Urdu Audio Emotions Open-Source Model - Free Deployment to Accurately Identify Four Types of Emotions in Urdu Audio

Urdu Audio Emotions

Developed by Talha

A fine-tuned Urdu audio emotion classification model based on facebook/wav2vec2-large-xlsr-53, supporting recognition of four emotions: anger, happiness, calmness, and sadness.

Audio Classification

Transformers

Open Source License:Apache-2.0 #Urdu Emotion Recognition #High Accuracy Audio Classification #wav2vec2 Fine-tuning

Downloads 66

Release Time : 7/2/2022

Model Overview

This model is designed for Urdu audio data emotion classification, capable of accurately identifying four emotional states in audio: anger, happiness, calmness, and sadness.

Model Features

High Accuracy

Achieves 97.5% classification accuracy on the evaluation set.

Multi-emotion Recognition

Can identify four emotional states: anger, happiness, calmness, and sadness.

Transfer Learning

Fine-tuned based on the pre-trained wav2vec2-large-xlsr-53 model.

Model Capabilities

Urdu Audio Processing

Emotion Classification

Speech Feature Extraction

Use Cases

Sentiment Analysis

Customer Service Call Analysis

Analyze customer emotional states in service calls.

Accurately identifies customer anger to improve service quality.

Mental Health Assessment

Assess user psychological state through voice analysis.

Identifies vocal features indicative of depressive tendencies.

🚀 Results

This model is a fine - tuned version of facebook/wav2vec2-large-xlsr-53 on the None dataset. It can classify Urdu audio into the following categories: Angry, Happy, Neutral, and Sad. On the evaluation set, it achieves the following results:

Loss: 0.1638
Accuracy: 0.975

🚀 Quick Start

Loss: 0.1638
Accuracy: 0.975

✨ Features

Classify Urdu audio into four emotional categories: Angry, Happy, Neutral, and Sad.
Achieve high accuracy (0.975) on the evaluation set.

📦 Installation

No specific installation steps are provided in the original document, so this section is skipped.

💻 Usage Examples

No code examples are provided in the original document, so this section is skipped.

📚 Documentation

Model description

The model processes Urdu audio and classifies it into the 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

🔧 Technical Details

The model is based on fine - tuning facebook/wav2vec2-large-xlsr-53. Through a series of hyperparameter settings and training processes, it can effectively classify Urdu audio into different emotional categories.

📄 License

The model is licensed under the Apache - 2.0 license.

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