@@ -23,10 +29,10 @@
- 👋 Join our community for discussion and support!
+ 👋 欢迎加入社区，参与讨论与交流！
- 
Feishu
+ 飞书群
|
@@ -34,67 +40,69 @@
-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.
+VoxCPM 是一个**无离散音频分词器**（Tokenizer-Free）的语音合成系统，通过端到端的**扩散自回归架构**直接生成连续语音表征，绕过对音频的离散编码步骤，实现高度自然且富有表现力的语音合成。
-**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](https://github.com/OpenBMB/MiniCPM) backbone.
+**VoxCPM2** 是最新的版本 — 基于 [MiniCPM-4](https://github.com/OpenBMB/MiniCPM) 基座构建，总计 **20亿** 参数，在超过 **200万小时** 的多语种音频数据上训练，支持 **30种全球语言+9种中文方言**、**音色设计**、**可控声音克隆**，原生输出 **48kHz** 高质量音频。
-### ✨ Highlights
+### ✨ 核心特性
-- 🌍 **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](https://github.com/a710128/nanovllm-voxcpm) or [vLLM-Omni](https://github.com/vllm-project/vllm-omni) — official vLLM omni-modal serving for VoxCPM2 with PagedAttention and an OpenAI-compatible API
-- 📜 **Fully Open-Source & Commercial-Ready** — Weights and code released under the [Apache-2.0](LICENSE) license, free for commercial use
+- 🌍 **30种语言语音合成** — 直接输入原始文本即可合成（支持语言详见下文），无需额外语言标签
+- 🎨 **音色设计** — 用自然语言描述（性别、年龄、音色、情绪、语速……）凭空创建全新音色，无需参考音频
+- 🎛️ **可控声音克隆** — 从参考音频片段克隆任意声音，可叠加风格指令控制情绪、语速和表现力，同时保持原始音色
+- 🎙️ **极致克隆** — 提供参考音频及其文本内容，模型接着参考音频进行无缝续写，从而精准还原声音细节特征（与 VoxCPM1.5 一致）
+- 🔊 **48kHz 高质量音频** — 输入 16kHz 参考音频，通过 AudioVAE V2 的非对称编解码设计直接输出 48kHz 高质量音频，内置超分能力
+- 🧠 **语境感知合成** — 根据文本内容自动推断合适的韵律和表现力
+- ⚡ **实时流式合成** — 在 NVIDIA RTX 4090 上 RTF 低至 ~0.3，通过 [Nano-vLLM](https://github.com/a710128/nanovllm-voxcpm) 或 [vLLM-Omni](https://github.com/vllm-project/vllm-omni)（官方 vLLM 全模态服务，原生支持 VoxCPM2，提供 PagedAttention 与 OpenAI 兼容 API）加速后可达 ~0.13
+- 📜 **完全开源，商用就绪** — 权重和代码基于 [Apache-2.0](LICENSE) 协议发布，免费商用
-**🌍 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
+
🌍 支持的语言（30种）
+
+阿拉伯语、缅甸语、中文、丹麦语、荷兰语、英语、芬兰语、法语、德语、希腊语、希伯来语、印地语、印尼语、意大利语、日语、高棉语、韩语、老挝语、马来语、挪威语、波兰语、葡萄牙语、俄语、西班牙语、斯瓦希里语、瑞典语、菲律宾语、泰语、土耳其语、越南语
-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
+中国方言：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
-### News
-- **[2026.04]** 🔥 We release **VoxCPM2** — 2B, 30 languages, Voice Design & Controllable Voice Cloning, 48kHz audio output! [Weights](https://huggingface.co/openbmb/VoxCPM2) | [Docs](https://voxcpm.readthedocs.io/en/latest/) | [Playground](https://huggingface.co/spaces/OpenBMB/VoxCPM-Demo) | [Technical Report](https://arxiv.org/abs/2606.06928)
-- **[2025.12]** 🎉 Open-source **VoxCPM1.5** [weights](https://huggingface.co/openbmb/VoxCPM1.5) with SFT & LoRA fine-tuning. (**🏆 #1 GitHub Trending**)
-- **[2025.09]** 🔥 Release VoxCPM [Technical Report](https://arxiv.org/abs/2509.24650).
-- **[2025.09]** 🎉 Open-source **VoxCPM-0.5B** [weights](https://huggingface.co/openbmb/VoxCPM-0.5B) (**🏆 #1 HuggingFace Trending**)
+### 最新动态
+
+* **[2026.04]** 🔥 发布 **VoxCPM2** — 20亿参数，30种语言，音色设计与可控声音克隆，48kHz 音频输出！[模型权重](https://huggingface.co/openbmb/VoxCPM2) | [使用文档](https://voxcpm.readthedocs.io/zh-cn/latest/) | [在线体验](https://huggingface.co/spaces/OpenBMB/VoxCPM-Demo) | [官网体验](https://voxcpm.modelbest.cn/) (适用国内访问) | [技术报告](https://arxiv.org/abs/2606.06928)
+* **[2025.12]** 🎉 开源 **VoxCPM1.5** [模型权重](https://huggingface.co/openbmb/VoxCPM1.5)，支持 SFT 和 LoRA 微调。(**🏆 GitHub Trending #1**)
+* **[2025.09]** 🔥 发布 VoxCPM [技术报告](https://arxiv.org/abs/2509.24650)。
+* **[2025.09]** 🎉 开源 **VoxCPM-0.5B** [模型权重](https://huggingface.co/openbmb/VoxCPM-0.5B) (**🏆 HuggingFace Trending #1**)
---
-## Contents
+## 目录
-- [Quick Start](#-quick-start)
- - [Installation](#installation)
+- [快速开始](#-快速开始)
+ - [安装](#安装)
- [Python API](#python-api)
- - [CLI Usage](#cli-usage)
+ - [命令行使用](#命令行使用)
- [Web Demo](#web-demo)
- - [Production Deployment](#-production-deployment-nano-vllm)
- - [On-Device Inference (llama.cpp-omni)](#-on-device-inference-llamacpp-omni)
-- [Models & Versions](#-models--versions)
-- [Performance](#-performance)
-- [Fine-tuning](#%EF%B8%8F-fine-tuning)
-- [Documentation](#-documentation)
-- [Ecosystem & Community](#-ecosystem--community)
-- [Risks and Limitations](#%EF%B8%8F-risks-and-limitations)
-- [Citation](#-citation)
+ - [生产部署](#-生产部署nano-vllm)
+ - [端侧推理（llama.cpp-omni）](#-端侧推理llamacpp-omni)
+- [模型与版本](#-模型与版本)
+- [性能评测](#-性能评测)
+- [微调](#%EF%B8%8F-微调)
+- [文档](#-文档)
+- [生态与社区](#-生态与社区)
+- [风险与局限性](#%EF%B8%8F-风险与局限性)
+- [引用](#-引用)
---
-## 🚀 Quick Start
+## 🚀 快速开始
-### Installation
+### 安装
```sh
pip install voxcpm
```
-> **Requirements:** Python ≥ 3.10 (<3.13), PyTorch ≥ 2.5.0, CUDA ≥ 12.0. See [Quick Start Docs](https://voxcpm.readthedocs.io/en/latest/quickstart.html) for details.
