Whisper-Small-Common-Voice / README.md

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	---
	language:
	- fa
	license: apache-2.0
	base_model: makhataei/Whisper-Small-Common-Voice
	tags:
	- fa-asr
	- generated_from_trainer
	datasets:
	- mozilla-foundation/common_voice_15_0
	metrics:
	- wer
	model-index:
	- name: Whisper Small Persian
	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. -->

	# Whisper Small Persian

	This model is a fine-tuned version of [makhataei/Whisper-Small-Common-Voice](https://huggingface.co/makhataei/Whisper-Small-Common-Voice) on the Common Voice 15.0 dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.9637
	- Wer: 53.0122

	## 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: 1e-06
	- train_batch_size: 10
	- eval_batch_size: 10
	- seed: 42
	- gradient_accumulation_steps: 4
	- total_train_batch_size: 40
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 500
	- training_steps: 10000

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|
	\| 0.0003 \| 0.14 \| 100 \| 0.8127 \| 50.1960 \|
	\| 0.0003 \| 0.28 \| 200 \| 0.8106 \| 50.8591 \|
	\| 0.0003 \| 0.42 \| 300 \| 0.8138 \| 50.2935 \|
	\| 0.0005 \| 0.56 \| 400 \| 0.8216 \| 51.2345 \|
	\| 0.0003 \| 0.7 \| 500 \| 0.8295 \| 50.0918 \|
	\| 0.0003 \| 0.83 \| 600 \| 0.8331 \| 53.4124 \|
	\| 0.0003 \| 0.97 \| 700 \| 0.8269 \| 54.9288 \|
	\| 0.0003 \| 1.11 \| 800 \| 0.8295 \| 51.0178 \|
	\| 0.0005 \| 1.25 \| 900 \| 0.8341 \| 50.1149 \|
	\| 0.0007 \| 1.39 \| 1000 \| 0.8423 \| 51.9819 \|
	\| 0.0005 \| 1.53 \| 1100 \| 0.8324 \| 52.3706 \|
	\| 0.0003 \| 1.67 \| 1200 \| 0.8411 \| 51.8662 \|
	\| 0.0002 \| 1.81 \| 1300 \| 0.8545 \| 52.8402 \|
	\| 0.0004 \| 1.95 \| 1400 \| 0.8619 \| 54.0242 \|
	\| 0.0002 \| 2.09 \| 1500 \| 0.8556 \| 54.8296 \|
	\| 0.0004 \| 2.23 \| 1600 \| 0.8291 \| 53.9581 \|
	\| 0.0003 \| 2.36 \| 1700 \| 0.8633 \| 51.2047 \|
	\| 0.0003 \| 2.5 \| 1800 \| 0.8557 \| 53.7249 \|
	\| 0.0005 \| 2.64 \| 1900 \| 0.8551 \| 51.7190 \|
	\| 0.0003 \| 2.78 \| 2000 \| 0.8418 \| 52.9030 \|
	\| 0.0002 \| 2.92 \| 2100 \| 0.8522 \| 50.9467 \|
	\| 0.0002 \| 3.06 \| 2200 \| 0.8798 \| 51.2047 \|
	\| 0.0003 \| 3.2 \| 2300 \| 0.8545 \| 51.4395 \|
	\| 0.0002 \| 3.34 \| 2400 \| 0.8633 \| 51.0212 \|
	\| 0.0007 \| 3.48 \| 2500 \| 0.8644 \| 53.8440 \|
	\| 0.0002 \| 3.62 \| 2600 \| 0.8598 \| 52.5029 \|
	\| 0.0002 \| 3.76 \| 2700 \| 0.8578 \| 52.0679 \|
	\| 0.0002 \| 3.89 \| 2800 \| 0.8672 \| 52.1027 \|
	\| 0.0001 \| 4.03 \| 2900 \| 0.8655 \| 52.3706 \|
	\| 0.0001 \| 4.17 \| 3000 \| 0.8741 \| 52.2350 \|
	\| 0.0001 \| 4.31 \| 3100 \| 0.8716 \| 53.0056 \|
	\| 0.0001 \| 4.45 \| 3200 \| 0.8758 \| 51.0327 \|
	\| 0.0005 \| 4.59 \| 3300 \| 0.8636 \| 51.8662 \|
	\| 0.0001 \| 4.73 \| 3400 \| 0.8725 \| 51.0807 \|
	\| 0.0001 \| 4.87 \| 3500 \| 0.8781 \| 51.1700 \|
	\| 0.0001 \| 5.01 \| 3600 \| 0.8806 \| 50.7450 \|
	\| 0.0001 \| 5.15 \| 3700 \| 0.8835 \| 50.6210 \|
	\| 0.0001 \| 5.29 \| 3800 \| 0.8852 \| 51.1121 \|
	\| 0.0001 \| 5.42 \| 3900 \| 0.8874 \| 51.1700 \|
	\| 0.0001 \| 5.56 \| 4000 \| 0.8894 \| 51.3998 \|
