An advanced neural voice synthesis platform implementing Transfer Learning from Speaker Verification to Multispeaker Text-To-Speech Synthesis (SV2TTS) for high-fidelity zero-shot voice cloning.
Source Code · Technical Specification · Video Demo · Live Demo
Authors · Overview · Features · Structure · Results · Quick Start · Usage Guidelines · License · About · Acknowledgments
Important
Special thanks to Mega Satish for her meaningful contributions, guidance, and support that helped shape this work.
Deepfake Audio is a multi-stage neural voice synthesis architecture designed to clone speaker identities and generate high-fidelity speech from textual input. By implementing the SV2TTS framework, this project translates skeletal vocal characteristics into a latent embedding, which then conditions a generative model to produce new vocalizations with strikingly natural prosody and timbre.
Important
This project builds upon the foundational research and implementation of the Real-Time-Voice-Cloning repository by Corentin Jemine.
Note
An audio deepfake is when a “cloned” voice that is potentially indistinguishable from the real person’s is used to produce synthetic audio. This process involves utilizing advanced neural architectures, such as the SV2TTS framework, to distillate high-dimensional vocal identities into latent embeddings. These embeddings then condition a generative model to synthesize new speech that mirrors the original speaker's prosody, timbre, and acoustic nuances with striking fidelity.
The repository serves as a digital study into the mechanics of neural cloning and signal processing, brought into a modern context via a Progressive Web App (PWA) interface, enabling high-performance voice synthesis through a decoupled engine architecture.
The classification engine is governed by strict computational design patterns ensuring fidelity and responsiveness:
- Speaker Normalization: The encoder utilizes a linear speaker verification pipeline, incrementally distilling lexical tokens into a global affective voice state.
- Zero-Shot Inference: Beyond simple playback, the system integrates a Tacotron 2-based synthesizer that dynamically refines its accuracy over time, simulating an organic learning curve for complex phonetic structures.
- Real-Time Vocoding: Audio reconstruction supports both streaming and batch generation, ensuring high-fidelity waveform response critical for interactive neural study.
Tip
Acoustic Precision Integration
To maximize cloning clarity, the engine employs a multi-stage neural pipeline. Latent filters refine the embedding stream, and probabilistic weights visualize the voice's confidence vector, strictly coupling acoustic flair with state changes. This ensures the user's mental model is constantly synchronized with the underlying neural simulation.
| Feature | Description |
|---|---|
| SV2TTS Core | Combines LSTM Speaker Encoders with Tacotron Synthesizers for comprehensive voice cloning. |
| PWA Architecture | Implements a robust standalone installable interface for immediate neural vocalization study. |
| Academic Clarity | In-depth and detailed comments integrated throughout the codebase for transparent logic study. |
| Neural Topology | Efficient Decoupled Engine execution via Gradio and Torch for native high-performance access. |
| Inference Pipeline | Asynchronous architecture ensuring stability and responsiveness on local clients. |
| Visual Feedback | Interactive Status Monitors that trigger on synthesis events for sensory reward. |
| State Feedback | Embedding-Based Indicators and waveform effects for high-impact acoustic feel. |
| Social Persistence | Interactive Footer Integration bridging the analysis to the source repository. |
Note
We have engineered a Logic-Driven State Manager that calibrates vocal scores across multiple vectors to simulate human-like identity transfer. The visual language focuses on the minimalist "Neon Mic" aesthetic, ensuring maximum focus on the interactive neural trajectory.
- Languages: Python 3.9+
- Logic: Neural Pipelines (SV2TTS & Signal Processing)
- Frameworks: PyTorch & TensorFlow (Inference)
- UI System: Modern Design (Gradio & Custom CSS)
- Deployment: Local execution / Hugging Face Spaces
- Architecture: Progressive Web App (PWA)
DEEPFAKE-AUDIO/
│
├── Dataset/ # Neural Assets
│ ├── samples/ # Voice Reference Audio
│ ├── encoder.pt # Speaker Verification Model
│ ├── synthesizer.pt # TTS Synthesis Model
│ └── vocoder.pt # Waveform Reconstruction Model
│
├── docs/ # Academic Documentation
│ └── SPECIFICATION.md # Technical Architecture
│
├── Mega/ # Attribution Assets
│ ├── Filly.jpg # Companion (Filly)
│ └── Mega.png # Profile Image (Mega Satish)
│
├── screenshots/ # Visual Gallery
│ ├── 01_landing_page.png
│ ├── 02_landing_page_footer.png
│ ├── 03_example_run_config.png
│ ├── 04_example_run_processing.png
│ ├── 05_example_run_results.png
│ ├── 06_example_run_results_footer.png
│ ├── 07_download_option.png
│ ├── Audio.wav # Sample Output
│ └── favicon.png # Project Icon
│
├── Source Code/ # Primary Application Layer
│ ├── app.py # Gradio Studio Interface
│ ├── app_ui_demo.py # UI-Only Verification Mode
│ ├── Dockerfile # Containerization Config
│ ├── requirements.txt # Dependency Manifest
│ ├── favicon.png # Application Icon
│ └── intro_message.wav # Audio Branding
│
├── .gitattributes # Signal Normalization
├── .gitignore # Deployment Exclusions
├── DEEPFAKE-AUDIO.ipynb # Research Notebook
├── DEEPFAKE-AUDIO.py # Research Script (Standalone CLI)
├── SECURITY.md # Security Protocols
├── CITATION.cff # Academic Citation Manifest
├── codemeta.json # Metadata Standard
├── LICENSE # MIT License
└── README.md # Project EntranceInitial system state with clean aesthetics and synchronized brand identity.
