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 (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

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

VoiceDialogue / docs /troubleshooting.md

liumaolin

Document API usage, system architecture, configuration, and troubleshooting

1858ba9 about 1 year ago

preview code

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	# 故障排除与性能优化

	## 🛠️ 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。
	```bash
	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)。
	- Linux 解决方案: `sudo usermod -a -G audio $USER`，然后重新登录。

	### 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。
	```bash
	# 使用 conda
	conda deactivate
	conda env remove -n voicedialogue

	# 使用 uv
	deactivate
	rm -rf .venv
	```

	### 5. 说话人角色不存在
	- 问题: 指定的说话人不在支持列表中。
	- 解决方案: 使用 `python main.py --help` 查看所有可用的说话人角色。

	### 6. 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：
	```bash
	# 检查 FFmpeg 安装
	ffmpeg -version

	# 重新安装 FFmpeg
	# macOS
	brew reinstall ffmpeg

	```

	### 7. 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 3.9+ 版本：
	```bash
	python --version
	# 如果版本过低，请升级或使用虚拟环境
	```

	### 8. 桌面应用相关问题
	- 问题: Electron 应用启动失败或功能异常。
	- 解决方案:
	- 确保 Node.js 版本 >= 16
	- 重新安装依赖：`cd electron-app && npm install`
	- 检查 Python 后端是否正常运行

	### 9. 构建打包问题
	- 问题: 使用构建脚本失败。
	- 解决方案:
	- 确保有执行权限：`chmod +x scripts/*.sh`
	- 检查所有依赖是否安装完成
	- 查看具体错误日志进行调试

	## 📊 性能优化建议

	### 硬件优化
	- 内存: 推荐 32GB RAM 以获得最佳性能
	- 存储: 使用 SSD 硬盘可显著提升模型加载速度
	- CPU: 多核处理器有助于多线程处理

	### 软件优化
	- 模型选择: 根据硬件配置选择合适大小的模型
	- 批处理优化: 调整 LLM 的 `n_batch` 参数
	- 音频缓冲: 根据延迟要求调整音频缓冲区大小