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: 0.8695
	- Wer: 50.4804

	## 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-08
	- 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 \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|
	\| 0.0002 \| 0.14 \| 100 \| 0.8627 \| 50.5962 \|
	\| 0.0002 \| 0.28 \| 200 \| 0.8627 \| 50.5945 \|
	\| 0.0002 \| 0.42 \| 300 \| 0.8628 \| 50.5945 \|
	\| 0.0001 \| 0.56 \| 400 \| 0.8631 \| 50.2720 \|
	\| 0.0001 \| 0.7 \| 500 \| 0.8634 \| 50.2869 \|
	\| 0.0002 \| 0.83 \| 600 \| 0.8637 \| 50.2638 \|
	\| 0.0002 \| 0.97 \| 700 \| 0.8638 \| 50.2704 \|
	\| 0.0002 \| 1.11 \| 800 \| 0.8639 \| 50.2935 \|
	\| 0.0002 \| 1.25 \| 900 \| 0.8640 \| 50.2704 \|
	\| 0.0002 \| 1.39 \| 1000 \| 0.8641 \| 50.2340 \|
	\| 0.0002 \| 1.53 \| 1100 \| 0.8644 \| 50.2538 \|
	\| 0.0002 \| 1.67 \| 1200 \| 0.8645 \| 50.2522 \|
	\| 0.0002 \| 1.81 \| 1300 \| 0.8646 \| 50.2671 \|
	\| 0.0002 \| 1.95 \| 1400 \| 0.8648 \| 50.2241 \|
	\| 0.0002 \| 2.09 \| 1500 \| 0.8650 \| 50.2390 \|
	\| 0.0001 \| 2.23 \| 1600 \| 0.8653 \| 50.2274 \|
	\| 0.0001 \| 2.36 \| 1700 \| 0.8653 \| 50.2257 \|
	\| 0.0002 \| 2.5 \| 1800 \| 0.8653 \| 50.2290 \|
	\| 0.0002 \| 2.64 \| 1900 \| 0.8653 \| 50.2373 \|
	\| 0.0002 \| 2.78 \| 2000 \| 0.8653 \| 50.2307 \|
	\| 0.0001 \| 2.92 \| 2100 \| 0.8655 \| 50.2158 \|
	\| 0.0002 \| 3.06 \| 2200 \| 0.8656 \| 50.2175 \|
	\| 0.0002 \| 3.2 \| 2300 \| 0.8658 \| 50.2108 \|
	\| 0.0002 \| 3.34 \| 2400 \| 0.8659 \| 50.2175 \|
	\| 0.0001 \| 3.48 \| 2500 \| 0.8660 \| 50.2274 \|
	\| 0.0001 \| 3.62 \| 2600 \| 0.8661 \| 50.2257 \|
	\| 0.0002 \| 3.76 \| 2700 \| 0.8662 \| 50.2323 \|
	\| 0.0002 \| 3.89 \| 2800 \| 0.8663 \| 50.2108 \|
	\| 0.0002 \| 4.03 \| 2900 \| 0.8665 \| 50.1827 \|
	\| 0.0002 \| 4.17 \| 3000 \| 0.8666 \| 50.2158 \|
	\| 0.0001 \| 4.31 \| 3100 \| 0.8668 \| 50.2191 \|
	\| 0.0002 \| 4.45 \| 3200 \| 0.8668 \| 50.2224 \|
	\| 0.0002 \| 4.59 \| 3300 \| 0.8669 \| 50.1976 \|
	\| 0.0002 \| 4.73 \| 3400 \| 0.8668 \| 50.1893 \|
	\| 0.0002 \| 4.87 \| 3500 \| 0.8668 \| 50.1976 \|
	\| 0.0001 \| 5.01 \| 3600 \| 0.8670 \| 50.1976 \|
	\| 0.0002 \| 5.15 \| 3700 \| 0.8671 \| 50.1893 \|
	\| 0.0001 \| 5.29 \| 3800 \| 0.8672 \| 50.1893 \|
