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", 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
- 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 new
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 new
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
- 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
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。
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。
# 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:
# 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:
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
- 确保有执行权限:
📊 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