Instructions to use MoYoYoTech/VoiceDialogue with libraries, inference providers, notebooks, and local apps. Follow these links to get started.

Libraries

How to use MoYoYoTech/VoiceDialogue with Transformers:

# Use a pipeline as a high-level helper
from transformers import pipeline

pipe = pipeline("text-to-speech", model="MoYoYoTech/VoiceDialogue")
messages = [
    {"role": "user", "content": "Who are you?"},
]
pipe(messages)

# Load model directly
from transformers import AutoModel
model = AutoModel.from_pretrained("MoYoYoTech/VoiceDialogue", dtype="auto")

llama-cpp-python

How to use MoYoYoTech/VoiceDialogue with llama-cpp-python:

# !pip install llama-cpp-python

from llama_cpp import Llama

llm = Llama.from_pretrained(
	repo_id="MoYoYoTech/VoiceDialogue",
	filename="assets/models/llm/qwen/Qwen3-8B-Q6_K.gguf",
)

llm.create_chat_completion(
	messages = "\"The answer to the universe is 42\""
)

Notebooks
Google Colab
Kaggle
Local Apps Settings

llama.cpp

How to use MoYoYoTech/VoiceDialogue with llama.cpp:

Install from brew

brew install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MoYoYoTech/VoiceDialogue:Q6_K
# Run inference directly in the terminal:
llama-cli -hf MoYoYoTech/VoiceDialogue:Q6_K

Install from WinGet (Windows)

winget install llama.cpp
# Start a local OpenAI-compatible server with a web UI:
llama-server -hf MoYoYoTech/VoiceDialogue:Q6_K
# Run inference directly in the terminal:
llama-cli -hf MoYoYoTech/VoiceDialogue:Q6_K

Use pre-built binary

# Download pre-built binary from:
# https://github.com/ggerganov/llama.cpp/releases
# Start a local OpenAI-compatible server with a web UI:
./llama-server -hf MoYoYoTech/VoiceDialogue:Q6_K
# Run inference directly in the terminal:
./llama-cli -hf MoYoYoTech/VoiceDialogue:Q6_K

Build from source code

git clone https://github.com/ggerganov/llama.cpp.git
cd llama.cpp
cmake -B build
cmake --build build -j --target llama-server llama-cli
# Start a local OpenAI-compatible server with a web UI:
./build/bin/llama-server -hf MoYoYoTech/VoiceDialogue:Q6_K
# Run inference directly in the terminal:
./build/bin/llama-cli -hf MoYoYoTech/VoiceDialogue:Q6_K

Use Docker

docker model run hf.co/MoYoYoTech/VoiceDialogue:Q6_K

LM Studio
Jan
Ollama
How to use MoYoYoTech/VoiceDialogue with Ollama:
```
ollama run hf.co/MoYoYoTech/VoiceDialogue:Q6_K
```

Unsloth Studio

How to use MoYoYoTech/VoiceDialogue with Unsloth Studio:

Install Unsloth Studio (macOS, Linux, WSL)

curl -fsSL https://unsloth.ai/install.sh | sh
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for MoYoYoTech/VoiceDialogue to start chatting

Install Unsloth Studio (Windows)

irm https://unsloth.ai/install.ps1 | iex
# Run unsloth studio
unsloth studio -H 0.0.0.0 -p 8888
# Then open http://localhost:8888 in your browser
# Search for MoYoYoTech/VoiceDialogue to start chatting

Using HuggingFace Spaces for Unsloth

# No setup required
# Open https://huggingface.co/spaces/unsloth/studio in your browser
# Search for MoYoYoTech/VoiceDialogue to start chatting

How to use MoYoYoTech/VoiceDialogue with Pi:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf MoYoYoTech/VoiceDialogue:Q6_K

Configure the model in Pi

# Install Pi:
npm install -g @mariozechner/pi-coding-agent
# Add to ~/.pi/agent/models.json:
{
  "providers": {
    "llama-cpp": {
      "baseUrl": "http://localhost:8080/v1",
      "api": "openai-completions",
      "apiKey": "none",
      "models": [
        {
          "id": "MoYoYoTech/VoiceDialogue:Q6_K"
        }
      ]
    }
  }
}

Run Pi

# Start Pi in your project directory:
pi

Hermes Agent new

How to use MoYoYoTech/VoiceDialogue with Hermes Agent:

Start the llama.cpp server

# Install llama.cpp:
brew install llama.cpp
# Start a local OpenAI-compatible server:
llama-server -hf MoYoYoTech/VoiceDialogue:Q6_K

