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VoxCPM is a tokenizer-free Text-to-Speech system that directly generates continuous speech representations via an end-to-end diffusion autoregressive architecture, bypassing discrete tokenization to achieve highly natural and expressive synthesis.

VoxCPM2 is the latest major release — a 2B parameter model trained on over 2 million hours of multilingual speech data, now supporting 30 languages, Voice Design, Controllable Voice Cloning, and 48kHz studio-quality audio output. Built on a MiniCPM-4 backbone.

• 🌍 30-Language Multilingual — Input text in any of the 30 supported languages and synthesize directly, no language tag needed
• 🎨 Voice Design — Create a brand-new voice from a natural-language description alone (gender, age, tone, emotion, pace …), no reference audio required
• 🎛️ Controllable Cloning — Clone any voice from a short reference clip, with optional style guidance to steer emotion, pace, and expression while preserving the original timbre
• 🎙️ Ultimate Cloning — Reproduce every vocal nuance: provide both reference audio and its transcript, and the model continues seamlessly from the reference, faithfully preserving every vocal detail — timbre, rhythm, emotion, and style (same as VoxCPM1.5)
• 🔊 48kHz High-Quality Audio — Accepts 16kHz reference audio and directly outputs 48kHz studio-quality audio via AudioVAE V2's asymmetric encode/decode design, with 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 — Weights and code released under the Apache-2.0 license, free for commercial use

Chinese Dialect: 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

• [2026.04] 🔥 We release VoxCPM2 — 2B, 30 languages, Voice Design & Controllable Voice Cloning, 48kHz audio output! Weights | Docs | Playground
• [2025.12] 🎉 Open-source VoxCPM1.5 weights with SFT & LoRA fine-tuning. (🏆 #1 GitHub Trending)
• [2025.09] 🔥 Release VoxCPM Technical Report.
• [2025.09] 🎉 Open-source VoxCPM-0.5B weights (🏆 #1 HuggingFace Trending)

• Quick Start
  • Installation
  • Python API
  • CLI Usage
  • Web Demo
  • Production Deployment
• Models & Versions
• Performance
• Fine-tuning
• Documentation
• Ecosystem & Community
• Risks and Limitations
• Citation

pip install voxcpm

Requirements: Python ≥ 3.10, PyTorch ≥ 2.5.0, CUDA ≥ 12.0. See Quick Start Docs for details.

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)
print("saved: demo.wav")

Create a voice from a natural-language description — no reference audio needed. Format: put the description in parentheses at the start of text(e.g. "(your voice description)The text 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)

Upload a reference audio. The model clones the timbre, and you can still use control instructions to adjust speed, emotion, or style.

wav = model.generate(
    text="This is a cloned voice generated by VoxCPM2.",
    reference_wav_path="path/to/voice.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="path/to/voice.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 audio-continuation-based cloning with every vocal nuance reproduced. For maximum cloning similarity, pass the same reference clip to both reference_wav_path and prompt_wav_path as shown below:

wav = model.generate(
    text="This is an ultimate cloning demonstration using VoxCPM2.",
    prompt_wav_path="path/to/voice.wav",
    prompt_text="The transcript of the reference audio.",
    reference_wav_path="path/to/voice.wav", # optional, for better simliarity 
)
sf.write("hifi_clone.wav", wav, model.tts_model.sample_rate)

import numpy as np

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

# Voice design (no reference audio needed)
voxcpm design \
  --text "VoxCPM2 brings studio-quality multilingual speech synthesis." \
  --output out.wav

# Controllable voice cloning with style control
voxcpm design \
  --text "VoxCPM2 brings studio-quality multilingual speech synthesis." \
  --control "Young female voice, warm and gentle, slightly smiling" \
  --output out.wav

# Voice cloning (reference audio)
voxcpm clone \
  --text "This is a voice cloning demo." \
  --reference-audio path/to/voice.wav \
  --output out.wav

# Ultimate cloning (prompt audio + transcript)
voxcpm clone \
  --text "This is a voice cloning demo." \
  --prompt-audio path/to/voice.wav \
  --prompt-text "reference transcript" \
  --reference-audio path/to/voice.wav \ # optional, for better simliarity
  --output out.wav

# Batch processing
voxcpm batch --input examples/input.txt --output-dir outs

# Help
voxcpm --help

python app.py   # then open http://localhost:7860

For high-throughput serving, use Nano-vLLM-VoxCPM — a dedicated inference engine built on Nano-vLLM with concurrent request support and an async API.

pip install nano-vllm-voxcpm

from nanovllm_voxcpm import VoxCPM
import numpy as np, soundfile as sf

server = VoxCPM.from_pretrained(model="/path/to/VoxCPM", devices=[0])
chunks = list(server.generate(target_text="Hello from VoxCPM!"))
sf.write("out.wav", np.concatenate(chunks), 48000)
server.stop()

RTF as low as ~0.13 on NVIDIA RTX 4090 (vs ~0.3 with the standard PyTorch implementation), with support for batched concurrent requests and a FastAPI HTTP server. See the Nano-vLLM-VoxCPM repo for deployment details.

Full parameter reference, multi-scenario examples, and voice cloning tips → Quick Start Guide | Usage Guide | Cookbook

VoxCPM2 is built on a tokenizer-free, diffusion autoregressive paradigm. The model operates entirely in the latent space of AudioVAE V2, following a four-stage pipeline: LocEnc → TSLM → RALM → LocDiT, enabling rich expressiveness and 48kHz native audio output.

For full architectural details, VoxCPM2-specific upgrades, and a model comparison table, see the Architecture Design.

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

VoxCPM supports both full fine-tuning (SFT) and LoRA fine-tuning. With as little as 5–10 minutes of audio, you can adapt to a specific speaker, language, or domain.

# LoRA fine-tuning (parameter-efficient, 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

# WebUI for training & inference
python lora_ft_webui.py   # then open http://localhost:7860

Full guide → Fine-tuning Guide (data preparation, configuration, training, LoRA hot-swapping, FAQ)

Full documentation: voxcpm.readthedocs.io

See the full Ecosystem in the docs. Community projects are not officially maintained by OpenBMB. Built something cool? Open an issue or PR to add it!

• Potential for Misuse: VoxCPM's voice cloning can generate highly realistic synthetic speech. It is strictly forbidden to use VoxCPM for impersonation, fraud, or disinformation. We strongly recommend clearly marking any AI-generated content.
• Controllable Generation Stability: Voice Design and Controllable Voice Cloning results can vary between runs — you may try to generate 1~3 times to obtain the desired voice or style. We are actively working on improving controllability consistency.
• Language Coverage: VoxCPM2 officially supports 30 languages. For languages not on the list, you are welcome to test directly or try fine-tuning on your own data. We plan to expand language coverage in future releases.
• Usage: This model is released under the Apache-2.0 license. For production deployments, we recommend conducting thorough testing and safety evaluation tailored to your use case.

If you find VoxCPM helpful, please consider citing our work and starring ⭐ the repository!

@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},
}

VoxCPM model weights and code are open-sourced under the Apache-2.0 license.

• DiTAR for the diffusion autoregressive backbone
• MiniCPM-4 for the language model foundation
• CosyVoice for the Flow Matching-based LocDiT implementation
• DAC for the Audio VAE backbone
• Our community users for trying VoxCPM, reporting issues, sharing ideas, and contributing—your support helps the project keep getting better

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