+> **环境要求：** Python ≥ 3.10 (<3.13)，PyTorch ≥ 2.5.0，CUDA ≥ 12.0。详见 [快速开始文档](https://voxcpm.readthedocs.io/zh-cn/latest/quickstart.html)。
### Python API
-#### 🗣️ Text-to-Speech
+#### 🗣️ 文本转语音
```python
from voxcpm import VoxCPM
@@ -106,16 +114,16 @@ model = VoxCPM.from_pretrained(
)
wav = model.generate(
- text="VoxCPM2 is the current recommended release for realistic multilingual speech synthesis.",
+ text="VoxCPM2 是目前推荐使用的多语言语音合成版本。",
cfg_value=2.0,
inference_timesteps=10,
seed=42,
)
sf.write("demo.wav", wav, model.tts_model.sample_rate)
-print("saved: demo.wav")
+print("已保存: demo.wav")
```
-If you prefer downloading from ModelScope first, you can use:
+如果你希望先从 ModelScope 下载模型到本地（适用于国内网络访问），可以使用：
```bash
pip install modelscope
@@ -123,14 +131,14 @@ pip install modelscope
```python
from modelscope import snapshot_download
-snapshot_download("OpenBMB/VoxCPM2", local_dir='./pretrained_models/VoxCPM2') # specify the local directory to save the model
+snapshot_download("OpenBMB/VoxCPM2", local_dir='./pretrained_models/VoxCPM2') # 指定模型保存的本地路径
from voxcpm import VoxCPM
import soundfile as sf
-model = VoxCPM.from_pretrained("./pretrained_models/VoxCPM2", load_denoiser=False)
+model = VoxCPM.from_pretrained('./pretrained_models/VoxCPM2', load_denoiser=False)
wav = model.generate(
- text="VoxCPM2 is the current recommended release for realistic multilingual speech synthesis.",
+ text="VoxCPM2 是目前推荐使用的多语言语音合成版本。",
cfg_value=2.0,
inference_timesteps=10,
seed=42,
@@ -138,13 +146,13 @@ wav = model.generate(
sf.write("demo.wav", wav, model.tts_model.sample_rate)
```
-#### 🎨 Voice Design
+#### 🎨 音色设计
-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."`):
+用自然语言描述创建全新音色，无需参考音频。**格式：** 在 `text` 开头用括号写入音色描述（如 `"(音色描述)要合成的文本。"`）：
```python
wav = model.generate(
- text="(A young woman, gentle and sweet voice)Hello, welcome to VoxCPM2!",
+ text="(年轻女性，声音温柔甜美)你好，欢迎使用VoxCPM2！",
cfg_value=2.0,
inference_timesteps=10,
seed=42,
@@ -152,19 +160,19 @@ wav = model.generate(
sf.write("voice_design.wav", wav, model.tts_model.sample_rate)
```
-#### 🎛️ Controllable Voice Cloning
+#### 🎛️ 可控声音克隆
-Upload a reference audio. The model clones the timbre, and you can still use control instructions to adjust speed, emotion, or style.
+上传一段参考音频，模型克隆其音色，同时可以使用控制指令调节语速、情绪或风格。
```python
wav = model.generate(
- text="This is a cloned voice generated by VoxCPM2.",
+ text="这是VoxCPM2生成的克隆语音。",
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.",
+ text="(稍快一点，欢快的语气)这是带风格控制的克隆语音。",
reference_wav_path="path/to/voice.wav",
cfg_value=2.0,
inference_timesteps=10,
@@ -173,78 +181,78 @@ wav = model.generate(
sf.write("controllable_clone.wav", wav, model.tts_model.sample_rate)
```
-#### 🎙️ Ultimate Cloning
+#### 🎙️ 极致克隆
-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:
+提供参考音频及其精确文本转录，实现基于音频续写的高保真克隆。为获得最高克隆相似度，可将同一音频同时传给 `reference_wav_path` 和 `prompt_wav_path`：
```python
wav = model.generate(
- text="This is an ultimate cloning demonstration using VoxCPM2.",
+ text="这是使用VoxCPM2的极致克隆演示。",
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
+ prompt_text="参考音频的文本转录。",
+ reference_wav_path="path/to/voice.wav", # 可选，提升相似度
)
sf.write("hifi_clone.wav", wav, model.tts_model.sample_rate)
```
-**🔄 Streaming API**
+
+🔄 流式 API
```python
import numpy as np
chunks = []
for chunk in model.generate_streaming(
- text="Streaming text to speech is easy with VoxCPM!",
+ text="使用VoxCPM进行流式语音合成非常简单！",
):
chunks.append(chunk)
wav = np.concatenate(chunks)
sf.write("streaming.wav", wav, model.tts_model.sample_rate)
```
+
-
-
-### CLI Usage
+### 命令行使用
```bash
-# Voice design (no reference audio needed)
+# 音色设计（无需参考音频）
voxcpm design \
- --text "VoxCPM2 brings studio-quality multilingual speech synthesis." \
+ --text "VoxCPM2带来全新语音合成体验。" \
--output out.wav
-# Controllable voice cloning with style control
+# 可控声音克隆（带风格控制）
voxcpm design \
- --text "VoxCPM2 brings studio-quality multilingual speech synthesis." \
- --control "Young female voice, warm and gentle, slightly smiling" \
+ --text "VoxCPM2带来全新语音合成体验。" \
+ --control "年轻女声，温暖温柔，略带微笑" \
--seed 42 \
--output out.wav
-# Voice cloning (reference audio)
+# 声音克隆（参考音频）
voxcpm clone \
- --text "This is a voice cloning demo." \
+ --text "这是一个声音克隆的演示。" \
--reference-audio path/to/voice.wav \
--output out.wav
-# Ultimate cloning (prompt audio + transcript)
+# 极致克隆（提示音频 + 转录文本）
voxcpm clone \
- --text "This is a voice cloning demo." \
+ --text "这是一个声音克隆的演示。" \
--prompt-audio path/to/voice.wav \
- --prompt-text "reference transcript" \
- --reference-audio path/to/voice.wav \ # optional, for better simliarity
+ --prompt-text "参考音频转录文本" \
+ --reference-audio path/to/voice.wav \
--output out.wav
-# Batch processing
+# 批量处理
voxcpm batch --input examples/input.txt --output-dir outs
-# Optional post-generation timestamps with stable-ts
+# 可选的生成后时间戳对齐（基于 stable-ts）
pip install "voxcpm[timestamps]"
voxcpm design \
- --text "VoxCPM2 brings studio-quality multilingual speech synthesis." \
+ --text "VoxCPM2带来全新语音合成体验。" \
--output out.wav \
--timestamps \
--timestamp-level word \
- --timestamp-language en
+ --timestamp-language zh
-# Character timestamps are best-effort and are derived from word alignment
+# 字级时间戳是 best-effort，会基于词级对齐结果拆分
voxcpm design \
--text "欢迎使用 VoxCPM2。" \
--output out.wav \
@@ -252,27 +260,27 @@ voxcpm design \
--timestamp-level char \
--timestamp-language zh
-# Help
+# 帮助
voxcpm --help
```
### Web Demo
```bash
-python app.py --port 8808 # then open in browser: http://localhost:8808
+python app.py --port 8808 # 然后在浏览器打开 http://localhost:8808
```
-Use `--device` to choose the runtime device:
+使用 `--device` 选择运行设备：
```bash
python app.py --device auto
```
-Supported values are `auto`, `cpu`, `mps`, `cuda`, and `cuda:N`. On Apple Silicon Macs, `auto` uses MPS when available.
+支持的取值包括 `auto`、`cpu`、`mps`、`cuda` 和 `cuda:N`。在 Apple Silicon Mac 上，`auto` 会在可用时使用 MPS。
-### 🚢 Production Deployment (Nano-vLLM)
+### 🚢 生产部署（Nano-vLLM）
-For high-throughput serving, use **[Nano-vLLM-VoxCPM](https://github.com/a710128/nanovllm-voxcpm)** — a dedicated inference engine built on Nano-vLLM with concurrent request support and an async API.
+如需高吞吐量部署，使用 [**Nano-vLLM-VoxCPM**](https://github.com/a710128/nanovllm-voxcpm) — 基于 Nano-vLLM 构建的专用推理引擎，支持并发请求和异步 API。
```bash
pip install nano-vllm-voxcpm
@@ -283,46 +291,46 @@ 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!"))
+chunks = list(server.generate(target_text="你好，我来自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](https://github.com/a710128/nanovllm-voxcpm) for deployment details.
+> **在 NVIDIA RTX 4090 上 RTF 低至 ~0.13**（标准 PyTorch 实现约 ~0.3），支持批量并发请求和 FastAPI HTTP 服务。详见 [Nano-vLLM-VoxCPM 仓库](https://github.com/a710128/nanovllm-voxcpm)。
-### 🏭 Production Serving (vLLM-Omni)
+### 🏭 生产环境部署（vLLM-Omni）
-For production multi-tenant deployments, use **[vLLM-Omni](https://github.com/vllm-project/vllm-omni)** — the official vLLM project's omni-modal extension with native **VoxCPM2** support. PagedAttention KV cache, continuous batching, and a drop-in **OpenAI-compatible** `/v1/audio/speech` endpoint.
