model_syllable_onSet0

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: 0.1789
• 0 Precision: 1.0
• 0 Recall: 0.9688
• 0 F1-score: 0.9841
• 0 Support: 32
• 1 Precision: 0.9667
• 1 Recall: 1.0
• 1 F1-score: 0.9831
• 1 Support: 29
• 2 Precision: 1.0
• 2 Recall: 1.0
• 2 F1-score: 1.0
• 2 Support: 29
• 3 Precision: 1.0
• 3 Recall: 1.0
• 3 F1-score: 1.0
• 3 Support: 8
• Accuracy: 0.9898
• Macro avg Precision: 0.9917
• Macro avg Recall: 0.9922
• Macro avg F1-score: 0.9918
• Macro avg Support: 98
• Weighted avg Precision: 0.9901
• Weighted avg Recall: 0.9898
• Weighted avg F1-score: 0.9898
• Weighted avg Support: 98
• Wer: 0.4059
• Mtrix: [[0, 1, 2, 3], [0, 31, 1, 0, 0], [1, 0, 29, 0, 0], [2, 0, 0, 29, 0], [3, 0, 0, 0, 8]]

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
1.6359	4.16	100	1.5622	0.0	0.0	0.0	32	0.0	0.0	0.0	29	0.2333	0.7241	0.3529	29	0.0	0.0	0.0	8	0.2143	0.0583	0.1810	0.0882	98	0.0690	0.2143	0.1044	98	0.9761	[[0, 1, 2, 3], [0, 0, 0, 32, 0], [1, 0, 0, 29, 0], [2, 8, 0, 21, 0], [3, 0, 0, 8, 0]]
1.4941	8.33	200	1.2550	0.0	0.0	0.0	32	0.0	0.0	0.0	29	0.2333	0.7241	0.3529	29	0.0	0.0	0.0	8	0.2143	0.0583	0.1810	0.0882	98	0.0690	0.2143	0.1044	98	0.9761	[[0, 1, 2, 3], [0, 0, 0, 32, 0], [1, 0, 0, 29, 0], [2, 8, 0, 21, 0], [3, 0, 0, 8, 0]]
1.1062	12.49	300	1.1919	0.0	0.0	0.0	32	0.0	0.0	0.0	29	0.2333	0.7241	0.3529	29	0.0	0.0	0.0	8	0.2143	0.0583	0.1810	0.0882	98	0.0690	0.2143	0.1044	98	0.9761	[[0, 1, 2, 3], [0, 0, 0, 32, 0], [1, 0, 0, 29, 0], [2, 8, 0, 21, 0], [3, 0, 0, 8, 0]]
1.0287	16.65	400	0.9334	0.0	0.0	0.0	32	0.0	0.0	0.0	29	0.2333	0.7241	0.3529	29	0.0	0.0	0.0	8	0.2143	0.0583	0.1810	0.0882	98	0.0690	0.2143	0.1044	98	0.9761	[[0, 1, 2, 3], [0, 0, 0, 32, 0], [1, 0, 0, 29, 0], [2, 8, 0, 21, 0], [3, 0, 0, 8, 0]]
0.9124	20.82	500	0.8485	0.0	0.0	0.0	32	0.0	0.0	0.0	29	0.2333	0.7241	0.3529	29	0.0	0.0	0.0	8	0.2143	0.0583	0.1810	0.0882	98	0.0690	0.2143	0.1044	98	0.9761	[[0, 1, 2, 3], [0, 0, 0, 32, 0], [1, 0, 0, 29, 0], [2, 8, 0, 21, 0], [3, 0, 0, 8, 0]]
0.8822	24.98	600	0.9073	0.0	0.0	0.0	32	0.0	0.0	0.0	29	0.2333	0.7241	0.3529	29	0.0	0.0	0.0	8	0.2143	0.0583	0.1810	0.0882	98	0.0690	0.2143	0.1044	98	0.9761	[[0, 1, 2, 3], [0, 0, 0, 32, 0], [1, 0, 0, 29, 0], [2, 8, 0, 21, 0], [3, 0, 0, 8, 0]]
0.8117	29.16	700	0.8052	1.0	0.9375	0.9677	32	0.9062	1.0	0.9508	29	1.0	0.9655	0.9825	29	1.0	1.0	1.0	8	0.9694	0.9766	0.9758	0.9753	98	0.9723	0.9694	0.9697	98	1.0	[[0, 1, 2, 3], [0, 30, 2, 0, 0], [1, 0, 29, 0, 0], [2, 0, 1, 28, 0], [3, 0, 0, 0, 8]]