	\| 0.0002 \| 5.42 \| 3900 \| 0.8673 \| 50.1860 \|
	\| 0.0001 \| 5.56 \| 4000 \| 0.8674 \| 50.1728 \|
	\| 0.0002 \| 5.7 \| 4100 \| 0.8674 \| 50.1927 \|
	\| 0.0002 \| 5.84 \| 4200 \| 0.8675 \| 50.2935 \|
	\| 0.0001 \| 5.98 \| 4300 \| 0.8676 \| 50.1943 \|
	\| 0.0002 \| 6.12 \| 4400 \| 0.8676 \| 50.2009 \|
	\| 0.0002 \| 6.26 \| 4500 \| 0.8677 \| 50.2605 \|
	\| 0.0002 \| 6.4 \| 4600 \| 0.8677 \| 50.2737 \|
	\| 0.0002 \| 6.54 \| 4700 \| 0.8679 \| 50.2638 \|
	\| 0.0001 \| 6.68 \| 4800 \| 0.8681 \| 50.2621 \|
	\| 0.0002 \| 6.82 \| 4900 \| 0.8681 \| 50.2654 \|
	\| 0.0001 \| 6.95 \| 5000 \| 0.8681 \| 50.2770 \|
	\| 0.0001 \| 7.09 \| 5100 \| 0.8682 \| 50.2638 \|
	\| 0.0002 \| 7.23 \| 5200 \| 0.8682 \| 50.2737 \|
	\| 0.0002 \| 7.37 \| 5300 \| 0.8682 \| 50.2886 \|
	\| 0.0002 \| 7.51 \| 5400 \| 0.8683 \| 50.2820 \|
	\| 0.0002 \| 7.65 \| 5500 \| 0.8683 \| 50.3084 \|
	\| 0.0001 \| 7.79 \| 5600 \| 0.8684 \| 50.2803 \|
	\| 0.0001 \| 7.93 \| 5700 \| 0.8685 \| 50.2952 \|
	\| 0.0001 \| 8.07 \| 5800 \| 0.8686 \| 50.2770 \|
	\| 0.0001 \| 8.21 \| 5900 \| 0.8687 \| 50.2803 \|
	\| 0.0001 \| 8.34 \| 6000 \| 0.8688 \| 50.2820 \|
	\| 0.0001 \| 8.48 \| 6100 \| 0.8689 \| 50.3018 \|
	\| 0.0002 \| 8.62 \| 6200 \| 0.8689 \| 50.2853 \|
	\| 0.0001 \| 8.76 \| 6300 \| 0.8689 \| 50.2886 \|
	\| 0.0002 \| 8.9 \| 6400 \| 0.8689 \| 50.2753 \|
	\| 0.0001 \| 9.04 \| 6500 \| 0.8689 \| 50.4606 \|
	\| 0.0001 \| 9.18 \| 6600 \| 0.8690 \| 50.4721 \|
	\| 0.0001 \| 9.32 \| 6700 \| 0.8690 \| 50.4754 \|
	\| 0.0002 \| 9.46 \| 6800 \| 0.8690 \| 50.4738 \|
	\| 0.0002 \| 9.6 \| 6900 \| 0.8691 \| 50.4655 \|
	\| 0.0001 \| 9.74 \| 7000 \| 0.8692 \| 50.4672 \|
	\| 0.0002 \| 9.87 \| 7100 \| 0.8692 \| 50.4705 \|
	\| 0.0002 \| 10.01 \| 7200 \| 0.8692 \| 50.4688 \|
	\| 0.0001 \| 10.15 \| 7300 \| 0.8692 \| 50.4771 \|
	\| 0.0001 \| 10.29 \| 7400 \| 0.8692 \| 50.4771 \|
	\| 0.0001 \| 10.43 \| 7500 \| 0.8692 \| 50.4771 \|
	\| 0.0001 \| 10.57 \| 7600 \| 0.8692 \| 50.4837 \|
	\| 0.0001 \| 10.71 \| 7700 \| 0.8693 \| 50.4820 \|
	\| 0.0001 \| 10.85 \| 7800 \| 0.8693 \| 50.4887 \|
	\| 0.0001 \| 10.99 \| 7900 \| 0.8693 \| 50.4820 \|
	\| 0.0001 \| 11.13 \| 8000 \| 0.8694 \| 50.4887 \|
