Back to Portfolio
AI & Voice Synthesis

RVC Custom Voice Cloning Pipeline

PythonPyTorchRVC ArchitectureAudio DSPHubert Feature MapsGPU Training

Project Overview

The RVC (Retrieval-Based Voice Conversion) Voice Cloning Pipeline is an advanced artificial intelligence and digital signal processing (DSP) application designed to train custom vocal weights and swap vocals/speech with extreme high-fidelity accuracy.

By combining advanced neural nets with voice retrieval models, the pipeline analyzes incoming source audio files, extracts core vocal vectors (such as Hubert and ContentVec representations), and maps them onto target speaker profiles while preserving original pitch trajectories, emphasis patterns, and emotional accents.

Core Features

  • High-Fidelity Audio Mapping: Implements Retrieval-Based Voice Conversion algorithms to convert speech with natural timbre, eliminating digital metallic distortions.
  • Hubert & ContentVec Vector Extraction: Extracts deep acoustic feature maps using Hubert encoders to represent pronunciation concepts, independent of the speaker's vocal pitch.
  • Vocal Pitch Trackers: Employs state-of-the-art pitch tracking algorithms (Harvest, Crepe, or PM) to calculate and transpose pitch frequencies smoothly.
  • Custom Dataset Audio Preprocessor: Standardizes input wav/mp3 files into clean vocal datasets, running denoising algorithms, volume normalizations, and silence trimmers automatically.
  • Model Weights Exporter: Automatically compiles and packages trained vocal profiles into small weight structures (`.pth` weights and `.index` files) for portable deployment.

What You Can Manage & Build

This RVC pipeline provides full developer-level management over advanced deep learning speech synthesis systems:

  • Dataset Processing Pipelines: Clean and partition raw, noisy audio recordings into targeted 10-second training samples using FFmpeg filters.
  • Model Configuration Adjustments: Configure batch sizes, learning rates, target sample rates (32K / 40K / 48K), epoch counts, and GPU allocation models.
  • Vocal Timbre & Pitch Control: Tune index rate factors, pitch transpositions (semitones scale), and source/target voice ratios to fine-tune voice clones.
  • Voice Conversion Web Apps: Build custom Flask/Next.js frontends to allow clients to upload voice clips and synthesise cloned audio tracks on demand.

DSP Audio Pipeline Workflow

The voice conversion workflow follows a detailed, multi-layered processing sequence on PyTorch:

  • Source Denoising: Raw audio is cleaned of background room ambient noise using deep learning spectral subtraction models.
  • Feature Extraction: Audio frames pass through a pretrained 12-layer HuBERT model, translating sound waves into semantic vectors.
  • Index Matching: A custom K-Nearest Neighbors (KNN) algorithm searches the speaker's trained index database to swap vocal characteristics.
  • Synthesizing: The target voice wave is compiled through a customized generator, mapped with original F0 pitch curves to preserve accent textures.