model_broadclass_onSet0try1

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: 0.9723
0 Precision: 0.7317
0 Recall: 0.9677
0 F1-score: 0.8333
0 Support: 31
1 Precision: 0.8276
1 Recall: 0.96
1 F1-score: 0.8889
1 Support: 25
2 Precision: 1.0
2 Recall: 0.7407
2 F1-score: 0.8511
2 Support: 27
3 Precision: 1.0
3 Recall: 0.5333
3 F1-score: 0.6957
3 Support: 15
Accuracy: 0.8367
Macro avg Precision: 0.8898
Macro avg Recall: 0.8005
Macro avg F1-score: 0.8172
Macro avg Support: 98
Weighted avg Precision: 0.8711
Weighted avg Recall: 0.8367
Weighted avg F1-score: 0.8313
Weighted avg Support: 98
Wer: 0.9220
Mtrix: [[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 1, 24, 0, 0], [2, 4, 3, 20, 0], [3, 6, 1, 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
2.329	4.16	100	2.2015	0.3163	1.0	0.4806	31	0.0	0.0	0.0	25	0.0	0.0	0.0	27	0.0	0.0	0.0	15	0.3163	0.0791	0.25	0.1202	98	0.1001	0.3163	0.1520	98	0.9847	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
2.2772	8.33	200	2.1792	0.3163	1.0	0.4806	31	0.0	0.0	0.0	25	0.0	0.0	0.0	27	0.0	0.0	0.0	15	0.3163	0.0791	0.25	0.1202	98	0.1001	0.3163	0.1520	98	0.9847	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
2.0617	12.49	300	2.0492	0.3163	1.0	0.4806	31	0.0	0.0	0.0	25	0.0	0.0	0.0	27	0.0	0.0	0.0	15	0.3163	0.0791	0.25	0.1202	98	0.1001	0.3163	0.1520	98	0.9847	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
1.9607	16.65	400	1.8299	0.3163	1.0	0.4806	31	0.0	0.0	0.0	25	0.0	0.0	0.0	27	0.0	0.0	0.0	15	0.3163	0.0791	0.25	0.1202	98	0.1001	0.3163	0.1520	98	0.9847	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
1.6665	20.82	500	1.5920	0.3163	1.0	0.4806	31	0.0	0.0	0.0	25	0.0	0.0	0.0	27	0.0	0.0	0.0	15	0.3163	0.0791	0.25	0.1202	98	0.1001	0.3163	0.1520	98	0.9847	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
1.6451	24.98	600	1.5898	0.3163	1.0	0.4806	31	0.0	0.0	0.0	25	0.0	0.0	0.0	27	0.0	0.0	0.0	15	0.3163	0.0791	0.25	0.1202	98	0.1001	0.3163	0.1520	98	0.9847	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
1.6024	29.16	700	1.5471	0.3163	1.0	0.4806	31	0.0	0.0	0.0	25	0.0	0.0	0.0	27	0.0	0.0	0.0	15	0.3163	0.0791	0.25	0.1202	98	0.1001	0.3163	0.1520	98	0.9847	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
1.5967	33.33	800	1.5154	0.3163	1.0	0.4806	31	0.0	0.0	0.0	25	0.0	0.0	0.0	27	0.0	0.0	0.0	15	0.3163	0.0791	0.25	0.1202	98	0.1001	0.3163	0.1520	98	0.9847	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
1.4451	37.49	900	1.4983	0.3163	1.0	0.4806	31	0.0	0.0	0.0	25	0.0	0.0	0.0	27	0.0	0.0	0.0	15	0.3163	0.0791	0.25	0.1202	98	0.1001	0.3163	0.1520	98	0.9847	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
0.9896	41.65	1000	0.9953	0.3163	1.0	0.4806	31	0.0	0.0	0.0	25	0.0	0.0	0.0	27	0.0	0.0	0.0	15	0.3163	0.0791	0.25	0.1202	98	0.1001	0.3163	0.1520	98	0.9842	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 25, 0, 0, 0], [2, 27, 0, 0, 0], [3, 15, 0, 0, 0]]
0.9559	45.82	1100	0.9747	0.3483	1.0	0.5167	31	1.0	0.24	0.3871	25	1.0	0.0741	0.1379	27	1.0	0.0667	0.125	15	0.4082	0.8371	0.3452	0.2917	98	0.7939	0.4082	0.3193	98	0.9650	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 19, 6, 0, 0], [2, 25, 0, 2, 0], [3, 14, 0, 0, 1]]
0.9441	49.98	1200	1.0000	0.4493	1.0	0.62	31	0.7857	0.44	0.5641	25	1.0	0.3333	0.5	27	1.0	0.4	0.5714	15	0.5816	0.8087	0.5433	0.5639	98	0.7711	0.5816	0.5652	98	0.9590	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 14, 11, 0, 0], [2, 15, 3, 9, 0], [3, 9, 0, 0, 6]]
0.9656	54.16	1300	0.9814	0.5741	1.0	0.7294	31	0.8	0.64	0.7111	25	1.0	0.4444	0.6154	27	1.0	0.8	0.8889	15	0.7245	0.8435	0.7211	0.7362	98	0.8142	0.7245	0.7177	98	0.9304	[[0, 1, 2, 3], [0, 31, 0, 0, 0], [1, 9, 16, 0, 0], [2, 12, 3, 12, 0], [3, 2, 1, 0, 12]]
0.9491	58.33	1400	0.9922	0.5	0.9677	0.6593	31	0.7778	0.56	0.6512	25	1.0	0.5185	0.6829	27	1.0	0.4	0.5714	15	0.6531	0.8194	0.6116	0.6412	98	0.7851	0.6531	0.6503	98	0.9383	[[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 11, 14, 0, 0], [2, 11, 2, 14, 0], [3, 8, 1, 0, 6]]
0.8918	62.49	1500	0.9883	0.6522	0.9677	0.7792	31	0.8846	0.92	0.9020	25	1.0	0.5556	0.7143	27	1.0	0.7333	0.8462	15	0.8061	0.8842	0.7942	0.8104	98	0.8605	0.8061	0.8029	98	0.9383	[[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 2, 23, 0, 0], [2, 11, 1, 15, 0], [3, 3, 1, 0, 11]]
0.8863	66.65	1600	0.9723	0.7317	0.9677	0.8333	31	0.8276	0.96	0.8889	25	1.0	0.7407	0.8511	27	1.0	0.5333	0.6957	15	0.8367	0.8898	0.8005	0.8172	98	0.8711	0.8367	0.8313	98	0.9220	[[0, 1, 2, 3], [0, 30, 1, 0, 0], [1, 1, 24, 0, 0], [2, 4, 3, 20, 0], [3, 6, 1, 0, 8]]

Framework versions

Transformers 4.25.1
Pytorch 1.13.0+cu116
Datasets 2.8.0
Tokenizers 0.13.2

model_broadclass_onSet0try1

Model description

Intended uses & limitations

Training and evaluation data

Training procedure

Training hyperparameters

Training results

Framework versions

NSDT 3DConvert

UnrealSynth

DreamTexture.js