	\| 0.0001 \| 11.27 \| 8100 \| 0.8694 \| 50.4804 \|
	\| 0.0002 \| 11.4 \| 8200 \| 0.8694 \| 50.4754 \|
	\| 0.0001 \| 11.54 \| 8300 \| 0.8694 \| 50.4721 \|
	\| 0.0002 \| 11.68 \| 8400 \| 0.8694 \| 50.4771 \|
	\| 0.0001 \| 11.82 \| 8500 \| 0.8694 \| 50.4738 \|
	\| 0.0002 \| 11.96 \| 8600 \| 0.8695 \| 50.4771 \|
	\| 0.0002 \| 12.1 \| 8700 \| 0.8695 \| 50.4787 \|
	\| 0.0001 \| 12.24 \| 8800 \| 0.8695 \| 50.4787 \|
	\| 0.0001 \| 12.38 \| 8900 \| 0.8695 \| 50.4754 \|
	\| 0.0001 \| 12.52 \| 9000 \| 0.8695 \| 50.4787 \|
	\| 0.0001 \| 12.66 \| 9100 \| 0.8695 \| 50.4787 \|
	\| 0.0001 \| 12.8 \| 9200 \| 0.8695 \| 50.4804 \|
	\| 0.0001 \| 12.93 \| 9300 \| 0.8695 \| 50.4804 \|
	\| 0.0001 \| 13.07 \| 9400 \| 0.8695 \| 50.4804 \|
	\| 0.0002 \| 13.21 \| 9500 \| 0.8695 \| 50.4804 \|
	\| 0.0002 \| 13.35 \| 9600 \| 0.8695 \| 50.4804 \|
	\| 0.0001 \| 13.49 \| 9700 \| 0.8695 \| 50.4804 \|
	\| 0.0002 \| 13.63 \| 9800 \| 0.8695 \| 50.4804 \|
	\| 0.0002 \| 13.77 \| 9900 \| 0.8695 \| 50.4804 \|
	\| 0.0001 \| 13.91 \| 10000 \| 0.8695 \| 50.4804 \|


	### 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: 0.8695
	- Wer: 50.4804

	## 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-08
	- 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 \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|
	\| 0.0002 \| 0.14 \| 100 \| 0.8627 \| 50.5962 \|
	\| 0.0002 \| 0.28 \| 200 \| 0.8627 \| 50.5945 \|
	\| 0.0002 \| 0.42 \| 300 \| 0.8628 \| 50.5945 \|
	\| 0.0001 \| 0.56 \| 400 \| 0.8631 \| 50.2720 \|
	\| 0.0001 \| 0.7 \| 500 \| 0.8634 \| 50.2869 \|
	\| 0.0002 \| 0.83 \| 600 \| 0.8637 \| 50.2638 \|
	\| 0.0002 \| 0.97 \| 700 \| 0.8638 \| 50.2704 \|
	\| 0.0002 \| 1.11 \| 800 \| 0.8639 \| 50.2935 \|
	\| 0.0002 \| 1.25 \| 900 \| 0.8640 \| 50.2704 \|
	\| 0.0002 \| 1.39 \| 1000 \| 0.8641 \| 50.2340 \|
	\| 0.0002 \| 1.53 \| 1100 \| 0.8644 \| 50.2538 \|
	\| 0.0002 \| 1.67 \| 1200 \| 0.8645 \| 50.2522 \|
	\| 0.0002 \| 1.81 \| 1300 \| 0.8646 \| 50.2671 \|
	\| 0.0002 \| 1.95 \| 1400 \| 0.8648 \| 50.2241 \|
	\| 0.0002 \| 2.09 \| 1500 \| 0.8650 \| 50.2390 \|
	\| 0.0001 \| 2.23 \| 1600 \| 0.8653 \| 50.2274 \|
	\| 0.0001 \| 2.36 \| 1700 \| 0.8653 \| 50.2257 \|