	\| 0.0002 \| 5.7 \| 4100 \| 0.8899 \| 51.4246 \|
	\| 0.0001 \| 5.84 \| 4200 \| 0.8927 \| 51.6992 \|
	\| 0.0001 \| 5.98 \| 4300 \| 0.8933 \| 51.8993 \|
	\| 0.0001 \| 6.12 \| 4400 \| 0.8966 \| 51.7835 \|
	\| 0.0001 \| 6.26 \| 4500 \| 0.8980 \| 51.8381 \|
	\| 0.0001 \| 6.4 \| 4600 \| 0.8973 \| 51.7107 \|
	\| 0.0001 \| 6.54 \| 4700 \| 0.9008 \| 51.5553 \|
	\| 0.0001 \| 6.68 \| 4800 \| 0.9029 \| 51.1220 \|
	\| 0.0001 \| 6.82 \| 4900 \| 0.9030 \| 51.3221 \|
	\| 0.0001 \| 6.95 \| 5000 \| 0.9039 \| 52.1605 \|
	\| 0.0001 \| 7.09 \| 5100 \| 0.9084 \| 52.1440 \|
	\| 0.0001 \| 7.23 \| 5200 \| 0.9106 \| 51.9505 \|
	\| 0.0001 \| 7.37 \| 5300 \| 0.9117 \| 52.6219 \|
	\| 0.0001 \| 7.51 \| 5400 \| 0.9133 \| 52.4830 \|
	\| 0.0002 \| 7.65 \| 5500 \| 0.9187 \| 51.3320 \|
	\| 0.0001 \| 7.79 \| 5600 \| 0.9184 \| 52.3954 \|
	\| 0.0001 \| 7.93 \| 5700 \| 0.9185 \| 52.5392 \|
	\| 0.0001 \| 8.07 \| 5800 \| 0.9209 \| 53.1263 \|
	\| 0.0001 \| 8.21 \| 5900 \| 0.9232 \| 53.0965 \|
	\| 0.0001 \| 8.34 \| 6000 \| 0.9242 \| 53.6737 \|
	\| 0.0001 \| 8.48 \| 6100 \| 0.9220 \| 52.6996 \|
	\| 0.0001 \| 8.62 \| 6200 \| 0.9228 \| 52.6500 \|
	\| 0.0001 \| 8.76 \| 6300 \| 0.9255 \| 52.3838 \|
	\| 0.0001 \| 8.9 \| 6400 \| 0.9269 \| 53.0138 \|
	\| 0.0001 \| 9.04 \| 6500 \| 0.9298 \| 52.9345 \|
	\| 0.0001 \| 9.18 \| 6600 \| 0.9317 \| 53.2222 \|
	\| 0.0001 \| 9.32 \| 6700 \| 0.9337 \| 53.1974 \|
	\| 0.0001 \| 9.46 \| 6800 \| 0.9354 \| 52.9130 \|
	\| 0.0001 \| 9.6 \| 6900 \| 0.9379 \| 52.8865 \|
	\| 0.0001 \| 9.74 \| 7000 \| 0.9407 \| 52.9560 \|
	\| 0.0001 \| 9.87 \| 7100 \| 0.9399 \| 52.5045 \|
	\| 0.0001 \| 10.01 \| 7200 \| 0.9394 \| 52.9113 \|
	\| 0.0001 \| 10.15 \| 7300 \| 0.9423 \| 52.9064 \|
	\| 0.0001 \| 10.29 \| 7400 \| 0.9422 \| 52.9477 \|
	\| 0.0001 \| 10.43 \| 7500 \| 0.9445 \| 53.2305 \|
	\| 0.0001 \| 10.57 \| 7600 \| 0.9452 \| 53.1842 \|
	\| 0.0001 \| 10.71 \| 7700 \| 0.9478 \| 53.3562 \|
	\| 0.0001 \| 10.85 \| 7800 \| 0.9451 \| 52.9113 \|
	\| 0.0001 \| 10.99 \| 7900 \| 0.9476 \| 52.6616 \|
	\| 0.0 \| 11.13 \| 8000 \| 0.9502 \| 52.3606 \|
	\| 0.0 \| 11.27 \| 8100 \| 0.9518 \| 52.7294 \|
	\| 0.0 \| 11.4 \| 8200 \| 0.9523 \| 52.8799 \|
	\| 0.0 \| 11.54 \| 8300 \| 0.9540 \| 52.8419 \|
	\| 0.0001 \| 11.68 \| 8400 \| 0.9542 \| 53.0486 \|
	\| 0.0 \| 11.82 \| 8500 \| 0.9569 \| 53.0453 \|
	\| 0.0 \| 11.96 \| 8600 \| 0.9576 \| 52.9576 \|
	\| 0.0 \| 12.1 \| 8700 \| 0.9589 \| 53.2371 \|
	\| 0.0 \| 12.24 \| 8800 \| 0.9599 \| 53.2057 \|
	\| 0.0 \| 12.38 \| 8900 \| 0.9605 \| 53.3165 \|
	\| 0.0 \| 12.52 \| 9000 \| 0.9603 \| 52.9576 \|
	\| 0.0 \| 12.66 \| 9100 \| 0.9608 \| 52.5789 \|
	\| 0.0 \| 12.8 \| 9200 \| 0.9609 \| 53.2288 \|
	\| 0.0 \| 12.93 \| 9300 \| 0.9611 \| 53.1759 \|
	\| 0.0 \| 13.07 \| 9400 \| 0.9618 \| 53.1296 \|
	\| 0.0001 \| 13.21 \| 9500 \| 0.9632 \| 53.0618 \|
	\| 0.0 \| 13.35 \| 9600 \| 0.9632 \| 52.9593 \|
	\| 0.0 \| 13.49 \| 9700 \| 0.9633 \| 52.9923 \|
	\| 0.0 \| 13.63 \| 9800 \| 0.9635 \| 53.1379 \|
	\| 0.0 \| 13.77 \| 9900 \| 0.9637 \| 53.0122 \|
	\| 0.0 \| 13.91 \| 10000 \| 0.9637 \| 53.0122 \|


