616 \| 0.4480 \| 0.2856 \| 0.0644 \|
	\| 0.0349 \| 55.0 \| 528220 \| 0.4560 \| 0.2845 \| 0.0647 \|
	\| 0.0339 \| 56.0 \| 537824 \| 0.4718 \| 0.2838 \| 0.0642 \|
	\| 0.0332 \| 57.0 \| 547428 \| 0.4646 \| 0.2854 \| 0.0642 \|
	\| 0.0324 \| 58.0 \| 557032 \| 0.4635 \| 0.2835 \| 0.0639 \|
	\| 0.0316 \| 59.0 \| 566636 \| 0.4836 \| 0.2847 \| 0.0638 \|
	\| 0.0302 \| 60.0 \| 576240 \| 0.4745 \| 0.2814 \| 0.0634 \|
	\| 0.0296 \| 61.0 \| 585844 \| 0.4663 \| 0.2786 \| 0.0630 \|
	\| 0.0285 \| 62.0 \| 595448 \| 0.4630 \| 0.2768 \| 0.0625 \|
	\| 0.028 \| 63.0 \| 605052 \| 0.4861 \| 0.2763 \| 0.0626 \|
	\| 0.027 \| 64.0 \| 614656 \| 0.5029 \| 0.2800 \| 0.0632 \|
	\| 0.0261 \| 65.0 \| 624260 \| 0.4905 \| 0.2791 \| 0.0625 \|
	\| 0.0253 \| 66.0 \| 633864 \| 0.4920 \| 0.2783 \| 0.0621 \|
	\| 0.0247 \| 67.0 \| 643468 \| 0.4926 \| 0.2796 \| 0.0620 \|
	\| 0.0238 \| 68.0 \| 653072 \| 0.5030 \| 0.2752 \| 0.0619 \|
	\| 0.0234 \| 69.0 \| 662676 \| 0.4909 \| 0.2734 \| 0.0614 \|
	\| 0.0229 \| 70.0 \| 672280 \| 0.5069 \| 0.2731 \| 0.0612 \|
	\| 0.022 \| 71.0 \| 681884 \| 0.5141 \| 0.2680 \| 0.0607 \|
	\| 0.0214 \| 72.0 \| 691488 \| 0.5336 \| 0.2692 \| 0.0605 \|
	\| 0.021 \| 73.0 \| 701092 \| 0.5030 \| 0.2676 \| 0.0606 \|
	\| 0.0204 \| 74.0 \| 710696 \| 0.5245 \| 0.2654 \| 0.0599 \|
	\| 0.0196 \| 75.0 \| 720300 \| 0.5345 \| 0.2653 \| 0.0602 \|
	\| 0.0194 \| 76.0 \| 729904 \| 0.5288 \| 0.2694 \| 0.0607 \|
	\| 0.0186 \| 77.0 \| 739508 \| 0.5339 \| 0.2666 \| 0.0599 \|
	\| 0.0182 \| 78.0 \| 749112 \| 0.5432 \| 0.2675 \| 0.0602 \|
	\| 0.018 \| 79.0 \| 758716 \| 0.5457 \| 0.2660 \| 0.0600 \|
	\| 0.018 \| 79.9917 \| 768240 \| 0.5459 \| 0.2673 \| 0.0600 \|


	### Framework versions

	- Transformers 4.47.0
	- Pytorch 2.1.0+cu118
	- Datasets 3.2.0
	- Tokenizers 0.21.0

	---
	library_name: transformers
	license: apache-2.0
	base_model: facebook/wav2vec2-xls-r-300m
	tags:
	- generated_from_trainer
	metrics:
	- wer
	model-index:
	- name: wav2vec2-xls-r-300m-lg-CV-Fleurs-200hrs-v11
	results: []
	---

	<!-- This model card has been generated automatically according to the information the Trainer had access to. You
	should probably proofread and complete it, then remove this comment. -->

	# wav2vec2-xls-r-300m-lg-CV-Fleurs-200hrs-v11

	This model is a fine-tuned version of [facebook/wav2vec2-xls-r-300m](https://huggingface.co/facebook/wav2vec2-xls-r-300m) on the None dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.5459
	- Wer: 0.2673
	- Cer: 0.0600

