Celebrity Voice Cloning /w AI | 매거진에 참여하세요

AI voice cloning is no longer a fringe experiment—it’s here, it’s real, and it’s eerily accurate.

From Hollywood actors to historical figures, voice synthesis tech can now replicate a person’s voice so convincingly that it’s being used in

marketing, content creation, film dubbing, and more.

But while it looks magical from the outside, the reality under the hood is complex—and not just technically. This tech raises some serious ethical and legal questions.

So let’s break it down: how does it work, and what does it take to actually build a voice clone?

The Tech Stack Behind AI Voice Cloning

1. Voice Data Collection & Preprocessing

• - Voice Samples:

• You’ll need at least 30 minutes to 3+ hours of high-quality, noise-free recordings of your target speaker.

• The more diverse and clean the data, the better.

• - Preprocessing Steps:

• Denoise the audio, normalize sample rate and volume, and prepare it for training.

2. Feature Extraction & Model Training

• - Acoustic Models:

• Models like Tacotron2, FastSpeech2, or VITS convert text into mel-spectrograms (visual representations of audio).

• - Vocoder:

• Converts the mel-spectrograms into waveforms you can actually listen to. Popular vocoders include WaveNet, HiFi-GAN, and Parallel - WaveGAN.

• - Voice Embeddings:

• Capture the “sound fingerprint” of a person’s voice—pitch, tone, style—and inject it into the acoustic model.

3. Text-to-Speech (TTS) Flow

Once trained, the system converts text → speech like this:

[ Text Input ] 
↓ 
[ Acoustic Model ]
↓ 
[ Mel-Spectrogram ] 
↓ 
[ Vocoder ]
↓ 
[ Realistic Voice Output ]

You can choose between real-time generation (streaming) or pre-rendered clips.

4. Development Process (For Engineers)

Step 1: Choose a Model

Use pretrained models from Hugging Face or GitHub. Popular choices:

• - VITS (by Kakao Brain)

• - FastSpeech2

• - Tacotron2

• - Bark (Suno AI)

• - Vall-E (Microsoft)

You can fine-tune them or use “zero-shot” capabilities for fast prototyping.

Step 2: Train Voice Embeddings

Voice cloning typically requires a Speaker Encoder like the GE2E model (used in SV2TTS).
Zero-shot models allow cloning from just 3–10 seconds of audio—but still benefit from quality data.

Step 3: Build the Voice Pipeline

[ Text ] 
↓ 
[ Text Processor ]→ [ Voice Embedding ] 
↓ [ Acoustic Model ] ←─── 
↓ [ Mel-Spectrogram ] 
↓ [ Vocoder ] 
↓ [ Output Audio ]

Output can be stored as audio files or streamed for real-time interaction.

Step 4: Tools & Frameworks

• - Languages: Python

• - Frameworks: PyTorch / TensorFlow

• - Popular Libraries:

  • ESPnet-TTS

  • Coqui TTS

  • Mozilla TTS

  • YourTTS

  • Descript Overdub

  • ElevenLabs API

• Hardware: You’ll need a GPU—preferably NVIDIA A100 or RTX 3090 or higher.

5. The Ethics of Voice Cloning

This isn’t just code and compute—it’s someone’s voice. And that comes with responsibility.

• Consent is non-negotiable: Using a person’s voice without permission can violate likeness rights and personality rights.

• Deepfake danger: Fake audio can be used for scams, misinformation, or defamation.

• Transparency is key: If AI-generated voice is used, it must be disclosed clearly.

Bottom line: just because you can doesn’t mean you should.

The Real Challenges (That Demos Don’t Show)

Most blogs and YouTube tutorials make voice cloning look easy. Just "enter text, get speech." But here’s the reality:

• - Good data is hard to find.

• Even celebrity voices online are noisy and inconsistent.

• - Model fine-tuning is a pain.

• You’ll deal with formatting issues, GPU bottlenecks, hyperparameter tuning, and long training times.

• - Natural speech is hard.

• Demos sound okay, but long-form speech lacks flow, emotion, and context stability.

• - UI & Deployment are non-trivial.

• To turn it into a real product, you’ll need backend APIs, streaming infrastructure, and scalable architecture.

Open Source Voice Models: Hit or Miss?

Some OSS models are fantastic. Coqui TTS, YourTTS, and Bark offer high-quality results, especially in English.

Many support zero-shot cloning and come with hosted demos.

But:

• Non-English support is spotty—especially for Korean, Japanese, and multilingual usage.

• Still needs tuning for real-world deployment.

• Not production-ready out of the box: You’ll need to integrate APIs, handle latency, and scale with care.

Final Take

AI voice cloning is now democratized—but responsibly using it is a whole different game.

If you’re a developer, understand that success doesn’t stop at “it works.

” You need to ask: is it ethical?, is it legal?, and is it being used for good?

AI is changing how we tell stories, sell products, and preserve memory.

Let’s make sure it does so with integrity.