+如需生产级多租户部署，使用 [**vLLM-Omni**](https://github.com/vllm-project/vllm-omni) — 官方 vLLM 项目的全模态扩展，原生支持 **VoxCPM2**。具备 PagedAttention KV 缓存、连续批处理，以及与 OpenAI 完全兼容的 `/v1/audio/speech` 接口。
```bash
-# Install from source (latest main — vllm-omni is rapidly evolving)
+# 从源码安装（最新 main 分支 —— vllm-omni 正在快速迭代）
uv pip install vllm==0.19.0 --torch-backend=auto
git clone https://github.com/vllm-project/vllm-omni.git && cd vllm-omni
uv pip install -e .
```
-See the [vLLM-Omni installation guide](https://vllm-omni.readthedocs.io/en/latest/getting_started/installation/) for other platforms (ROCm, XPU, MUSA, NPU) and Docker images.
+其他平台（ROCm、XPU、MUSA、NPU）与 Docker 镜像请参考 [vLLM-Omni 安装文档](https://vllm-omni.readthedocs.io/en/latest/getting_started/installation/)。
```bash
-# Launch an OpenAI-compatible TTS server (--omni enables omni-modal serving)
+# 启动 OpenAI 兼容的 TTS 服务（--omni 启用全模态服务）
vllm serve openbmb/VoxCPM2 --omni --port 8000
-# Call it from any OpenAI client
+# 任意 OpenAI 客户端均可调用
curl http://localhost:8000/v1/audio/speech \
-H "Content-Type: application/json" \
- -d '{"model":"openbmb/VoxCPM2","input":"Hello from VoxCPM2 on vLLM-Omni!","voice":"default"}' \
+ -d '{"model":"openbmb/VoxCPM2","input":"你好，欢迎使用 VoxCPM2 on vLLM-Omni！","voice":"default"}' \
--output out.wav
```
-> Built on the upstream vLLM scheduler, with batched concurrent requests, streaming chunk delivery, and multi-GPU deployment out of the box. See the [VoxCPM2 example](https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/voxcpm2) for full deployment recipes.
+> 基于上游 vLLM 调度器构建，开箱即用支持批量并发、流式分块输出和多 GPU 部署。完整示例见 [VoxCPM2 部署样例](https://github.com/vllm-project/vllm-omni/tree/main/examples/online_serving/voxcpm2)。
-### 📱 On-Device Inference (llama.cpp-omni)
+### 📱 端侧推理（llama.cpp-omni）
-For on-device / edge deployment without Python, use **[llama.cpp-omni](https://github.com/tc-mb/llama.cpp-omni)** — a high-performance C++ inference engine built on llama.cpp, with native VoxCPM2 GGUF support on **CPU / Metal / CUDA / Vulkan**.
+如需在端侧/消费级硬件上无 Python 运行，使用 **[llama.cpp-omni](https://github.com/tc-mb/llama.cpp-omni)** — 基于 llama.cpp 的高性能 C++ 推理引擎，原生支持 VoxCPM2 GGUF，可在 **CPU / Metal / CUDA / Vulkan** 上运行。
-**1. Download GGUF weights** from [HuggingFace](https://huggingface.co/DennisHuang648/VoxCPM2-GGUF) | [ModelScope](https://modelscope.cn/models/DennisHuang/VoxCPM2-GGUF) — you need one **BaseLM** (F16 or Q8_0) + the **Acoustic** file. Q8_0 halves the download with negligible quality loss.
+**1. 下载 GGUF 权重**：从 [HF下载](https://huggingface.co/DennisHuang648/VoxCPM2-GGUF) | [ModelScope](https://modelscope.cn/models/DennisHuang/VoxCPM2-GGUF)，需要一个 **BaseLM**（F16 或 Q8_0）+ **Acoustic** 文件。Q8_0 体积减半，质量损失可忽略。
-**2. Build**
+**2. 编译**
```bash
git clone https://github.com/tc-mb/llama.cpp-omni.git && cd llama.cpp-omni
@@ -330,330 +338,317 @@ cmake -B build -DCMAKE_BUILD_TYPE=Release
cmake --build build --target voxcpm2-cli -j
```
-> CMake auto-detects Metal (macOS) or CUDA (Linux with NVIDIA GPU).
+> CMake 会自动检测并启用 Metal（macOS）或 CUDA（Linux + NVIDIA GPU）。
-**3. Run**
+**3. 运行**
```bash
-# Basic TTS
+# 基础 TTS
./build/bin/voxcpm2-cli \
- -t "Hello, this is VoxCPM2 running through llama.cpp-omni." \
+ -t "你好，我是通过 llama.cpp-omni 运行的 VoxCPM2。" \
-o output.wav VoxCPM2-BaseLM-Q8_0.gguf VoxCPM2-Acoustic-F16.gguf
-# Voice cloning (reference audio)
+# 声音克隆（参考音频）
./build/bin/voxcpm2-cli \
- -t "Cloned voice." -r speaker.wav -o clone.wav \
+ -t "克隆的声音。" -r speaker.wav -o clone.wav \
VoxCPM2-BaseLM-Q8_0.gguf VoxCPM2-Acoustic-F16.gguf
-# Ultimate cloning (reference audio + transcript)
+# 精准克隆（参考音频 + 转写文本）
./build/bin/voxcpm2-cli \
- -t "Target text." --prompt-wav speaker.wav --prompt-text "transcript of speaker.wav" \
+ -t "目标文本。" --prompt-wav speaker.wav --prompt-text "参考音频的转写文本" \
-o clone.wav VoxCPM2-BaseLM-Q8_0.gguf VoxCPM2-Acoustic-F16.gguf
```
-> **RTF ~1.76 (Q8_0) on Apple M4 Pro / Metal.** Key flags: `--cfg` (guidance scale), `--timesteps` (CFM steps), `--seed`, `--temperature`, `--stream`. See the [llama.cpp-omni repo](https://github.com/tc-mb/llama.cpp-omni) and [GGUF weights page](https://huggingface.co/DennisHuang648/VoxCPM2-GGUF) for full details.
+> **在 Apple M4 Pro / Metal 上 RTF ~1.76（Q8_0）。** 主要参数：`--cfg`（引导尺度）、`--timesteps`（CFM 步数）、`--seed`、`--temperature`、`--stream`。详见 [llama.cpp-omni 仓库](https://github.com/tc-mb/llama.cpp-omni) 和 [GGUF 权重页面](https://huggingface.co/DennisHuang648/VoxCPM2-GGUF)。
-> **Full parameter reference, multi-scenario examples, and voice cloning tips →** [Quick Start Guide](https://voxcpm.readthedocs.io/en/latest/quickstart.html) | [Usage Guide](https://voxcpm.readthedocs.io/en/latest/usage_guide.html) | [Cookbook](https://voxcpm.readthedocs.io/en/latest/cookbook.html)
+> **完整参数说明、多场景示例与声音克隆技巧 →** [快速开始指南](https://voxcpm.readthedocs.io/zh-cn/latest/quickstart.html) | [使用指南](https://voxcpm.readthedocs.io/zh-cn/latest/usage_guide.html) | [Cookbook](https://voxcpm.readthedocs.io/zh-cn/latest/cookbook.html)
