Phase 6: Automatic Speech Recognition · ~75 minutes · Python

Voice Cloning & Voice Conversion

Voice cloning reads your text in someone else's voice. Voice conversion rewrites your voice into someone else's while preserving what you said. Both hang on the same decomposition: separate speaker identity from content.

Hiring signal: Understanding of voice cloning & voice conversion internals

What you will learn

Implement voice cloning & voice conversion from scratch
Understand the math and intuition behind the algorithm
Use production libraries for the same task
Ship a reusable artifact

Introduction

Type: Build Languages: Python Prerequisites: Phase 6 · 06 (Speaker Recognition), Phase 6 · 07 (TTS) Time: ~75 minutes

The Problem

In 2026, a 5-second audio clip is enough to produce a high-quality clone of anyone's voice with a consumer GPU. ElevenLabs, F5-TTS, OpenVoice v2, VoiceBox all ship zero-shot or few-shot cloning. The technology is a blessing (accessibility TTS, dubbing, assistive voices) and a weapon (scam calls, political deepfakes, IP theft).

Two closely-related tasks:

Voice cloning (TTS-side): text + 5-second reference voice → audio in that voice.
Voice conversion (speech-side): source audio (person A saying X) + reference voice of person B → audio of B saying X.

Both factor a waveform into (content, speaker, prosody) and recombine content from one source with speaker from another.

Key constraint you now ship under in 2026: watermarking and consent gates are legally required in the EU (AI Act, enforceable August 2026) and in California (AB 2905, effective 2025). Your pipeline must emit an inaudible watermark and refuse non-consensual clones.

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Key terms in this lesson

Zero-Shot