model_syllable_onSet2

JovialValley/model_syllable_onSet2

model_syllable_onSet2 is a automatic speech recognition model from JovialValley. Use it when you need speech turned into text. It is set up for transformers. The card lists the license as apache-2.0.

should probably proofread and complete it, then remove this comment. --

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At a glance

Task: Automatic Speech Recognition
Library: transformers
License: apache-2.0
Model type: wav2vec2
Access: Public
Created: Dec 21, 2022
Updated: Dec 21, 2022
SHA: d34ed3a5

Model card

From the Hugging Face model README

model_syllable_onSet2

This model is a fine-tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset. It achieves the following results on the evaluation set:

Loss: 0.4231
0 Precision: 1.0
0 Recall: 0.96
0 F1-score: 0.9796
0 Support: 25
1 Precision: 0.9643
1 Recall: 0.9643
1 F1-score: 0.9643
1 Support: 28
2 Precision: 1.0
2 Recall: 0.9643
2 F1-score: 0.9818
2 Support: 28
3 Precision: 0.8889
3 Recall: 1.0
3 F1-score: 0.9412
3 Support: 16
Accuracy: 0.9691
Macro avg Precision: 0.9633
Macro avg Recall: 0.9721
Macro avg F1-score: 0.9667
Macro avg Support: 97
Weighted avg Precision: 0.9714
Weighted avg Recall: 0.9691
Weighted avg F1-score: 0.9695
Weighted avg Support: 97
Wer: 0.2827
Mtrix: [[0, 1, 2, 3], [0, 24, 1, 0, 0], [1, 0, 27, 0, 1], [2, 0, 0, 27, 1], [3, 0, 0, 0, 16]]

