model_syllable_onSet3

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: 0.1590
0 Precision: 0.9688
0 Recall: 1.0
0 F1-score: 0.9841
0 Support: 31
1 Precision: 1.0
1 Recall: 1.0
1 F1-score: 1.0
1 Support: 25
2 Precision: 1.0
2 Recall: 0.9474
2 F1-score: 0.9730
2 Support: 19
3 Precision: 0.9545
3 Recall: 0.9545
3 F1-score: 0.9545
3 Support: 22
Accuracy: 0.9794
Macro avg Precision: 0.9808
Macro avg Recall: 0.9755
Macro avg F1-score: 0.9779
Macro avg Support: 97
Weighted avg Precision: 0.9797
Weighted avg Recall: 0.9794
Weighted avg F1-score: 0.9793
Weighted avg Support: 97
Wer: 0.2202
Mtrix: [[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 0, 18, 1], [3, 1, 0, 0, 21]]

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
1.642	4.16	100	1.5891	1.0	0.2581	0.4103	31	0.0	0.0	0.0	25	0.2135	1.0	0.3519	19	0.0	0.0	0.0	22	0.2784	0.3034	0.3145	0.1905	97	0.3614	0.2784	0.2000	97	0.9780	[[0, 1, 2, 3], [0, 8, 0, 23, 0], [1, 0, 0, 25, 0], [2, 0, 0, 19, 0], [3, 0, 0, 22, 0]]
1.4791	8.33	200	1.3227	1.0	0.2581	0.4103	31	0.0	0.0	0.0	25	0.2135	1.0	0.3519	19	0.0	0.0	0.0	22	0.2784	0.3034	0.3145	0.1905	97	0.3614	0.2784	0.2000	97	0.9780	[[0, 1, 2, 3], [0, 8, 0, 23, 0], [1, 0, 0, 25, 0], [2, 0, 0, 19, 0], [3, 0, 0, 22, 0]]
1.2376	12.49	300	1.0446	1.0	0.2581	0.4103	31	0.0	0.0	0.0	25	0.2135	1.0	0.3519	19	0.0	0.0	0.0	22	0.2784	0.3034	0.3145	0.1905	97	0.3614	0.2784	0.2000	97	0.9780	[[0, 1, 2, 3], [0, 8, 0, 23, 0], [1, 0, 0, 25, 0], [2, 0, 0, 19, 0], [3, 0, 0, 22, 0]]
0.9622	16.65	400	0.8811	1.0	0.2581	0.4103	31	0.0	0.0	0.0	25	0.2135	1.0	0.3519	19	0.0	0.0	0.0	22	0.2784	0.3034	0.3145	0.1905	97	0.3614	0.2784	0.2000	97	0.9780	[[0, 1, 2, 3], [0, 8, 0, 23, 0], [1, 0, 0, 25, 0], [2, 0, 0, 19, 0], [3, 0, 0, 22, 0]]
0.8614	20.82	500	0.8174	1.0	0.2581	0.4103	31	0.0	0.0	0.0	25	0.2135	1.0	0.3519	19	0.0	0.0	0.0	22	0.2784	0.3034	0.3145	0.1905	97	0.3614	0.2784	0.2000	97	0.9780	[[0, 1, 2, 3], [0, 8, 0, 23, 0], [1, 0, 0, 25, 0], [2, 0, 0, 19, 0], [3, 0, 0, 22, 0]]
0.8344	24.98	600	0.7498	1.0	1.0	1.0	31	1.0	1.0	1.0	25	1.0	1.0	1.0	19	1.0	1.0	1.0	22	1.0	1.0	1.0	1.0	97	1.0	1.0	1.0	97	1.0	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 0, 19, 0], [3, 0, 0, 0, 22]]
0.8105	29.16	700	0.7907	0.9688	1.0	0.9841	31	1.0	0.96	0.9796	25	0.95	1.0	0.9744	19	1.0	0.9545	0.9767	22	0.9794	0.9797	0.9786	0.9787	97	0.9802	0.9794	0.9794	97	1.0	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 24, 1, 0], [2, 0, 0, 19, 0], [3, 1, 0, 0, 21]]