	## 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: 4
	- seed: 42
	- gradient_accumulation_steps: 2
	- total_train_batch_size: 16
	- optimizer: Use adamw_torch with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
	- lr_scheduler_type: linear
	- num_epochs: 80
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \|
	\|:-------------:\|:-------:\|:------:\|:---------------:\|:------:\|:------:\|
	\| 0.5847 \| 1.0 \| 9604 \| 0.3651 \| 0.4380 \| 0.1030 \|
	\| 0.3444 \| 2.0 \| 19208 \| 0.3452 \| 0.4003 \| 0.0948 \|
	\| 0.3045 \| 3.0 \| 28812 \| 0.3180 \| 0.3849 \| 0.0894 \|
	\| 0.2762 \| 4.0 \| 38416 \| 0.3008 \| 0.3744 \| 0.0859 \|
	\| 0.2549 \| 5.0 \| 48020 \| 0.3076 \| 0.3745 \| 0.0862 \|
	\| 0.2392 \| 6.0 \| 57624 \| 0.2865 \| 0.3569 \| 0.0814 \|
	\| 0.225 \| 7.0 \| 67228 \| 0.2839 \| 0.3475 \| 0.0799 \|
	\| 0.2137 \| 8.0 \| 76832 \| 0.2730 \| 0.3496 \| 0.0793 \|
	\| 0.2031 \| 9.0 \| 86436 \| 0.2895 \| 0.3487 \| 0.0803 \|
	\| 0.1922 \| 10.0 \| 96040 \| 0.2667 \| 0.3519 \| 0.0801 \|
	\| 0.184 \| 11.0 \| 105644 \| 0.2853 \| 0.3340 \| 0.0762 \|
	\| 0.1748 \| 12.0 \| 115248 \| 0.2582 \| 0.3318 \| 0.0756 \|
	\| 0.1666 \| 13.0 \| 124852 \| 0.2601 \| 0.3324 \| 0.0743 \|
	\| 0.1605 \| 14.0 \| 134456 \| 0.2627 \| 0.3282 \| 0.0752 \|
	\| 0.1532 \| 15.0 \| 144060 \| 0.2682 \| 0.3248 \| 0.0748 \|
	\| 0.1462 \| 16.0 \| 153664 \| 0.2782 \| 0.3255 \| 0.0745 \|
	\| 0.139 \| 17.0 \| 163268 \| 0.2798 \| 0.3255 \| 0.0737 \|
	\| 0.1328 \| 18.0 \| 172872 \| 0.2794 \| 0.3212 \| 0.0725 \|
	\| 0.1267 \| 19.0 \| 182476 \| 0.2855 \| 0.3158 \| 0.0723 \|
	\| 0.121 \| 20.0 \| 192080 \| 0.2801 \| 0.3108 \| 0.0702 \|
	\| 0.1151 \| 21.0 \| 201684 \| 0.2737 \| 0.3060 \| 0.0689 \|
	\| 0.1104 \| 22.0 \| 211288 \| 0.2774 \| 0.3211 \| 0.0727 \|
	\| 0.1052 \| 23.0 \| 220892 \| 0.2842 \| 0.3125 \| 0.0716 \|
	\| 0.1001 \| 24.0 \| 230496 \| 0.2951 \| 0.3120 \| 0.0698 \|
	\| 0.095 \| 25.0 \| 240100 \| 0.2926 \| 0.3101 \| 0.0703 \|
	\| 0.0912 \| 26.0 \| 249704 \| 0.2863 \| 0.3042 \| 0.0701 \|
	\| 0.0881 \| 27.0 \| 259308 \| 0.3057 \| 0.3106 \| 0.0701 \|
	\| 0.0838 \| 28.0 \| 268912 \| 0.3083 \| 0.3099 \| 0.0704 \|
	\| 0.0811 \| 29.0 \| 278516 \| 0.3272 \| 0.3107 \| 0.0701 \|
	\| 0.0779 \| 30.0 \| 288120 \| 0.3337 \| 0.3110 \| 0.0696 \|
	\| 0.0746 \| 31.0 \| 297724 \| 0.3389 \| 0.3006 \| 0.0679 \|
	\| 0.0716 \| 32.0 \| 307328 \| 0.3380 \| 0.3021 \| 0.0686 \|
	\| 0.0691 \| 33.0 \| 316932 \| 0.3334 \| 0.3036 \| 0.0676 \|
	\| 0.0667 \| 34.0 \| 326536 \| 0.3280 \| 0.3033 \| 0.0676 \|
	\| 0.0646 \| 35.0 \| 336140 \| 0.3451 \| 0.3076 \| 0.0682 \|
	\| 0.0623 \| 36.0 \| 345744 \| 0.3544 \| 0.3012 \| 0.0677 \|
	\| 0.0604 \| 37.0 \| 355348 \| 0.3688 \| 0.3036 \| 0.0686 \|
	\| 0.0581 \| 38.0 \| 364952 \| 0.3706 \| 0.3040 \| 0.0683 \|