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: Adam with betas=(0.9,0.999) and epsilon=1e-08
lr_scheduler_type: linear
lr_scheduler_warmup_steps: 200
num_epochs: 70
mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	0 Precision	0 Recall	0 F1-score	0 Support	1 Precision	1 Recall	1 F1-score	1 Support	2 Precision	2 Recall	2 F1-score	2 Support	3 Precision	3 Recall	3 F1-score	3 Support	Accuracy	Macro avg Precision	Macro avg Recall	Macro avg F1-score	Macro avg Support	Weighted avg Precision	Weighted avg Recall	Weighted avg F1-score	Weighted avg Support	Wer	Mtrix
1.3102	4.16	100	1.2133	0.125	0.04	0.0606	25	0.0	0.0	0.0	28	0.3146	1.0	0.4786	28	0.0	0.0	0.0	16	0.2990	0.1099	0.26	0.1348	97	0.1230	0.2990	0.1538	97	0.9676	[[0, 1, 2, 3], [0, 1, 0, 24, 0], [1, 7, 0, 21, 0], [2, 0, 0, 28, 0], [3, 0, 0, 16, 0]]
0.7368	8.33	200	0.7100	1.0	0.72	0.8372	25	0.3333	0.0357	0.0645	28	0.3684	1.0	0.5385	28	0.0	0.0	0.0	16	0.4845	0.4254	0.4389	0.3600	97	0.4603	0.4845	0.3898	97	0.8227	[[0, 1, 2, 3], [0, 18, 2, 5, 0], [1, 0, 1, 27, 0], [2, 0, 0, 28, 0], [3, 0, 0, 16, 0]]
0.3813	12.49	300	0.3802	0.8519	0.92	0.8846	25	0.7333	0.7857	0.7586	28	0.9231	0.8571	0.8889	28	0.9286	0.8125	0.8667	16	0.8454	0.8592	0.8438	0.8497	97	0.8509	0.8454	0.8465	97	0.7694	[[0, 1, 2, 3], [0, 23, 2, 0, 0], [1, 4, 22, 2, 0], [2, 0, 3, 24, 1], [3, 0, 3, 0, 13]]
0.2761	16.65	400	0.2263	1.0	1.0	1.0	25	1.0	0.9643	0.9818	28	1.0	0.9643	0.9818	28	0.8889	1.0	0.9412	16	0.9794	0.9722	0.9821	0.9762	97	0.9817	0.9794	0.9798	97	0.4392	[[0, 1, 2, 3], [0, 25, 0, 0, 0], [1, 0, 27, 0, 1], [2, 0, 0, 27, 1], [3, 0, 0, 0, 16]]
0.1596	20.82	500	0.2283	1.0	0.96	0.9796	25	0.9310	0.9643	0.9474	28	0.9643	0.9643	0.9643	28	0.9375	0.9375	0.9375	16	0.9588	0.9582	0.9565	0.9572	97	0.9595	0.9588	0.9589	97	0.4971	[[0, 1, 2, 3], [0, 24, 1, 0, 0], [1, 0, 27, 1, 0], [2, 0, 0, 27, 1], [3, 0, 1, 0, 15]]
0.124	24.98	600	0.1841	1.0	0.96	0.9796	25	0.9655	1.0	0.9825	28	1.0	0.9643	0.9818	28	0.9412	1.0	0.9697	16	0.9794	0.9767	0.9811	0.9784	97	0.9803	0.9794	0.9794	97	0.2955	[[0, 1, 2, 3], [0, 24, 1, 0, 0], [1, 0, 28, 0, 0], [2, 0, 0, 27, 1], [3, 0, 0, 0, 16]]
0.1162	29.16	700	0.2286	1.0	0.96	0.9796	25	0.9333	1.0	0.9655	28	1.0	0.9286	0.9630	28	0.9412	1.0	0.9697	16	0.9691	0.9686	0.9721	0.9694	97	0.9711	0.9691	0.9691	97	0.3627	[[0, 1, 2, 3], [0, 24, 1, 0, 0], [1, 0, 28, 0, 0], [2, 0, 1, 26, 1], [3, 0, 0, 0, 16]]
0.1576	33.33	800	0.2259	1.0	0.92	0.9583	25	0.9333	1.0	0.9655	28	1.0	0.9643	0.9818	28	0.9412	1.0	0.9697	16	0.9691	0.9686	0.9711	0.9688	97	0.9711	0.9691	0.9691	97	0.3210	[[0, 1, 2, 3], [0, 23, 2, 0, 0], [1, 0, 28, 0, 0], [2, 0, 0, 27, 1], [3, 0, 0, 0, 16]]
0.0957	37.49	900	0.2757	1.0	0.96	0.9796	25	0.9643	0.9643	0.9643	28	0.9643	0.9643	0.9643	28	0.9412	1.0	0.9697	16	0.9691	0.9674	0.9721	0.9695	97	0.9697	0.9691	0.9691	97	0.3499	[[0, 1, 2, 3], [0, 24, 1, 0, 0], [1, 0, 27, 1, 0], [2, 0, 0, 27, 1], [3, 0, 0, 0, 16]]
0.1145	41.65	1000	0.2951	1.0	0.96	0.9796	25	1.0	0.9643	0.9818	28	1.0	0.9643	0.9818	28	0.8421	1.0	0.9143	16	0.9691	0.9605	0.9721	0.9644	97	0.9740	0.9691	0.9701	97	0.3024	[[0, 1, 2, 3], [0, 24, 0, 0, 1], [1, 0, 27, 0, 1], [2, 0, 0, 27, 1], [3, 0, 0, 0, 16]]
0.121	45.82	1100	0.3262	1.0	0.96	0.9796	25	1.0	0.9643	0.9818	28	1.0	0.9643	0.9818	28	0.8421	1.0	0.9143	16	0.9691	0.9605	0.9721	0.9644	97	0.9740	0.9691	0.9701	97	0.2885	[[0, 1, 2, 3], [0, 24, 0, 0, 1], [1, 0, 27, 0, 1], [2, 0, 0, 27, 1], [3, 0, 0, 0, 16]]
0.079	49.98	1200	0.3615	1.0	0.96	0.9796	25	0.9643	0.9643	0.9643	28	1.0	0.9643	0.9818	28	0.8889	1.0	0.9412	16	0.9691	0.9633	0.9721	0.9667	97	0.9714	0.9691	0.9695	97	0.3615	[[0, 1, 2, 3], [0, 24, 1, 0, 0], [1, 0, 27, 0, 1], [2, 0, 0, 27, 1], [3, 0, 0, 0, 16]]
0.0733	54.16	1300	0.3891	1.0	0.96	0.9796	25	0.9643	0.9643	0.9643	28	1.0	0.9643	0.9818	28	0.8889	1.0	0.9412	16	0.9691	0.9633	0.9721	0.9667	97	0.9714	0.9691	0.9695	97	0.3082	[[0, 1, 2, 3], [0, 24, 1, 0, 0], [1, 0, 27, 0, 1], [2, 0, 0, 27, 1], [3, 0, 0, 0, 16]]
0.0962	58.33	1400	0.3620	1.0	0.96	0.9796	25	0.9643	0.9643	0.9643	28	1.0	0.9643	0.9818	28	0.8889	1.0	0.9412	16	0.9691	0.9633	0.9721	0.9667	97	0.9714	0.9691	0.9695	97	0.2851	[[0, 1, 2, 3], [0, 24, 1, 0, 0], [1, 0, 27, 0, 1], [2, 0, 0, 27, 1], [3, 0, 0, 0, 16]]
0.0628	62.49	1500	0.4084	1.0	0.96	0.9796	25	0.9630	0.9286	0.9455	28	0.9643	0.9643	0.9643	28	0.8889	1.0	0.9412	16	0.9588	0.9540	0.9632	0.9576	97	0.9607	0.9588	0.9590	97	0.3001	[[0, 1, 2, 3], [0, 24, 1, 0, 0], [1, 0, 26, 1, 1], [2, 0, 0, 27, 1], [3, 0, 0, 0, 16]]
0.0675	66.65	1600	0.4231	1.0	0.96	0.9796	25	0.9643	0.9643	0.9643	28	1.0	0.9643	0.9818	28	0.8889	1.0	0.9412	16	0.9691	0.9633	0.9721	0.9667	97	0.9714	0.9691	0.9695	97	0.2827	[[0, 1, 2, 3], [0, 24, 1, 0, 0], [1, 0, 27, 0, 1], [2, 0, 0, 27, 1], [3, 0, 0, 0, 16]]

Framework versions

Transformers 4.25.1
Pytorch 1.13.0+cu116
Datasets 2.8.0
Tokenizers 0.13.2

Task: Automatic Speech Recognition
Library: transformers
Type: wav2vec2
License: apache-2.0
Created: Dec 21, 2022
Updated: Dec 21, 2022