docs/SPECIFICATION.md

Technical Specification: Deepfake Audio

Architectural Overview

Deepfake Audio is a multi-stage neural voice synthesis architecture designed to clone speaker identities and generate high-fidelity speech from textual input. The system utilizes a Transfer Learning framework known as SV2TTS (Speaker Verification to Multispeaker Text-To-Speech Synthesis), integrating three distinct deep learning components to achieve zero-shot voice cloning.

Neural Pipeline Flow

graph TD
    Start["User Input (Audio + Text)"] --> Encoder["Speaker Encoder (LSTM)"]
    Encoder --> Embedding["Speaker Embedding (d-vector)"]
    Embedding --> Synthesizer["Tacotron 2 Synthesizer"]
    Start --> Synthesizer
    Synthesizer --> Spectrogram["Mel-Spectrogram"]
    Spectrogram --> Vocoder["WaveGlow / MelGAN Vocoder"]
    Vocoder --> Output["Generated Audio Waveform"]
    Output --> UI["Update Interface Assets"]

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Technical Implementations

1. Engine Architecture

• Core Interface: Built on Gradio, providing a highly responsive and intuitive web-based HMI for real-time interaction and synthesis monitoring.
• Neural Topology: Employs a three-stage decoupled architecture (Encoder, Synthesizer, Vocoder), allowing for independent optimization and high-dimensional speaker representation.

2. Logic & Inference

• Speaker Encoding: Utilizes a pre-trained LSTM network to extract a fixed-dimensional speaker embedding from a short reference audio clip, capturing core vocal characteristics.
• Sequence Synthesis: Implements a modified Tacotron 2 architecture to generate frame-level mel-spectrograms conditioned on both the speaker embedding and target text.
• Waveform Reconstruction: Employs neural vocoding (MelGAN/WaveGlow) to transcode mel-spectrograms into high-fidelity time-domain waveforms in real-time.

3. Deployment Pipeline

• Local Runtime: Optimized for execution on Python 3.9+ with Torch/TensorFlow backends, supporting both CPU and GPU-accelerated inference.
• Progressive Web App: The application is configured as a PWA, enabling native-like installation on desktop and mobile platforms for an integrated user experience.

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Technical Prerequisites

• Runtime: Python 3.9.x environment with Git and FFmpeg installed.
• Hardware: Minimum 8GB RAM; NVIDIA GPU with CUDA support recommended for low-latency synthesis.

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Technical Specification | Python | Version 1.0