---
-## 📦 Models & Versions
+## 📦 模型与版本
+| | **VoxCPM2** | **VoxCPM1.5** | **VoxCPM-0.5B** |
+|---|:---:|:---:|:---:|
+| **状态** | 🟢 最新版本 | 稳定版 | 旧版 |
+| **主模型参数量** | 2B | 0.6B | 0.5B |
+| **音频采样率** | 48kHz | 44.1kHz | 16kHz |
+| **LM处理码率** | 6.25Hz | 6.25Hz | 12.5Hz |
+| **语言支持数量** | 30 | 2（中文、英文） | 2（中文、英文） |
+| **克隆模式** | 隔离参考音频（无需文本） & 音频续写 | 仅音频续写 | 仅音频续写 |
+| **音色设计** | ✅ | — | — |
+| **可控声音克隆** | ✅ | — | — |
+| **SFT / LoRA** | ✅ | ✅ | ✅ |
+| **RTF (RTX 4090)** | ~0.30 | ~0.15 | ~0.17 |
+| **RTF Nano-VLLM (RTX 4090)** | ~0.13 | ~0.08 | ~0.10 |
+| **显存占用** | ~8 GB | ~6 GB | ~5 GB |
+| **模型权重** | [🤗 HF](https://huggingface.co/openbmb/VoxCPM2) / [MS](https://modelscope.cn/models/OpenBMB/VoxCPM2) | [🤗 HF](https://huggingface.co/openbmb/VoxCPM1.5) / [MS](https://modelscope.cn/models/OpenBMB/VoxCPM1.5) | [🤗 HF](https://huggingface.co/openbmb/VoxCPM-0.5B) / [MS](https://modelscope.cn/models/OpenBMB/VoxCPM-0.5B) |
+| **技术报告** | [arXiv](https://arxiv.org/abs/2606.06928) | — | [arXiv](https://arxiv.org/abs/2509.24650) [ICLR 2026](https://openreview.net/forum?id=h5KLpGoqzC) |
+| **Demo 页面** | [音频示例](https://openbmb.github.io/voxcpm2-demopage) | — | [音频示例](https://openbmb.github.io/VoxCPM-demopage) |
-| | **VoxCPM2** | **VoxCPM1.5** | **VoxCPM-0.5B** |
-| ------------------------------- | ---------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------------------ |
-| **Status** | 🟢 Latest | Stable | Legacy |
-| **Backbone Parameters** | 2B | 0.6B | 0.5B |
-| **Audio Sample Rate** | 48kHz | 44.1kHz | 16kHz |
-| **LM Token Rate** | 6.25Hz | 6.25Hz | 12.5Hz |
-| **Languages** | 30 | 2 (zh, en) | 2 (zh, en) |
-| **Cloning Mode** | Isolated Reference & Continuation | Continuation only | Continuation only |
-| **Voice Design** | ✅ | — | — |
-| **Controllable Voice Cloning** | ✅ | — | — |
-| **SFT / LoRA** | ✅ | ✅ | ✅ |
-| **RTF (RTX 4090)** | ~0.30 | ~0.15 | ~0.17 |
-| **RTF in Nano-VLLM (RTX 4090)** | ~0.13 | ~0.08 | ~0.10 |
-| **VRAM** | ~8 GB | ~6 GB | ~5 GB |
-| **Weights** | [🤗 HF](https://huggingface.co/openbmb/VoxCPM2) / [MS](https://modelscope.cn/models/OpenBMB/VoxCPM2) | [🤗 HF](https://huggingface.co/openbmb/VoxCPM1.5) / [MS](https://modelscope.cn/models/OpenBMB/VoxCPM1.5) | [🤗 HF](https://huggingface.co/openbmb/VoxCPM-0.5B) / [MS](https://modelscope.cn/models/OpenBMB/VoxCPM-0.5B) |
-| **Technical Report** | [arXiv](https://arxiv.org/abs/2606.06928) | — | [arXiv](https://arxiv.org/abs/2509.24650) [ICLR 2026](https://openreview.net/forum?id=h5KLpGoqzC) |
-| **Demo Page** | [Audio Samples](https://openbmb.github.io/voxcpm2-demopage) | — | [Audio Samples](https://openbmb.github.io/VoxCPM-demopage) |
-
-
-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.
+VoxCPM2 采用**连续音频表征、扩散自回归**范式，模型在 **AudioVAE** 的连续隐空间中通过四阶段处理：**LocEnc → TSLM → RALM → LocDiT**，实现丰富的表现力语音合成和 48kHz 原生音频输出。
-
+
-> For full architectural details, VoxCPM2-specific upgrades, and a model comparison table, see the [Architecture Design](https://voxcpm.readthedocs.io/en/latest/models/architecture.html).
+> 完整架构细节、VoxCPM2 升级内容和模型对比表见 [架构设计文档](https://voxcpm.readthedocs.io/zh-cn/latest/models/architecture.html)。
---
-## 📊 Performance
+## 📊 性能评测
-VoxCPM2 achieves state-of-the-art or comparable results on public zero-shot and controllable TTS benchmarks.
+VoxCPM2 在公开的零样本和可控 TTS 基准测试中取得了 SOTA 或可比的结果。
### Seed-TTS-eval
-**Seed-TTS-eval WER(⬇)&SIM(⬆) Results (click to expand)**
-
-
-| Model | Parameters | Open-Source | test-EN | | test-ZH | | test-Hard | |
-| ----------------- | ---------- | ----------- | ------- | ------ | ------- | ------ | --------- | ------ |
-| | | | WER/%⬇ | SIM/%⬆ | CER/%⬇ | SIM/%⬆ | CER/%⬇ | SIM/%⬆ |
-| MegaTTS3 | 0.5B | ❌ | 2.79 | 77.1 | 1.52 | 79.0 | - | - |
-| DiTAR | 0.6B | ❌ | 1.69 | 73.5 | 1.02 | 75.3 | - | - |
-| CosyVoice3 | 0.5B | ❌ | 2.02 | 71.8 | 1.16 | 78.0 | 6.08 | 75.8 |
-| CosyVoice3 | 1.5B | ❌ | 2.22 | 72.0 | 1.12 | 78.1 | 5.83 | 75.8 |
-| Seed-TTS | - | ❌ | 2.25 | 76.2 | 1.12 | 79.6 | 7.59 | 77.6 |
-| MiniMax-Speech | - | ❌ | 1.65 | 69.2 | 0.83 | 78.3 | - | - |
-| F5-TTS | 0.3B | ✅ | 2.00 | 67.0 | 1.53 | 76.0 | 8.67 | 71.3 |
-| MaskGCT | 1B | ✅ | 2.62 | 71.7 | 2.27 | 77.4 | - | - |
-| CosyVoice | 0.3B | ✅ | 4.29 | 60.9 | 3.63 | 72.3 | 11.75 | 70.9 |
-| CosyVoice2 | 0.5B | ✅ | 3.09 | 65.9 | 1.38 | 75.7 | 6.83 | 72.4 |
-| SparkTTS | 0.5B | ✅ | 3.14 | 57.3 | 1.54 | 66.0 | - | - |
-| FireRedTTS | 0.5B | ✅ | 3.82 | 46.0 | 1.51 | 63.5 | 17.45 | 62.1 |
-| FireRedTTS-2 | 1.5B | ✅ | 1.95 | 66.5 | 1.14 | 73.6 | - | - |
-| Qwen2.5-Omni | 7B | ✅ | 2.72 | 63.2 | 1.70 | 75.2 | 7.97 | 74.7 |
-| Qwen3-Omni | 30B-A3B | ✅ | 1.39 | - | 1.07 | - | - | - |
-| OpenAudio-s1-mini | 0.5B | ✅ | 1.94 | 55.0 | 1.18 | 68.5 | 23.37 | 64.3 |
-| IndexTTS2 | 1.5B | ✅ | 2.23 | 70.6 | 1.03 | 76.5 | 7.12 | 75.5 |
-| VibeVoice | 1.5B | ✅ | 3.04 | 68.9 | 1.16 | 74.4 | - | - |
