Kokoro-82M gains zero-shot voice tuning

Kokoro-82M combines fast synthesis, stable output, and a library of fixed voice packs. The new kokoro-inno-clone-tuner adapter adds zero-shot voice matching, which creates a voice from an unseen reference clip without per-speaker training. It keeps the base model frozen and produces native Kokoro voice packs, so existing pipelines can use the result without changing synthesis models.

A native voice pack from one clip

The adapter wraps Kokoro-82M and converts reference audio into a tensor with Kokoro’s standard [510, 1, 256] shape. Applications can pass that tensor directly to KPipeline or save it as a reusable voice file with torch.save.

Enroll and synthesize

# Shell
pip install inno-kokoro

import torch

from inno_kokoro.enroll import Tuner, enroll, read
from kokoro import KPipeline

tuner = Tuner()
pack, _ = enroll(*read("my_ref.wav"), tuner)

torch.save(pack, "my_voice.pt")

pipeline = KPipeline(lang_code="a")
result = next(
    pipeline("Hello from a tuned voice.", voice=pack)
)
audio = result.audio

Reference clips must be between 3 and 30 seconds, contain one speaker, and have reasonably clean audio. The current release supports English.

Drop-in deployment, split licenses

Kokoro-FastAPI includes the tuner from version 0.9.0 onward, allowing self-hosted installations to add enrollment without rebuilding their serving layer. The bundled speaker encoder also avoids a separate model download.

• Adapter code and weights: Apache-2.0
• Bundled speaker encoder: CC BY-SA 3.0

Deployments that redistribute the full package should review the obligations of both licenses, particularly the attribution and share-alike terms attached to the speaker encoder.