wav2vec2-large-sw-cv-20hr-v1 / README.md

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	---
	license: apache-2.0
	base_model: facebook/wav2vec2-large
	tags:
	- generated_from_trainer
	metrics:
	- wer
	model-index:
	- name: wav2vec2-large-sw-cv-20hr-v1
	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. -->

	[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/asr-africa-research-team/ASR%20Africa/runs/w0c4nymx)
	# wav2vec2-large-sw-cv-20hr-v1

	This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: inf
	- Model Preparation Time: 0.0059
	- Wer: 0.3464
	- Cer: 0.1302

	## 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: 0.0001
	- train_batch_size: 32
	- eval_batch_size: 8
	- seed: 42
	- gradient_accumulation_steps: 2
	- total_train_batch_size: 64
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: cosine
	- lr_scheduler_warmup_steps: 500
	- num_epochs: 100
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Model Preparation Time \| Wer \| Cer \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:----------------------:\|:------:\|:------:\|
	\| 4.0167 \| 0.9976 \| 210 \| 1.3039 \| 0.0059 \| 0.9301 \| 0.3195 \|
	\| 0.7784 \| 2.0 \| 421 \| 0.7306 \| 0.0059 \| 0.5818 \| 0.1788 \|
	\| 0.5359 \| 2.9976 \| 631 \| 0.5886 \| 0.0059 \| 0.5048 \| 0.1517 \|
	\| 0.427 \| 4.0 \| 842 \| 0.5274 \| 0.0059 \| 0.4460 \| 0.1345 \|
	\| 0.3657 \| 4.9976 \| 1052 \| 0.5617 \| 0.0059 \| 0.4620 \| 0.1430 \|
	\| 0.3219 \| 6.0 \| 1263 \| 0.5162 \| 0.0059 \| 0.408 \| 0.1240 \|
	\| 0.2922 \| 6.9976 \| 1473 \| 0.4861 \| 0.0059 \| 0.4074 \| 0.1256 \|
	\| 0.2681 \| 8.0 \| 1684 \| 0.5076 \| 0.0059 \| 0.404 \| 0.1253 \|
	\| 0.2459 \| 8.9976 \| 1894 \| 0.5042 \| 0.0059 \| 0.3915 \| 0.1205 \|
	\| 0.2332 \| 10.0 \| 2105 \| 0.5051 \| 0.0059 \| 0.3706 \| 0.1120 \|
	\| 0.2181 \| 10.9976 \| 2315 \| 0.5370 \| 0.0059 \| 0.3750 \| 0.1149 \|
	\| 0.2073 \| 12.0 \| 2526 \| 0.5231 \| 0.0059 \| 0.3860 \| 0.1249 \|
	\| 0.1982 \| 12.9976 \| 2736 \| 0.5290 \| 0.0059 \| 0.4045 \| 0.1239 \|
	\| 0.1875 \| 14.0 \| 2947 \| 0.5184 \| 0.0059 \| 0.3755 \| 0.1153 \|
	\| 0.1782 \| 14.9976 \| 3157 \| 0.5215 \| 0.0059 \| 0.3587 \| 0.1100 \|
	\| 0.1684 \| 16.0 \| 3368 \| 0.5395 \| 0.0059 \| 0.371 \| 0.1142 \|
	\| 0.1629 \| 16.9976 \| 3578 \| 0.5499 \| 0.0059 \| 0.3608 \| 0.1101 \|
	\| 0.1563 \| 18.0 \| 3789 \| 0.5478 \| 0.0059 \| 0.3577 \| 0.1107 \|
	\| 0.1516 \| 18.9976 \| 3999 \| 0.5290 \| 0.0059 \| 0.3649 \| 0.1148 \|
	\| 0.1431 \| 20.0 \| 4210 \| 0.5765 \| 0.0059 \| 0.3657 \| 0.1167 \|
	\| 0.1366 \| 20.9976 \| 4420 \| 0.5604 \| 0.0059 \| 0.3617 \| 0.1137 \|
	\| 0.1345 \| 22.0 \| 4631 \| 0.5546 \| 0.0059 \| 0.3604 \| 0.1118 \|
	\| 0.1303 \| 22.9976 \| 4841 \| 0.5284 \| 0.0059 \| 0.3511 \| 0.1089 \|
	\| 0.122 \| 24.0 \| 5052 \| 0.5668 \| 0.0059 \| 0.3555 \| 0.1111 \|
