Talha/urdu-audio-emotions

results

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: 0.1638
• Accuracy: 0.975

Model description

The model Urdu audio and classify in following categories

• Angry
• Happy
• Neutral
• Sad

Training and evaluation data

The dataset is available at https://www.kaggle.com/datasets/kingabzpro/urdu-emotion-dataset

Training procedure

Training code is available at https://www.kaggle.com/code/chtalhaanwar/urdu-emotions-hf

Training hyperparameters

The following hyperparameters were used during training:

• learning_rate: 5e-05
• train_batch_size: 32
• eval_batch_size: 32
• seed: 42
• optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-08
• lr_scheduler_type: linear
• num_epochs: 50
• mixed_precision_training: Native AMP

Training results

Training Loss	Epoch	Step	Validation Loss	Accuracy
1.3838	1.0	10	1.3907	0.225
1.3732	2.0	20	1.3872	0.2125
1.3354	3.0	30	1.3116	0.6625
1.2689	4.0	40	1.1820	0.6375
1.1179	5.0	50	1.0075	0.7
0.9962	6.0	60	0.8707	0.7125
0.8842	7.0	70	0.7485	0.7625
0.786	8.0	80	0.6326	0.8
0.6757	9.0	90	0.5995	0.8
0.6104	10.0	100	0.4835	0.825
0.5821	11.0	110	0.3886	0.9
0.4721	12.0	120	0.3935	0.8625
0.3976	13.0	130	0.3020	0.925
0.4483	14.0	140	0.3171	0.9
0.2665	15.0	150	0.3016	0.9125
0.2119	16.0	160	0.2722	0.925
0.3376	17.0	170	0.3163	0.8875
0.1518	18.0	180	0.2681	0.9125
0.1559	19.0	190	0.3074	0.925
0.1031	20.0	200	0.3526	0.8875
0.1557	21.0	210	0.2254	0.9375
0.0846	22.0	220	0.2410	0.9375
0.0733	23.0	230	0.2369	0.925
0.0964	24.0	240	0.2273	0.9375
0.0574	25.0	250	0.2066	0.95
0.1113	26.0	260	0.2941	0.9125
0.1313	27.0	270	0.2715	0.925
0.0851	28.0	280	0.1725	0.9625
0.0402	29.0	290	0.2221	0.95
0.1075	30.0	300	0.2199	0.9625
0.0418	31.0	310	0.1699	0.95
0.1869	32.0	320	0.2287	0.9625
0.0637	33.0	330	0.3230	0.9125
0.0483	34.0	340	0.1602	0.975
0.0891	35.0	350	0.1615	0.975
0.0359	36.0	360	0.1571	0.975
0.1006	37.0	370	0.1809	0.9625
0.0417	38.0	380	0.1923	0.9625
0.0346	39.0	390	0.2035	0.9625
0.0417	40.0	400	0.1737	0.9625
0.0396	41.0	410	0.1833	0.9625
0.0202	42.0	420	0.1946	0.9625
0.0137	43.0	430	0.1785	0.9625
0.0214	44.0	440	0.1841	0.9625
0.0304	45.0	450	0.1690	0.9625
0.0199	46.0	460	0.1646	0.975
0.0122	47.0	470	0.1622	0.975
0.0324	48.0	480	0.1615	0.975
0.0269	49.0	490	0.1625	0.975
0.0245	50.0	500	0.1638	0.975

Framework versions

• Transformers 4.18.0
• Pytorch 1.11.0
• Datasets 2.1.0
• Tokenizers 0.12.1