wav2vec2-base-librispeech-model / README.md

End of training

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	---
	library_name: transformers
	license: apache-2.0
	base_model: facebook/wav2vec2-base-960h
	tags:
	- automatic-speech-recognition
	- libri10h
	- mms
	- generated_from_trainer
	metrics:
	- wer
	model-index:
	- name: wav2vec2-base-librispeech-model
	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. -->

	# wav2vec2-base-librispeech-model

	This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the LIBRI10H - ENG dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.8515
	- Wer: 0.7226

	## 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: 8
	- eval_batch_size: 8
	- seed: 42
	- gradient_accumulation_steps: 2
	- 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_steps: 200
	- num_epochs: 100.0
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:------:\|
	\| 4.7426 \| 1.1565 \| 200 \| 2.8968 \| 1.0 \|
	\| 2.7493 \| 2.3130 \| 400 \| 2.2712 \| 0.9987 \|
	\| 2.0118 \| 3.4696 \| 600 \| 1.6905 \| 0.9768 \|
	\| 1.7815 \| 4.6261 \| 800 \| 1.5406 \| 0.9588 \|
	\| 1.667 \| 5.7826 \| 1000 \| 1.4410 \| 0.9385 \|
	\| 1.5898 \| 6.9391 \| 1200 \| 1.3799 \| 0.9282 \|
	\| 1.5366 \| 8.0928 \| 1400 \| 1.3415 \| 0.9165 \|
	\| 1.4917 \| 9.2493 \| 1600 \| 1.3144 \| 0.9205 \|
	\| 1.455 \| 10.4058 \| 1800 \| 1.2746 \| 0.9068 \|
	\| 1.4266 \| 11.5623 \| 2000 \| 1.2521 \| 0.9102 \|
	\| 1.3925 \| 12.7188 \| 2200 \| 1.2213 \| 0.8971 \|
	\| 1.3754 \| 13.8754 \| 2400 \| 1.2028 \| 0.8938 \|
	\| 1.3452 \| 15.0290 \| 2600 \| 1.1931 \| 0.8826 \|
	\| 1.3265 \| 16.1855 \| 2800 \| 1.1682 \| 0.8860 \|
	\| 1.3106 \| 17.3420 \| 3000 \| 1.1645 \| 0.8752 \|
	\| 1.2917 \| 18.4986 \| 3200 \| 1.1686 \| 0.8780 \|
	\| 1.2745 \| 19.6551 \| 3400 \| 1.1385 \| 0.8670 \|
	\| 1.2639 \| 20.8116 \| 3600 \| 1.1301 \| 0.8666 \|
	\| 1.2432 \| 21.9681 \| 3800 \| 1.1173 \| 0.8670 \|
	\| 1.2294 \| 23.1217 \| 4000 \| 1.1098 \| 0.8620 \|
	\| 1.2203 \| 24.2783 \| 4200 \| 1.1077 \| 0.8711 \|
	\| 1.2037 \| 25.4348 \| 4400 \| 1.0964 \| 0.8626 \|
	\| 1.1965 \| 26.5913 \| 4600 \| 1.0910 \| 0.8581 \|
	\| 1.181 \| 27.7478 \| 4800 \| 1.0842 \| 0.8533 \|
	\| 1.1711 \| 28.9043 \| 5000 \| 1.0692 \| 0.8465 \|
	\| 1.1573 \| 30.0580 \| 5200 \| 1.0724 \| 0.8464 \|
	\| 1.1472 \| 31.2145 \| 5400 \| 1.0529 \| 0.8404 \|
	\| 1.1375 \| 32.3710 \| 5600 \| 1.0506 \| 0.8403 \|
	\| 1.1276 \| 33.5275 \| 5800 \| 1.0432 \| 0.8398 \|
	\| 1.1149 \| 34.6841 \| 6000 \| 1.0371 \| 0.8330 \|
	\| 1.1099 \| 35.8406 \| 6200 \| 1.0372 \| 0.8341 \|
	\| 1.0959 \| 36.9971 \| 6400 \| 1.0296 \| 0.8370 \|
	\| 1.0838 \| 38.1507 \| 6600 \| 1.0136 \| 0.8232 \|
	\| 1.0761 \| 39.3072 \| 6800 \| 1.0355 \| 0.8288 \|
	\| 1.069 \| 40.4638 \| 7000 \| 1.0072 \| 0.8211 \|