-| HiggsAudio-v2 | 3B | ✅ | 2.44 | 67.7 | 1.50 | 74.0 | 55.07 | 65.6 |
-| VoxCPM-0.5B | 0.6B | ✅ | 1.85 | 72.9 | 0.93 | 77.2 | 8.87 | 73.0 |
-| VoxCPM1.5 | 0.8B | ✅ | 2.12 | 71.4 | 1.18 | 77.0 | 7.74 | 73.1 |
-| MOSS-TTS | | ✅ | 1.85 | 73.4 | 1.20 | 78.8 | - | - |
-| Qwen3-TTS | 1.7B | ✅ | 1.23 | 71.7 | 1.22 | 77.0 | 6.76 | 74.8 |
-| FishAudio S2 | 4B | ✅ | 0.99 | - | 0.54 | - | 5.99 | - |
-| LongCat-Audio-DiT | 3.5B | ✅ | 1.50 | 78.6 | 1.09 | 81.8 | 6.04 | 79.7 |
-| **VoxCPM2** | 2B | ✅ | 1.84 | 75.3 | 0.97 | 79.5 | 8.13 | 75.3 |
-
-
-
-
-### CV3-eval
-
-**CV3-eval Multilingual WER/CER(⬇) Results (click to expand)**
-
-
-| Model | zh | en | hard-zh | hard-en | ja | ko | de | es | fr | it | ru |
-| --------------- | ---- | ---- | ------- | ------- | ---- | ---- | ---- | ---- | ---- | ---- | ---- |
-| CosyVoice2 | 4.08 | 6.32 | 12.58 | 11.96 | 9.13 | 19.7 | - | - | - | - | - |
-| CosyVoice3-1.5B | 3.91 | 4.99 | 9.77 | 10.55 | 7.57 | 5.69 | 6.43 | 4.47 | 11.8 | 10.5 | 6.64 |
-| Fish Audio S2 | 2.65 | 2.43 | 9.10 | 4.40 | 3.96 | 2.76 | 2.22 | 2.00 | 6.26 | 2.04 | 2.78 |
-| **VoxCPM2** | 3.65 | 5.00 | 8.55 | 8.48 | 5.96 | 5.69 | 4.77 | 3.80 | 9.85 | 4.25 | 5.21 |
+
+Seed-TTS-eval WER(⬇)&SIM(⬆) 结果（点击展开）
+
+| Model | Parameters | Open-Source | test-EN | | test-ZH | | test-Hard | |
+|------|------|------|:------------:|:--:|:------------:|:--:|:-------------:|:--:|
+| | | | WER/%⬇ | SIM/%⬆| CER/%⬇| SIM/%⬆ | CER/%⬇ | SIM/%⬆ |
+| MegaTTS3 | 0.5B | ❌ | 2.79 | 77.1 | 1.52 | 79.0 | - | - |
+| DiTAR | 0.6B | ❌ | 1.69 | 73.5 | 1.02 | 75.3 | - | - |
+| CosyVoice3 | 0.5B | ❌ | 2.02 | 71.8 | 1.16 | 78.0 | 6.08 | 75.8 |
+| CosyVoice3 | 1.5B | ❌ | 2.22 | 72.0 | 1.12 | 78.1 | 5.83 | 75.8 |
+| Seed-TTS | - | ❌ | 2.25 | 76.2 | 1.12 | 79.6 | 7.59 | 77.6 |
+| MiniMax-Speech | - | ❌ | 1.65 | 69.2 | 0.83 | 78.3 | - | - |
+| F5-TTS | 0.3B | ✅ | 2.00 | 67.0 | 1.53 | 76.0 | 8.67 | 71.3 |
+| MaskGCT | 1B | ✅ | 2.62 | 71.7 | 2.27 | 77.4 | - | - |
+| CosyVoice | 0.3B | ✅ | 4.29 | 60.9 | 3.63 | 72.3 | 11.75 | 70.9 |
+| CosyVoice2 | 0.5B | ✅ | 3.09 | 65.9 | 1.38 | 75.7 | 6.83 | 72.4 |
+| SparkTTS | 0.5B | ✅ | 3.14 | 57.3 | 1.54 | 66.0 | - | - |
+| FireRedTTS | 0.5B | ✅ | 3.82 | 46.0 | 1.51 | 63.5 | 17.45 | 62.1 |
+| FireRedTTS-2 | 1.5B | ✅ | 1.95 | 66.5 | 1.14 | 73.6 | - | - |
+| Qwen2.5-Omni | 7B | ✅ | 2.72 | 63.2 | 1.70 | 75.2 | 7.97 | 74.7 |
+| Qwen3-Omni | 30B-A3B | ✅ | 1.39 | - | 1.07 | - | - | - |
+| OpenAudio-s1-mini | 0.5B | ✅ | 1.94 | 55.0 | 1.18 | 68.5 | 23.37 | 64.3 |
+| IndexTTS2 | 1.5B | ✅ | 2.23 | 70.6 | 1.03 | 76.5 | 7.12 | 75.5 |
+| VibeVoice | 1.5B | ✅ | 3.04 | 68.9 | 1.16 | 74.4 | - | - |
+| HiggsAudio-v2 | 3B | ✅ | 2.44 | 67.7 | 1.50 | 74.0 | 55.07 | 65.6 |
+| VoxCPM-0.5B | 0.6B | ✅ | 1.85 | 72.9 | 0.93 | 77.2 | 8.87 | 73.0 |
+| VoxCPM1.5 | 0.8B | ✅ | 2.12 | 71.4 | 1.18 | 77.0 | 7.74 | 73.1 |
+| MOSS-TTS | | ✅ | 1.85 | 73.4 | 1.20 | 78.8 | - | - |
+| Qwen3-TTS | 1.7B | ✅ | 1.23 | 71.7 | 1.22 | 77.0 | 6.76 | 74.8 |
+| FishAudio S2 | 4B | ✅ | 0.99 | - | 0.54 | - | 5.99 | - |
+| LongCat-Audio-DiT | 3.5B | ✅ | 1.50 | 78.6 | 1.09 | 81.8 | 6.04 | 79.7 |
+| **VoxCPM2** | 2B | ✅ | 1.84 | 75.3 | 0.97| 79.5| 8.13 | 75.3 |
+
+### CV3-eval
+
+CV3-eval 多语言 WER/CER(⬇) 结果（点击展开）
+| Model | zh | en | hard-zh | hard-en | ja | ko | de | es | fr | it | ru |
+|-------|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
+| CosyVoice2 | 4.08 | 6.32 | 12.58| 11.96| 9.13 | 19.7 |- | - | - | - | - |
+| CosyVoice3-1.5B | 3.91 | 4.99 | 9.77 | 10.55 | 7.57 | 5.69 | 6.43 | 4.47 | 11.8 | 10.5 | 6.64 |
+| Fish Audio S2 | 2.65 | 2.43 | 9.10 | 4.40 | 3.96 | 2.76 | 2.22 | 2.00 | 6.26 | 2.04 | 2.78 |
+| **VoxCPM2** | 3.65 | 5.00 | 8.55 | 8.48 | 5.96 | 5.69 | 4.77 | 3.80 | 9.85 | 4.25 | 5.21 |
+
### MiniMax-Multilingual-Test
-**Minimax-MLS-test WER(⬇) Results (click to expand)**
+
+Minimax-MLS-test WER(⬇) 结果（点击展开）
+| Language | Minimax | ElevenLabs | Qwen3-TTS | FishAudio S2 | **VoxCPM2** |
+|----------|:-------:|:----------:|:--------------------:|:------------:|:-----------:|
+| Arabic | **1.665** | 1.666 | – | 3.500 | 13.046 |
+| Cantonese | 34.111 | 51.513 | – | **30.670** | 38.584 |
+| Chinese | 2.252 | 16.026 | 0.928 | **0.730** | 1.136 |
+| Czech | 3.875 | **2.108** | – | 2.840 | 24.132 |
+| Dutch | 1.143 | **0.803** | – | 0.990 | 0.913 |
+| English | 2.164 | 2.339 | **0.934** | 1.620 | 2.289 |
+| Finnish | 4.666 | 2.964 | – | 3.330 | **2.632** |
+| French | 4.099 | 5.216 | **2.858** | 3.050 | 4.534 |
+| German | 1.906 | 0.572 | 1.235 | **0.550** | 0.679 |
+| Greek | 2.016 | **0.991** | – | 5.740 | 2.844 |
+| Hindi | 6.962 | **5.827** | – | 14.640 | 19.699 |
+| Indonesian | 1.237 | **1.059** | – | 1.460 | 1.084 |
+| Italian | 1.543 | 1.743 | **0.948** | 1.270 | 1.563 |
+| Japanese | 3.519 | 10.646 | 3.823 | **2.760** | 4.628 |
+| Korean | 1.747 | 1.865 | 1.755 | **1.180** | 1.962 |
+| Polish | 1.415 | **0.766** | – | 1.260 | 1.141 |
+| Portuguese | 1.877 | 1.331 | 1.526 | **1.140** | 1.938 |
