asr-africa/MMS-Wolof-20-hour-Mixed-dataset

01089d5 verified 2 months ago

6.88 kB

	---
	library_name: transformers
	license: cc-by-nc-4.0
	base_model: facebook/mms-1b-all
	tags:
	- generated_from_trainer
	datasets:
	- fleurs
	metrics:
	- wer
	model-index:
	- name: MMS-Wolof-20-hour-Mixed-dataset
	results:
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: fleurs
	type: fleurs
	config: wo_sn
	split: None
	args: wo_sn
	metrics:
	- name: Wer
	type: wer
	value: 0.4748502317169662
	---

	<!-- 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/j93wiewo)
	# MMS-Wolof-20-hour-Mixed-dataset

	This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the fleurs dataset.
	It achieves the following results on the evaluation set:
	- Loss: 1.3654
	- Wer: 0.4749
	- Cer: 0.1708

	## 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: 4
	- eval_batch_size: 8
	- seed: 42
	- gradient_accumulation_steps: 4
	- 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
	- lr_scheduler_warmup_ratio: 0.1
	- num_epochs: 50

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:------:\|:------:\|
	\| 9.5604 \| 0.7369 \| 500 \| 0.7373 \| 0.5227 \| 0.1732 \|
	\| 2.6975 \| 1.4738 \| 1000 \| 0.6973 \| 0.5301 \| 0.1779 \|
	\| 2.726 \| 2.2108 \| 1500 \| 0.8814 \| 0.5966 \| 0.2092 \|
	\| 2.8093 \| 2.9477 \| 2000 \| 0.8858 \| 0.6151 \| 0.2303 \|
	\| 2.78 \| 3.6846 \| 2500 \| 0.8017 \| 0.5918 \| 0.2146 \|
	\| 2.8066 \| 4.4215 \| 3000 \| 1.0143 \| 0.6655 \| 0.2453 \|
	\| 2.8916 \| 5.1584 \| 3500 \| 0.8609 \| 0.6299 \| 0.2351 \|
	\| 2.7853 \| 5.8954 \| 4000 \| 0.8444 \| 0.6100 \| 0.2304 \|
	\| 2.6374 \| 6.6323 \| 4500 \| 0.9584 \| 0.6420 \| 0.2505 \|
	\| 2.5197 \| 7.3692 \| 5000 \| 0.9859 \| 0.6703 \| 0.2562 \|
	\| 2.4943 \| 8.1061 \| 5500 \| 0.8587 \| 0.6267 \| 0.2405 \|
	\| 2.39 \| 8.8430 \| 6000 \| 0.9456 \| 0.6221 \| 0.2388 \|
	\| 2.2478 \| 9.5800 \| 6500 \| 0.9030 \| 0.6598 \| 0.2536 \|
	\| 2.1688 \| 10.3169 \| 7000 \| 0.8462 \| 0.6527 \| 0.2567 \|
	\| 2.0468 \| 11.0538 \| 7500 \| 0.8851 \| 0.6080 \| 0.2302 \|
	\| 1.9318 \| 11.7907 \| 8000 \| 0.8898 \| 0.6276 \| 0.2301 \|
	\| 1.8937 \| 12.5276 \| 8500 \| 0.7846 \| 0.5826 \| 0.2191 \|
	\| 1.7943 \| 13.2646 \| 9000 \| 0.8560 \| 0.6137 \| 0.2326 \|
	\| 1.7337 \| 14.0015 \| 9500 \| 0.8789 \| 0.6005 \| 0.2271 \|
	\| 1.608 \| 14.7384 \| 10000 \| 0.8736 \| 0.6092 \| 0.2270 \|
	\| 1.5533 \| 15.4753 \| 10500 \| 0.9045 \| 0.5951 \| 0.2287 \|
	\| 1.4501 \| 16.2122 \| 11000 \| 0.8505 \| 0.6288 \| 0.2415 \|
	\| 1.4074 \| 16.9492 \| 11500 \| 0.8023 \| 0.5853 \| 0.2227 \|
	\| 1.3339 \| 17.6861 \| 12000 \| 0.8177 \| 0.5967 \| 0.2211 \|
	\| 1.2208 \| 18.4230 \| 12500 \| 0.8922 \| 0.5761 \| 0.2126 \|