	\| 1.0624 \| 41.6203 \| 7200 \| 1.0019 \| 0.8217 \|
	\| 1.0502 \| 42.7768 \| 7400 \| 1.0021 \| 0.8329 \|
	\| 1.0423 \| 43.9333 \| 7600 \| 0.9960 \| 0.8153 \|
	\| 1.0334 \| 45.0870 \| 7800 \| 0.9903 \| 0.8134 \|
	\| 1.0203 \| 46.2435 \| 8000 \| 0.9787 \| 0.8116 \|
	\| 1.0212 \| 47.4 \| 8200 \| 0.9690 \| 0.8029 \|
	\| 1.0062 \| 48.5565 \| 8400 \| 0.9864 \| 0.8030 \|
	\| 1.0029 \| 49.7130 \| 8600 \| 0.9658 \| 0.8000 \|
	\| 0.9922 \| 50.8696 \| 8800 \| 0.9552 \| 0.7964 \|
	\| 0.9784 \| 52.0232 \| 9000 \| 0.9563 \| 0.7978 \|
	\| 0.9761 \| 53.1797 \| 9200 \| 0.9442 \| 0.7898 \|
	\| 0.9649 \| 54.3362 \| 9400 \| 0.9495 \| 0.7898 \|
	\| 0.9567 \| 55.4928 \| 9600 \| 0.9448 \| 0.7927 \|
	\| 0.9556 \| 56.6493 \| 9800 \| 0.9303 \| 0.7851 \|
	\| 0.9454 \| 57.8058 \| 10000 \| 0.9304 \| 0.7784 \|
	\| 0.9356 \| 58.9623 \| 10200 \| 0.9202 \| 0.7718 \|
	\| 0.927 \| 60.1159 \| 10400 \| 0.9264 \| 0.7730 \|
	\| 0.9172 \| 61.2725 \| 10600 \| 0.9252 \| 0.7736 \|
	\| 0.9177 \| 62.4290 \| 10800 \| 0.9087 \| 0.7682 \|
	\| 0.9107 \| 63.5855 \| 11000 \| 0.9119 \| 0.7663 \|
	\| 0.9017 \| 64.7420 \| 11200 \| 0.9014 \| 0.7609 \|
	\| 0.899 \| 65.8986 \| 11400 \| 0.8962 \| 0.7597 \|
	\| 0.8854 \| 67.0522 \| 11600 \| 0.8976 \| 0.7533 \|
	\| 0.8841 \| 68.2087 \| 11800 \| 0.8952 \| 0.7554 \|
	\| 0.8792 \| 69.3652 \| 12000 \| 0.8951 \| 0.7535 \|
	\| 0.8697 \| 70.5217 \| 12200 \| 0.8913 \| 0.7513 \|
	\| 0.8677 \| 71.6783 \| 12400 \| 0.8820 \| 0.7496 \|
	\| 0.862 \| 72.8348 \| 12600 \| 0.8834 \| 0.7447 \|
	\| 0.8573 \| 73.9913 \| 12800 \| 0.8824 \| 0.7437 \|
	\| 0.8527 \| 75.1449 \| 13000 \| 0.8747 \| 0.7388 \|
	\| 0.8451 \| 76.3014 \| 13200 \| 0.8806 \| 0.7399 \|
	\| 0.8435 \| 77.4580 \| 13400 \| 0.8713 \| 0.7401 \|
	\| 0.8393 \| 78.6145 \| 13600 \| 0.8734 \| 0.7387 \|
	\| 0.8353 \| 79.7710 \| 13800 \| 0.8702 \| 0.7367 \|
	\| 0.834 \| 80.9275 \| 14000 \| 0.8661 \| 0.7335 \|
	\| 0.8265 \| 82.0812 \| 14200 \| 0.8642 \| 0.7312 \|
	\| 0.8183 \| 83.2377 \| 14400 \| 0.8638 \| 0.7334 \|
	\| 0.8238 \| 84.3942 \| 14600 \| 0.8643 \| 0.7311 \|
	\| 0.8176 \| 85.5507 \| 14800 \| 0.8640 \| 0.7309 \|
	\| 0.8183 \| 86.7072 \| 15000 \| 0.8603 \| 0.7294 \|
	\| 0.8121 \| 87.8638 \| 15200 \| 0.8586 \| 0.7270 \|
	\| 0.8033 \| 89.0174 \| 15400 \| 0.8585 \| 0.7265 \|
	\| 0.8116 \| 90.1739 \| 15600 \| 0.8560 \| 0.7254 \|
	\| 0.8058 \| 91.3304 \| 15800 \| 0.8553 \| 0.7262 \|
	\| 0.7992 \| 92.4870 \| 16000 \| 0.8548 \| 0.7263 \|
	\| 0.7979 \| 93.6435 \| 16200 \| 0.8528 \| 0.7236 \|
	\| 0.7979 \| 94.8 \| 16400 \| 0.8529 \| 0.7235 \|
	\| 0.7978 \| 95.9565 \| 16600 \| 0.8526 \| 0.7242 \|
	\| 0.7934 \| 97.1101 \| 16800 \| 0.8519 \| 0.7238 \|
	\| 0.7915 \| 98.2667 \| 17000 \| 0.8520 \| 0.7233 \|
	\| 0.7996 \| 99.4232 \| 17200 \| 0.8515 \| 0.7230 \|