+| Romanian | 2.878 | **1.347** | – | 10.740 | 21.577 |
+| Russian | 4.281 | 3.878 | 3.212 | **2.400** | 3.634 |
+| Spanish | 1.029 | 1.084 | 1.126 | **0.910** | 1.438 |
+| Thai | 2.701 | 73.936 | – | 4.230 | 2.961 |
+| Turkish | 1.52 | 0.699 | – | 0.870 | 0.817 |
+| Ukrainian | 1.082 | **0.997** | – | 2.300 | 6.316 |
+| Vietnamese | **0.88** | 73.415 | – | 7.410 | 3.307 |
-| Language | Minimax | ElevenLabs | Qwen3-TTS | FishAudio S2 | **VoxCPM2** |
-| ---------- | --------- | ---------- | --------- | ------------ | ----------- |
-| Arabic | **1.665** | 1.666 | – | 3.500 | 13.046 |
-| Cantonese | 34.111 | 51.513 | – | **30.670** | 38.584 |
-| Chinese | 2.252 | 16.026 | 0.928 | **0.730** | 1.136 |
-| Czech | 3.875 | **2.108** | – | 2.840 | 24.132 |
-| Dutch | 1.143 | **0.803** | – | 0.990 | 0.913 |
-| English | 2.164 | 2.339 | **0.934** | 1.620 | 2.289 |
-| Finnish | 4.666 | 2.964 | – | 3.330 | **2.632** |
-| French | 4.099 | 5.216 | **2.858** | 3.050 | 4.534 |
-| German | 1.906 | 0.572 | 1.235 | **0.550** | 0.679 |
-| Greek | 2.016 | **0.991** | – | 5.740 | 2.844 |
-| Hindi | 6.962 | **5.827** | – | 14.640 | 19.699 |
-| Indonesian | 1.237 | **1.059** | – | 1.460 | 1.084 |
-| Italian | 1.543 | 1.743 | **0.948** | 1.270 | 1.563 |
-| Japanese | 3.519 | 10.646 | 3.823 | **2.760** | 4.628 |
-| Korean | 1.747 | 1.865 | 1.755 | **1.180** | 1.962 |
-| Polish | 1.415 | **0.766** | – | 1.260 | 1.141 |
-| Portuguese | 1.877 | 1.331 | 1.526 | **1.140** | 1.938 |
-| Romanian | 2.878 | **1.347** | – | 10.740 | 21.577 |
-| Russian | 4.281 | 3.878 | 3.212 | **2.400** | 3.634 |
-| Spanish | 1.029 | 1.084 | 1.126 | **0.910** | 1.438 |
-| Thai | 2.701 | 73.936 | – | 4.230 | 2.961 |
-| Turkish | 1.52 | 0.699 | – | 0.870 | 0.817 |
-| Ukrainian | 1.082 | **0.997** | – | 2.300 | 6.316 |
-| Vietnamese | **0.88** | 73.415 | – | 7.410 | 3.307 |
-
-
-
-
-**Minimax-MLS-test SIM(⬆) Results (click to expand)**
-
-
-| Language | Minimax | ElevenLabs | Qwen3-TTS | FishAudio S2 | **VoxCPM2** |
-| ---------- | -------- | ---------- | --------- | ------------ | ----------- |
-| Arabic | 73.6 | 70.6 | – | 75.0 | **79.1** |
-| Cantonese | 77.8 | 67.0 | – | 80.5 | **83.5** |
-| Chinese | 78.0 | 67.7 | 79.9 | 81.6 | **82.5** |
-| Czech | 79.6 | 68.5 | – | **79.8** | 78.3 |
-| Dutch | 73.8 | 68.0 | – | 73.0 | **80.8** |
-| English | 75.6 | 61.3 | 77.5 | 79.7 | **85.4** |
-| Finnish | 83.5 | 75.9 | – | 81.9 | **89.0** |
-| French | 62.8 | 53.5 | 62.8 | 69.8 | **73.5** |
-| German | 73.3 | 61.4 | 77.5 | 76.7 | **80.3** |
-| Greek | 82.6 | 73.3 | – | 79.5 | **86.0** |
-| Hindi | 81.8 | 73.0 | – | 82.1 | **85.6** |
-| Indonesian | 72.9 | 66.0 | – | 76.3 | **80.0** |
-| Italian | 69.9 | 57.9 | 81.7 | 74.7 | **78.0** |
-| Japanese | 77.6 | 73.8 | 78.8 | 79.6 | **82.8** |
-| Korean | 77.6 | 70.0 | 79.9 | 81.7 | **83.3** |
-| Polish | 80.2 | 72.9 | – | 81.9 | **88.4** |
-| Portuguese | 80.5 | 71.1 | 81.7 | 78.1 | **83.7** |
-| Romanian | **80.9** | 69.9 | – | 73.3 | 79.7 |
-| Russian | 76.1 | 67.6 | 79.2 | 79.0 | **81.1** |
-| Spanish | 76.2 | 61.5 | 81.4 | 77.6 | **83.1** |
-| Thai | 80.0 | 58.8 | – | 78.6 | **84.0** |
-| Turkish | 77.9 | 59.6 | – | 83.5 | **87.1** |
-| Ukrainian | 73.0 | 64.7 | – | 74.7 | **79.8** |
-| Vietnamese | 74.3 | 36.9 | – | 74.0 | **80.6** |
+
+
+Minimax-MLS-test SIM(⬆) 结果（点击展开）
+| Language | Minimax | ElevenLabs | Qwen3-TTS | FishAudio S2 | **VoxCPM2** |
+|----------|:-------:|:----------:|:--------------------:|:------------:|:-----------:|
+| Arabic | 73.6 | 70.6 | – | 75.0 | **79.1** |
+| Cantonese | 77.8 | 67.0 | – | 80.5 | **83.5** |
+| Chinese | 78.0 | 67.7 | 79.9 | 81.6 | **82.5** |
+| Czech | 79.6 | 68.5 | – | **79.8** | 78.3 |
+| Dutch | 73.8 | 68.0 | – | 73.0 | **80.8** |
+| English | 75.6 | 61.3 | 77.5 | 79.7 | **85.4** |
+| Finnish | 83.5 | 75.9 | – | 81.9 | **89.0** |
+| French | 62.8 | 53.5 | 62.8 | 69.8 | **73.5** |
+| German | 73.3 | 61.4 | 77.5 | 76.7 | **80.3** |
+| Greek | 82.6 | 73.3 | – | 79.5 | **86.0** |
+| Hindi | 81.8 | 73.0 | – | 82.1 | **85.6** |
+| Indonesian | 72.9 | 66.0 | – | 76.3 | **80.0** |
+| Italian | 69.9 | 57.9 | 81.7 | 74.7 | **78.0** |
+| Japanese | 77.6 | 73.8 | 78.8 | 79.6 | **82.8** |
+| Korean | 77.6 | 70.0 | 79.9 | 81.7 | **83.3** |
+| Polish | 80.2 | 72.9 | – | 81.9 | **88.4** |
+| Portuguese | 80.5 | 71.1 | 81.7 | 78.1 | **83.7** |
+| Romanian | **80.9** | 69.9 | – | 73.3 | 79.7 |
+| Russian | 76.1 | 67.6 | 79.2 | 79.0 | **81.1** |
+| Spanish | 76.2 | 61.5 | 81.4 | 77.6 | **83.1** |
+| Thai | 80.0 | 58.8 | – | 78.6 | **84.0** |
+| Turkish | 77.9 | 59.6 | – | 83.5 | **87.1** |
+| Ukrainian | 73.0 | 64.7 | – | 74.7 | **79.8** |
+| Vietnamese | 74.3 | 36.9 | – | 74.0 | **80.6** |
+
### Internal 30-Language ASR Benchmark
-We additionally run an internal multilingual intelligibility benchmark with **30 languages × 500 samples**. ASR transcription is evaluated via **Gemini 3.1 Flash Lite API**.