	\| 0.0002 \| 2.5 \| 1800 \| 0.8653 \| 50.2290 \|
	\| 0.0002 \| 2.64 \| 1900 \| 0.8653 \| 50.2373 \|
	\| 0.0002 \| 2.78 \| 2000 \| 0.8653 \| 50.2307 \|
	\| 0.0001 \| 2.92 \| 2100 \| 0.8655 \| 50.2158 \|
	\| 0.0002 \| 3.06 \| 2200 \| 0.8656 \| 50.2175 \|
	\| 0.0002 \| 3.2 \| 2300 \| 0.8658 \| 50.2108 \|
	\| 0.0002 \| 3.34 \| 2400 \| 0.8659 \| 50.2175 \|
	\| 0.0001 \| 3.48 \| 2500 \| 0.8660 \| 50.2274 \|
	\| 0.0001 \| 3.62 \| 2600 \| 0.8661 \| 50.2257 \|
	\| 0.0002 \| 3.76 \| 2700 \| 0.8662 \| 50.2323 \|
	\| 0.0002 \| 3.89 \| 2800 \| 0.8663 \| 50.2108 \|
	\| 0.0002 \| 4.03 \| 2900 \| 0.8665 \| 50.1827 \|
	\| 0.0002 \| 4.17 \| 3000 \| 0.8666 \| 50.2158 \|
	\| 0.0001 \| 4.31 \| 3100 \| 0.8668 \| 50.2191 \|
	\| 0.0002 \| 4.45 \| 3200 \| 0.8668 \| 50.2224 \|
	\| 0.0002 \| 4.59 \| 3300 \| 0.8669 \| 50.1976 \|
	\| 0.0002 \| 4.73 \| 3400 \| 0.8668 \| 50.1893 \|
	\| 0.0002 \| 4.87 \| 3500 \| 0.8668 \| 50.1976 \|
	\| 0.0001 \| 5.01 \| 3600 \| 0.8670 \| 50.1976 \|
	\| 0.0002 \| 5.15 \| 3700 \| 0.8671 \| 50.1893 \|
	\| 0.0001 \| 5.29 \| 3800 \| 0.8672 \| 50.1893 \|
	\| 0.0002 \| 5.42 \| 3900 \| 0.8673 \| 50.1860 \|
	\| 0.0001 \| 5.56 \| 4000 \| 0.8674 \| 50.1728 \|
	\| 0.0002 \| 5.7 \| 4100 \| 0.8674 \| 50.1927 \|
	\| 0.0002 \| 5.84 \| 4200 \| 0.8675 \| 50.2935 \|
	\| 0.0001 \| 5.98 \| 4300 \| 0.8676 \| 50.1943 \|
	\| 0.0002 \| 6.12 \| 4400 \| 0.8676 \| 50.2009 \|
	\| 0.0002 \| 6.26 \| 4500 \| 0.8677 \| 50.2605 \|
	\| 0.0002 \| 6.4 \| 4600 \| 0.8677 \| 50.2737 \|
	\| 0.0002 \| 6.54 \| 4700 \| 0.8679 \| 50.2638 \|
	\| 0.0001 \| 6.68 \| 4800 \| 0.8681 \| 50.2621 \|
	\| 0.0002 \| 6.82 \| 4900 \| 0.8681 \| 50.2654 \|
	\| 0.0001 \| 6.95 \| 5000 \| 0.8681 \| 50.2770 \|
	\| 0.0001 \| 7.09 \| 5100 \| 0.8682 \| 50.2638 \|
	\| 0.0002 \| 7.23 \| 5200 \| 0.8682 \| 50.2737 \|
	\| 0.0002 \| 7.37 \| 5300 \| 0.8682 \| 50.2886 \|
	\| 0.0002 \| 7.51 \| 5400 \| 0.8683 \| 50.2820 \|
	\| 0.0002 \| 7.65 \| 5500 \| 0.8683 \| 50.3084 \|
	\| 0.0001 \| 7.79 \| 5600 \| 0.8684 \| 50.2803 \|
	\| 0.0001 \| 7.93 \| 5700 \| 0.8685 \| 50.2952 \|
	\| 0.0001 \| 8.07 \| 5800 \| 0.8686 \| 50.2770 \|
	\| 0.0001 \| 8.21 \| 5900 \| 0.8687 \| 50.2803 \|
	\| 0.0001 \| 8.34 \| 6000 \| 0.8688 \| 50.2820 \|
