Introduction to Neural Voice
Behind every AI cover is a complex architecture of Deep Learning. Unlike old-fashioned voice changers, modern synthesis doesn't just "filter" your voice; it hallucinates new audio based on a learned model.
1. Feature Extraction: The Ear of the AI
The first step is for the AI to convert audio into numerical data. It uses a Spectrogram (a visual representation of sound frequencies) to analyze "mel-frequency cepstral coefficients" (MFCCs). This tells the AI the unique timbre and harmonic texture of the voice.
2. The Core: RVC (Retrieval-based Voice Conversion)
OG Voice utilizes RVC technology.
- The Pitch Estimator (Harvest/Crepe): This identifies the exact frequency (pitch) of the source singer.
- The Indexer: This maps the source singer's features to the target singer's dataset.
- The Vocoder: This reconstructs the numerical data back into high-fidelity audio waves.
3. Training the Model
When you click "Train," the AI plays a "matching game" millions of times. It takes a piece of audio, tries to replicate it using its current understanding, and checks its own accuracy. This iterative process is what allows the model to learn the specific nuances like "raspiness" or "breathiness."
4. Latency and Processing
Current voice synthesis is computationally heavy. This is why complex generations take a few minutes — billions of mathematical operations are happening to ensure the vocal timing and emotion remain intact.
Summary: Voice synthesis is a blend of signal processing and advanced mathematics. At OG Voice, we leverage these complex neural networks to give you studio-quality outputs in a user-friendly interface.