End of training

814c436 over 1 year ago

7.78 kB

	---
	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: 0.8843
	- Wer: 48.6448

	## 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-05
	- train_batch_size: 14
	- eval_batch_size: 10
	- seed: 42
	- gradient_accumulation_steps: 4
	- total_train_batch_size: 56
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 50
	- training_steps: 10000
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|
	\| 0.1801 \| 0.39 \| 100 \| 0.4976 \| 49.1260 \|
	\| 0.1597 \| 0.79 \| 200 \| 0.4624 \| 46.7497 \|
	\| 0.0776 \| 1.18 \| 300 \| 0.4794 \| 43.1761 \|
	\| 0.083 \| 1.57 \| 400 \| 0.4823 \| 43.8028 \|
	\| 0.0786 \| 1.96 \| 500 \| 0.4883 \| 44.3915 \|
	\| 0.0331 \| 2.36 \| 600 \| 0.5385 \| 46.2437 \|
	\| 0.0353 \| 2.75 \| 700 \| 0.5605 \| 44.9439 \|
	\| 0.0139 \| 3.14 \| 800 \| 0.5941 \| 45.2812 \|
	\| 0.0152 \| 3.53 \| 900 \| 0.5978 \| 49.2930 \|
	\| 0.0155 \| 3.93 \| 1000 \| 0.6114 \| 49.9777 \|
	\| 0.0063 \| 4.32 \| 1100 \| 0.6467 \| 50.0041 \|
	\| 0.0079 \| 4.71 \| 1200 \| 0.6383 \| 48.0875 \|
	\| 0.0046 \| 5.1 \| 1300 \| 0.6500 \| 45.4995 \|
	\| 0.0042 \| 5.5 \| 1400 \| 0.6476 \| 47.4492 \|
	\| 0.0052 \| 5.89 \| 1500 \| 0.6685 \| 52.1870 \|
	\| 0.0023 \| 6.28 \| 1600 \| 0.6794 \| 44.2510 \|
	\| 0.0032 \| 6.67 \| 1700 \| 0.6724 \| 45.7161 \|
	\| 0.0021 \| 7.07 \| 1800 \| 0.6820 \| 47.6013 \|
	\| 0.0015 \| 7.46 \| 1900 \| 0.6925 \| 46.6720 \|
	\| 0.0024 \| 7.85 \| 2000 \| 0.7104 \| 50.2902 \|
	\| 0.0029 \| 8.24 \| 2100 \| 0.6837 \| 46.4173 \|
	\| 0.0016 \| 8.64 \| 2200 \| 0.7191 \| 46.0088 \|
	\| 0.0017 \| 9.03 \| 2300 \| 0.7105 \| 47.5964 \|
	\| 0.0014 \| 9.42 \| 2400 \| 0.7293 \| 44.7603 \|
	\| 0.0018 \| 9.81 \| 2500 \| 0.7365 \| 49.8966 \|
	\| 0.0008 \| 10.21 \| 2600 \| 0.7378 \| 47.4740 \|
	\| 0.0016 \| 10.6 \| 2700 \| 0.7303 \| 45.9691 \|
	\| 0.0011 \| 10.99 \| 2800 \| 0.7330 \| 47.7254 \|
	\| 0.0014 \| 11.38 \| 2900 \| 0.7448 \| 44.8579 \|
	\| 0.0013 \| 11.78 \| 3000 \| 0.7471 \| 46.5116 \|
	\| 0.0015 \| 12.17 \| 3100 \| 0.7513 \| 47.5699 \|
	\| 0.0014 \| 12.56 \| 3200 \| 0.7380 \| 46.4008 \|
	\| 0.0015 \| 12.95 \| 3300 \| 0.7520 \| 45.9658 \|
	\| 0.0009 \| 13.35 \| 3400 \| 0.7482 \| 49.2269 \|
	\| 0.0021 \| 13.74 \| 3500 \| 0.7619 \| 47.1234 \|
	\| 0.0013 \| 14.13 \| 3600 \| 0.7453 \| 49.3956 \|
	\| 0.001 \| 14.52 \| 3700 \| 0.7582 \| 47.6741 \|
	\| 0.0009 \| 14.92 \| 3800 \| 0.7637 \| 46.9713 \|
	\| 0.0014 \| 15.31 \| 3900 \| 0.7722 \| 47.2706 \|
	\| 0.001 \| 15.7 \| 4000 \| 0.7692 \| 46.9663 \|
	\| 0.0003 \| 16.09 \| 4100 \| 0.7744 \| 47.1730 \|
	\| 0.0004 \| 16.49 \| 4200 \| 0.7842 \| 47.3351 \|
	\| 0.0003 \| 16.88 \| 4300 \| 0.7784 \| 47.0771 \|
	\| 0.0002 \| 17.27 \| 4400 \| 0.7879 \| 45.7641 \|
	\| 0.0005 \| 17.66 \| 4500 \| 0.7965 \| 50.0240 \|
