Prometheus stole fire and got chained to a rock for eternity. We'll take AI—and hope the gods don't notice.

So, how do we bring this digital me to life? The 'brain' is Hermes, a sophisticated language model adept at text-based conversations. But for a true digital twin, it needs *my* voice. This is where the challenge lies. I'm exploring cutting-edge Text-to-Speech (TTS) technologies, specifically Tortoise TTS and Coqui TTS, to find the perfect vocal match.

For these TTS models to replicate *my* voice, they need to learn from it. This means feeding them high-quality audio samples – not just words, but a diverse range of sounds, intonations, and even emotional expressions. The goal is to capture the nuances of my natural speech. As a starting point, I've recorded a comprehensive audio sample, including all alphabetic sounds and my name, to benchmark the learning process. Listen to the input below.

Finding the *right* TTS model was a significant undertaking. The high-quality commercial TTS space is often closed-source and expensive, making truly exceptional open-source alternatives a rare find. After extensive research, I've narrowed the field to a few promising contenders. Let's dive into how they compare.

First on the list is Coqui TTS, a prominent name in the open-source TTS landscape. It's a versatile library boasting various models like Tacotron, VITS, and Glow-TTS. Coqui excels in multilingual support, voice cloning, and speaker adaptation. It also provides robust tools for custom model training and fine-tuning, backed by a supportive community. This makes it a strong candidate for both experimental and production-level applications.

Next, we have E2/F5 TTS. While also a comprehensive TTS library, E2/F5 particularly stands out for its voice cloning capabilities, especially its 'one-shot' cloning. This feature allows it to generate a remarkably accurate voice mimicry from just a brief audio sample. Let's hear how it performs.

Another notable option in the voice synthesis space is Resemble.ai. While often known for its comprehensive platform and enterprise solutions, Resemble.ai also offers powerful voice cloning technology, including rapid cloning from minimal data. Their focus is on creating highly realistic and emotionally nuanced synthetic voices. Let's evaluate its output.

After rigorous comparison, E2/F5 TTS emerges as the standout choice for realistic voice cloning in this project. Its 'one-shot' capability is particularly impressive, capturing the subtle nuances of my voice from minimal input. It also demonstrates superior performance in maintaining vocal authenticity and avoiding common accent artifacts, resulting in a cleaner and more convincing voice clone.

With the voice synthesis refined, the next frontier is visual representation. The aim is to create a digital likeness – a 3D avatar that not only resembles me but can also be animated and controlled. This visual component is crucial for achieving a truly interactive and believable digital twin.

Creating a high-fidelity digital human from scratch is complex, and truly open-source end-to-end solutions are scarce. However, the video game industry offers a wealth of tools and techniques for realistic character creation. They're pioneers in facial scanning, motion capture, and creating lifelike digital actors. This provides a promising avenue: leveraging game development technologies to build a rigged facial mesh for lip-syncing and expressions. The initial step is a detailed 3D scan of my head and shoulders, which will then be transformed into a functional facial mesh.

For capturing the initial 3D model, I'm exploring tools like RealityScan. This app, developed by Epic Games, allows for creating 3D models from a series of photos taken with a smartphone, leveraging photogrammetry techniques. This technology can provide a detailed starting point for the digital likeness.

Before diving into a full head scan, I tested RealityScan with my hand first. This little experiment taught me some really important things about photogrammetry: you need to get multiple angles in vertical slices around the whole subject for good coverage. Staying completely still is crucial - the software needs stable reference points to stitch the images together properly. Ambient lighting works much better than harsh directional light that creates shadows. And the subject should be as matte as possible so light reflections don't mess with the image boundaries. These lessons will definitely come in handy when I tackle the more complex head scan.