Configure Hermes

# Install Hermes:
curl -fsSL https://hermes-agent.nousresearch.com/install.sh | bash
hermes setup
# Point Hermes at the local server:
hermes config set model.provider custom
hermes config set model.base_url http://127.0.0.1:8080/v1
hermes config set model.default MoYoYoTech/VoiceDialogue:Q6_K

Run Hermes

hermes

Atomic Chat new
Docker Model Runner
How to use MoYoYoTech/VoiceDialogue with Docker Model Runner:
```
docker model run hf.co/MoYoYoTech/VoiceDialogue:Q6_K
```

Lemonade

How to use MoYoYoTech/VoiceDialogue with Lemonade:

Pull the model

# Download Lemonade from https://lemonade-server.ai/
lemonade pull MoYoYoTech/VoiceDialogue:Q6_K

Run and chat with the model

lemonade run user.VoiceDialogue-Q6_K

List all available models

lemonade list

VoiceDialogue / Vibevoice /installation.md

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Add detailed installation guide and restructure README setup section

28762c8 about 1 year ago

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VoiceDialogue 安装指南

本文档提供 VoiceDialogue 智能语音对话系统的详细安装说明。

系统要求

在开始安装之前，请确保您的系统满足以下要求：

操作系统: macOS 14+ (推荐)
Python 版本: 3.9 或更高版本
内存要求: 至少 16GB RAM (推荐 32GB 用于大模型)
存储空间: 至少 20GB 可用空间 (用于模型文件)

安装步骤

1. 克隆项目

git clone https://huggingface.co/MoYoYoTech/VoiceDialogue
cd VoiceDialogue

2. 创建并激活虚拟环境

建议使用虚拟环境来避免依赖冲突：

# 使用 uv (推荐)
pip install uv
uv venv
source .venv/bin/activate

# 或使用 conda
conda create -n voicedialogue python=3.11
conda activate voicedialogue

# 或使用 venv
python -m venv voicedialogue
source voicedialogue/bin/activate

3. 安装项目依赖

# 使用 uv (推荐)
WHISPER_COREML=1 CMAKE_ARGS="-DGGML_METAL=on" uv sync

# 或使用 pip
WHISPER_COREML=1 CMAKE_ARGS="-DGGML_METAL=on" pip install -r requirements.txt

4. 安装音频处理工具

# macOS
brew install ffmpeg

5. 安装额外依赖

# 安装 kokoro-onnx
uv pip install kokoro-onnx
# 或
pip install kokoro-onnx

# 重新安装指定版本的 numpy
uv pip install numpy==1.26.4
# 或
pip install numpy==1.26.4

验证安装

安装完成后，可以通过以下命令验证安装是否成功：

# 查看帮助信息
python main.py --help

# 启动系统（默认使用中文，沈逸角色）
python main.py

如果看到 "服务启动成功" 提示，说明安装成功。

故障排除

1. 模型下载失败

问题: 网络连接超时或模型下载失败。
解决方案: 设置 Hugging Face 镜像。

export HF_ENDPOINT=https://hf-mirror.com
pip install -U huggingface_hub

2. 音频设备问题

问题: 找不到音频设备或权限被拒绝。
macOS 解决方案: 系统设置 → 隐私与安全性 → 麦克风 → 启用你的终端应用 (如 iTerm, Terminal)。

3. 内存不足错误 (OOM)

问题: CUDA out of memory 或 RAM 不足。
解决方案: LLM 是主要的内存消耗者。你可以通过修改 src/VoiceDialogue/services/text/generator.py 来降低资源消耗：
- 更换模型: 将模型路径指向一个更小的模型（如 7B Q4 量化模型）。
- 减少批处理大小: 减小模型参数中的 n_batch 值（如 256）。
- 减少上下文长度: 减小 n_ctx 的值（如 1024）。

4. 依赖包冲突

问题: 包版本冲突或导入错误。
解决方案: 强烈建议在虚拟环境中安装。如果遇到问题，尝试重建虚拟环境。

# 使用 conda
conda deactivate
conda env remove -n voicedialogue

# 使用 uv
rm -rf .venv
uv venv

5. FFmpeg 相关错误

问题: 音频处理失败或编解码错误。
解决方案: 确保正确安装 FFmpeg：

# 检查 FFmpeg 安装
ffmpeg -version

# 重新安装 FFmpeg
# macOS
brew reinstall ffmpeg

6. Python 版本兼容性

问题: Python 版本过低导致的兼容性问题。
解决方案: 确保使用 Python 3.11+ 版本：

python --version
# 如果版本过低，请升级或使用虚拟环境

下一步

安装完成后，您可以：

查看使用指南了解如何使用系统
查看配置选项了解如何自定义配置
查看系统架构了解系统工作原理

如果遇到其他问题，请查看完整故障排除指南或提交 Issue。