	\| 0.0004 \| 18.06 \| 4600 \| 0.8001 \| 48.4381 \|
	\| 0.0002 \| 18.45 \| 4700 \| 0.8016 \| 49.0037 \|
	\| 0.0002 \| 18.84 \| 4800 \| 0.8066 \| 50.0868 \|
	\| 0.0009 \| 19.23 \| 4900 \| 0.8021 \| 47.2276 \|
	\| 0.0005 \| 19.63 \| 5000 \| 0.8162 \| 47.3500 \|
	\| 0.0003 \| 20.02 \| 5100 \| 0.8091 \| 48.7225 \|
	\| 0.0003 \| 20.41 \| 5200 \| 0.8060 \| 51.5024 \|
	\| 0.0003 \| 20.8 \| 5300 \| 0.8220 \| 51.4875 \|
	\| 0.0003 \| 21.2 \| 5400 \| 0.8098 \| 45.8617 \|
	\| 0.0003 \| 21.59 \| 5500 \| 0.8132 \| 44.8711 \|
	\| 0.0009 \| 21.98 \| 5600 \| 0.8006 \| 45.3937 \|
	\| 0.0003 \| 22.37 \| 5700 \| 0.8008 \| 45.6186 \|
	\| 0.0002 \| 22.77 \| 5800 \| 0.8081 \| 46.3247 \|
	\| 0.0002 \| 23.16 \| 5900 \| 0.8082 \| 46.1279 \|
	\| 0.0002 \| 23.55 \| 6000 \| 0.8238 \| 46.1775 \|
	\| 0.0005 \| 23.95 \| 6100 \| 0.8119 \| 49.9727 \|
	\| 0.0002 \| 24.34 \| 6200 \| 0.8315 \| 49.0863 \|
	\| 0.0001 \| 24.73 \| 6300 \| 0.8224 \| 47.2243 \|
	\| 0.0001 \| 25.12 \| 6400 \| 0.8259 \| 47.1681 \|
	\| 0.0001 \| 25.52 \| 6500 \| 0.8219 \| 48.5737 \|
	\| 0.0002 \| 25.91 \| 6600 \| 0.8400 \| 48.9077 \|
	\| 0.0005 \| 26.3 \| 6700 \| 0.8319 \| 47.5567 \|
	\| 0.0001 \| 26.69 \| 6800 \| 0.8394 \| 50.2357 \|
	\| 0.0001 \| 27.09 \| 6900 \| 0.8480 \| 48.4629 \|
	\| 0.0001 \| 27.48 \| 7000 \| 0.8498 \| 47.1151 \|
	\| 0.0002 \| 27.87 \| 7100 \| 0.8342 \| 48.9243 \|
	\| 0.0003 \| 28.26 \| 7200 \| 0.8184 \| 47.3731 \|
	\| 0.0001 \| 28.66 \| 7300 \| 0.8278 \| 47.9288 \|
	\| 0.0002 \| 29.05 \| 7400 \| 0.8439 \| 47.8610 \|
	\| 0.0001 \| 29.44 \| 7500 \| 0.8461 \| 49.9463 \|
	\| 0.0001 \| 29.83 \| 7600 \| 0.8449 \| 48.4861 \|
	\| 0.0001 \| 30.23 \| 7700 \| 0.8512 \| 49.0003 \|
	\| 0.0001 \| 30.62 \| 7800 \| 0.8555 \| 48.2777 \|
	\| 0.0001 \| 31.01 \| 7900 \| 0.8543 \| 48.6795 \|
	\| 0.0001 \| 31.4 \| 8000 \| 0.8566 \| 48.7655 \|
	\| 0.0001 \| 31.8 \| 8100 \| 0.8605 \| 48.6779 \|
	\| 0.0 \| 32.19 \| 8200 \| 0.8634 \| 49.3691 \|
	\| 0.0 \| 32.58 \| 8300 \| 0.8663 \| 50.0438 \|
	\| 0.0 \| 32.97 \| 8400 \| 0.8685 \| 49.7280 \|
	\| 0.0 \| 33.37 \| 8500 \| 0.8704 \| 49.1641 \|
	\| 0.0 \| 33.76 \| 8600 \| 0.8724 \| 48.8416 \|
	\| 0.0 \| 34.15 \| 8700 \| 0.8736 \| 49.2286 \|
	\| 0.0 \| 34.54 \| 8800 \| 0.8755 \| 48.6134 \|
	\| 0.0 \| 34.94 \| 8900 \| 0.8767 \| 48.9259 \|
	\| 0.0 \| 35.33 \| 9000 \| 0.8778 \| 48.9805 \|
	\| 0.0 \| 35.72 \| 9100 \| 0.8791 \| 49.3212 \|
	\| 0.0 \| 36.11 \| 9200 \| 0.8801 \| 49.3724 \|
	\| 0.0 \| 36.51 \| 9300 \| 0.8813 \| 49.4336 \|
	\| 0.0 \| 36.9 \| 9400 \| 0.8819 \| 49.1045 \|
	\| 0.0 \| 37.29 \| 9500 \| 0.8826 \| 49.2633 \|
	\| 0.0 \| 37.68 \| 9600 \| 0.8832 \| 49.4237 \|
	\| 0.0 \| 38.08 \| 9700 \| 0.8837 \| 48.6316 \|
	\| 0.0 \| 38.47 \| 9800 \| 0.8841 \| 48.6465 \|
	\| 0.0 \| 38.86 \| 9900 \| 0.8842 \| 48.9342 \|
	\| 0.0 \| 39.25 \| 10000 \| 0.8843 \| 48.6448 \|