	\| 0.0567 \| 39.0 \| 374556 \| 0.3936 \| 0.2999 \| 0.0679 \|
	\| 0.0552 \| 40.0 \| 384160 \| 0.3663 \| 0.3034 \| 0.0678 \|
	\| 0.0529 \| 41.0 \| 393764 \| 0.3894 \| 0.3009 \| 0.0681 \|
	\| 0.052 \| 42.0 \| 403368 \| 0.3807 \| 0.2945 \| 0.0669 \|
	\| 0.0498 \| 43.0 \| 412972 \| 0.3960 \| 0.2945 \| 0.0668 \|
	\| 0.0487 \| 44.0 \| 422576 \| 0.4331 \| 0.2949 \| 0.0677 \|
	\| 0.0471 \| 45.0 \| 432180 \| 0.4023 \| 0.2926 \| 0.0663 \|
	\| 0.0458 \| 46.0 \| 441784 \| 0.3923 \| 0.2919 \| 0.0660 \|
	\| 0.0447 \| 47.0 \| 451388 \| 0.4166 \| 0.2957 \| 0.0659 \|
	\| 0.0428 \| 48.0 \| 460992 \| 0.4066 \| 0.2932 \| 0.0656 \|
	\| 0.0417 \| 49.0 \| 470596 \| 0.4177 \| 0.2929 \| 0.0671 \|
	\| 0.0409 \| 50.0 \| 480200 \| 0.4262 \| 0.2909 \| 0.0656 \|
	\| 0.0391 \| 51.0 \| 489804 \| 0.4366 \| 0.2875 \| 0.0655 \|
	\| 0.0381 \| 52.0 \| 499408 \| 0.4492 \| 0.2916 \| 0.0647 \|
	\| 0.0374 \| 53.0 \| 509012 \| 0.4400 \| 0.2822 \| 0.0639 \|
	\| 0.0362 \| 54.0 \| 518616 \| 0.4480 \| 0.2856 \| 0.0644 \|
	\| 0.0349 \| 55.0 \| 528220 \| 0.4560 \| 0.2845 \| 0.0647 \|
	\| 0.0339 \| 56.0 \| 537824 \| 0.4718 \| 0.2838 \| 0.0642 \|
	\| 0.0332 \| 57.0 \| 547428 \| 0.4646 \| 0.2854 \| 0.0642 \|
	\| 0.0324 \| 58.0 \| 557032 \| 0.4635 \| 0.2835 \| 0.0639 \|
	\| 0.0316 \| 59.0 \| 566636 \| 0.4836 \| 0.2847 \| 0.0638 \|
	\| 0.0302 \| 60.0 \| 576240 \| 0.4745 \| 0.2814 \| 0.0634 \|
	\| 0.0296 \| 61.0 \| 585844 \| 0.4663 \| 0.2786 \| 0.0630 \|
	\| 0.0285 \| 62.0 \| 595448 \| 0.4630 \| 0.2768 \| 0.0625 \|
	\| 0.028 \| 63.0 \| 605052 \| 0.4861 \| 0.2763 \| 0.0626 \|
	\| 0.027 \| 64.0 \| 614656 \| 0.5029 \| 0.2800 \| 0.0632 \|
	\| 0.0261 \| 65.0 \| 624260 \| 0.4905 \| 0.2791 \| 0.0625 \|
	\| 0.0253 \| 66.0 \| 633864 \| 0.4920 \| 0.2783 \| 0.0621 \|
	\| 0.0247 \| 67.0 \| 643468 \| 0.4926 \| 0.2796 \| 0.0620 \|
	\| 0.0238 \| 68.0 \| 653072 \| 0.5030 \| 0.2752 \| 0.0619 \|
	\| 0.0234 \| 69.0 \| 662676 \| 0.4909 \| 0.2734 \| 0.0614 \|
	\| 0.0229 \| 70.0 \| 672280 \| 0.5069 \| 0.2731 \| 0.0612 \|
	\| 0.022 \| 71.0 \| 681884 \| 0.5141 \| 0.2680 \| 0.0607 \|
	\| 0.0214 \| 72.0 \| 691488 \| 0.5336 \| 0.2692 \| 0.0605 \|
	\| 0.021 \| 73.0 \| 701092 \| 0.5030 \| 0.2676 \| 0.0606 \|
	\| 0.0204 \| 74.0 \| 710696 \| 0.5245 \| 0.2654 \| 0.0599 \|
	\| 0.0196 \| 75.0 \| 720300 \| 0.5345 \| 0.2653 \| 0.0602 \|
	\| 0.0194 \| 76.0 \| 729904 \| 0.5288 \| 0.2694 \| 0.0607 \|
	\| 0.0186 \| 77.0 \| 739508 \| 0.5339 \| 0.2666 \| 0.0599 \|
	\| 0.0182 \| 78.0 \| 749112 \| 0.5432 \| 0.2675 \| 0.0602 \|
	\| 0.018 \| 79.0 \| 758716 \| 0.5457 \| 0.2660 \| 0.0600 \|
	\| 0.018 \| 79.9917 \| 768240 \| 0.5459 \| 0.2673 \| 0.0600 \|


	### Framework versions

	- Transformers 4.47.0
	- Pytorch 2.1.0+cu118
	- Datasets 3.2.0
	- Tokenizers 0.21.0