Text-to-Speech
Transformers
ONNX
GGUF
Chinese
English
voice-dialogue
speech-recognition
large-language-model
asr
tts
llm
chinese
english
real-time
conversational
Instructions to use MoYoYoTech/VoiceDialogue with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
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", device_map="auto") - Notebooks
- Google Colab
- Kaggle
- Local Apps Settings
- llama.cpp
How to use MoYoYoTech/VoiceDialogue with llama.cpp:
Install (macOS, Linux)
curl -LsSf https://llama.app/install.sh | sh # Start a local OpenAI-compatible server with a web UI: llama serve -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 serve -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
- Pi
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 serve -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 serve -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
- OpenClaw new
How to use MoYoYoTech/VoiceDialogue with OpenClaw:
Start the llama.cpp server
# Install llama.cpp: brew install llama.cpp # Start a local OpenAI-compatible server: llama serve -hf MoYoYoTech/VoiceDialogue:Q6_K
Configure OpenClaw
# Install OpenClaw: npm install -g openclaw@latest # Register the local server and set it as the default model: openclaw onboard --non-interactive --mode local \ --auth-choice custom-api-key \ --custom-base-url http://127.0.0.1:8080/v1 \ --custom-model-id "MoYoYoTech/VoiceDialogue:Q6_K" \ --custom-provider-id llama-cpp \ --custom-compatibility openai \ --custom-text-input \ --accept-risk \ --skip-health
Run OpenClaw
openclaw agent --local --agent main --message "Hello from Hugging Face"
- 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
| title: VoiceDialogue - 智能语音对话系统 | |
| license: mit | |
| language: | |
| - zh | |
| - en | |
| pipeline_tag: text-to-speech | |
| tags: | |
| - voice-dialogue | |
| - speech-recognition | |
| - text-to-speech | |
| - large-language-model | |
| - asr | |
| - tts | |
| - llm | |
| - chinese | |
| - english | |
| - real-time | |
| library_name: transformers | |
| # VoiceDialogue - 智能语音对话系统 | |
| <div align="center"> | |
|  | |
|  | |
|  | |
|  | |
| 一个集成了语音识别(ASR)、大语言模型(LLM)和文本转语音(TTS)的实时语音对话系统 | |
| [快速开始](#-快速开始) • [文档导航](#-文档导航) • [贡献指南](docs/contributing.md) | |
| </div> | |
| ## 🎯 项目简介 | |
| VoiceDialogue 是一个基于 Python 的完整语音对话系统,实现了端到端的语音交互体验。系统采用模块化设计,具备实时、高精度、多角色的特点。 | |
| - 🖥️ **图形界面**: 内置 Web 图形界面,浏览器即可使用(选音色、切语言、看实时字幕) | |
| - 🎤 **实时语音识别**: 基于 Qwen3-ASR 的高精度中英文转录(自带标点,支持 52 种语言) | |
| - 🤖 **智能对话生成**: 集成 Qwen3 等大语言模型 | |
| - 🔊 **高质量语音合成**: 支持多角色、多风格的语音输出 | |
| - 🌐 **Web API 服务**: 提供 HTTP 接口,方便集成 | |
| - ⚡ **低延迟处理**: 优化的音频流处理管道 | |