	### Framework versions

	- Transformers 4.35.0
	- Pytorch 2.0.1+cu117
	- Datasets 2.14.6
	- Tokenizers 0.14.1

	---
	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: 0.8843
	- Wer: 48.6448

	## 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-05
	- train_batch_size: 14
	- eval_batch_size: 10
	- seed: 42
	- gradient_accumulation_steps: 4
	- total_train_batch_size: 56
	- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
	- lr_scheduler_type: linear
	- lr_scheduler_warmup_steps: 50
	- training_steps: 10000
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \|
	\|:-------------:\|:-----:\|:-----:\|:---------------:\|:-------:\|
	\| 0.1801 \| 0.39 \| 100 \| 0.4976 \| 49.1260 \|
	\| 0.1597 \| 0.79 \| 200 \| 0.4624 \| 46.7497 \|
	\| 0.0776 \| 1.18 \| 300 \| 0.4794 \| 43.1761 \|
	\| 0.083 \| 1.57 \| 400 \| 0.4823 \| 43.8028 \|
	\| 0.0786 \| 1.96 \| 500 \| 0.4883 \| 44.3915 \|
	\| 0.0331 \| 2.36 \| 600 \| 0.5385 \| 46.2437 \|
	\| 0.0353 \| 2.75 \| 700 \| 0.5605 \| 44.9439 \|
	\| 0.0139 \| 3.14 \| 800 \| 0.5941 \| 45.2812 \|
	\| 0.0152 \| 3.53 \| 900 \| 0.5978 \| 49.2930 \|
	\| 0.0155 \| 3.93 \| 1000 \| 0.6114 \| 49.9777 \|
	\| 0.0063 \| 4.32 \| 1100 \| 0.6467 \| 50.0041 \|
	\| 0.0079 \| 4.71 \| 1200 \| 0.6383 \| 48.0875 \|
	\| 0.0046 \| 5.1 \| 1300 \| 0.6500 \| 45.4995 \|
	\| 0.0042 \| 5.5 \| 1400 \| 0.6476 \| 47.4492 \|
	\| 0.0052 \| 5.89 \| 1500 \| 0.6685 \| 52.1870 \|
	\| 0.0023 \| 6.28 \| 1600 \| 0.6794 \| 44.2510 \|
	\| 0.0032 \| 6.67 \| 1700 \| 0.6724 \| 45.7161 \|
	\| 0.0021 \| 7.07 \| 1800 \| 0.6820 \| 47.6013 \|
	\| 0.0015 \| 7.46 \| 1900 \| 0.6925 \| 46.6720 \|
	\| 0.0024 \| 7.85 \| 2000 \| 0.7104 \| 50.2902 \|
	\| 0.0029 \| 8.24 \| 2100 \| 0.6837 \| 46.4173 \|
	\| 0.0016 \| 8.64 \| 2200 \| 0.7191 \| 46.0088 \|
	\| 0.0017 \| 9.03 \| 2300 \| 0.7105 \| 47.5964 \|
	\| 0.0014 \| 9.42 \| 2400 \| 0.7293 \| 44.7603 \|
	\| 0.0018 \| 9.81 \| 2500 \| 0.7365 \| 49.8966 \|
	\| 0.0008 \| 10.21 \| 2600 \| 0.7378 \| 47.4740 \|
