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Home/Categories/AI & Machine Learning/VoxCPM
VoxCPM icon

VoxCPM

Open source alternative to ElevenLabs

  • 39.6K stars
  • Apache-2.0
  • Active this month
  • Last commit 14 days ago

Last verified Oct 20, 2026

Generate multilingual speech with an Apache-2.0 TTS model for voice cloning, voice design, and self-hosted 48kHz audio output.

Visit websiteGitHub39.6K stars
Branded cover for VoxCPM
TL;DR

VoxCPM is an Apache-2.0 multilingual text-to-speech model for teams building voice generation, voice cloning, and speech design workflows. It replaces per-character cloud TTS APIs when AI teams need to run speech generation on their own hardware and tune the pipeline.

Read the full breakdownHide the full breakdownWho it is for, how it works, trade-offs and the FAQ
who it's for

Who VoxCPM is for#

AI teams building multilingual voice products

Use VoxCPM when product quality depends on controlling voice generation across languages and styles.

Skip if:

Skip if you need a hosted API with support, billing, and uptime guarantees.

Researchers testing TTS architectures

The open model and code are useful for studying multilingual and controllable speech generation.

Skip if:

Skip if your needs are simple narration clips and you do not have GPU access.

the problem

The problem it solves#

Cloud text-to-speech APIs are convenient, but high-volume voice generation can become expensive and constrained by provider limits. Teams building voice products also face privacy questions when scripts, prompts, reference voices, or generated audio pass through a third-party API.

Multilingual voice work adds another problem: quality and style controls vary by language, and many tools force developers into a fixed hosted model with limited room for research or product-specific tuning.

how VoxCPM solves it

How it solves it#

Multilingual speech generation

Targets multilingual TTS use cases where developers need one project for multiple languages and voice styles.

Voice cloning and voice design

Supports workflows around reference voices and controllable voice generation for custom voice applications.

Self-hosted model path

The Apache-2.0 project lets AI teams run and adapt the model locally instead of paying per character to a hosted API.

strengths · trade-offs

Strengths and trade-offs#

Strengths

  • Useful for privacy-sensitive voice workRunning the model yourself keeps scripts, reference audio, and generated speech inside your environment.
  • Permissive license for buildersApache-2.0 licensing supports commercial experimentation and integration with fewer restrictions than many model releases.

Trade-offs

  • -GPU and ML operations requiredVoxCPM is a model project, not a polished SaaS recorder. Teams need hardware, serving, audio evaluation, and model operations skill.
versus alternatives

VoxCPM vs alternatives#

VoxCPM vs ElevenLabs

VoxCPM is a stronger fit for teams that need to run multilingual text-to-speech and voice cloning on their own infrastructure. ElevenLabs is easier to start with because it is a hosted commercial product, but VoxCPM avoids per-character usage pricing and keeps prompts, reference voices, and generated audio inside the operator's environment.

VoxCPM vs Azure AI Speech

Azure AI Speech gives enterprises a managed cloud service with support, billing, and production uptime handled by Microsoft. VoxCPM asks the team to own GPU serving and model operations, but it gives researchers and builders direct access to an Apache-2.0 model for experiments, privacy-sensitive workloads, and product-specific speech generation.

tech stack · detected from GitHub

What it's built on#

Languages
Python
Frameworks
PyTorch
frequently asked

FAQ#

Is VoxCPM open source?

Yes. VoxCPM is open source under the Apache-2.0 license.

What is VoxCPM used for?

VoxCPM is used for multilingual text-to-speech, voice cloning, and controllable voice generation.

How does VoxCPM compare to ElevenLabs?

ElevenLabs is a hosted voice generation service. VoxCPM is an open source model path for teams that can operate speech generation themselves.

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