	\| 0.1183 \| 24.9976 \| 5262 \| 0.5874 \| 0.0059 \| 0.3521 \| 0.1088 \|
	\| 0.1151 \| 26.0 \| 5473 \| 0.5539 \| 0.0059 \| 0.3379 \| 0.1044 \|
	\| 0.1108 \| 26.9976 \| 5683 \| 0.6110 \| 0.0059 \| 0.3375 \| 0.1051 \|
	\| 0.1089 \| 28.0 \| 5894 \| 0.5582 \| 0.0059 \| 0.3397 \| 0.1029 \|
	\| 0.1064 \| 28.9976 \| 6104 \| 0.5774 \| 0.0059 \| 0.3432 \| 0.1062 \|
	\| 0.1026 \| 30.0 \| 6315 \| 0.6042 \| 0.0059 \| 0.3420 \| 0.1062 \|
	\| 0.0983 \| 30.9976 \| 6525 \| 0.5793 \| 0.0059 \| 0.3402 \| 0.1046 \|
	\| 0.0952 \| 32.0 \| 6736 \| 0.6083 \| 0.0059 \| 0.3423 \| 0.1074 \|
	\| 0.0927 \| 32.9976 \| 6946 \| 0.6015 \| 0.0059 \| 0.3363 \| 0.1035 \|
	\| 0.0895 \| 34.0 \| 7157 \| 0.5790 \| 0.0059 \| 0.3368 \| 0.1041 \|
	\| 0.0889 \| 34.9976 \| 7367 \| 0.5530 \| 0.0059 \| 0.3338 \| 0.1023 \|
	\| 0.0865 \| 36.0 \| 7578 \| 0.5598 \| 0.0059 \| 0.3267 \| 0.1009 \|
	\| 0.0828 \| 36.9976 \| 7788 \| 0.5699 \| 0.0059 \| 0.3249 \| 0.1001 \|
	\| 0.0814 \| 38.0 \| 7999 \| 0.5756 \| 0.0059 \| 0.3237 \| 0.0996 \|
	\| 0.0819 \| 38.9976 \| 8209 \| 0.5878 \| 0.0059 \| 0.3363 \| 0.1052 \|
	\| 0.077 \| 40.0 \| 8420 \| 0.5852 \| 0.0059 \| 0.3216 \| 0.0984 \|
	\| 0.075 \| 40.9976 \| 8630 \| 0.5940 \| 0.0059 \| 0.3295 \| 0.1022 \|
	\| 0.0725 \| 42.0 \| 8841 \| 0.5779 \| 0.0059 \| 0.3219 \| 0.0997 \|
	\| 0.0701 \| 42.9976 \| 9051 \| 0.5962 \| 0.0059 \| 0.3144 \| 0.0965 \|
	\| 0.0693 \| 44.0 \| 9262 \| 0.6192 \| 0.0059 \| 0.317 \| 0.0975 \|
	\| 0.0659 \| 44.9976 \| 9472 \| 0.5989 \| 0.0059 \| 0.3126 \| 0.0964 \|
	\| 0.0662 \| 46.0 \| 9683 \| 0.6069 \| 0.0059 \| 0.3112 \| 0.0975 \|
	\| 0.0646 \| 46.9976 \| 9893 \| 0.6309 \| 0.0059 \| 0.3164 \| 0.0986 \|
	\| 0.0626 \| 48.0 \| 10104 \| 0.6266 \| 0.0059 \| 0.3199 \| 0.1007 \|
	\| 0.062 \| 48.9976 \| 10314 \| 0.6403 \| 0.0059 \| 0.3116 \| 0.0963 \|
	\| 0.0591 \| 50.0 \| 10525 \| 0.6140 \| 0.0059 \| 0.3133 \| 0.0965 \|
	\| 0.0568 \| 50.9976 \| 10735 \| 0.5947 \| 0.0059 \| 0.3078 \| 0.0950 \|
	\| 0.0538 \| 52.0 \| 10946 \| 0.6202 \| 0.0059 \| 0.3029 \| 0.0939 \|
	\| 0.0544 \| 52.9976 \| 11156 \| 0.6215 \| 0.0059 \| 0.312 \| 0.0966 \|
	\| 0.0526 \| 54.0 \| 11367 \| 0.6637 \| 0.0059 \| 0.3093 \| 0.0959 \|
	\| 0.05 \| 54.9976 \| 11577 \| 0.6513 \| 0.0059 \| 0.3079 \| 0.0955 \|
	\| 0.0518 \| 56.0 \| 11788 \| 0.6611 \| 0.0059 \| 0.3070 \| 0.0948 \|
	\| 0.0493 \| 56.9976 \| 11998 \| 0.6415 \| 0.0059 \| 0.3041 \| 0.0941 \|
	\| 0.0482 \| 58.0 \| 12209 \| 0.6386 \| 0.0059 \| 0.3042 \| 0.0939 \|
	\| 0.0461 \| 58.9976 \| 12419 \| 0.6664 \| 0.0059 \| 0.316 \| 0.0995 \|
	\| 0.0445 \| 60.0 \| 12630 \| 0.6472 \| 0.0059 \| 0.3057 \| 0.0963 \|
	\| 0.0449 \| 60.9976 \| 12840 \| 0.6510 \| 0.0059 \| 0.3103 \| 0.0972 \|
	\| 0.0437 \| 62.0 \| 13051 \| 0.6696 \| 0.0059 \| 0.3166 \| 0.1005 \|


