This project is a Voice Identification System built using Python, leveraging SpeechBrain and ECAPA-TDNN for speaker verification. The system identifies users by comparing their voice embeddings with stored data, providing a secure and efficient method for user recognition.
The Voice Identification System is designed to recognize users based on their voice data. It uses state-of-the-art technologies like SpeechBrain and ECAPA-TDNN to extract robust speaker embeddings and perform accurate speaker verification. The system also features a Streamlit-based web app for seamless interaction, allowing users to upload audio files, verify their identity, and view results in real-time.
The main goals of this project are:
- Accurate Speaker Recognition: Build a system that accurately identifies users based on their voice data.
- Efficient Storage and Retrieval: Store and retrieve speaker embeddings efficiently for quick verification.
- User-Friendly Interface: Create an intuitive web application using Streamlit for easy interaction.
Python serves as the core language for this project, offering flexibility and a rich ecosystem of libraries for machine learning, audio processing, and web development.
SpeechBrain is an open-source toolkit for speech processing. This project leverages SpeechBrain for feature extraction and speaker verification tasks.
ECAPA-TDNN (Emphasized Channel Attention, Propagation, and Aggregation Time-Delay Neural Network) is a state-of-the-art neural network architecture for speaker recognition. It generates robust and discriminative speaker embeddings, which are used as unique voiceprints for each user.
Streamlit creates a user-friendly web interface. It allows users to upload audio files, view verification results, and navigate through different sections of the app seamlessly.
- Audio Upload: Users upload or record audio directly in the Streamlit app.
- Embedding Extraction: SpeechBrain, powered by the ECAPA-TDNN model, extracts speaker embeddings from the uploaded audio file.
- Database Matching:
- The extracted embeddings are compared against stored user embeddings in the database.
- The system retrieves and displays the user’s profile details if a match is found.
- Verification Results: Verification results and user details are displayed on a dedicated page.
The system processes audio files uploaded through the Streamlit interface. It normalizes and prepares the audio for embedding extraction using SpeechBrain.
The core of the verification process lies in generating reliable speaker embeddings using the ECAPA-TDNN model. These embeddings serve as unique voiceprints for each user, enabling accurate speaker verification.
- Extracted embeddings are stored in a SQLite database as binary blobs.
- During verification, embeddings are retrieved and compared to new input embeddings using cosine similarity to confirm the user’s identity.