KokoClone: Offline Kokoro Voice Cloning Guide

KokoClone Quick Answer

KokoClone is a separate community voice-cloning project built on Kokoro-ONNX. Its official model card documents a local Python 3.12 installation, a Gradio app, command-line use, and a KokoClone.generate() Python API. Supply text and a clean 3-10 second reference-audio clip; the project describes this as zero-shot voice cloning without per-speaker training. OfflineTTS does not embed KokoClone, so use the official repository or model card and obtain the speaker’s consent before generating a cloned voice.

KokoClone is a community voice-cloning project built on Kokoro-ONNX. Its current workflow accepts text plus a short reference recording and generates speech intended to carry characteristics of that reference voice. This page focuses on the documented setup and API, without treating ordinary Kokoro voices, Kitten expressions, or Piper training as interchangeable cloning systems.

Important: the OfflineTTS Kokoro tool does not currently run KokoClone. It provides 54 built-in Kokoro voices across 9 language groups. The separate voice mixer blends existing Kokoro voice embeddings; it does not clone a speaker from uploaded audio.

What KokoClone Currently Provides

According to the KokoClone model card, the project provides:

• zero-shot generation from a 3-10 second reference clip;
• CPU and GPU installation paths;
• a Gradio web interface, command-line interface, and Python API;
• automatic model downloads on first run;
• language codes for English, Hindi, French, Japanese, Chinese, Italian, Portuguese, and Spanish.

KokoClone is not an official feature of the Kokoro model or OfflineTTS. It is a separate community implementation, so check its repository and model card for changes before integrating it into a production workflow.

Install KokoClone Locally

The documented setup uses Python 3.12 in a Conda environment:

git clone https://github.com/Ashish-Patnaik/kokoclone.git
cd kokoclone

conda create -n kokoclone python=3.12.12 -y
conda activate kokoclone

# CPU installation
pip install torch torchaudio --index-url https://download.pytorch.org/whl/cpu
pip install -r requirements.txt

For an NVIDIA setup, follow the repository’s current GPU instructions rather than assuming a particular CUDA version. The required model files are downloaded when the project first runs.

Use the Web Interface or CLI

Launch the included Gradio interface:

python app.py

Or generate a WAV file from the command line:

python cli.py \
  --text "This is a local KokoClone test." \
  --lang en \
  --ref reference.wav \
  --out output.wav

Use a clean, single-speaker reference recording. Music, room echo, overlapping speech, and aggressive noise removal can all make the target voice less consistent.

Current Python API

The current model card documents this API:

from core.cloner import KokoClone

cloner = KokoClone()
cloner.generate(
    text="This voice is generated with KokoClone.",
    lang="en",
    reference_audio="reference.wav",
    output_path="output.wav",
)

Older examples using from kokoclone import KokoClone or a text_to_speech() method may no longer match the repository. Pin a known commit for production and test the code against that version.

How to Prepare a Better Reference Clip

The model card recommends 3-10 seconds, but duration alone does not guarantee a useful result. A practical reference clip should have:

1. One speaker and no overlap.
2. Minimal room echo and steady microphone distance.
3. Natural speaking volume rather than a whisper or shout, unless that delivery is intentional.
4. No music, sound effects, or strong background noise.
5. A file you are authorized to use.

Test more than one reference segment. A clean sentence with varied phonemes often gives a more useful signal than a clip containing long pauses or repeated words.

Supported Languages

The current model card lists these language codes:

Cross-language cloning can be harder than same-language generation. Pronunciation, accent transfer, and speaker similarity can vary, so evaluate output with native listeners before publishing.

KokoClone vs OfflineTTS Engines

Piper has a documented model-training workflow for teams with a prepared dataset, but that is different from instant voice cloning. Kitten’s OfflineTTS integration exposes expression embeddings, not an official custom-speaker training pipeline.

If you need a reliable built-in voice rather than a clone, see the Kokoro vs Piper vs Kitten browser TTS comparison or open the browser TTS tool.

Privacy, Consent, and Misuse

Running a model locally reduces the need to upload reference audio to a hosted service, but local processing does not remove your responsibilities.

• Get the speaker’s informed consent before cloning their voice.
• Define where the generated voice may be used and how long files will be retained.
• Label synthetic audio when context could make listeners believe it is authentic.
• Do not impersonate public figures, coworkers, family members, or customers.
• Review the model license and the rights attached to your source recording.

Voice recordings can be sensitive biometric data. Store references and generated files carefully, and delete them when they are no longer needed.

When KokoClone Is a Reasonable Fit

KokoClone is most useful for consented experiments, accessibility prototypes, research, and local creative workflows where a short reference-driven setup matters. It is less suitable when you need guaranteed identity similarity, formal vendor support, or a managed compliance process.

For production use, test multiple speakers and languages on the exact hardware you plan to support. Avoid publishing universal quality, latency, or memory claims unless you have a reproducible benchmark for that environment.