model_syllable_onSet1

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: 0.1815
0 Precision: 1.0
0 Recall: 0.9677
0 F1-score: 0.9836
0 Support: 31
1 Precision: 0.9545
1 Recall: 1.0
1 F1-score: 0.9767
1 Support: 21
2 Precision: 1.0
2 Recall: 1.0
2 F1-score: 1.0
2 Support: 30
3 Precision: 1.0
3 Recall: 1.0
3 F1-score: 1.0
3 Support: 16
Accuracy: 0.9898
Macro avg Precision: 0.9886
Macro avg Recall: 0.9919
Macro avg F1-score: 0.9901
Macro avg Support: 98
Weighted avg Precision: 0.9903
Weighted avg Recall: 0.9898
Weighted avg F1-score: 0.9898
Weighted avg Support: 98
Wer: 0.7883
Mtrix: [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 21, 0, 0], [2, 0, 0, 30, 0], [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
1.6949	4.16	100	1.6177	1.0	0.2581	0.4103	31	0.0	0.0	0.0	21	0.3333	1.0	0.5	30	0.0	0.0	0.0	16	0.3878	0.3333	0.3145	0.2276	98	0.4184	0.3878	0.2828	98	0.9655	[[0, 1, 2, 3], [0, 8, 0, 23, 0], [1, 0, 0, 21, 0], [2, 0, 0, 30, 0], [3, 0, 0, 16, 0]]
1.5778	8.33	200	1.3535	1.0	0.2581	0.4103	31	0.0	0.0	0.0	21	0.3333	1.0	0.5	30	0.0	0.0	0.0	16	0.3878	0.3333	0.3145	0.2276	98	0.4184	0.3878	0.2828	98	0.9655	[[0, 1, 2, 3], [0, 8, 0, 23, 0], [1, 0, 0, 21, 0], [2, 0, 0, 30, 0], [3, 0, 0, 16, 0]]
1.2861	12.49	300	1.0938	1.0	0.2581	0.4103	31	0.0	0.0	0.0	21	0.3333	1.0	0.5	30	0.0	0.0	0.0	16	0.3878	0.3333	0.3145	0.2276	98	0.4184	0.3878	0.2828	98	0.9655	[[0, 1, 2, 3], [0, 8, 0, 23, 0], [1, 0, 0, 21, 0], [2, 0, 0, 30, 0], [3, 0, 0, 16, 0]]
0.954	16.65	400	0.9480	1.0	0.2581	0.4103	31	0.0	0.0	0.0	21	0.3333	1.0	0.5	30	0.0	0.0	0.0	16	0.3878	0.3333	0.3145	0.2276	98	0.4184	0.3878	0.2828	98	0.9655	[[0, 1, 2, 3], [0, 8, 0, 23, 0], [1, 0, 0, 21, 0], [2, 0, 0, 30, 0], [3, 0, 0, 16, 0]]
0.8849	20.82	500	0.9231	1.0	0.2581	0.4103	31	0.0	0.0	0.0	21	0.3333	1.0	0.5	30	0.0	0.0	0.0	16	0.3878	0.3333	0.3145	0.2276	98	0.4184	0.3878	0.2828	98	0.9655	[[0, 1, 2, 3], [0, 8, 0, 23, 0], [1, 0, 0, 21, 0], [2, 0, 0, 30, 0], [3, 0, 0, 16, 0]]
0.8674	24.98	600	0.8767	1.0	0.2581	0.4103	31	0.0	0.0	0.0	21	0.3333	1.0	0.5	30	0.0	0.0	0.0	16	0.3878	0.3333	0.3145	0.2276	98	0.4184	0.3878	0.2828	98	0.9655	[[0, 1, 2, 3], [0, 8, 0, 23, 0], [1, 0, 0, 21, 0], [2, 0, 0, 30, 0], [3, 0, 0, 16, 0]]
0.7921	29.16	700	0.7519	1.0	0.9677	0.9836	31	0.9545	1.0	0.9767	21	1.0	1.0	1.0	30	1.0	1.0	1.0	16	0.9898	0.9886	0.9919	0.9901	98	0.9903	0.9898	0.9898	98	1.0	[[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 21, 0, 0], [2, 0, 0, 30, 0], [3, 0, 0, 0, 16]]
