mms-1b-naijavoices_500h-hau-ft / README.md

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	---
	library_name: transformers
	license: cc-by-nc-4.0
	base_model: facebook/mms-1b-all
	tags:
	- automatic-speech-recognition
	- /mnt/md0/synvoices/data/naijavoices_500h
	- mms
	- generated_from_trainer
	metrics:
	- wer
	model-index:
	- name: mms-1b-naijavoices_500h-hau-ft
	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. -->

	# mms-1b-naijavoices_500h-hau-ft

	This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the /MNT/MD0/SYNVOICES/DATA/NAIJAVOICES_500H - NA dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.3121
	- Wer: 0.3303
	- Cer: 0.0830

	## 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: 0.001
	- train_batch_size: 8
	- eval_batch_size: 8
	- seed: 42
	- distributed_type: multi-GPU
	- num_devices: 2
	- gradient_accumulation_steps: 2
	- total_train_batch_size: 32
	- total_eval_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
	- lr_scheduler_warmup_steps: 100
	- num_epochs: 2.0
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Cer \| Validation Loss \| Wer \|
	\|:-------------:\|:------:\|:-----:\|:------:\|:---------------:\|:------:\|
	\| 0.5183 \| 0.0279 \| 500 \| 0.1051 \| 0.3932 \| 0.4076 \|
	\| 0.4379 \| 0.0559 \| 1000 \| 0.1009 \| 0.3776 \| 0.3964 \|
	\| 0.5709 \| 0.0838 \| 1500 \| 0.1001 \| 0.3829 \| 0.3935 \|
	\| 0.6257 \| 0.1117 \| 2000 \| 0.0994 \| 0.3710 \| 0.3930 \|
	\| 0.5065 \| 0.1397 \| 2500 \| 0.1008 \| 0.3796 \| 0.3937 \|
	\| 0.4613 \| 0.1676 \| 3000 \| 0.0977 \| 0.3637 \| 0.3849 \|
	\| 0.5305 \| 0.1955 \| 3500 \| 0.0971 \| 0.3674 \| 0.3801 \|
	\| 0.4303 \| 0.2235 \| 4000 \| 0.0980 \| 0.3663 \| 0.3875 \|
	\| 0.6497 \| 0.2514 \| 4500 \| 0.0959 \| 0.3617 \| 0.3741 \|
	\| 0.3887 \| 0.2794 \| 5000 \| 0.0985 \| 0.3620 \| 0.3925 \|
	\| 0.4604 \| 0.3073 \| 5500 \| 0.3570 \| 0.3727 \| 0.0947 \|
	\| 0.4349 \| 0.3352 \| 6000 \| 0.3529 \| 0.3686 \| 0.0940 \|
	\| 0.5403 \| 0.3632 \| 6500 \| 0.3518 \| 0.3722 \| 0.0941 \|
	\| 0.4455 \| 0.3911 \| 7000 \| 0.3522 \| 0.3695 \| 0.0937 \|
	\| 0.4454 \| 0.4190 \| 7500 \| 0.3527 \| 0.3722 \| 0.0941 \|
	\| 0.3582 \| 0.4470 \| 8000 \| 0.3479 \| 0.3696 \| 0.0932 \|
	\| 0.6661 \| 0.4749 \| 8500 \| 0.3453 \| 0.3597 \| 0.0911 \|
	\| 0.4702 \| 0.5028 \| 9000 \| 0.3472 \| 0.3672 \| 0.0929 \|
	\| 0.3877 \| 0.5308 \| 9500 \| 0.3467 \| 0.3772 \| 0.0952 \|
	\| 0.5848 \| 0.5587 \| 10000 \| 0.3422 \| 0.3652 \| 0.0920 \|
	\| 0.4943 \| 0.5866 \| 10500 \| 0.3444 \| 0.3701 \| 0.0926 \|
	\| 0.5451 \| 0.6146 \| 11000 \| 0.3441 \| 0.3583 \| 0.0908 \|
	\| 0.4033 \| 0.6425 \| 11500 \| 0.3424 \| 0.3572 \| 0.0907 \|
	\| 0.4437 \| 0.6704 \| 12000 \| 0.3427 \| 0.3576 \| 0.0906 \|
	\| 0.4541 \| 0.6984 \| 12500 \| 0.3375 \| 0.3574 \| 0.0901 \|
	\| 0.3769 \| 0.7263 \| 13000 \| 0.3381 \| 0.3605 \| 0.0901 \|