0.7944	33.33	800	0.7554	1.0	0.9688	0.9841	32	0.9355	1.0	0.9667	29	1.0	0.9655	0.9825	29	1.0	1.0	1.0	8	0.9796	0.9839	0.9836	0.9833	98	0.9809	0.9796	0.9798	98	1.0	[[0, 1, 2, 3], [0, 31, 1, 0, 0], [1, 0, 29, 0, 0], [2, 0, 1, 28, 0], [3, 0, 0, 0, 8]]
0.7473	37.49	900	0.7203	1.0	0.9688	0.9841	32	0.9667	1.0	0.9831	29	1.0	1.0	1.0	29	1.0	1.0	1.0	8	0.9898	0.9917	0.9922	0.9918	98	0.9901	0.9898	0.9898	98	1.0	[[0, 1, 2, 3], [0, 31, 1, 0, 0], [1, 0, 29, 0, 0], [2, 0, 0, 29, 0], [3, 0, 0, 0, 8]]
0.3694	41.65	1000	0.3012	1.0	0.9688	0.9841	32	0.9667	1.0	0.9831	29	1.0	1.0	1.0	29	1.0	1.0	1.0	8	0.9898	0.9917	0.9922	0.9918	98	0.9901	0.9898	0.9898	98	0.6408	[[0, 1, 2, 3], [0, 31, 1, 0, 0], [1, 0, 29, 0, 0], [2, 0, 0, 29, 0], [3, 0, 0, 0, 8]]
0.2322	45.82	1100	0.2035	1.0	0.9688	0.9841	32	0.9667	1.0	0.9831	29	1.0	1.0	1.0	29	1.0	1.0	1.0	8	0.9898	0.9917	0.9922	0.9918	98	0.9901	0.9898	0.9898	98	0.7970	[[0, 1, 2, 3], [0, 31, 1, 0, 0], [1, 0, 29, 0, 0], [2, 0, 0, 29, 0], [3, 0, 0, 0, 8]]
0.1993	49.98	1200	0.1834	1.0	0.9688	0.9841	32	0.9667	1.0	0.9831	29	1.0	1.0	1.0	29	1.0	1.0	1.0	8	0.9898	0.9917	0.9922	0.9918	98	0.9901	0.9898	0.9898	98	0.6420	[[0, 1, 2, 3], [0, 31, 1, 0, 0], [1, 0, 29, 0, 0], [2, 0, 0, 29, 0], [3, 0, 0, 0, 8]]
0.2195	54.16	1300	0.1791	1.0	0.9688	0.9841	32	0.9667	1.0	0.9831	29	1.0	1.0	1.0	29	1.0	1.0	1.0	8	0.9898	0.9917	0.9922	0.9918	98	0.9901	0.9898	0.9898	98	0.7617	[[0, 1, 2, 3], [0, 31, 1, 0, 0], [1, 0, 29, 0, 0], [2, 0, 0, 29, 0], [3, 0, 0, 0, 8]]
0.1691	58.33	1400	0.1660	1.0	0.9688	0.9841	32	0.9667	1.0	0.9831	29	1.0	1.0	1.0	29	1.0	1.0	1.0	8	0.9898	0.9917	0.9922	0.9918	98	0.9901	0.9898	0.9898	98	0.7058	[[0, 1, 2, 3], [0, 31, 1, 0, 0], [1, 0, 29, 0, 0], [2, 0, 0, 29, 0], [3, 0, 0, 0, 8]]
0.154	62.49	1500	0.1797	1.0	0.9688	0.9841	32	0.9667	1.0	0.9831	29	1.0	1.0	1.0	29	1.0	1.0	1.0	8	0.9898	0.9917	0.9922	0.9918	98	0.9901	0.9898	0.9898	98	0.4367	[[0, 1, 2, 3], [0, 31, 1, 0, 0], [1, 0, 29, 0, 0], [2, 0, 0, 29, 0], [3, 0, 0, 0, 8]]
0.15	66.65	1600	0.1790	1.0	0.9688	0.9841	32	0.9667	1.0	0.9831	29	1.0	1.0	1.0	29	1.0	1.0	1.0	8	0.9898	0.9917	0.9922	0.9918	98	0.9901	0.9898	0.9898	98	0.3888	[[0, 1, 2, 3], [0, 31, 1, 0, 0], [1, 0, 29, 0, 0], [2, 0, 0, 29, 0], [3, 0, 0, 0, 8]]

Framework versions

• Transformers 4.25.1
• Pytorch 1.13.0+cu116
• Datasets 2.8.0
• Tokenizers 0.13.2