	\| 1.1803 \| 19.1599 \| 13000 \| 0.8207 \| 0.5637 \| 0.2076 \|
	\| 1.1159 \| 19.8968 \| 13500 \| 0.8114 \| 0.5473 \| 0.2013 \|
	\| 1.0415 \| 20.6338 \| 14000 \| 0.8646 \| 0.5533 \| 0.2036 \|
	\| 0.9767 \| 21.3707 \| 14500 \| 0.9001 \| 0.5569 \| 0.2088 \|
	\| 0.9803 \| 22.1076 \| 15000 \| 0.8485 \| 0.5696 \| 0.2100 \|
	\| 0.8933 \| 22.8445 \| 15500 \| 0.8164 \| 0.5452 \| 0.2041 \|
	\| 0.8509 \| 23.5814 \| 16000 \| 0.9136 \| 0.5545 \| 0.2037 \|
	\| 0.8398 \| 24.3183 \| 16500 \| 0.8095 \| 0.5350 \| 0.1950 \|
	\| 0.7741 \| 25.0553 \| 17000 \| 0.9116 \| 0.5448 \| 0.1998 \|
	\| 0.7303 \| 25.7922 \| 17500 \| 0.9380 \| 0.5339 \| 0.1948 \|
	\| 0.7132 \| 26.5291 \| 18000 \| 0.8357 \| 0.5143 \| 0.1888 \|
	\| 0.6655 \| 27.2660 \| 18500 \| 0.9127 \| 0.5495 \| 0.2049 \|
	\| 0.6452 \| 28.0029 \| 19000 \| 0.8722 \| 0.5258 \| 0.1933 \|
	\| 0.5913 \| 28.7399 \| 19500 \| 0.9262 \| 0.5227 \| 0.1929 \|
	\| 0.5792 \| 29.4768 \| 20000 \| 0.9722 \| 0.5239 \| 0.1883 \|
	\| 0.5528 \| 30.2137 \| 20500 \| 0.9868 \| 0.5259 \| 0.1937 \|
	\| 0.5488 \| 30.9506 \| 21000 \| 0.9860 \| 0.5268 \| 0.1945 \|
	\| 0.5023 \| 31.6875 \| 21500 \| 0.9549 \| 0.5134 \| 0.1874 \|
	\| 0.4668 \| 32.4245 \| 22000 \| 1.0188 \| 0.5200 \| 0.1943 \|
	\| 0.4751 \| 33.1614 \| 22500 \| 1.0139 \| 0.5112 \| 0.1852 \|
	\| 0.434 \| 33.8983 \| 23000 \| 1.0354 \| 0.5073 \| 0.1815 \|
	\| 0.4149 \| 34.6352 \| 23500 \| 0.9920 \| 0.5170 \| 0.1874 \|
	\| 0.4044 \| 35.3721 \| 24000 \| 1.1387 \| 0.5051 \| 0.1840 \|
	\| 0.3839 \| 36.1091 \| 24500 \| 1.1052 \| 0.5034 \| 0.1848 \|
	\| 0.3576 \| 36.8460 \| 25000 \| 1.0593 \| 0.4889 \| 0.1811 \|
	\| 0.3379 \| 37.5829 \| 25500 \| 1.0930 \| 0.5007 \| 0.1823 \|
	\| 0.336 \| 38.3198 \| 26000 \| 1.1091 \| 0.4968 \| 0.1808 \|
	\| 0.3148 \| 39.0567 \| 26500 \| 1.1871 \| 0.4993 \| 0.1810 \|
	\| 0.3005 \| 39.7937 \| 27000 \| 1.1890 \| 0.4993 \| 0.1801 \|
	\| 0.2964 \| 40.5306 \| 27500 \| 1.1436 \| 0.4899 \| 0.1758 \|
	\| 0.2731 \| 41.2675 \| 28000 \| 1.1677 \| 0.4940 \| 0.1780 \|
	\| 0.2641 \| 42.0044 \| 28500 \| 1.1943 \| 0.4943 \| 0.1783 \|
	\| 0.2435 \| 42.7413 \| 29000 \| 1.2838 \| 0.4906 \| 0.1787 \|
	\| 0.2486 \| 43.4783 \| 29500 \| 1.2935 \| 0.4938 \| 0.1767 \|
	\| 0.2259 \| 44.2152 \| 30000 \| 1.3013 \| 0.4898 \| 0.1749 \|
	\| 0.218 \| 44.9521 \| 30500 \| 1.2965 \| 0.4856 \| 0.1749 \|
	\| 0.2143 \| 45.6890 \| 31000 \| 1.2891 \| 0.4823 \| 0.1739 \|
	\| 0.195 \| 46.4259 \| 31500 \| 1.3284 \| 0.4791 \| 0.1724 \|
	\| 0.1943 \| 47.1629 \| 32000 \| 1.3182 \| 0.4805 \| 0.1734 \|
	\| 0.1851 \| 47.8998 \| 32500 \| 1.3429 \| 0.4769 \| 0.1719 \|
	\| 0.1833 \| 48.6367 \| 33000 \| 1.3515 \| 0.4723 \| 0.1708 \|
	\| 0.1739 \| 49.3736 \| 33500 \| 1.3654 \| 0.4749 \| 0.1708 \|