	\| 0.0016 \| 10.6 \| 2700 \| 0.7303 \| 45.9691 \|
	\| 0.0011 \| 10.99 \| 2800 \| 0.7330 \| 47.7254 \|
	\| 0.0014 \| 11.38 \| 2900 \| 0.7448 \| 44.8579 \|
	\| 0.0013 \| 11.78 \| 3000 \| 0.7471 \| 46.5116 \|
	\| 0.0015 \| 12.17 \| 3100 \| 0.7513 \| 47.5699 \|
	\| 0.0014 \| 12.56 \| 3200 \| 0.7380 \| 46.4008 \|
	\| 0.0015 \| 12.95 \| 3300 \| 0.7520 \| 45.9658 \|
	\| 0.0009 \| 13.35 \| 3400 \| 0.7482 \| 49.2269 \|
	\| 0.0021 \| 13.74 \| 3500 \| 0.7619 \| 47.1234 \|
	\| 0.0013 \| 14.13 \| 3600 \| 0.7453 \| 49.3956 \|
	\| 0.001 \| 14.52 \| 3700 \| 0.7582 \| 47.6741 \|
	\| 0.0009 \| 14.92 \| 3800 \| 0.7637 \| 46.9713 \|
	\| 0.0014 \| 15.31 \| 3900 \| 0.7722 \| 47.2706 \|
	\| 0.001 \| 15.7 \| 4000 \| 0.7692 \| 46.9663 \|
	\| 0.0003 \| 16.09 \| 4100 \| 0.7744 \| 47.1730 \|
	\| 0.0004 \| 16.49 \| 4200 \| 0.7842 \| 47.3351 \|
	\| 0.0003 \| 16.88 \| 4300 \| 0.7784 \| 47.0771 \|
	\| 0.0002 \| 17.27 \| 4400 \| 0.7879 \| 45.7641 \|
	\| 0.0005 \| 17.66 \| 4500 \| 0.7965 \| 50.0240 \|
	\| 0.0004 \| 18.06 \| 4600 \| 0.8001 \| 48.4381 \|
	\| 0.0002 \| 18.45 \| 4700 \| 0.8016 \| 49.0037 \|
	\| 0.0002 \| 18.84 \| 4800 \| 0.8066 \| 50.0868 \|
	\| 0.0009 \| 19.23 \| 4900 \| 0.8021 \| 47.2276 \|
	\| 0.0005 \| 19.63 \| 5000 \| 0.8162 \| 47.3500 \|
	\| 0.0003 \| 20.02 \| 5100 \| 0.8091 \| 48.7225 \|
	\| 0.0003 \| 20.41 \| 5200 \| 0.8060 \| 51.5024 \|
	\| 0.0003 \| 20.8 \| 5300 \| 0.8220 \| 51.4875 \|
	\| 0.0003 \| 21.2 \| 5400 \| 0.8098 \| 45.8617 \|
	\| 0.0003 \| 21.59 \| 5500 \| 0.8132 \| 44.8711 \|
	\| 0.0009 \| 21.98 \| 5600 \| 0.8006 \| 45.3937 \|
	\| 0.0003 \| 22.37 \| 5700 \| 0.8008 \| 45.6186 \|
	\| 0.0002 \| 22.77 \| 5800 \| 0.8081 \| 46.3247 \|
	\| 0.0002 \| 23.16 \| 5900 \| 0.8082 \| 46.1279 \|
	\| 0.0002 \| 23.55 \| 6000 \| 0.8238 \| 46.1775 \|
	\| 0.0005 \| 23.95 \| 6100 \| 0.8119 \| 49.9727 \|
	\| 0.0002 \| 24.34 \| 6200 \| 0.8315 \| 49.0863 \|
	\| 0.0001 \| 24.73 \| 6300 \| 0.8224 \| 47.2243 \|