0.7851	33.33	800	0.8212	1.0	0.9032	0.9492	31	0.84	1.0	0.9130	21	1.0	1.0	1.0	30	1.0	0.9375	0.9677	16	0.9592	0.96	0.9602	0.9575	98	0.9657	0.9592	0.9600	98	1.0	[[0, 1, 2, 3], [0, 28, 3, 0, 0], [1, 0, 21, 0, 0], [2, 0, 0, 30, 0], [3, 0, 1, 0, 15]]
0.7657	37.49	900	0.7504	1.0	0.9677	0.9836	31	0.9130	1.0	0.9545	21	1.0	1.0	1.0	30	1.0	0.9375	0.9677	16	0.9796	0.9783	0.9763	0.9765	98	0.9814	0.9796	0.9798	98	1.0	[[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 21, 0, 0], [2, 0, 0, 30, 0], [3, 0, 1, 0, 15]]
0.688	41.65	1000	0.6897	1.0	1.0	1.0	31	0.9130	1.0	0.9545	21	1.0	0.9667	0.9831	30	1.0	0.9375	0.9677	16	0.9796	0.9783	0.9760	0.9763	98	0.9814	0.9796	0.9798	98	0.7008	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 21, 0, 0], [2, 0, 1, 29, 0], [3, 0, 1, 0, 15]]
0.4415	45.82	1100	0.1917	1.0	1.0	1.0	31	1.0	1.0	1.0	21	1.0	1.0	1.0	30	1.0	1.0	1.0	16	1.0	1.0	1.0	1.0	98	1.0	1.0	1.0	98	0.6974	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 21, 0, 0], [2, 0, 0, 30, 0], [3, 0, 0, 0, 16]]
0.3074	49.98	1200	0.1865	1.0	0.9677	0.9836	31	0.9545	1.0	0.9767	21	1.0	1.0	1.0	30	1.0	1.0	1.0	16	0.9898	0.9886	0.9919	0.9901	98	0.9903	0.9898	0.9898	98	0.6686	[[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 21, 0, 0], [2, 0, 0, 30, 0], [3, 0, 0, 0, 16]]
0.2069	54.16	1300	0.1821	1.0	0.9677	0.9836	31	0.9545	1.0	0.9767	21	1.0	1.0	1.0	30	1.0	1.0	1.0	16	0.9898	0.9886	0.9919	0.9901	98	0.9903	0.9898	0.9898	98	0.7043	[[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 21, 0, 0], [2, 0, 0, 30, 0], [3, 0, 0, 0, 16]]
0.1791	58.33	1400	0.1866	1.0	0.9677	0.9836	31	0.9130	1.0	0.9545	21	1.0	0.9667	0.9831	30	1.0	1.0	1.0	16	0.9796	0.9783	0.9836	0.9803	98	0.9814	0.9796	0.9799	98	0.6893	[[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 21, 0, 0], [2, 0, 1, 29, 0], [3, 0, 0, 0, 16]]
0.1717	62.49	1500	0.1839	1.0	0.9677	0.9836	31	0.9545	1.0	0.9767	21	1.0	1.0	1.0	30	1.0	1.0	1.0	16	0.9898	0.9886	0.9919	0.9901	98	0.9903	0.9898	0.9898	98	0.7848	[[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 21, 0, 0], [2, 0, 0, 30, 0], [3, 0, 0, 0, 16]]
0.1571	66.65	1600	0.1799	1.0	0.9677	0.9836	31	0.9545	1.0	0.9767	21	1.0	1.0	1.0	30	1.0	1.0	1.0	16	0.9898	0.9886	0.9919	0.9901	98	0.9903	0.9898	0.9898	98	0.7929	[[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 0, 21, 0, 0], [2, 0, 0, 30, 0], [3, 0, 0, 0, 16]]

Framework versions

Transformers 4.25.1
Pytorch 1.13.0+cu116
Datasets 2.8.0
Tokenizers 0.13.2

model_syllable_onSet1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

NSDT 3DConvert

UnrealSynth

DreamTexture.js