💡 Interactive Element: Engage the title header to activate the system's auditory introduction.
Interactive Polish: Footer Integration
Seamlessly integrated authorship and social persistence.
Synthesis Setup: Adaptive Config
Configuring target text and reference identity for neural cloning.
Neural Processing: Real-Time Inference
System Distillery extracting acoustic embeddings and synthesizing mel-spectrograms.
Quantified Output: Generated Results
Successful high-fidelity audio synthesis with precise identity fidelity.
Complete User Flow: Result & Footer
Comprehensive view of the post-synthesis state.
System Options: Audio Export
Exporting synthesized waveforms for downstream academic reference.
Generated Result Output: Audio Signal
Interactive verified output from the neural synthesis pipeline.
Listen to Generated Sample
- Python 3.9+: Required for runtime execution. Download Python
- Git: For version control and cloning. Download Git
Warning
The synthesis engine relies on pre-trained neural models. Ensure you have the weights (encoder.pt, synthesizer.pt, vocoder.pt) placed in the Dataset/ directory. Failure to synchronize these assets will result in initialization errors.
Open your terminal and clone the repository:
git clone https://github.com/Amey-Thakur/DEEPFAKE-AUDIO.git
cd DEEPFAKE-AUDIOPrepare an isolated environment to manage dependencies:
Windows (Command Prompt / PowerShell):
python -m venv venv
venv\Scripts\activatemacOS / Linux (Terminal):
python3 -m venv venv
source venv/bin/activateEnsure your environment is active, then install the required libraries:
pip install -r "Source Code/requirements.txt"Launch the primary Gradio-based studio engine:
python "Source Code/app.py"PWA Installation: Once the studio is running, you can click the "Install" icon in your browser's address bar to add the Deepfake Audio Studio to your desktop as a standalone application.
Tip
Experience the interactive Deepfake Audio cloning environment directly in your browser through the working Hugging Face Space. This platform features a Multispeaker Text-To-Speech (SV2TTS) architecture integrated with a Tacotron 2 synthesizer and WaveGlow vocoder to synthesize continuous vocal patterns, providing a visual demonstration of acoustic identity transfer and generative phoneme boundaries.
For automated synthesis or command-line research workflows:
# Example: Using a preset identity
python DEEPFAKE-AUDIO.py --preset "Steve Jobs.wav" --text "Neural cloning active."
# Example: Using a custom voice file
python DEEPFAKE-AUDIO.py --input "my_voice.wav" --text "Synthesizing new speech."Important
Execute the complete Neural Voice Synthesis Research directly in the cloud. This interactive Google Colab Notebook provides a zero-setup environment for orchestrating high-fidelity speaker identity transfers, offering a scholarly gateway to the underlying Python-based multispeaker synthesis architecture.
This repository is openly shared to support learning and knowledge exchange across the academic community.
For Students
Use this project as reference material for understanding Neural Voice Synthesis, Transfer Learning (SV2TTS), and real-time audio inference. The source code is available for study to facilitate self-paced learning and exploration of Python-based deep learning pipelines and PWA integration.
For Educators
This project may serve as a practical lab example or supplementary teaching resource for Deep Learning, Acoustic Science, and Interactive System Architecture courses. Attribution is appreciated when utilizing content.
For Researchers
The documentation and architectural approach may provide insights into academic project structuring, neural identity representation, and hybrid multi-stage synthesis pipelines.
This repository and all its creative and technical assets are made available under the MIT License. See the LICENSE file for complete terms.
Note
Summary: You are free to share and adapt this content for any purpose, even commercially, as long as you provide appropriate attribution to the original authors.
Copyright © 2021 Amey Thakur & Mega Satish
Created & Maintained by: Amey Thakur & Mega Satish
This project features Deepfake Audio, a three-stage neural voice synthesis system. It represents a personal exploration into Deep Learning-based identity transfer and high-performance interactive application architecture via Gradio.
Connect: GitHub · LinkedIn · ORCID
Grateful acknowledgment to Mega Satish for her exceptional collaboration and partnership on this neural voice cloning research. Her constant support, technical clarity, and dedication to software quality were instrumental in achieving the system's functional objectives. Learning alongside her was a transformative experience; her thoughtful approach to problem-solving and steady encouragement turned complex requirements into meaningful learning moments. This work reflects the growth and insights gained from our side-by-side academic journey. Thank you, Mega, for everything you shared and taught along the way.
Special thanks to Corentin Jemine for the foundational research and open-source implementation of the Real-Time-Voice-Cloning repository, which served as the cornerstone for this project's technical architecture.
Authors · Overview · Features · Structure · Results · Quick Start · Usage Guidelines · License · About · Acknowledgments
Computer Engineering (B.E.) - University of Mumbai
Semester-wise curriculum, laboratories, projects, and academic notes.