	\| 0.3915 \| 0.7543 \| 13500 \| 0.3357 \| 0.3538 \| 0.0892 \|
	\| 0.5068 \| 0.7822 \| 14000 \| 0.3373 \| 0.3518 \| 0.0892 \|
	\| 0.3922 \| 0.8101 \| 14500 \| 0.3362 \| 0.3556 \| 0.0895 \|
	\| 0.3928 \| 0.8381 \| 15000 \| 0.3337 \| 0.3489 \| 0.0887 \|
	\| 0.487 \| 0.8660 \| 15500 \| 0.3350 \| 0.3580 \| 0.0897 \|
	\| 0.3974 \| 0.8939 \| 16000 \| 0.3330 \| 0.3545 \| 0.0892 \|
	\| 0.3988 \| 0.9219 \| 16500 \| 0.3339 \| 0.3468 \| 0.0880 \|
	\| 0.5077 \| 0.9498 \| 17000 \| 0.3322 \| 0.3529 \| 0.0884 \|
	\| 0.4159 \| 0.9777 \| 17500 \| 0.3320 \| 0.3509 \| 0.0885 \|
	\| 0.46 \| 1.0057 \| 18000 \| 0.3313 \| 0.3469 \| 0.0881 \|
	\| 0.4727 \| 1.0336 \| 18500 \| 0.3320 \| 0.3588 \| 0.0893 \|
	\| 0.5366 \| 1.0616 \| 19000 \| 0.3292 \| 0.3519 \| 0.0891 \|
	\| 0.7299 \| 1.0895 \| 19500 \| 0.3292 \| 0.3518 \| 0.0882 \|
	\| 0.4359 \| 1.1174 \| 20000 \| 0.3292 \| 0.3470 \| 0.0872 \|
	\| 0.4212 \| 1.1454 \| 20500 \| 0.3266 \| 0.3449 \| 0.0873 \|
	\| 0.4532 \| 1.1733 \| 21000 \| 0.3264 \| 0.3443 \| 0.0868 \|
	\| 0.5725 \| 1.2012 \| 21500 \| 0.3264 \| 0.3393 \| 0.0857 \|
	\| 0.4016 \| 1.2292 \| 22000 \| 0.3249 \| 0.3398 \| 0.0861 \|
	\| 0.4479 \| 1.2571 \| 22500 \| 0.3243 \| 0.3519 \| 0.0875 \|
	\| 0.3502 \| 1.2851 \| 23000 \| 0.3253 \| 0.3463 \| 0.0867 \|
	\| 0.4566 \| 1.3130 \| 23500 \| 0.3207 \| 0.3387 \| 0.0854 \|
	\| 0.4414 \| 1.3409 \| 24000 \| 0.3218 \| 0.3431 \| 0.0858 \|
	\| 0.4479 \| 1.3689 \| 24500 \| 0.3243 \| 0.3445 \| 0.0864 \|
	\| 0.4601 \| 1.3968 \| 25000 \| 0.3197 \| 0.3405 \| 0.0858 \|
	\| 0.4091 \| 1.4247 \| 25500 \| 0.3219 \| 0.3371 \| 0.0851 \|
	\| 0.3548 \| 1.4527 \| 26000 \| 0.3207 \| 0.3417 \| 0.0856 \|
	\| 0.4587 \| 1.4806 \| 26500 \| 0.3183 \| 0.3360 \| 0.0851 \|
	\| 0.51 \| 1.5085 \| 27000 \| 0.3196 \| 0.3393 \| 0.0853 \|
	\| 0.4705 \| 1.5365 \| 27500 \| 0.3187 \| 0.3382 \| 0.0853 \|
	\| 0.5046 \| 1.5644 \| 28000 \| 0.3194 \| 0.3337 \| 0.0843 \|
	\| 0.4924 \| 1.5923 \| 28500 \| 0.3149 \| 0.3327 \| 0.0840 \|
	\| 0.3216 \| 1.6203 \| 29000 \| 0.3173 \| 0.3395 \| 0.0850 \|
	\| 0.4593 \| 1.6482 \| 29500 \| 0.3156 \| 0.3297 \| 0.0833 \|
	\| 0.3163 \| 1.6761 \| 30000 \| 0.3141 \| 0.3355 \| 0.0843 \|
	\| 0.3492 \| 1.7041 \| 30500 \| 0.3147 \| 0.3337 \| 0.0840 \|
	\| 0.4529 \| 1.7320 \| 31000 \| 0.3150 \| 0.3363 \| 0.0843 \|
	\| 0.2976 \| 1.7600 \| 31500 \| 0.3149 \| 0.3320 \| 0.0838 \|
	\| 0.3691 \| 1.7879 \| 32000 \| 0.3141 \| 0.3313 \| 0.0836 \|
	\| 0.3154 \| 1.8158 \| 32500 \| 0.3128 \| 0.3296 \| 0.0831 \|
	\| 0.429 \| 1.8438 \| 33000 \| 0.3134 \| 0.3346 \| 0.0841 \|
	\| 0.3474 \| 1.8717 \| 33500 \| 0.3136 \| 0.3311 \| 0.0836 \|
	\| 0.4074 \| 1.8996 \| 34000 \| 0.3130 \| 0.3318 \| 0.0832 \|
	\| 0.3559 \| 1.9276 \| 34500 \| 0.3123 \| 0.3334 \| 0.0835 \|
	\| 0.3606 \| 1.9555 \| 35000 \| 0.3119 \| 0.3318 \| 0.0834 \|
	\| 0.4125 \| 1.9834 \| 35500 \| 0.3123 \| 0.3301 \| 0.0830 \|


