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. [](https://github.com/OpenBMB/VoxCPM) [](https://voxcpm.readthedocs.io/en/latest/) [](https://huggingface.co/spaces/OpenBMB/VoxCPM-Demo) [](https://openbmb.github.io/voxcpm2-demopage) [](https://discord.gg/KZUx7tVNwz) [](https://applink.feishu.cn/client/chat/chatter/add_by_link?link_token=acds0b9d-23d8-4d7e-b696-d200f3e22a7f) - 🌍 **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](https://github.com/a710128/nanovllm-voxcpm) - 📜 **Fully Open-Source & Commercial-Ready** — Apache-2.0 license, free for commercial use 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 pip install voxcpm **Requirements:** Python ≥ 3.10, PyTorch ≥ 2.5.0, CUDA ≥ 12.0 · [Full Quick Start →](https://voxcpm.readthedocs.io/en/latest/quickstart.html) 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) 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) 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) 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) 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) 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) VoxCPM2 achieves state-of-the-art or competitive results on major zero-shot and controllable TTS benchmarks. See the [GitHub repo](https://github.com/OpenBMB/VoxCPM#-performance) for full benchmark tables (Seed-TTS-eval, CV3-eval, InstructTTSEval, MiniMax Multilingual Test). VoxCPM2 supports both full SFT and LoRA fine-tuning with as little as 5–10 minutes of audio: python scripts/train_voxcpm_finetune.py \ --config_path conf/voxcpm_v2/voxcpm_finetune_lora.yaml python scripts/train_voxcpm_finetune.py \ --config_path conf/voxcpm_v2/voxcpm_finetune_all.yaml See the [Fine-tuning Guide](https://voxcpm.readthedocs.io/en/latest/finetuning/finetune.html) for full instructions. - 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. @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}, } Released under the [Apache-2.0](https://www.apache.org/licenses/LICENSE-2.0) license, free for commercial use. For production deployments, we recommend thorough testing and safety evaluation tailored to your use case.