wav2vec-turkish-300m-xls / README.md

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	---
	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
	---

	<!-- 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. -->

	# 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

	---
	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
	---

	<!-- 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. -->

	# 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