	### Framework versions

	- Transformers 4.48.1
	- Pytorch 2.5.1+cu121
	- Datasets 3.2.0
	- Tokenizers 0.21.0

	---
	library_name: transformers
	license: cc-by-nc-4.0
	base_model: facebook/mms-1b-all
	tags:
	- automatic-speech-recognition
	- /mnt/md0/synvoices/data/naijavoices_500h
	- mms
	- generated_from_trainer
	metrics:
	- wer
	model-index:
	- name: mms-1b-naijavoices_500h-hau-ft
	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. -->

	# mms-1b-naijavoices_500h-hau-ft

	This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the /MNT/MD0/SYNVOICES/DATA/NAIJAVOICES_500H - NA dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.3121
	- Wer: 0.3303
	- Cer: 0.0830

	## 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: 0.001
	- train_batch_size: 8
	- eval_batch_size: 8
	- seed: 42
	- distributed_type: multi-GPU
	- num_devices: 2
	- gradient_accumulation_steps: 2
	- total_train_batch_size: 32
	- total_eval_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
	- lr_scheduler_warmup_steps: 100
	- num_epochs: 2.0
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Cer \| Validation Loss \| Wer \|
	\|:-------------:\|:------:\|:-----:\|:------:\|:---------------:\|:------:\|
	\| 0.5183 \| 0.0279 \| 500 \| 0.1051 \| 0.3932 \| 0.4076 \|
	\| 0.4379 \| 0.0559 \| 1000 \| 0.1009 \| 0.3776 \| 0.3964 \|
	\| 0.5709 \| 0.0838 \| 1500 \| 0.1001 \| 0.3829 \| 0.3935 \|
	\| 0.6257 \| 0.1117 \| 2000 \| 0.0994 \| 0.3710 \| 0.3930 \|
	\| 0.5065 \| 0.1397 \| 2500 \| 0.1008 \| 0.3796 \| 0.3937 \|
	\| 0.4613 \| 0.1676 \| 3000 \| 0.0977 \| 0.3637 \| 0.3849 \|
	\| 0.5305 \| 0.1955 \| 3500 \| 0.0971 \| 0.3674 \| 0.3801 \|
	\| 0.4303 \| 0.2235 \| 4000 \| 0.0980 \| 0.3663 \| 0.3875 \|
	\| 0.6497 \| 0.2514 \| 4500 \| 0.0959 \| 0.3617 \| 0.3741 \|
	\| 0.3887 \| 0.2794 \| 5000 \| 0.0985 \| 0.3620 \| 0.3925 \|
	\| 0.4604 \| 0.3073 \| 5500 \| 0.3570 \| 0.3727 \| 0.0947 \|
	\| 0.4349 \| 0.3352 \| 6000 \| 0.3529 \| 0.3686 \| 0.0940 \|
