VoxCPM

// Overview

VoxCPM is an open-source text-to-speech (TTS) system from OpenBMB. Instead of turning speech into discrete tokens, it directly generates continuous speech representations through an end-to-end diffusion autoregressive architecture, which the team says produces highly natural and expressive audio.

The latest release, VoxCPM2, is a 2-billion-parameter model trained on over 2 million hours of multilingual speech and built on a MiniCPM-4 backbone. It supports 30 languages, voice design from a plain-text description, controllable voice cloning, and 48kHz studio-quality output. The weights and code are released under the Apache-2.0 license, so the project is free to use commercially.

// What it does

• Multilingual synthesis across 30 languages plus several Chinese dialects, with no language tag needed in the input text
• Voice Design: create a brand-new voice from a natural-language description (gender, age, tone, emotion, pace) without any reference audio
• Controllable voice cloning from a short reference clip, with optional style guidance to steer emotion, pace, and expression while keeping the original timbre
• Ultimate cloning that reproduces fine vocal detail by continuing from a reference clip paired with its transcript
• 48kHz high-quality output with built-in super-resolution, accepting 16kHz reference audio and needing no external upsampler
• Real-time streaming generation, with much faster inference available through Nano-vLLM or the official vLLM-Omni serving engine

// Getting started

VoxCPM ships as a Python package. Install it with pip, then load a pretrained model and generate audio with a few lines of code, or use the bundled command-line tool. It requires Python 3.10 or newer (below 3.13), PyTorch 2.5.0 or newer, and CUDA 12.0 or newer.

pip install voxcpm

from voxcpm import VoxCPM
import soundfile as sf

model = VoxCPM.from_pretrained(
  "openbmb/VoxCPM2",
  load_denoiser=False,
)

wav = model.generate(
    text="VoxCPM2 is the current recommended release for realistic multilingual speech synthesis.",
    cfg_value=2.0,
    inference_timesteps=10,
)
sf.write("demo.wav", wav, model.tts_model.sample_rate)

# Design a voice from a description, no reference audio needed
voxcpm design \
  --text "VoxCPM2 brings studio-quality multilingual speech synthesis." \
  --output out.wav

# Clone a voice from a reference recording
voxcpm clone \
  --text "This is a voice cloning demo." \
  --reference-audio path/to/voice.wav \
  --output out.wav

python app.py --port 8808  # then open http://localhost:8808

Commands and code are distilled from the project's own documentation — always check the official repo for the latest.

// When to use it

• Adding natural multilingual voiceovers to videos, audiobooks, and other content across 30 languages
• Designing a custom brand or character voice from a written description when no recording is available
• Cloning a specific voice from a short clip for narration, dubbing, or accessibility tools
• Building real-time voice features into apps using streaming generation and high-throughput serving engines

// How VoxCPM compares

VoxCPM alongside other open-source audio, music & voice tools AI/TLDR tracks, ranked by GitHub stars.