	\| 0.0001 \| 8.48 \| 6100 \| 0.8689 \| 50.3018 \|
	\| 0.0002 \| 8.62 \| 6200 \| 0.8689 \| 50.2853 \|
	\| 0.0001 \| 8.76 \| 6300 \| 0.8689 \| 50.2886 \|
	\| 0.0002 \| 8.9 \| 6400 \| 0.8689 \| 50.2753 \|
	\| 0.0001 \| 9.04 \| 6500 \| 0.8689 \| 50.4606 \|
	\| 0.0001 \| 9.18 \| 6600 \| 0.8690 \| 50.4721 \|
	\| 0.0001 \| 9.32 \| 6700 \| 0.8690 \| 50.4754 \|
	\| 0.0002 \| 9.46 \| 6800 \| 0.8690 \| 50.4738 \|
	\| 0.0002 \| 9.6 \| 6900 \| 0.8691 \| 50.4655 \|
	\| 0.0001 \| 9.74 \| 7000 \| 0.8692 \| 50.4672 \|
	\| 0.0002 \| 9.87 \| 7100 \| 0.8692 \| 50.4705 \|
	\| 0.0002 \| 10.01 \| 7200 \| 0.8692 \| 50.4688 \|
	\| 0.0001 \| 10.15 \| 7300 \| 0.8692 \| 50.4771 \|
	\| 0.0001 \| 10.29 \| 7400 \| 0.8692 \| 50.4771 \|
	\| 0.0001 \| 10.43 \| 7500 \| 0.8692 \| 50.4771 \|
	\| 0.0001 \| 10.57 \| 7600 \| 0.8692 \| 50.4837 \|
	\| 0.0001 \| 10.71 \| 7700 \| 0.8693 \| 50.4820 \|
	\| 0.0001 \| 10.85 \| 7800 \| 0.8693 \| 50.4887 \|
	\| 0.0001 \| 10.99 \| 7900 \| 0.8693 \| 50.4820 \|
	\| 0.0001 \| 11.13 \| 8000 \| 0.8694 \| 50.4887 \|
	\| 0.0001 \| 11.27 \| 8100 \| 0.8694 \| 50.4804 \|
	\| 0.0002 \| 11.4 \| 8200 \| 0.8694 \| 50.4754 \|
	\| 0.0001 \| 11.54 \| 8300 \| 0.8694 \| 50.4721 \|
	\| 0.0002 \| 11.68 \| 8400 \| 0.8694 \| 50.4771 \|
	\| 0.0001 \| 11.82 \| 8500 \| 0.8694 \| 50.4738 \|
	\| 0.0002 \| 11.96 \| 8600 \| 0.8695 \| 50.4771 \|
	\| 0.0002 \| 12.1 \| 8700 \| 0.8695 \| 50.4787 \|
	\| 0.0001 \| 12.24 \| 8800 \| 0.8695 \| 50.4787 \|
	\| 0.0001 \| 12.38 \| 8900 \| 0.8695 \| 50.4754 \|
	\| 0.0001 \| 12.52 \| 9000 \| 0.8695 \| 50.4787 \|
	\| 0.0001 \| 12.66 \| 9100 \| 0.8695 \| 50.4787 \|
	\| 0.0001 \| 12.8 \| 9200 \| 0.8695 \| 50.4804 \|
	\| 0.0001 \| 12.93 \| 9300 \| 0.8695 \| 50.4804 \|
	\| 0.0001 \| 13.07 \| 9400 \| 0.8695 \| 50.4804 \|
	\| 0.0002 \| 13.21 \| 9500 \| 0.8695 \| 50.4804 \|
	\| 0.0002 \| 13.35 \| 9600 \| 0.8695 \| 50.4804 \|
	\| 0.0001 \| 13.49 \| 9700 \| 0.8695 \| 50.4804 \|
	\| 0.0002 \| 13.63 \| 9800 \| 0.8695 \| 50.4804 \|
	\| 0.0002 \| 13.77 \| 9900 \| 0.8695 \| 50.4804 \|
	\| 0.0001 \| 13.91 \| 10000 \| 0.8695 \| 50.4804 \|


	### Framework versions

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