| > 想要了解更多?请查看 [功能特性详解](docs/features.md)。 | |
| ## 🚀 快速开始 | |
| > **最简单的方式**:克隆仓库 → 安装依赖 → 启动 → 在浏览器打开图形界面,即可开始语音对话。 | |
| > 目前仅支持 **macOS(Apple Silicon)**。 | |
| ### 1. 克隆并安装 | |
| > **模型分两部分**: | |
| > - **随仓库下载(约 12GB,Git LFS)**:大语言模型、语音合成、参考音色等。 | |
| > - **首次启动自动下载(约 4.4GB)**:语音识别引擎 **Qwen3-ASR**,由程序在第一次运行时从 HuggingFace 拉取并缓存到 `~/.cache/huggingface`,之后无需重复下载。 | |
| > | |
| > ⚠️ **必须先安装 [Git LFS](https://git-lfs.com)**,否则克隆下来的模型只是几百字节的占位指针,应用无法启动。 | |
| ```bash | |
| # 1) 安装并初始化 Git LFS(只需一次) | |
| brew install git-lfs # 如未安装 Homebrew,见 https://git-lfs.com | |
| git lfs install | |
| # 2) 克隆项目(包含约 12GB 模型,体积较大,请耐心等待) | |
| git clone https://huggingface.co/MoYoYoTech/VoiceDialogue | |
| cd VoiceDialogue | |
| # 3) 校验模型确实拉取成功(应显示 GB 级大小,而非 100+ 字节) | |
| # 若显示很小,说明 Git LFS 未生效,执行:git lfs pull | |
| ls -lh assets/models/llm/qwen/Qwen3-8B-Q6_K.gguf | |
| # 4) 安装依赖(推荐使用 uv) | |
| pip install uv | |
| uv venv | |
| source .venv/bin/activate | |
| WHISPER_COREML=1 CMAKE_ARGS="-DGGML_METAL=on" uv sync | |
| # 5) 安装额外依赖 | |
| uv pip install kokoro-onnx # kokoro-onnx(英文 TTS) | |
| uv pip install numpy==1.26.4 # 固定 numpy 版本 | |
| ``` | |
| > 📖 需要更详细的步骤?请查阅 [安装指南](docs/installation.md),其中包含系统要求和常见问题。 | |
| ### 2. 启动图形界面(推荐) | |
| ```bash | |
| python main.py --mode api | |
| ``` | |
| 启动后,在浏览器中打开:**http://localhost:8000/app/** | |
| 在界面中即可完成全部操作: | |
| - 点击右下角 **⚙️ 设置**,选择**麦克风、回音消除、识别语言、音色**,也可切换**中 / 英界面语言**; | |
| - 点击 **「开始对话」**,即可与 AI 实时语音对话,**字幕会实时显示**。 | |
| > **首次启动较慢,属正常现象**:程序会自动下载 Qwen3-ASR 模型(约 4.4GB,需联网,下载进度会打印在终端)并转换一次 TTS 权重格式。全部完成后才会就绪,整个过程约几分钟(取决于网速);之后每次启动只需数十秒。 | |
| > 若终端长时间停在下载步骤,请检查网络是否能访问 `huggingface.co`。 | |
| ### 3. 命令行模式(CLI) | |
| 如果不需要图形界面,也可以直接在终端运行语音对话: | |
| ```bash | |
| # 启动语音对话(默认中文) | |
| python main.py | |
| # 指定语言与音色 | |
| python main.py --language en --speaker Heart | |
| # 列出可用音频输入设备(如外置麦克风阵列) | |
| python main.py --list-audio-devices | |
| # 指定输入设备 | |
| python main.py --input-device <设备索引> | |
| ``` | |
| > 详细使用方法请参考 [配置指南](docs/configuration.md) 和 [API 服务指南](docs/api-guide.md)。 | |
| ## 📚 文档导航 | |
| - 📖 **[安装指南](docs/installation.md)**: 详细的安装步骤和系统要求。 | |
| - ⚙️ **[配置指南](docs/configuration.md)**: 如何配置系统参数和高级选项。 | |
| - 🎭 **[功能特性](docs/features.md)**: 深入了解项目的所有功能。 | |
| - 🌐 **[API 指南](docs/api-guide.md)**: 如何使用和集成 API 服务。 | |
| - 🏗️ **[系统架构](docs/architecture.md)**: 了解系统的内部工作原理。 | |
| - 📁 **[项目结构](docs/project-structure.md)**: 浏览项目代码和文件组织。 | |
| - 🛠️ **[故障排除](docs/troubleshooting.md)**: 常见问题和解决方案。 | |
| - 🤝 **[贡献指南](docs/contributing.md)**: 如何为项目做出贡献。 | |
| ## 📄 许可证 | |
| 本项目采用 MIT 许可证开源。 | |
| ## 🙏 致谢 | |
| 如果这个项目对您有帮助,请给我们一个 ⭐️! |