	### Framework versions

	- Transformers 4.46.1
	- Pytorch 2.1.0+cu118
	- Datasets 2.17.0
	- Tokenizers 0.20.3

	---
	library_name: transformers
	license: cc-by-nc-4.0
	base_model: facebook/mms-1b-all
	tags:
	- generated_from_trainer
	datasets:
	- fleurs
	metrics:
	- wer
	model-index:
	- name: MMS-Wolof-20-hour-Mixed-dataset
	results:
	- task:
	name: Automatic Speech Recognition
	type: automatic-speech-recognition
	dataset:
	name: fleurs
	type: fleurs
	config: wo_sn
	split: None
	args: wo_sn
	metrics:
	- name: Wer
	type: wer
	value: 0.4748502317169662
	---

	<!-- 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/j93wiewo)
	# MMS-Wolof-20-hour-Mixed-dataset

	This model is a fine-tuned version of [facebook/mms-1b-all](https://huggingface.co/facebook/mms-1b-all) on the fleurs dataset.
	It achieves the following results on the evaluation set:
	- Loss: 1.3654
	- Wer: 0.4749
	- Cer: 0.1708

	## 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: 4
	- eval_batch_size: 8
	- seed: 42
	- gradient_accumulation_steps: 4
	- 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
	- lr_scheduler_warmup_ratio: 0.1
	- num_epochs: 50