	### Framework versions

	- Transformers 4.49.0
	- Pytorch 2.6.0+cu124
	- Datasets 3.3.2
	- Tokenizers 0.21.0

	---
	library_name: transformers
	license: apache-2.0
	base_model: facebook/wav2vec2-base-960h
	tags:
	- automatic-speech-recognition
	- libri10h
	- mms
	- generated_from_trainer
	metrics:
	- wer
	model-index:
	- name: wav2vec2-base-librispeech-model
	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. -->

	# wav2vec2-base-librispeech-model

	This model is a fine-tuned version of [facebook/wav2vec2-base-960h](https://huggingface.co/facebook/wav2vec2-base-960h) on the LIBRI10H - ENG dataset.
	It achieves the following results on the evaluation set:
	- Loss: 0.8515
	- Wer: 0.7226

	## 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: 8
	- eval_batch_size: 8
	- seed: 42
	- gradient_accumulation_steps: 2
	- 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_steps: 200
	- num_epochs: 100.0
	- mixed_precision_training: Native AMP

	### Training results

	\| Training Loss \| Epoch \| Step \| Validation Loss \| Wer \|
	\|:-------------:\|:-------:\|:-----:\|:---------------:\|:------:\|
	\| 4.7426 \| 1.1565 \| 200 \| 2.8968 \| 1.0 \|
	\| 2.7493 \| 2.3130 \| 400 \| 2.2712 \| 0.9987 \|
	\| 2.0118 \| 3.4696 \| 600 \| 1.6905 \| 0.9768 \|
	\| 1.7815 \| 4.6261 \| 800 \| 1.5406 \| 0.9588 \|
	\| 1.667 \| 5.7826 \| 1000 \| 1.4410 \| 0.9385 \|
	\| 1.5898 \| 6.9391 \| 1200 \| 1.3799 \| 0.9282 \|
	\| 1.5366 \| 8.0928 \| 1400 \| 1.3415 \| 0.9165 \|
	\| 1.4917 \| 9.2493 \| 1600 \| 1.3144 \| 0.9205 \|
	\| 1.455 \| 10.4058 \| 1800 \| 1.2746 \| 0.9068 \|
	\| 1.4266 \| 11.5623 \| 2000 \| 1.2521 \| 0.9102 \|
	\| 1.3925 \| 12.7188 \| 2200 \| 1.2213 \| 0.8971 \|
	\| 1.3754 \| 13.8754 \| 2400 \| 1.2028 \| 0.8938 \|
	\| 1.3452 \| 15.0290 \| 2600 \| 1.1931 \| 0.8826 \|
	\| 1.3265 \| 16.1855 \| 2800 \| 1.1682 \| 0.8860 \|
	\| 1.3106 \| 17.3420 \| 3000 \| 1.1645 \| 0.8752 \|
	\| 1.2917 \| 18.4986 \| 3200 \| 1.1686 \| 0.8780 \|