+我们额外进行了内部多语言可懂度评测：**30 语种 × 500 样本**，ASR 转写评估使用 **Gemini 3.1 Flash Lite API**。
-**Internal 30-Language ASR Benchmark (click to expand)**
-
-
-| Language | Metric | VoxCPM2 | Fish S2-Pro |
-| ---------------------- | ------ | --------- | ----------- |
-| ar (Arabic) | CER | 1.23% | 0.30% |
-| da (Danish) | WER | 2.70% | 3.52% |
-| de (German) | WER | 0.96% | 0.64% |
-| el (Greek) | WER | 3.17% | 4.61% |
-| en (English) | WER | 0.42% | 1.03% |
-| es (Spanish) | WER | 1.33% | 0.64% |
-| fi (Finnish) | WER | 2.24% | 2.80% |
-| fr (French) | WER | 2.16% | 2.34% |
-| he (Hebrew) | CER | 2.98% | 15.27% |
-| hi (Hindi) | CER | 0.79% | 0.91% |
-| id (Indonesian) | WER | 1.36% | 1.68% |
-| it (Italian) | WER | 1.65% | 1.08% |
-| ja (Japanese) | CER | 2.40% | 1.82% |
-| km (Khmer) | CER | 2.05% | 75.15% |
-| ko (Korean) | CER | 0.95% | 0.29% |
-| lo (Lao) | CER | 1.90% | 87.40% |
-| ms (Malay) | WER | 1.75% | 1.41% |
-| my (Burmese) | CER | 1.42% | 85.27% |
-| nl (Dutch) | WER | 1.25% | 1.68% |
-| no (Norwegian) | WER | 2.49% | 3.76% |
-| pl (Polish) | WER | 1.90% | 1.65% |
-| pt (Portuguese) | WER | 1.48% | 1.49% |
-| ru (Russian) | WER | 0.90% | 0.86% |
-| sv (Swedish) | WER | 2.22% | 2.63% |
-| sw (Swahili) | CER | 1.07% | 2.02% |
-| th (Thai) | CER | 0.94% | 1.92% |
-| tl (Tagalog) | WER | 2.63% | 4.00% |
-| tr (Turkish) | WER | 1.65% | 1.65% |
-| vi (Vietnamese) | WER | 1.56% | 5.56% |
-| zh (Chinese) | CER | 0.92% | 1.02% |
-| Average (30 languages) | | **1.68%** | - |
+
+内部30语种评测集ASR结果（点击展开）
+| 语言 | 指标 | VoxCPM2 | Fish S2-Pro |
+|---|---:|---:|---:|
+| ar (阿拉伯语) | CER | 1.23% | 0.30% |
+| da (丹麦语) | WER | 2.70% | 3.52% |
+| de (德语) | WER | 0.96% | 0.64% |
+| el (希腊语) | WER | 3.17% | 4.61% |
+| en (英语) | WER | 0.42% | 1.03% |
+| es (西班牙语) | WER | 1.33% | 0.64% |
+| fi (芬兰语) | WER | 2.24% | 2.80% |
+| fr (法语) | WER | 2.16% | 2.34% |
+| he (希伯来语) | CER | 2.98% | 15.27% |
+| hi (印地语) | CER | 0.79% | 0.91% |
+| id (印尼语) | WER | 1.36% | 1.68% |
+| it (意大利语) | WER | 1.65% | 1.08% |
+| ja (日语) | CER | 2.40% | 1.82% |
+| km (高棉语) | CER | 2.05% | 75.15% |
+| ko (韩语) | CER | 0.95% | 0.29% |
+| lo (老挝语) | CER | 1.90% | 87.40% |
+| ms (马来语) | WER | 1.75% | 1.41% |
+| my (缅甸语) | CER | 1.42% | 85.27% |
+| nl (荷兰语) | WER | 1.25% | 1.68% |
+| no (挪威语) | WER | 2.49% | 3.76% |
+| pl (波兰语) | WER | 1.90% | 1.65% |
+| pt (葡萄牙语) | WER | 1.48% | 1.49% |
+| ru (俄语) | WER | 0.90% | 0.86% |
+| sv (瑞典语) | WER | 2.22% | 2.63% |
+| sw (斯瓦希里语) | CER | 1.07% | 2.02% |
+| th (泰语) | CER | 0.94% | 1.92% |
+| tl (菲律宾语) | WER | 2.63% | 4.00% |
+| tr (土耳其语) | WER | 1.65% | 1.65% |
+| vi (越南语) | WER | 1.56% | 5.56% |
+| zh (中文) | CER | 0.92% | 1.02% |
+| 平均（30 语种） | | **1.68%** | - |
+
### InstructTTSEval
-**Instruction-Guided Voice Design Results (click to expand)**
-
-
-| Model | InstructTTSEval-ZH | | | InstructTTSEval-EN | | |
-| ---------------------- | ------------------ | -------- | -------- | ------------------ | -------- | -------- |
-| | APS⬆ | DSD⬆ | RP⬆ | APS⬆ | DSD⬆ | RP⬆ |
-| Hume | – | – | – | 83.0 | 75.3 | 54.3 |
-| VoxInstruct | 47.5 | 52.3 | 42.6 | 54.9 | 57.0 | 39.3 |
-| Parler-tts-mini | – | – | – | 63.4 | 48.7 | 28.6 |
-| Parler-tts-large | – | – | – | 60.0 | 45.9 | 31.2 |
-| PromptTTS | – | – | – | 64.3 | 47.2 | 31.4 |
-| PromptStyle | – | – | – | 57.4 | 46.4 | 30.9 |
-| VoiceSculptor | 75.7 | 64.7 | 61.5 | – | – | – |
-| Mimo-Audio-7B-Instruct | 75.7 | 74.3 | 61.5 | 80.6 | 77.6 | 59.5 |
-| Qwen3TTS-12Hz-1.7B-VD | **85.2** | **81.1** | **65.1** | 82.9 | 82.4 | 68.4 |
-| **VoxCPM2** | **85.2** | 71.5 | 60.8 | **84.2** | **83.2** | **71.4** |
-
+
+指令驱动音色设计结果 (点击展开)
+| Model | InstructTTSEval-ZH | | | InstructTTSEval-EN | | |
+|-------|:---:|:----:|:----:|:----:|:----:|:----:|
+| | APS⬆| DSD⬆ | RP⬆| APS⬆ | DSD⬆ | RP⬆ |
+| Hume | – | – | – | 83.0 | 75.3 | 54.3 |
+| VoxInstruct | 47.5 | 52.3 | 42.6 | 54.9 | 57.0 | 39.3 |
+| Parler-tts-mini | – | – | – | 63.4 | 48.7 | 28.6 |
+| Parler-tts-large | – | – | – | 60.0 | 45.9 | 31.2 |
+| PromptTTS | – | – | – | 64.3 | 47.2 | 31.4 |
+| PromptStyle | – | – | – | 57.4 | 46.4 | 30.9 |
+| VoiceSculptor | 75.7 | 64.7 | 61.5 | – | – | – |
+| Mimo-Audio-7B-Instruct | 75.7 | 74.3 | 61.5 | 80.6 | 77.6 | 59.5 |
+| Qwen3TTS-12Hz-1.7B-VD | **85.2** | **81.1** | **65.1** | 82.9 | 82.4 | 68.4 |
+| **VoxCPM2** | **85.2** | 71.5 | 60.8 | **84.2** | **83.2** | **71.4** |
+
---
-## ⚙️ Fine-tuning
+## ⚙️ 微调
-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.