	### Framework versions

	- Transformers 4.43.1
	- Pytorch 2.2.0+cu121
	- Datasets 2.20.0
	- Tokenizers 0.19.1

	---
	license: apache-2.0
	base_model: facebook/wav2vec2-large
	tags:
	- generated_from_trainer
	metrics:
	- wer
	model-index:
	- name: wav2vec2-large-sw-cv-20hr-v1
	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. -->

	[<img src="https://raw.githubusercontent.com/wandb/assets/main/wandb-github-badge-28.svg" alt="Visualize in Weights & Biases" width="200" height="32"/>](https://wandb.ai/asr-africa-research-team/ASR%20Africa/runs/w0c4nymx)
	# wav2vec2-large-sw-cv-20hr-v1

	This model is a fine-tuned version of [facebook/wav2vec2-large](https://huggingface.co/facebook/wav2vec2-large) on an unknown dataset.
	It achieves the following results on the evaluation set:
	- Loss: inf
	- Model Preparation Time: 0.0059
	- Wer: 0.3464
	- Cer: 0.1302

	## 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: 0.0001
	- train_batch_size: 32
	- eval_batch_size: 8
	- seed: 42
	- gradient_accumulation_steps: 2
	- total_train_batch_size: 64
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: cosine
	- lr_scheduler_warmup_steps: 500
	- num_epochs: 100
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Model Preparation Time \| Wer \| Cer \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:----------------------:\|:------:\|:------:\|
	\| 4.0167 \| 0.9976 \| 210 \| 1.3039 \| 0.0059 \| 0.9301 \| 0.3195 \|
	\| 0.7784 \| 2.0 \| 421 \| 0.7306 \| 0.0059 \| 0.5818 \| 0.1788 \|
	\| 0.5359 \| 2.9976 \| 631 \| 0.5886 \| 0.0059 \| 0.5048 \| 0.1517 \|
	\| 0.427 \| 4.0 \| 842 \| 0.5274 \| 0.0059 \| 0.4460 \| 0.1345 \|
	\| 0.3657 \| 4.9976 \| 1052 \| 0.5617 \| 0.0059 \| 0.4620 \| 0.1430 \|
	\| 0.3219 \| 6.0 \| 1263 \| 0.5162 \| 0.0059 \| 0.408 \| 0.1240 \|
	\| 0.2922 \| 6.9976 \| 1473 \| 0.4861 \| 0.0059 \| 0.4074 \| 0.1256 \|
	\| 0.2681 \| 8.0 \| 1684 \| 0.5076 \| 0.0059 \| 0.404 \| 0.1253 \|
	\| 0.2459 \| 8.9976 \| 1894 \| 0.5042 \| 0.0059 \| 0.3915 \| 0.1205 \|