	\| 0.5403 \| 0.3632 \| 6500 \| 0.3518 \| 0.3722 \| 0.0941 \|
	\| 0.4455 \| 0.3911 \| 7000 \| 0.3522 \| 0.3695 \| 0.0937 \|
	\| 0.4454 \| 0.4190 \| 7500 \| 0.3527 \| 0.3722 \| 0.0941 \|
	\| 0.3582 \| 0.4470 \| 8000 \| 0.3479 \| 0.3696 \| 0.0932 \|
	\| 0.6661 \| 0.4749 \| 8500 \| 0.3453 \| 0.3597 \| 0.0911 \|
	\| 0.4702 \| 0.5028 \| 9000 \| 0.3472 \| 0.3672 \| 0.0929 \|
	\| 0.3877 \| 0.5308 \| 9500 \| 0.3467 \| 0.3772 \| 0.0952 \|
	\| 0.5848 \| 0.5587 \| 10000 \| 0.3422 \| 0.3652 \| 0.0920 \|
	\| 0.4943 \| 0.5866 \| 10500 \| 0.3444 \| 0.3701 \| 0.0926 \|
	\| 0.5451 \| 0.6146 \| 11000 \| 0.3441 \| 0.3583 \| 0.0908 \|
	\| 0.4033 \| 0.6425 \| 11500 \| 0.3424 \| 0.3572 \| 0.0907 \|
	\| 0.4437 \| 0.6704 \| 12000 \| 0.3427 \| 0.3576 \| 0.0906 \|
	\| 0.4541 \| 0.6984 \| 12500 \| 0.3375 \| 0.3574 \| 0.0901 \|
	\| 0.3769 \| 0.7263 \| 13000 \| 0.3381 \| 0.3605 \| 0.0901 \|
	\| 0.3915 \| 0.7543 \| 13500 \| 0.3357 \| 0.3538 \| 0.0892 \|
	\| 0.5068 \| 0.7822 \| 14000 \| 0.3373 \| 0.3518 \| 0.0892 \|
	\| 0.3922 \| 0.8101 \| 14500 \| 0.3362 \| 0.3556 \| 0.0895 \|
	\| 0.3928 \| 0.8381 \| 15000 \| 0.3337 \| 0.3489 \| 0.0887 \|
	\| 0.487 \| 0.8660 \| 15500 \| 0.3350 \| 0.3580 \| 0.0897 \|
	\| 0.3974 \| 0.8939 \| 16000 \| 0.3330 \| 0.3545 \| 0.0892 \|
	\| 0.3988 \| 0.9219 \| 16500 \| 0.3339 \| 0.3468 \| 0.0880 \|
	\| 0.5077 \| 0.9498 \| 17000 \| 0.3322 \| 0.3529 \| 0.0884 \|
	\| 0.4159 \| 0.9777 \| 17500 \| 0.3320 \| 0.3509 \| 0.0885 \|
	\| 0.46 \| 1.0057 \| 18000 \| 0.3313 \| 0.3469 \| 0.0881 \|
	\| 0.4727 \| 1.0336 \| 18500 \| 0.3320 \| 0.3588 \| 0.0893 \|
	\| 0.5366 \| 1.0616 \| 19000 \| 0.3292 \| 0.3519 \| 0.0891 \|
	\| 0.7299 \| 1.0895 \| 19500 \| 0.3292 \| 0.3518 \| 0.0882 \|
	\| 0.4359 \| 1.1174 \| 20000 \| 0.3292 \| 0.3470 \| 0.0872 \|
	\| 0.4212 \| 1.1454 \| 20500 \| 0.3266 \| 0.3449 \| 0.0873 \|
	\| 0.4532 \| 1.1733 \| 21000 \| 0.3264 \| 0.3443 \| 0.0868 \|
