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Bark Voice Cloning Hubert Quantizer

A Python repository for training and running Bark voice cloning, with a HuBERT quantizer that converts audio into semantic features and tokens for speaker prompts.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Bark Voice Cloning Hubert Quantizer — OpenViST project profile in Audio & Voice AI.
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Understand the project

Practical overview

When it fits

Use the repository when you need the Python implementation for Bark voice cloning or want to follow its custom model-training workflow. For a user-facing route, use one of the interfaces linked in its README.

Who it is for

  • Developers adding Bark voice cloning to a Python project.
  • Users exploring voice cloning through the linked audio web UI, hosted Space, or interactive notebook.
  • Practitioners preparing data to train a HuBERT quantizer model.

Documented uses

  • Extract semantic features and tokens from audio for use in a Bark speaker prompt.
  • Run Bark voice-cloning inference using the repository's code and linked developer examples.
  • Prepare audio and semantic data, train a model, and test the latest model.
  • Choose among listed pretrained English, Polish, and German quantizer checkpoints.
Before you choose

Requirements & limits

The repository is public and licensed under MIT.

  • Python 3.10 is the stated environment; older versions may have issues.
  • The README recommends clean, clearly spoken, single-speaker audio that ends naturally, and suggests about ten seconds of prompt data.
  • The repository lists pretrained English checkpoints and community Polish and German checkpoints.
  • The README links to a notebook with CPU and CUDA usage examples, but does not establish compatibility guarantees.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Train a custom HuBERT quantizer model

Prepare a dataset, generate HuBERT semantic vectors, train a model, and test the latest model using the documented process.py stages.

Before you start

  • Python 3.10 is the documented version; older versions may have issues, and other versions have not been tested.
  • A dataset arranged as described in the README, with audio files and semantic data in zip files inside a folder such as Literature.
  • The repository's process.py script.

Steps

  1. Run process.py --path Literature --mode prepare to extract the dataset into one directory.

  2. Run process.py --path Literature --mode prepare2 to create HuBERT semantic vectors for training.

  3. Run process.py --path Literature --mode train to train the model.

  4. When training has progressed enough, run process.py --path Literature --mode test to test the latest model.

Expected outcome

The documented workflow prepares data, trains a model, and tests the latest model.

Guide limitations

  • The README warns that creating audio can take as long as or longer than creating semantic data.
  • The documentation does not specify training duration or a stopping threshold.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
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Sources checked
7 November 2026
Original website title
gitmylo/bark-voice-cloning-HuBERT-quantizer · Official homepage title · retained in its source language
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