	\| 0.2332 \| 10.0 \| 2105 \| 0.5051 \| 0.0059 \| 0.3706 \| 0.1120 \|
	\| 0.2181 \| 10.9976 \| 2315 \| 0.5370 \| 0.0059 \| 0.3750 \| 0.1149 \|
	\| 0.2073 \| 12.0 \| 2526 \| 0.5231 \| 0.0059 \| 0.3860 \| 0.1249 \|
	\| 0.1982 \| 12.9976 \| 2736 \| 0.5290 \| 0.0059 \| 0.4045 \| 0.1239 \|
	\| 0.1875 \| 14.0 \| 2947 \| 0.5184 \| 0.0059 \| 0.3755 \| 0.1153 \|
	\| 0.1782 \| 14.9976 \| 3157 \| 0.5215 \| 0.0059 \| 0.3587 \| 0.1100 \|
	\| 0.1684 \| 16.0 \| 3368 \| 0.5395 \| 0.0059 \| 0.371 \| 0.1142 \|
	\| 0.1629 \| 16.9976 \| 3578 \| 0.5499 \| 0.0059 \| 0.3608 \| 0.1101 \|
	\| 0.1563 \| 18.0 \| 3789 \| 0.5478 \| 0.0059 \| 0.3577 \| 0.1107 \|
	\| 0.1516 \| 18.9976 \| 3999 \| 0.5290 \| 0.0059 \| 0.3649 \| 0.1148 \|
	\| 0.1431 \| 20.0 \| 4210 \| 0.5765 \| 0.0059 \| 0.3657 \| 0.1167 \|
	\| 0.1366 \| 20.9976 \| 4420 \| 0.5604 \| 0.0059 \| 0.3617 \| 0.1137 \|
	\| 0.1345 \| 22.0 \| 4631 \| 0.5546 \| 0.0059 \| 0.3604 \| 0.1118 \|
	\| 0.1303 \| 22.9976 \| 4841 \| 0.5284 \| 0.0059 \| 0.3511 \| 0.1089 \|
	\| 0.122 \| 24.0 \| 5052 \| 0.5668 \| 0.0059 \| 0.3555 \| 0.1111 \|
	\| 0.1183 \| 24.9976 \| 5262 \| 0.5874 \| 0.0059 \| 0.3521 \| 0.1088 \|
	\| 0.1151 \| 26.0 \| 5473 \| 0.5539 \| 0.0059 \| 0.3379 \| 0.1044 \|
	\| 0.1108 \| 26.9976 \| 5683 \| 0.6110 \| 0.0059 \| 0.3375 \| 0.1051 \|
	\| 0.1089 \| 28.0 \| 5894 \| 0.5582 \| 0.0059 \| 0.3397 \| 0.1029 \|
	\| 0.1064 \| 28.9976 \| 6104 \| 0.5774 \| 0.0059 \| 0.3432 \| 0.1062 \|
	\| 0.1026 \| 30.0 \| 6315 \| 0.6042 \| 0.0059 \| 0.3420 \| 0.1062 \|
	\| 0.0983 \| 30.9976 \| 6525 \| 0.5793 \| 0.0059 \| 0.3402 \| 0.1046 \|
	\| 0.0952 \| 32.0 \| 6736 \| 0.6083 \| 0.0059 \| 0.3423 \| 0.1074 \|
	\| 0.0927 \| 32.9976 \| 6946 \| 0.6015 \| 0.0059 \| 0.3363 \| 0.1035 \|
	\| 0.0895 \| 34.0 \| 7157 \| 0.5790 \| 0.0059 \| 0.3368 \| 0.1041 \|
	\| 0.0889 \| 34.9976 \| 7367 \| 0.5530 \| 0.0059 \| 0.3338 \| 0.1023 \|
	\| 0.0865 \| 36.0 \| 7578 \| 0.5598 \| 0.0059 \| 0.3267 \| 0.1009 \|