	### Framework versions

	- Transformers 4.35.2
	- Pytorch 2.0.1+cu117
	- Datasets 2.15.0
	- Tokenizers 0.15.0

	---
	language:
	- fa
	license: apache-2.0
	base_model: makhataei/Whisper-Small-Common-Voice
	tags:
	- fa-asr
	- generated_from_trainer
	datasets:
	- mozilla-foundation/common_voice_15_0
	metrics:
	- wer
	model-index:
	- name: Whisper Small Persian
	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. -->

	# Whisper Small Persian

	This model is a fine-tuned version of [makhataei/Whisper-Small-Common-Voice](https://huggingface.co/makhataei/Whisper-Small-Common-Voice) on the Common Voice 15.0 dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.9637
	- Wer: 53.0122

	## 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: 1e-06
	- train_batch_size: 10
	- eval_batch_size: 10
	- seed: 42
	- gradient_accumulation_steps: 4
	- total_train_batch_size: 40
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 500
	- training_steps: 10000

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|
	\| 0.0003 \| 0.14 \| 100 \| 0.8127 \| 50.1960 \|
	\| 0.0003 \| 0.28 \| 200 \| 0.8106 \| 50.8591 \|
	\| 0.0003 \| 0.42 \| 300 \| 0.8138 \| 50.2935 \|
	\| 0.0005 \| 0.56 \| 400 \| 0.8216 \| 51.2345 \|
	\| 0.0003 \| 0.7 \| 500 \| 0.8295 \| 50.0918 \|
	\| 0.0003 \| 0.83 \| 600 \| 0.8331 \| 53.4124 \|
	\| 0.0003 \| 0.97 \| 700 \| 0.8269 \| 54.9288 \|
	\| 0.0003 \| 1.11 \| 800 \| 0.8295 \| 51.0178 \|
	\| 0.0005 \| 1.25 \| 900 \| 0.8341 \| 50.1149 \|
	\| 0.0007 \| 1.39 \| 1000 \| 0.8423 \| 51.9819 \|
	\| 0.0005 \| 1.53 \| 1100 \| 0.8324 \| 52.3706 \|
	\| 0.0003 \| 1.67 \| 1200 \| 0.8411 \| 51.8662 \|
	\| 0.0002 \| 1.81 \| 1300 \| 0.8545 \| 52.8402 \|
	\| 0.0004 \| 1.95 \| 1400 \| 0.8619 \| 54.0242 \|
	\| 0.0002 \| 2.09 \| 1500 \| 0.8556 \| 54.8296 \|
	\| 0.0004 \| 2.23 \| 1600 \| 0.8291 \| 53.9581 \|
	\| 0.0003 \| 2.36 \| 1700 \| 0.8633 \| 51.2047 \|
	\| 0.0003 \| 2.5 \| 1800 \| 0.8557 \| 53.7249 \|
	\| 0.0005 \| 2.64 \| 1900 \| 0.8551 \| 51.7190 \|
	\| 0.0003 \| 2.78 \| 2000 \| 0.8418 \| 52.9030 \|
	\| 0.0002 \| 2.92 \| 2100 \| 0.8522 \| 50.9467 \|
