Language Classifier - Indian Languages (Marathi, Telugu, Malayalam)

This model classifies audio samples into three Indian languages: Marathi, Telugu, and Malayalam.

Model Description

Model Architecture

1D Convolutional Neural Network (CNN) with the following key components:
- 3 Convolutional blocks with increasing filters (64, 128, 256)
- Batch Normalization and ReLU activation after each convolution
- MaxPooling and Dropout for regularization
- Dense layers with 256 units followed by a Softmax output layer
Input: Audio features (MFCC + Delta features)
Output: Language classification probabilities

Training Data

The model was trained on:

Total samples per language: 1000
- Training: 700 samples
- Validation: 150 samples
- Test: 150 samples

Features

MFCC (Mel-frequency cepstral coefficients) with delta features
Number of MFCC coefficients: 13
Maximum padding length: 174
Feature type: MFCC with delta and delta-delta features

Training Hyperparameters

Optimizer: AdamW
Learning rate: 0.001
Batch size: 64
Early stopping with patience of 10
Learning rate reduction on plateau
Loss function: Categorical Cross-entropy

Performance

The model achieves strong performance in distinguishing between Marathi, Telugu, and Malayalam speech samples.

Intended Use

This model is designed for:

Language identification in audio samples
Speech processing applications focusing on Indian languages
Research and development in multilingual speech systems

Limitations

Limited to three languages: Marathi, Telugu, Malayalam
Fixed input length requirement
May not perform well on very noisy audio
Not suitable for real-time processing without proper preprocessing

Usage

import tensorflow as tf
import numpy as np
import joblib
import json
import librosa

# Load the model, scaler, and config
model = tf.keras.models.load_model('indic_language_classifier_mtm.keras')
scaler = joblib.load('audio_feature_scaler_mtm.pkl')
with open('config_mtm.json', 'r') as f:
    config = json.load(f)

def extract_features(audio_path, config):
    audio, sr = librosa.load(audio_path, sr=None)
    mfccs = librosa.feature.mfcc(y=audio, sr=sr, n_mfcc=config['n_mfcc'])
    delta_mfccs = librosa.feature.delta(mfccs)
    delta2_mfccs = librosa.feature.delta(mfccs, order=2)
    features = np.concatenate((mfccs, delta_mfccs, delta2_mfccs), axis=0)
    
    # Pad or truncate
    if features.shape[1] > config['max_pad_len']:
        features = features[:, :config['max_pad_len']]
    else:
        pad_width = config['max_pad_len'] - features.shape[1]
        features = np.pad(features, pad_width=((0, 0), (0, pad_width)))
    
    return features.T

hriteshMaikap
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languageClassifier