Voice of Nation – Indian Language Identification System
Designed and implemented an end-to-end deep learning model to automatically identify spoken language (English or Mandarin) from short, spontaneous audio clips. Voice of Nation is an AI-powered multilingual speech classification system developed to identify and classify 10 Indian languages from short audio recordings. The project focuses on solving the challenge of language recognition in a diverse multilingual environment using modern deep learning and transformer-based architectures.
Technologies Used
PythonPyTorchPandasScikit-learnNumPyKerasHugging FaceAudio Signal ProcessingWorkflow of the SystemFeature Extraction using HuBERT/WhisperDeep Learning Classification
Project Details
Project Overview:Voice of Nation is an AI-powered multilingual speech classification system developed to identify and classify 10 Indian languages from short audio recordings. The project focuses on solving the challenge of language recognition in a diverse multilingual environment using modern deep learning and transformer-based architectures.
The system leverages powerful pretrained models such as HuBERT and OpenAI Whisper for extracting high-level speech representations and performing accurate language classification. The model is capable of handling variations in accents, pronunciation, and noisy audio conditions, making it suitable for real-world speech applications.
What we Did:
(i)Built a complete deep learning pipeline for multilingual audio classification
(ii)Processed and cleaned speech datasets for training and testing
(iii)Used transformer-based models (HuBERT & Whisper) for feature extraction
(iii)Implemented audio preprocessing and spectrogram generation
(iv)Trained and evaluated the model on multiple Indian language datasets
(v)Improved model robustness for noisy and real-time speech inputs
(vi)Tested performance across different accents and speaking styles
Core Features:
Identification of 10 Indian languages
Real-time speech language prediction
Noise-resistant audio processing
Deep learning-based feature extraction
Scalable multilingual speech recognition pipeline
Transformer-based speech understanding
Technologies & Tools:
Python
PyTorch
Deep Learning
HuBERT
OpenAI Whisper
Librosa
NumPy & Pandas
Jupyter Notebook
Audio Signal Processing
Workflow of the System
Audio Input Collection
Audio Preprocessing & Noise Reduction
Feature Extraction using HuBERT/Whisper
Deep Learning Classification
Language Prediction Output
Project Outcome:
Successfully developed an intelligent multilingual speech recognition system capable of identifying Indian languages with high accuracy. The project demonstrates the practical use of transformer-based AI models in speech processing, NLP, and real-time language understanding applications.
Domain:
Artificial Intelligence | Deep Learning | Speech Processing | Natural Language Processing | Audio Analytics