	\| 0.0002 \| 3.06 \| 2200 \| 0.8798 \| 51.2047 \|
	\| 0.0003 \| 3.2 \| 2300 \| 0.8545 \| 51.4395 \|
	\| 0.0002 \| 3.34 \| 2400 \| 0.8633 \| 51.0212 \|
	\| 0.0007 \| 3.48 \| 2500 \| 0.8644 \| 53.8440 \|
	\| 0.0002 \| 3.62 \| 2600 \| 0.8598 \| 52.5029 \|
	\| 0.0002 \| 3.76 \| 2700 \| 0.8578 \| 52.0679 \|
	\| 0.0002 \| 3.89 \| 2800 \| 0.8672 \| 52.1027 \|
	\| 0.0001 \| 4.03 \| 2900 \| 0.8655 \| 52.3706 \|
	\| 0.0001 \| 4.17 \| 3000 \| 0.8741 \| 52.2350 \|
	\| 0.0001 \| 4.31 \| 3100 \| 0.8716 \| 53.0056 \|
	\| 0.0001 \| 4.45 \| 3200 \| 0.8758 \| 51.0327 \|
	\| 0.0005 \| 4.59 \| 3300 \| 0.8636 \| 51.8662 \|
	\| 0.0001 \| 4.73 \| 3400 \| 0.8725 \| 51.0807 \|
	\| 0.0001 \| 4.87 \| 3500 \| 0.8781 \| 51.1700 \|
	\| 0.0001 \| 5.01 \| 3600 \| 0.8806 \| 50.7450 \|
	\| 0.0001 \| 5.15 \| 3700 \| 0.8835 \| 50.6210 \|
	\| 0.0001 \| 5.29 \| 3800 \| 0.8852 \| 51.1121 \|
	\| 0.0001 \| 5.42 \| 3900 \| 0.8874 \| 51.1700 \|
	\| 0.0001 \| 5.56 \| 4000 \| 0.8894 \| 51.3998 \|
	\| 0.0002 \| 5.7 \| 4100 \| 0.8899 \| 51.4246 \|
	\| 0.0001 \| 5.84 \| 4200 \| 0.8927 \| 51.6992 \|
	\| 0.0001 \| 5.98 \| 4300 \| 0.8933 \| 51.8993 \|
	\| 0.0001 \| 6.12 \| 4400 \| 0.8966 \| 51.7835 \|
	\| 0.0001 \| 6.26 \| 4500 \| 0.8980 \| 51.8381 \|
	\| 0.0001 \| 6.4 \| 4600 \| 0.8973 \| 51.7107 \|
	\| 0.0001 \| 6.54 \| 4700 \| 0.9008 \| 51.5553 \|
	\| 0.0001 \| 6.68 \| 4800 \| 0.9029 \| 51.1220 \|
	\| 0.0001 \| 6.82 \| 4900 \| 0.9030 \| 51.3221 \|
	\| 0.0001 \| 6.95 \| 5000 \| 0.9039 \| 52.1605 \|
	\| 0.0001 \| 7.09 \| 5100 \| 0.9084 \| 52.1440 \|
	\| 0.0001 \| 7.23 \| 5200 \| 0.9106 \| 51.9505 \|
	\| 0.0001 \| 7.37 \| 5300 \| 0.9117 \| 52.6219 \|
	\| 0.0001 \| 7.51 \| 5400 \| 0.9133 \| 52.4830 \|
	\| 0.0002 \| 7.65 \| 5500 \| 0.9187 \| 51.3320 \|
	\| 0.0001 \| 7.79 \| 5600 \| 0.9184 \| 52.3954 \|
	\| 0.0001 \| 7.93 \| 5700 \| 0.9185 \| 52.5392 \|
	\| 0.0001 \| 8.07 \| 5800 \| 0.9209 \| 53.1263 \|
	\| 0.0001 \| 8.21 \| 5900 \| 0.9232 \| 53.0965 \|
	\| 0.0001 \| 8.34 \| 6000 \| 0.9242 \| 53.6737 \|
	\| 0.0001 \| 8.48 \| 6100 \| 0.9220 \| 52.6996 \|