	\| 0.0001 \| 25.12 \| 6400 \| 0.8259 \| 47.1681 \|
	\| 0.0001 \| 25.52 \| 6500 \| 0.8219 \| 48.5737 \|
	\| 0.0002 \| 25.91 \| 6600 \| 0.8400 \| 48.9077 \|
	\| 0.0005 \| 26.3 \| 6700 \| 0.8319 \| 47.5567 \|
	\| 0.0001 \| 26.69 \| 6800 \| 0.8394 \| 50.2357 \|
	\| 0.0001 \| 27.09 \| 6900 \| 0.8480 \| 48.4629 \|
	\| 0.0001 \| 27.48 \| 7000 \| 0.8498 \| 47.1151 \|
	\| 0.0002 \| 27.87 \| 7100 \| 0.8342 \| 48.9243 \|
	\| 0.0003 \| 28.26 \| 7200 \| 0.8184 \| 47.3731 \|
	\| 0.0001 \| 28.66 \| 7300 \| 0.8278 \| 47.9288 \|
	\| 0.0002 \| 29.05 \| 7400 \| 0.8439 \| 47.8610 \|
	\| 0.0001 \| 29.44 \| 7500 \| 0.8461 \| 49.9463 \|
	\| 0.0001 \| 29.83 \| 7600 \| 0.8449 \| 48.4861 \|
	\| 0.0001 \| 30.23 \| 7700 \| 0.8512 \| 49.0003 \|
	\| 0.0001 \| 30.62 \| 7800 \| 0.8555 \| 48.2777 \|
	\| 0.0001 \| 31.01 \| 7900 \| 0.8543 \| 48.6795 \|
	\| 0.0001 \| 31.4 \| 8000 \| 0.8566 \| 48.7655 \|
	\| 0.0001 \| 31.8 \| 8100 \| 0.8605 \| 48.6779 \|
	\| 0.0 \| 32.19 \| 8200 \| 0.8634 \| 49.3691 \|
	\| 0.0 \| 32.58 \| 8300 \| 0.8663 \| 50.0438 \|
	\| 0.0 \| 32.97 \| 8400 \| 0.8685 \| 49.7280 \|
	\| 0.0 \| 33.37 \| 8500 \| 0.8704 \| 49.1641 \|
	\| 0.0 \| 33.76 \| 8600 \| 0.8724 \| 48.8416 \|
	\| 0.0 \| 34.15 \| 8700 \| 0.8736 \| 49.2286 \|
	\| 0.0 \| 34.54 \| 8800 \| 0.8755 \| 48.6134 \|
	\| 0.0 \| 34.94 \| 8900 \| 0.8767 \| 48.9259 \|
	\| 0.0 \| 35.33 \| 9000 \| 0.8778 \| 48.9805 \|
	\| 0.0 \| 35.72 \| 9100 \| 0.8791 \| 49.3212 \|
	\| 0.0 \| 36.11 \| 9200 \| 0.8801 \| 49.3724 \|
	\| 0.0 \| 36.51 \| 9300 \| 0.8813 \| 49.4336 \|
	\| 0.0 \| 36.9 \| 9400 \| 0.8819 \| 49.1045 \|
	\| 0.0 \| 37.29 \| 9500 \| 0.8826 \| 49.2633 \|
	\| 0.0 \| 37.68 \| 9600 \| 0.8832 \| 49.4237 \|
	\| 0.0 \| 38.08 \| 9700 \| 0.8837 \| 48.6316 \|
	\| 0.0 \| 38.47 \| 9800 \| 0.8841 \| 48.6465 \|
	\| 0.0 \| 38.86 \| 9900 \| 0.8842 \| 48.9342 \|
	\| 0.0 \| 39.25 \| 10000 \| 0.8843 \| 48.6448 \|


	### Framework versions

	- Transformers 4.35.0
	- Pytorch 2.0.1+cu117
	- Datasets 2.14.6
	- Tokenizers 0.14.1