	\| 0.5725 \| 1.2012 \| 21500 \| 0.3264 \| 0.3393 \| 0.0857 \|
	\| 0.4016 \| 1.2292 \| 22000 \| 0.3249 \| 0.3398 \| 0.0861 \|
	\| 0.4479 \| 1.2571 \| 22500 \| 0.3243 \| 0.3519 \| 0.0875 \|
	\| 0.3502 \| 1.2851 \| 23000 \| 0.3253 \| 0.3463 \| 0.0867 \|
	\| 0.4566 \| 1.3130 \| 23500 \| 0.3207 \| 0.3387 \| 0.0854 \|
	\| 0.4414 \| 1.3409 \| 24000 \| 0.3218 \| 0.3431 \| 0.0858 \|
	\| 0.4479 \| 1.3689 \| 24500 \| 0.3243 \| 0.3445 \| 0.0864 \|
	\| 0.4601 \| 1.3968 \| 25000 \| 0.3197 \| 0.3405 \| 0.0858 \|
	\| 0.4091 \| 1.4247 \| 25500 \| 0.3219 \| 0.3371 \| 0.0851 \|
	\| 0.3548 \| 1.4527 \| 26000 \| 0.3207 \| 0.3417 \| 0.0856 \|
	\| 0.4587 \| 1.4806 \| 26500 \| 0.3183 \| 0.3360 \| 0.0851 \|
	\| 0.51 \| 1.5085 \| 27000 \| 0.3196 \| 0.3393 \| 0.0853 \|
	\| 0.4705 \| 1.5365 \| 27500 \| 0.3187 \| 0.3382 \| 0.0853 \|
	\| 0.5046 \| 1.5644 \| 28000 \| 0.3194 \| 0.3337 \| 0.0843 \|
	\| 0.4924 \| 1.5923 \| 28500 \| 0.3149 \| 0.3327 \| 0.0840 \|
	\| 0.3216 \| 1.6203 \| 29000 \| 0.3173 \| 0.3395 \| 0.0850 \|
	\| 0.4593 \| 1.6482 \| 29500 \| 0.3156 \| 0.3297 \| 0.0833 \|
	\| 0.3163 \| 1.6761 \| 30000 \| 0.3141 \| 0.3355 \| 0.0843 \|
	\| 0.3492 \| 1.7041 \| 30500 \| 0.3147 \| 0.3337 \| 0.0840 \|
	\| 0.4529 \| 1.7320 \| 31000 \| 0.3150 \| 0.3363 \| 0.0843 \|
	\| 0.2976 \| 1.7600 \| 31500 \| 0.3149 \| 0.3320 \| 0.0838 \|
	\| 0.3691 \| 1.7879 \| 32000 \| 0.3141 \| 0.3313 \| 0.0836 \|
	\| 0.3154 \| 1.8158 \| 32500 \| 0.3128 \| 0.3296 \| 0.0831 \|
	\| 0.429 \| 1.8438 \| 33000 \| 0.3134 \| 0.3346 \| 0.0841 \|
	\| 0.3474 \| 1.8717 \| 33500 \| 0.3136 \| 0.3311 \| 0.0836 \|
	\| 0.4074 \| 1.8996 \| 34000 \| 0.3130 \| 0.3318 \| 0.0832 \|
	\| 0.3559 \| 1.9276 \| 34500 \| 0.3123 \| 0.3334 \| 0.0835 \|
	\| 0.3606 \| 1.9555 \| 35000 \| 0.3119 \| 0.3318 \| 0.0834 \|
	\| 0.4125 \| 1.9834 \| 35500 \| 0.3123 \| 0.3301 \| 0.0830 \|


	### Framework versions

	- Transformers 4.48.1
	- Pytorch 2.5.1+cu121
	- Datasets 3.2.0
	- Tokenizers 0.21.0