VoxCPM2

VoxCPM2 is a tokenizer-free, diffusion autoregressive Text-to-Speech model — 2B parameters, 30 languages, 48kHz audio output, trained on over 2 million hours of multilingual speech data.

Highlights

• 🌍 30-Language Multilingual — No language tag needed; input text in any supported language directly
• 🎨 Voice Design — Generate a novel voice from a natural-language description alone (gender, age, tone, emotion, pace…); no reference audio required
• 🎛️ Controllable Cloning — Clone any voice from a short clip, with optional style guidance to steer emotion, pace, and expression while preserving timbre
• 🎙️ Ultimate Cloning — Provide reference audio + its transcript for audio-continuation cloning; every vocal nuance faithfully reproduced
• 🔊 48kHz Studio-Quality Output — Accepts 16kHz reference; outputs 48kHz via AudioVAE V2's built-in super-resolution, no external upsampler needed
• 🧠 Context-Aware Synthesis — Automatically infers appropriate prosody and expressiveness from text content
• ⚡ Real-Time Streaming — RTF as low as ~0.3 on NVIDIA RTX 4090, and ~0.13 accelerated by Nano-VLLM
• 📜 Fully Open-Source & Commercial-Ready — Apache-2.0 license, free for commercial use

Supported Languages (30)

Arabic, Burmese, Chinese, Danish, Dutch, English, Finnish, French, German, Greek, Hebrew, Hindi, Indonesian, Italian, Japanese, Khmer, Korean, Lao, Malay, Norwegian, Polish, Portuguese, Russian, Spanish, Swahili, Swedish, Tagalog, Thai, Turkish, Vietnamese

Chinese Dialects: 四川话, 粤语, 吴语, 东北话, 河南话, 陕西话, 山东话, 天津话, 闽南话

Quick Start

Installation

pip install voxcpm

Requirements: Python ≥ 3.10, PyTorch ≥ 2.5.0, CUDA ≥ 12.0 · Full Quick Start →

Text-to-Speech

from voxcpm import VoxCPM
import soundfile as sf

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

wav = model.generate(
    text="VoxCPM2 brings multilingual support, creative voice design, and controllable voice cloning.",
    cfg_value=2.0,
    inference_timesteps=10,
)
sf.write("output.wav", wav, model.tts_model.sample_rate)

Voice Design

Put the voice description in parentheses at the start of text, followed by the content to synthesize:

wav = model.generate(
    text="(A young woman, gentle and sweet voice)Hello, welcome to VoxCPM2!",
    cfg_value=2.0,
    inference_timesteps=10,
)
sf.write("voice_design.wav", wav, model.tts_model.sample_rate)

Controllable Voice Cloning

# Basic cloning
wav = model.generate(
    text="This is a cloned voice generated by VoxCPM2.",
    reference_wav_path="speaker.wav",
)
sf.write("clone.wav", wav, model.tts_model.sample_rate)

# Cloning with style control
wav = model.generate(
    text="(slightly faster, cheerful tone)This is a cloned voice with style control.",
    reference_wav_path="speaker.wav",
    cfg_value=2.0,
    inference_timesteps=10,
)
sf.write("controllable_clone.wav", wav, model.tts_model.sample_rate)

Ultimate Cloning

Provide both the reference audio and its exact transcript for maximum fidelity. Pass the same clip to both reference_wav_path and prompt_wav_path for highest similarity:

wav = model.generate(
    text="This is an ultimate cloning demonstration using VoxCPM2.",
    prompt_wav_path="speaker_reference.wav",
    prompt_text="The transcript of the reference audio.",
    reference_wav_path="speaker_reference.wav",
)
sf.write("hifi_clone.wav", wav, model.tts_model.sample_rate)

Streaming

import numpy as np

chunks = []
for chunk in model.generate_streaming(text="Streaming is easy with VoxCPM!"):
    chunks.append(chunk)
wav = np.concatenate(chunks)
sf.write("streaming.wav", wav, model.tts_model.sample_rate)

Model Details

Property	Value
Architecture	Tokenizer-free Diffusion Autoregressive (LocEnc → TSLM → RALM → LocDiT)
Backbone	Based on MiniCPM-4, totally 2B parameters
Audio VAE	AudioVAE V2 (asymmetric encode/decode, 16kHz in → 48kHz out)
Training Data	2M+ hours multilingual speech
LM Token Rate	6.25 Hz
Max Sequence Length	8192 tokens
dtype	bfloat16
VRAM	~8 GB
RTF (RTX 4090)	~0.30 (standard) / ~0.13 (Nano-vLLM)

Performance

VoxCPM2 achieves state-of-the-art or competitive results on major zero-shot and controllable TTS benchmarks.

See the GitHub repo for full benchmark tables (Seed-TTS-eval, CV3-eval, InstructTTSEval, MiniMax Multilingual Test).

Fine-tuning

VoxCPM2 supports both full SFT and LoRA fine-tuning with as little as 5–10 minutes of audio:

# LoRA fine-tuning (recommended)
python scripts/train_voxcpm_finetune.py \
    --config_path conf/voxcpm_v2/voxcpm_finetune_lora.yaml

# Full fine-tuning
python scripts/train_voxcpm_finetune.py \
    --config_path conf/voxcpm_v2/voxcpm_finetune_all.yaml

See the Fine-tuning Guide for full instructions.

Limitations

• Voice Design and Style Control results may vary between runs; generating 1–3 times is recommended to obtain the desired output.
• Performance varies across languages depending on training data availability.
• Occasional instability may occur with very long or highly expressive inputs.
• Strictly forbidden to use for impersonation, fraud, or disinformation. AI-generated content should be clearly labeled.

Citation

@article{voxcpm2_2026,
  title   = {VoxCPM2: Tokenizer-Free TTS for Multilingual Speech Generation, Creative Voice Design, and True-to-Life Cloning},
  author  = {VoxCPM Team},
  journal = {GitHub},
  year    = {2026},
}

@article{voxcpm2025,
  title   = {VoxCPM: Tokenizer-Free TTS for Context-Aware Speech Generation and True-to-Life Voice Cloning},
  author  = {Zhou, Yixuan and Zeng, Guoyang and Liu, Xin and Li, Xiang and
             Yu, Renjie and Wang, Ziyang and Ye, Runchuan and Sun, Weiyue and
             Gui, Jiancheng and Li, Kehan and Wu, Zhiyong and Liu, Zhiyuan},
  journal = {arXiv preprint arXiv:2509.24650},
  year    = {2025},
}

License

Released under the Apache-2.0 license, free for commercial use. For production deployments, we recommend thorough testing and safety evaluation tailored to your use case.