+VoxCPM 支持**全参数微调（SFT）** 和 **LoRA 微调**。仅需 **5-10分钟** 的音频数据，即可适配特定说话人、语言或领域。
```bash
-# LoRA fine-tuning (parameter-efficient, recommended)
+# LoRA 微调（参数高效，推荐）
python scripts/train_voxcpm_finetune.py \
--config_path conf/voxcpm_v2/voxcpm_finetune_lora.yaml
-# Full fine-tuning
+# 全参数微调
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
+# WebUI 训练与推理
+python lora_ft_webui.py # 然后打开 http://localhost:7860
```
-> **Full guide →** [Fine-tuning Guide](https://voxcpm.readthedocs.io/en/latest/finetuning/finetune.html) (data preparation, configuration, training, LoRA hot-swapping, FAQ)
+> **完整指南 →** [微调文档](https://voxcpm.readthedocs.io/zh-cn/latest/finetuning/finetune.html)（数据准备、配置、训练、LoRA 热切换、常见问题）
---
-## 📚 Documentation
+## 📚 文档
-Full documentation: **[voxcpm.readthedocs.io](https://voxcpm.readthedocs.io/en/latest/)**
-
-
-| Topic | Link |
-| -------------------------- | ------------------------------------------------------------------------------------- |
-| Quick Start & Installation | [Quick Start](https://voxcpm.readthedocs.io/en/latest/quickstart.html) |
-| Usage Guide & Cookbook | [User Guide](https://voxcpm.readthedocs.io/en/latest/usage_guide.html) |
-| VoxCPM Series | [Models](https://voxcpm.readthedocs.io/en/latest/models/version_history.html) |
-| Fine-tuning (SFT & LoRA) | [Fine-tuning Guide](https://voxcpm.readthedocs.io/en/latest/finetuning/finetune.html) |
-| FAQ & Troubleshooting | [FAQ](https://voxcpm.readthedocs.io/en/latest/faq.html) |
+完整文档：**[voxcpm.readthedocs.io](https://voxcpm.readthedocs.io/zh-cn/latest/)**
+| 主题 | 链接 |
+|---|---|
+| 快速开始与安装 | [快速开始](https://voxcpm.readthedocs.io/zh-cn/latest/quickstart.html) |
+| 使用指南与 Cookbook | [使用指南](https://voxcpm.readthedocs.io/zh-cn/latest/usage_guide.html) |
+| VoxCPM 系列模型 | [模型列表](https://voxcpm.readthedocs.io/zh-cn/latest/models/version_history.html) |
+| 微调（SFT & LoRA） | [微调指南](https://voxcpm.readthedocs.io/zh-cn/latest/finetuning/finetune.html) |
+| 常见问题 | [FAQ](https://voxcpm.readthedocs.io/zh-cn/latest/faq.html) |
---
-## 🌟 Ecosystem & Community
+## 🌟 生态与社区
+| 项目 | 说明 |
+|---|---|
+| [**Nano-vLLM**](https://github.com/a710128/nanovllm-voxcpm) | 高吞吐快速 GPU 推理引擎 |
+| [**vLLM-Omni**](https://github.com/vllm-project/vllm-omni) | 官方 vLLM 全模态服务（原生支持 VoxCPM2）— PagedAttention、OpenAI 兼容 API |
+| [**llama.cpp-omni**](https://github.com/tc-mb/llama.cpp-omni) | 全双工全模态推理引擎 — VoxCPM2 GGUF，支持 CPU / Metal / CUDA / Vulkan |
+| [**VoxCPM.cpp**](https://github.com/bluryar/VoxCPM.cpp) | GGML/GGUF：CPU、CUDA、Vulkan 推理 |
+| [**audio.cpp**](https://github.com/0xShug0/audio.cpp) | 基于 ggml 的统一 C++ 推理框架 — CPU/CUDA/Vulkan/Metal，CLI 与服务端，无需 Python |
+| [**VoxCPM-ONNX**](https://github.com/bluryar/VoxCPM-ONNX) | ONNX 导出，支持 CPU 推理 |
+| [**VoxCPMANE**](https://github.com/0seba/VoxCPMANE) | Apple Neural Engine 后端 |
+| [**voxcpm_rs**](https://github.com/madushan1000/voxcpm_rs) | Rust 重新实现 |
+| [**ComfyUI-VoxCPM**](https://github.com/wildminder/ComfyUI-VoxCPM) | ComfyUI 节点工作流 |
+| [**ComfyUI_RH_VoxCPM**](https://github.com/HM-RunningHub/ComfyUI_RH_VoxCPM) | 面向 VoxCPM 2 的功能更完整的 ComfyUI 工作流，支持多说话人、LoRA 和自动 ASR |
+| [**ComfyUI-VoxCPMTTS**](https://github.com/1038lab/ComfyUI-VoxCPMTTS) | ComfyUI TTS 扩展 |
+| [**TTS WebUI**](https://github.com/rsxdalv/tts_webui_extension.vox_cpm) | 浏览器端 TTS 扩展 |
-| Project | Description |
-| --------------------------------------------------------------------------- | ------------------------------------------------------------------------------------------------ |
-| **[Nano-vLLM](https://github.com/a710128/nanovllm-voxcpm)** | High-throughput and Fast GPU serving |
-| **[vLLM-Omni](https://github.com/vllm-project/vllm-omni)** | Official vLLM omni-modal serving for VoxCPM2 — PagedAttention, OpenAI-compatible API |
-| **[llama.cpp-omni](https://github.com/tc-mb/llama.cpp-omni)** | Full-duplex omni inference engine — VoxCPM2 GGUF on CPU / Metal / CUDA / Vulkan |
-| **[VoxCPM.cpp](https://github.com/bluryar/VoxCPM.cpp)** | GGML/GGUF: CPU, CUDA, Vulkan inference |
-| **[audio.cpp](https://github.com/0xShug0/audio.cpp)** | ggml-based unified C++ inference framework — CPU/CUDA/Vulkan/Metal, CLI & server, no Python |
-| **[VoxCPM-ONNX](https://github.com/bluryar/VoxCPM-ONNX)** | ONNX export for CPU inference |
-| **[VoxCPMANE](https://github.com/0seba/VoxCPMANE)** | Apple Neural Engine backend |
-| **[voxcpm_rs](https://github.com/madushan1000/voxcpm_rs)** | Rust re-implementation |
-| **[ComfyUI-VoxCPM](https://github.com/wildminder/ComfyUI-VoxCPM)** | ComfyUI node-based workflows |
-| **[ComfyUI_RH_VoxCPM](https://github.com/HM-RunningHub/ComfyUI_RH_VoxCPM)** | Feature-complete ComfyUI workflow for VoxCPM 2 with multi-speaker generation, LoRA, and auto-ASR |
-| **[ComfyUI-VoxCPMTTS](https://github.com/1038lab/ComfyUI-VoxCPMTTS)** | ComfyUI TTS extension |
-| **[TTS WebUI](https://github.com/rsxdalv/tts_webui_extension.vox_cpm)** | Browser-based TTS extension |
-
-
-> See the full [Ecosystem](https://voxcpm.readthedocs.io/en/latest/) in the docs. Community projects are not officially maintained by OpenBMB. Built something cool? [Open an issue or PR](https://github.com/OpenBMB/VoxCPM/issues) to add it!
+> 完整生态见[文档](https://voxcpm.readthedocs.io/zh-cn/latest/)。社区项目非 OpenBMB 官方维护。做了什么有趣的东西？[提 Issue 或 PR](https://github.com/OpenBMB/VoxCPM/issues) 把它加进来！
---
-## ⚠️ Risks and Limitations
+## ⚠️ 风险与局限性
-- **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.
+- **滥用风险：** VoxCPM 的声音克隆能力可生成高度逼真的合成语音。**严禁**将 VoxCPM 用于冒充他人、欺诈或虚假信息传播。我们强烈建议对所有 AI 生成的内容进行明确标注。
+- **可控生成稳定性：** 音色设计和可控声音克隆的结果可能因生成次数而异 — 建议尝试生成 1~3 次以获得理想的音色或风格。我们正在积极提升可控性的一致性。
+- **语言覆盖：** VoxCPM2 官方支持 30 种语言。对于未列入的语言，欢迎直接测试或使用自有数据进行微调。我们计划在未来版本中扩展语言覆盖。
+- **使用说明：** 本模型基于 Apache-2.0 协议发布。用于生产部署时，我们建议针对具体场景进行充分的测试和安全评估。
---
-## 📖 Citation
+## 📖 引用
-If you find VoxCPM helpful, please consider citing our work and starring ⭐ the repository!
+如果 VoxCPM 对您有帮助，请考虑引用我们的工作并为仓库加星 ⭐！
```bib
@article{zhou2026voxcpm2,
@@ -671,22 +666,26 @@ If you find VoxCPM helpful, please consider citing our work and starring ⭐ the
}
```
-## 📄 License
+## 📄 许可证
-VoxCPM model weights and code are open-sourced under the [Apache-2.0](LICENSE) license.
+VoxCPM 模型权重和代码基于 [Apache-2.0](LICENSE) 协议开源。
-## 🙏 Acknowledgments
+## 🙏 致谢
-- [DiTAR](https://arxiv.org/abs/2502.03930) for the diffusion autoregressive backbone
-- [MiniCPM-4](https://github.com/OpenBMB/MiniCPM) for the language model foundation
-- [CosyVoice](https://github.com/FunAudioLLM/CosyVoice) for the Flow Matching-based LocDiT implementation
-- [DAC](https://github.com/descriptinc/descript-audio-codec) 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
+- [DiTAR](https://arxiv.org/abs/2502.03930) 扩散自回归骨干架构
+- [MiniCPM-4](https://github.com/OpenBMB/MiniCPM) 语言模型基座
+- [CosyVoice](https://github.com/FunAudioLLM/CosyVoice) 基于 Flow Matching 的 LocDiT 实现
+- [DAC](https://github.com/descriptinc/descript-audio-codec) Audio VAE 骨干
+- 感谢所有社区用户试用 VoxCPM、反馈问题、分享想法和贡献——你们的支持让项目持续进步
-## Institutions
+## 机构
-[ModelBest](https://modelbest.cn/) [THUHCSI](https://github.com/thuhcsi)
+
+ 
面壁智能
+
+ 
清华大学人机交互实验室
+
-## ⭐ Star History
+## ⭐ Star 历史
-[Star History Chart](https://star-history.com/#OpenBMB/VoxCPM&Date)
\ No newline at end of file
+[](https://star-history.com/#OpenBMB/VoxCPM&Date)
- 
+ 
- 
+
Feishu
+