0.6168	33.33	800	0.5496	0.9688	1.0	0.9841	31	1.0	0.96	0.9796	25	0.95	1.0	0.9744	19	1.0	0.9545	0.9767	22	0.9794	0.9797	0.9786	0.9787	97	0.9802	0.9794	0.9794	97	0.5840	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 24, 1, 0], [2, 0, 0, 19, 0], [3, 1, 0, 0, 21]]
0.2701	37.49	900	0.2587	1.0	1.0	1.0	31	1.0	0.96	0.9796	25	0.9474	0.9474	0.9474	19	0.9565	1.0	0.9778	22	0.9794	0.9760	0.9768	0.9762	97	0.9798	0.9794	0.9794	97	0.2375	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 24, 1, 0], [2, 0, 0, 18, 1], [3, 0, 0, 0, 22]]
0.1745	41.65	1000	0.2219	0.9688	1.0	0.9841	31	1.0	1.0	1.0	25	1.0	0.9474	0.9730	19	0.9545	0.9545	0.9545	22	0.9794	0.9808	0.9755	0.9779	97	0.9797	0.9794	0.9793	97	0.2445	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 0, 18, 1], [3, 1, 0, 0, 21]]
0.1494	45.82	1100	0.2548	0.9688	1.0	0.9841	31	1.0	0.96	0.9796	25	1.0	0.9474	0.9730	19	0.9130	0.9545	0.9333	22	0.9691	0.9704	0.9655	0.9675	97	0.9703	0.9691	0.9693	97	0.2352	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 24, 0, 1], [2, 0, 0, 18, 1], [3, 1, 0, 0, 21]]
0.1213	49.98	1200	0.1756	0.9688	1.0	0.9841	31	0.9615	1.0	0.9804	25	1.0	0.9474	0.9730	19	1.0	0.9545	0.9767	22	0.9794	0.9826	0.9755	0.9786	97	0.9801	0.9794	0.9793	97	0.2260	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 1, 18, 0], [3, 1, 0, 0, 21]]
0.0964	54.16	1300	0.1884	0.9688	1.0	0.9841	31	1.0	1.0	1.0	25	1.0	0.9474	0.9730	19	0.9545	0.9545	0.9545	22	0.9794	0.9808	0.9755	0.9779	97	0.9797	0.9794	0.9793	97	0.2260	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 0, 18, 1], [3, 1, 0, 0, 21]]
0.0859	58.33	1400	0.1212	0.9688	1.0	0.9841	31	1.0	1.0	1.0	25	1.0	1.0	1.0	19	1.0	0.9545	0.9767	22	0.9897	0.9922	0.9886	0.9902	97	0.9900	0.9897	0.9897	97	0.2202	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 0, 19, 0], [3, 1, 0, 0, 21]]
0.0845	62.49	1500	0.1254	0.9688	1.0	0.9841	31	1.0	1.0	1.0	25	1.0	1.0	1.0	19	1.0	0.9545	0.9767	22	0.9897	0.9922	0.9886	0.9902	97	0.9900	0.9897	0.9897	97	0.2178	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 0, 19, 0], [3, 1, 0, 0, 21]]
0.0831	66.65	1600	0.1590	0.9688	1.0	0.9841	31	1.0	1.0	1.0	25	1.0	0.9474	0.9730	19	0.9545	0.9545	0.9545	22	0.9794	0.9808	0.9755	0.9779	97	0.9797	0.9794	0.9793	97	0.2202	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 0, 25, 0, 0], [2, 0, 0, 18, 1], [3, 1, 0, 0, 21]]

Framework versions

Transformers 4.25.1
Pytorch 1.13.0+cu116
Datasets 2.8.0
Tokenizers 0.13.2

model_syllable_onSet3

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

NSDT 3DConvert

UnrealSynth

DreamTexture.js