	\| 0.0001 \| 8.62 \| 6200 \| 0.9228 \| 52.6500 \|
	\| 0.0001 \| 8.76 \| 6300 \| 0.9255 \| 52.3838 \|
	\| 0.0001 \| 8.9 \| 6400 \| 0.9269 \| 53.0138 \|
	\| 0.0001 \| 9.04 \| 6500 \| 0.9298 \| 52.9345 \|
	\| 0.0001 \| 9.18 \| 6600 \| 0.9317 \| 53.2222 \|
	\| 0.0001 \| 9.32 \| 6700 \| 0.9337 \| 53.1974 \|
	\| 0.0001 \| 9.46 \| 6800 \| 0.9354 \| 52.9130 \|
	\| 0.0001 \| 9.6 \| 6900 \| 0.9379 \| 52.8865 \|
	\| 0.0001 \| 9.74 \| 7000 \| 0.9407 \| 52.9560 \|
	\| 0.0001 \| 9.87 \| 7100 \| 0.9399 \| 52.5045 \|
	\| 0.0001 \| 10.01 \| 7200 \| 0.9394 \| 52.9113 \|
	\| 0.0001 \| 10.15 \| 7300 \| 0.9423 \| 52.9064 \|
	\| 0.0001 \| 10.29 \| 7400 \| 0.9422 \| 52.9477 \|
	\| 0.0001 \| 10.43 \| 7500 \| 0.9445 \| 53.2305 \|
	\| 0.0001 \| 10.57 \| 7600 \| 0.9452 \| 53.1842 \|
	\| 0.0001 \| 10.71 \| 7700 \| 0.9478 \| 53.3562 \|
	\| 0.0001 \| 10.85 \| 7800 \| 0.9451 \| 52.9113 \|
	\| 0.0001 \| 10.99 \| 7900 \| 0.9476 \| 52.6616 \|
	\| 0.0 \| 11.13 \| 8000 \| 0.9502 \| 52.3606 \|
	\| 0.0 \| 11.27 \| 8100 \| 0.9518 \| 52.7294 \|
	\| 0.0 \| 11.4 \| 8200 \| 0.9523 \| 52.8799 \|
	\| 0.0 \| 11.54 \| 8300 \| 0.9540 \| 52.8419 \|
	\| 0.0001 \| 11.68 \| 8400 \| 0.9542 \| 53.0486 \|
	\| 0.0 \| 11.82 \| 8500 \| 0.9569 \| 53.0453 \|
	\| 0.0 \| 11.96 \| 8600 \| 0.9576 \| 52.9576 \|
	\| 0.0 \| 12.1 \| 8700 \| 0.9589 \| 53.2371 \|
	\| 0.0 \| 12.24 \| 8800 \| 0.9599 \| 53.2057 \|
	\| 0.0 \| 12.38 \| 8900 \| 0.9605 \| 53.3165 \|
	\| 0.0 \| 12.52 \| 9000 \| 0.9603 \| 52.9576 \|
	\| 0.0 \| 12.66 \| 9100 \| 0.9608 \| 52.5789 \|
	\| 0.0 \| 12.8 \| 9200 \| 0.9609 \| 53.2288 \|
	\| 0.0 \| 12.93 \| 9300 \| 0.9611 \| 53.1759 \|
	\| 0.0 \| 13.07 \| 9400 \| 0.9618 \| 53.1296 \|
	\| 0.0001 \| 13.21 \| 9500 \| 0.9632 \| 53.0618 \|
	\| 0.0 \| 13.35 \| 9600 \| 0.9632 \| 52.9593 \|
	\| 0.0 \| 13.49 \| 9700 \| 0.9633 \| 52.9923 \|
	\| 0.0 \| 13.63 \| 9800 \| 0.9635 \| 53.1379 \|
	\| 0.0 \| 13.77 \| 9900 \| 0.9637 \| 53.0122 \|
	\| 0.0 \| 13.91 \| 10000 \| 0.9637 \| 53.0122 \|


	### Framework versions

	- Transformers 4.35.2
	- Pytorch 2.0.1+cu117
	- Datasets 2.15.0
	- Tokenizers 0.15.0