VoxCPM

VoxCPM

Next-Generation Tokenizer-Free TTS Zero-Shot Voice Cloning Technology

Text to Speech

Choose a voice, write your script, and generate a clean audio take.

Generate Speech
Enter text in multiple supported languages, and the system will generate speech in the detected language.
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🚀
Elon Musk
EN
Deep, conversational and confident male voice. Perfect for online courses and e-learning. Incredible emotional authenticity.
🧽
SpongeBob
EN
Exceptionally energetic and playful voice. Vibrant, child-like male voice perfect for motivational content and sports commentary.
🦅
Donald Trump
EN
Deep, authoritative male voice. Confident, conversational tone perfect for podcasts and voiceovers.
👩
Friendly Women
EN
Warm and friendly female voice. Natural and engaging, perfect for customer service and narration.
🎙️
Energetic Male
EN
Energetic youthful male voice. Great for commercials, social media, and dynamic content.
👩
AD学姐
ZH
自信俏皮活力的女声,语速较快,适合短视频、抖音内容、直播带货。AI TTS技术,声音真实自然。
🐴
赛马娘
ZH
充满朝气、励志且友好的年轻女声。适合短视频、TikTok内容,以及客户服务与虚拟助手。声音富有感染力。
🐺
灰太狼
ZH
青青草原狼大王的雄音,浑厚有力的男声,适合角色扮演和故事叙述。
☯️
太乙真人
ZH
哪吒太乙真人声音,沉稳大气的男性声音,适合神话故事和角色扮演。
🧔
丁真
ZH
年轻温暖的男声,富有叙事感,适合短视频、TikTok内容创作。声音自然流畅。

VoxCPM Technical Architecture

Deep dive into VoxCPM's core technical principles and innovative architectural design

MiniCPM-4 Backbone Network

Built on the edge-deployment optimized MiniCPM-4 large language model as the core architecture, achieving effective integration of text semantic understanding and speech feature extraction through hierarchical language modeling technology, supporting context-aware speech generation.

Tokenizer-Free End-to-End Architecture

Abandons traditional TTS text tokenization preprocessing, directly modeling in continuous speech space. Through end-to-end diffusion autoregressive architecture, achieves lossless conversion from text to speech while maintaining natural speech fluency.

FSQ Quantization Technology

Adopts Finite Scalar Quantization technology for efficient encoding of speech features, significantly reducing computational complexity and storage requirements while maintaining audio quality.

Local Diffusion Transformer

Combines the advantages of diffusion models and Transformer architecture, achieving high-quality speech generation through local diffusion mechanisms while ensuring audio quality and achieving RTF 0.17 efficient inference performance.

Zero-Shot Voice Cloning

With just a small amount of reference audio (3-10 seconds), it can extract subtle features of the speaker's timbre, accent, and emotional tone, achieving high-fidelity voice cloning.

Technical Performance Metrics

Based on authoritative international benchmarks, VoxCPM excels in multiple key indicators

0.17
Real-Time Factor (RTF)

6x faster than playback speed

0.93%
Character Error Rate (CER)

Chinese speech recognition accuracy

77.2%
Voice Similarity

Chinese voice cloning similarity

1.85%
English Error Rate (WER)

English speech recognition accuracy

Based on Seed-TTS-eval and other authoritative benchmark evaluation results

Technical Comparison Matrix

Comprehensive performance comparison between VoxCPM and mainstream TTS models

VoxCPM
RTF
0.17
CER (%)
0.93
Similarity(%)
77.2
Zero-Shot
Multilingual
Open Source
CosyVoice
RTF
0.25
CER (%)
3.2
Similarity(%)
0.88
Zero-Shot
Multilingual
Open Source
F5-TTS
RTF
0.42
CER (%)
4.1
Similarity(%)
0.85
Zero-Shot
Multilingual
Open Source
SparkTTS
RTF
0.31
CER (%)
2.8
Similarity(%)
0.89
Zero-Shot
Multilingual
Open Source

VoxCPM leads in speed, accuracy, and feature completeness among similar products

Core Capability Audio Demonstrations

Experience VoxCPM's exceptional performance in cross-language cloning, emotional expression, and context-aware generation

Cross-Language - EN→CN

English speaker voice cloned to Chinese speech

Cross-Language - CN→EN

Chinese speaker voice cloned to English speech

Emotion - Happy

Emotionally rich happy tone expression

Emotion - Sad

Emotionally rich sad tone expression

Context-Aware - News

Intelligent news broadcasting style

Context-Aware - Story

Intelligent storytelling style

More audio samples available at Official Demo Page

Technical Deep Dive

Explore VoxCPM's technical details and access development resources and academic research

Academic Paper

Detailed technical principles, experimental results, and performance evaluation reports

Read Paper

Open Source Code

Complete source code, model weights, and training scripts

Visit Repository

Quick Start

Deploy and use VoxCPM model in just a few simple steps

Get Started

Model Download

Pre-trained model weights ready for direct inference

Download Model

API Documentation

Detailed API interface descriptions and usage examples

View Documentation

Community Support

Join the developer community for technical support and discussions

Join Discussion

Technical Highlights

Apache 2.0Open Source License
500MModel Parameters
1.8M HoursTraining Data
RTX 4090Recommended Hardware

Application Scenarios

VoxCPM demonstrates exceptional performance across multiple domains, providing powerful support for innovative applications

Audiobook Production

Rapidly generate high-quality audiobooks with consistent voice

Language Learning

Personalized speech education with multilingual accent training

Content Creation

Professional voice solutions for video dubbing and podcast production

Accessibility Applications

Personalized reading experiences for visually impaired individuals

Quick Start

Get started with VoxCPM in just a few steps to deploy and experience tokenizer-free TTS technology

1

Environment Setup

First clone the repository and install dependencies:

git clone https://github.com/OpenBMB/VoxCPM.git
cd VoxCPM
pip install -r requirements.txt
2

Model Download

Download pre-trained model weights:

huggingface-cli download openbmb/VoxCPM --local-dir ./checkpoints/VoxCPM
3

Quick Usage

Use Python script for speech synthesis:

Initialize model
from voxcpm import VoxCPM
model = VoxCPM.from_pretrained("./checkpoints/VoxCPM")
Speech synthesis
text = 'Hello, this is VoxCPM speech synthesis demo'
audio = model.tts(text)
audio.save("output.wav")

System Requirements

Python 3.8+
PyTorch 1.13.0+
CUDA 11.6+ (Recommended RTX 4090 or higher)
Memory: 16GB+ RAM
VRAM: 12GB+ VRAM

Frequently Asked Questions

Common questions and answers about VoxCPM technology and usage