	\| 1.2745 \| 19.6551 \| 3400 \| 1.1385 \| 0.8670 \|
	\| 1.2639 \| 20.8116 \| 3600 \| 1.1301 \| 0.8666 \|
	\| 1.2432 \| 21.9681 \| 3800 \| 1.1173 \| 0.8670 \|
	\| 1.2294 \| 23.1217 \| 4000 \| 1.1098 \| 0.8620 \|
	\| 1.2203 \| 24.2783 \| 4200 \| 1.1077 \| 0.8711 \|
	\| 1.2037 \| 25.4348 \| 4400 \| 1.0964 \| 0.8626 \|
	\| 1.1965 \| 26.5913 \| 4600 \| 1.0910 \| 0.8581 \|
	\| 1.181 \| 27.7478 \| 4800 \| 1.0842 \| 0.8533 \|
	\| 1.1711 \| 28.9043 \| 5000 \| 1.0692 \| 0.8465 \|
	\| 1.1573 \| 30.0580 \| 5200 \| 1.0724 \| 0.8464 \|
	\| 1.1472 \| 31.2145 \| 5400 \| 1.0529 \| 0.8404 \|
	\| 1.1375 \| 32.3710 \| 5600 \| 1.0506 \| 0.8403 \|
	\| 1.1276 \| 33.5275 \| 5800 \| 1.0432 \| 0.8398 \|
	\| 1.1149 \| 34.6841 \| 6000 \| 1.0371 \| 0.8330 \|
	\| 1.1099 \| 35.8406 \| 6200 \| 1.0372 \| 0.8341 \|
	\| 1.0959 \| 36.9971 \| 6400 \| 1.0296 \| 0.8370 \|
	\| 1.0838 \| 38.1507 \| 6600 \| 1.0136 \| 0.8232 \|
	\| 1.0761 \| 39.3072 \| 6800 \| 1.0355 \| 0.8288 \|
	\| 1.069 \| 40.4638 \| 7000 \| 1.0072 \| 0.8211 \|
	\| 1.0624 \| 41.6203 \| 7200 \| 1.0019 \| 0.8217 \|
	\| 1.0502 \| 42.7768 \| 7400 \| 1.0021 \| 0.8329 \|
	\| 1.0423 \| 43.9333 \| 7600 \| 0.9960 \| 0.8153 \|
	\| 1.0334 \| 45.0870 \| 7800 \| 0.9903 \| 0.8134 \|
	\| 1.0203 \| 46.2435 \| 8000 \| 0.9787 \| 0.8116 \|
	\| 1.0212 \| 47.4 \| 8200 \| 0.9690 \| 0.8029 \|
	\| 1.0062 \| 48.5565 \| 8400 \| 0.9864 \| 0.8030 \|
	\| 1.0029 \| 49.7130 \| 8600 \| 0.9658 \| 0.8000 \|
	\| 0.9922 \| 50.8696 \| 8800 \| 0.9552 \| 0.7964 \|
	\| 0.9784 \| 52.0232 \| 9000 \| 0.9563 \| 0.7978 \|
	\| 0.9761 \| 53.1797 \| 9200 \| 0.9442 \| 0.7898 \|
	\| 0.9649 \| 54.3362 \| 9400 \| 0.9495 \| 0.7898 \|
	\| 0.9567 \| 55.4928 \| 9600 \| 0.9448 \| 0.7927 \|
	\| 0.9556 \| 56.6493 \| 9800 \| 0.9303 \| 0.7851 \|
	\| 0.9454 \| 57.8058 \| 10000 \| 0.9304 \| 0.7784 \|
	\| 0.9356 \| 58.9623 \| 10200 \| 0.9202 \| 0.7718 \|
	\| 0.927 \| 60.1159 \| 10400 \| 0.9264 \| 0.7730 \|