	\| 0.0828 \| 36.9976 \| 7788 \| 0.5699 \| 0.0059 \| 0.3249 \| 0.1001 \|
	\| 0.0814 \| 38.0 \| 7999 \| 0.5756 \| 0.0059 \| 0.3237 \| 0.0996 \|
	\| 0.0819 \| 38.9976 \| 8209 \| 0.5878 \| 0.0059 \| 0.3363 \| 0.1052 \|
	\| 0.077 \| 40.0 \| 8420 \| 0.5852 \| 0.0059 \| 0.3216 \| 0.0984 \|
	\| 0.075 \| 40.9976 \| 8630 \| 0.5940 \| 0.0059 \| 0.3295 \| 0.1022 \|
	\| 0.0725 \| 42.0 \| 8841 \| 0.5779 \| 0.0059 \| 0.3219 \| 0.0997 \|
	\| 0.0701 \| 42.9976 \| 9051 \| 0.5962 \| 0.0059 \| 0.3144 \| 0.0965 \|
	\| 0.0693 \| 44.0 \| 9262 \| 0.6192 \| 0.0059 \| 0.317 \| 0.0975 \|
	\| 0.0659 \| 44.9976 \| 9472 \| 0.5989 \| 0.0059 \| 0.3126 \| 0.0964 \|
	\| 0.0662 \| 46.0 \| 9683 \| 0.6069 \| 0.0059 \| 0.3112 \| 0.0975 \|
	\| 0.0646 \| 46.9976 \| 9893 \| 0.6309 \| 0.0059 \| 0.3164 \| 0.0986 \|
	\| 0.0626 \| 48.0 \| 10104 \| 0.6266 \| 0.0059 \| 0.3199 \| 0.1007 \|
	\| 0.062 \| 48.9976 \| 10314 \| 0.6403 \| 0.0059 \| 0.3116 \| 0.0963 \|
	\| 0.0591 \| 50.0 \| 10525 \| 0.6140 \| 0.0059 \| 0.3133 \| 0.0965 \|
	\| 0.0568 \| 50.9976 \| 10735 \| 0.5947 \| 0.0059 \| 0.3078 \| 0.0950 \|
	\| 0.0538 \| 52.0 \| 10946 \| 0.6202 \| 0.0059 \| 0.3029 \| 0.0939 \|
	\| 0.0544 \| 52.9976 \| 11156 \| 0.6215 \| 0.0059 \| 0.312 \| 0.0966 \|
	\| 0.0526 \| 54.0 \| 11367 \| 0.6637 \| 0.0059 \| 0.3093 \| 0.0959 \|
	\| 0.05 \| 54.9976 \| 11577 \| 0.6513 \| 0.0059 \| 0.3079 \| 0.0955 \|
	\| 0.0518 \| 56.0 \| 11788 \| 0.6611 \| 0.0059 \| 0.3070 \| 0.0948 \|
	\| 0.0493 \| 56.9976 \| 11998 \| 0.6415 \| 0.0059 \| 0.3041 \| 0.0941 \|
	\| 0.0482 \| 58.0 \| 12209 \| 0.6386 \| 0.0059 \| 0.3042 \| 0.0939 \|
	\| 0.0461 \| 58.9976 \| 12419 \| 0.6664 \| 0.0059 \| 0.316 \| 0.0995 \|
	\| 0.0445 \| 60.0 \| 12630 \| 0.6472 \| 0.0059 \| 0.3057 \| 0.0963 \|
	\| 0.0449 \| 60.9976 \| 12840 \| 0.6510 \| 0.0059 \| 0.3103 \| 0.0972 \|
	\| 0.0437 \| 62.0 \| 13051 \| 0.6696 \| 0.0059 \| 0.3166 \| 0.1005 \|


	### Framework versions

	- Transformers 4.43.1
	- Pytorch 2.2.0+cu121
	- Datasets 2.20.0
	- Tokenizers 0.19.1