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \| Cer \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:------:\|:------:\|
	\| 9.5604 \| 0.7369 \| 500 \| 0.7373 \| 0.5227 \| 0.1732 \|
	\| 2.6975 \| 1.4738 \| 1000 \| 0.6973 \| 0.5301 \| 0.1779 \|
	\| 2.726 \| 2.2108 \| 1500 \| 0.8814 \| 0.5966 \| 0.2092 \|
	\| 2.8093 \| 2.9477 \| 2000 \| 0.8858 \| 0.6151 \| 0.2303 \|
	\| 2.78 \| 3.6846 \| 2500 \| 0.8017 \| 0.5918 \| 0.2146 \|
	\| 2.8066 \| 4.4215 \| 3000 \| 1.0143 \| 0.6655 \| 0.2453 \|
	\| 2.8916 \| 5.1584 \| 3500 \| 0.8609 \| 0.6299 \| 0.2351 \|
	\| 2.7853 \| 5.8954 \| 4000 \| 0.8444 \| 0.6100 \| 0.2304 \|
	\| 2.6374 \| 6.6323 \| 4500 \| 0.9584 \| 0.6420 \| 0.2505 \|
	\| 2.5197 \| 7.3692 \| 5000 \| 0.9859 \| 0.6703 \| 0.2562 \|
	\| 2.4943 \| 8.1061 \| 5500 \| 0.8587 \| 0.6267 \| 0.2405 \|
	\| 2.39 \| 8.8430 \| 6000 \| 0.9456 \| 0.6221 \| 0.2388 \|
	\| 2.2478 \| 9.5800 \| 6500 \| 0.9030 \| 0.6598 \| 0.2536 \|
	\| 2.1688 \| 10.3169 \| 7000 \| 0.8462 \| 0.6527 \| 0.2567 \|
	\| 2.0468 \| 11.0538 \| 7500 \| 0.8851 \| 0.6080 \| 0.2302 \|
	\| 1.9318 \| 11.7907 \| 8000 \| 0.8898 \| 0.6276 \| 0.2301 \|
	\| 1.8937 \| 12.5276 \| 8500 \| 0.7846 \| 0.5826 \| 0.2191 \|
	\| 1.7943 \| 13.2646 \| 9000 \| 0.8560 \| 0.6137 \| 0.2326 \|
	\| 1.7337 \| 14.0015 \| 9500 \| 0.8789 \| 0.6005 \| 0.2271 \|
	\| 1.608 \| 14.7384 \| 10000 \| 0.8736 \| 0.6092 \| 0.2270 \|
	\| 1.5533 \| 15.4753 \| 10500 \| 0.9045 \| 0.5951 \| 0.2287 \|
	\| 1.4501 \| 16.2122 \| 11000 \| 0.8505 \| 0.6288 \| 0.2415 \|
	\| 1.4074 \| 16.9492 \| 11500 \| 0.8023 \| 0.5853 \| 0.2227 \|
	\| 1.3339 \| 17.6861 \| 12000 \| 0.8177 \| 0.5967 \| 0.2211 \|
	\| 1.2208 \| 18.4230 \| 12500 \| 0.8922 \| 0.5761 \| 0.2126 \|
	\| 1.1803 \| 19.1599 \| 13000 \| 0.8207 \| 0.5637 \| 0.2076 \|
	\| 1.1159 \| 19.8968 \| 13500 \| 0.8114 \| 0.5473 \| 0.2013 \|
	\| 1.0415 \| 20.6338 \| 14000 \| 0.8646 \| 0.5533 \| 0.2036 \|
	\| 0.9767 \| 21.3707 \| 14500 \| 0.9001 \| 0.5569 \| 0.2088 \|
	\| 0.9803 \| 22.1076 \| 15000 \| 0.8485 \| 0.5696 \| 0.2100 \|
	\| 0.8933 \| 22.8445 \| 15500 \| 0.8164 \| 0.5452 \| 0.2041 \|
	\| 0.8509 \| 23.5814 \| 16000 \| 0.9136 \| 0.5545 \| 0.2037 \|
	\| 0.8398 \| 24.3183 \| 16500 \| 0.8095 \| 0.5350 \| 0.1950 \|
	\| 0.7741 \| 25.0553 \| 17000 \| 0.9116 \| 0.5448 \| 0.1998 \|