	\| 0.9172 \| 61.2725 \| 10600 \| 0.9252 \| 0.7736 \|
	\| 0.9177 \| 62.4290 \| 10800 \| 0.9087 \| 0.7682 \|
	\| 0.9107 \| 63.5855 \| 11000 \| 0.9119 \| 0.7663 \|
	\| 0.9017 \| 64.7420 \| 11200 \| 0.9014 \| 0.7609 \|
	\| 0.899 \| 65.8986 \| 11400 \| 0.8962 \| 0.7597 \|
	\| 0.8854 \| 67.0522 \| 11600 \| 0.8976 \| 0.7533 \|
	\| 0.8841 \| 68.2087 \| 11800 \| 0.8952 \| 0.7554 \|
	\| 0.8792 \| 69.3652 \| 12000 \| 0.8951 \| 0.7535 \|
	\| 0.8697 \| 70.5217 \| 12200 \| 0.8913 \| 0.7513 \|
	\| 0.8677 \| 71.6783 \| 12400 \| 0.8820 \| 0.7496 \|
	\| 0.862 \| 72.8348 \| 12600 \| 0.8834 \| 0.7447 \|
	\| 0.8573 \| 73.9913 \| 12800 \| 0.8824 \| 0.7437 \|
	\| 0.8527 \| 75.1449 \| 13000 \| 0.8747 \| 0.7388 \|
	\| 0.8451 \| 76.3014 \| 13200 \| 0.8806 \| 0.7399 \|
	\| 0.8435 \| 77.4580 \| 13400 \| 0.8713 \| 0.7401 \|
	\| 0.8393 \| 78.6145 \| 13600 \| 0.8734 \| 0.7387 \|
	\| 0.8353 \| 79.7710 \| 13800 \| 0.8702 \| 0.7367 \|
	\| 0.834 \| 80.9275 \| 14000 \| 0.8661 \| 0.7335 \|
	\| 0.8265 \| 82.0812 \| 14200 \| 0.8642 \| 0.7312 \|
	\| 0.8183 \| 83.2377 \| 14400 \| 0.8638 \| 0.7334 \|
	\| 0.8238 \| 84.3942 \| 14600 \| 0.8643 \| 0.7311 \|
	\| 0.8176 \| 85.5507 \| 14800 \| 0.8640 \| 0.7309 \|
	\| 0.8183 \| 86.7072 \| 15000 \| 0.8603 \| 0.7294 \|
	\| 0.8121 \| 87.8638 \| 15200 \| 0.8586 \| 0.7270 \|
	\| 0.8033 \| 89.0174 \| 15400 \| 0.8585 \| 0.7265 \|
	\| 0.8116 \| 90.1739 \| 15600 \| 0.8560 \| 0.7254 \|
	\| 0.8058 \| 91.3304 \| 15800 \| 0.8553 \| 0.7262 \|
	\| 0.7992 \| 92.4870 \| 16000 \| 0.8548 \| 0.7263 \|
	\| 0.7979 \| 93.6435 \| 16200 \| 0.8528 \| 0.7236 \|
	\| 0.7979 \| 94.8 \| 16400 \| 0.8529 \| 0.7235 \|
	\| 0.7978 \| 95.9565 \| 16600 \| 0.8526 \| 0.7242 \|
	\| 0.7934 \| 97.1101 \| 16800 \| 0.8519 \| 0.7238 \|
	\| 0.7915 \| 98.2667 \| 17000 \| 0.8520 \| 0.7233 \|
	\| 0.7996 \| 99.4232 \| 17200 \| 0.8515 \| 0.7230 \|


	### Framework versions

	- Transformers 4.49.0
	- Pytorch 2.6.0+cu124
	- Datasets 3.3.2
	- Tokenizers 0.21.0