	\| 0.7303 \| 25.7922 \| 17500 \| 0.9380 \| 0.5339 \| 0.1948 \|
	\| 0.7132 \| 26.5291 \| 18000 \| 0.8357 \| 0.5143 \| 0.1888 \|
	\| 0.6655 \| 27.2660 \| 18500 \| 0.9127 \| 0.5495 \| 0.2049 \|
	\| 0.6452 \| 28.0029 \| 19000 \| 0.8722 \| 0.5258 \| 0.1933 \|
	\| 0.5913 \| 28.7399 \| 19500 \| 0.9262 \| 0.5227 \| 0.1929 \|
	\| 0.5792 \| 29.4768 \| 20000 \| 0.9722 \| 0.5239 \| 0.1883 \|
	\| 0.5528 \| 30.2137 \| 20500 \| 0.9868 \| 0.5259 \| 0.1937 \|
	\| 0.5488 \| 30.9506 \| 21000 \| 0.9860 \| 0.5268 \| 0.1945 \|
	\| 0.5023 \| 31.6875 \| 21500 \| 0.9549 \| 0.5134 \| 0.1874 \|
	\| 0.4668 \| 32.4245 \| 22000 \| 1.0188 \| 0.5200 \| 0.1943 \|
	\| 0.4751 \| 33.1614 \| 22500 \| 1.0139 \| 0.5112 \| 0.1852 \|
	\| 0.434 \| 33.8983 \| 23000 \| 1.0354 \| 0.5073 \| 0.1815 \|
	\| 0.4149 \| 34.6352 \| 23500 \| 0.9920 \| 0.5170 \| 0.1874 \|
	\| 0.4044 \| 35.3721 \| 24000 \| 1.1387 \| 0.5051 \| 0.1840 \|
	\| 0.3839 \| 36.1091 \| 24500 \| 1.1052 \| 0.5034 \| 0.1848 \|
	\| 0.3576 \| 36.8460 \| 25000 \| 1.0593 \| 0.4889 \| 0.1811 \|
	\| 0.3379 \| 37.5829 \| 25500 \| 1.0930 \| 0.5007 \| 0.1823 \|
	\| 0.336 \| 38.3198 \| 26000 \| 1.1091 \| 0.4968 \| 0.1808 \|
	\| 0.3148 \| 39.0567 \| 26500 \| 1.1871 \| 0.4993 \| 0.1810 \|
	\| 0.3005 \| 39.7937 \| 27000 \| 1.1890 \| 0.4993 \| 0.1801 \|
	\| 0.2964 \| 40.5306 \| 27500 \| 1.1436 \| 0.4899 \| 0.1758 \|
	\| 0.2731 \| 41.2675 \| 28000 \| 1.1677 \| 0.4940 \| 0.1780 \|
	\| 0.2641 \| 42.0044 \| 28500 \| 1.1943 \| 0.4943 \| 0.1783 \|
	\| 0.2435 \| 42.7413 \| 29000 \| 1.2838 \| 0.4906 \| 0.1787 \|
	\| 0.2486 \| 43.4783 \| 29500 \| 1.2935 \| 0.4938 \| 0.1767 \|
	\| 0.2259 \| 44.2152 \| 30000 \| 1.3013 \| 0.4898 \| 0.1749 \|
	\| 0.218 \| 44.9521 \| 30500 \| 1.2965 \| 0.4856 \| 0.1749 \|
	\| 0.2143 \| 45.6890 \| 31000 \| 1.2891 \| 0.4823 \| 0.1739 \|
	\| 0.195 \| 46.4259 \| 31500 \| 1.3284 \| 0.4791 \| 0.1724 \|
	\| 0.1943 \| 47.1629 \| 32000 \| 1.3182 \| 0.4805 \| 0.1734 \|
	\| 0.1851 \| 47.8998 \| 32500 \| 1.3429 \| 0.4769 \| 0.1719 \|
	\| 0.1833 \| 48.6367 \| 33000 \| 1.3515 \| 0.4723 \| 0.1708 \|
	\| 0.1739 \| 49.3736 \| 33500 \| 1.3654 \| 0.4749 \| 0.1708 \|


	### Framework versions

	- Transformers 4.46.1
	- Pytorch 2.1.0+cu118
	- Datasets 2.17.0
	- Tokenizers 0.20.3