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")

# pip install -U transformers accelerate
# 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 Desktop

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 @earendil-works/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

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

Hermes Agent

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

OpenClaw

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"

VoiceDialogue / src /voice_dialogue /tts /runtime /interface.py

liumaolin

refactor(core): Architecturally decouple Audio, ASR, and TTS modules

60f8238 about 1 year ago

Raw History Blame Contribute Delete

2.4 kB

from abc import ABC, abstractmethod
from typing import Tuple

import numpy as np

from ..models.base import BaseTTSConfig


class TTSInterface(ABC):
    """TTS服务的抽象接口"""

    def __init__(self, config: BaseTTSConfig):
        self.config = config
        self._is_ready = False

    @abstractmethod
    def setup(self, **kwargs) -> None:
        """
        初始化TTS服务
        
        Args:
            **kwargs: 额外的初始化参数
        """
        pass

    @abstractmethod
    def warmup(self, warmup_steps: int = 1) -> None:
        """
        预热TTS引擎
        
        Args:
            warmup_steps: 预热步数
        """
        pass

    @abstractmethod
    def synthesize(self, text: str, **kwargs) -> Tuple[np.ndarray, int]:
        """
        将文本转换为语音
        
        Args:
            text: 要转换的文本
            **kwargs: 额外的合成参数
            
        Returns:
            Tuple[np.ndarray, int]: (音频数据, 采样率)
        """
        pass

    @property
    def is_ready(self) -> bool:
        """
        检查TTS服务是否准备就绪
        
        Returns:
            bool: 是否准备就绪
        """
        return self._is_ready

    @is_ready.setter
    def is_ready(self, value: bool):
        self._is_ready = value

    def get_config(self) -> BaseTTSConfig:
        """获取当前配置"""
        return self.config


class TTSFactory:
    """TTS工厂类，用于创建不同的TTS实现"""

    _registry = {}

    @classmethod
    def register(cls, provider_name: str, tts_class):
        """注册TTS提供者"""
        cls._registry[provider_name] = tts_class

    @classmethod
    def create(cls, config: BaseTTSConfig) -> TTSInterface:
        """
        根据配置创建TTS实例
        
        Args:
            config: TTS配置
            
        Returns:
            TTSInterface: TTS实例
            
        Raises:
            ValueError: 不支持的TTS提供者
        """
        provider = config.provider.value
        if provider not in cls._registry:
            raise ValueError(f"不支持的TTS提供者: {provider}")

        tts_class = cls._registry[provider]
        return tts_class(config)

    @classmethod
    def list_providers(cls):
        """列出所有已注册的TTS提供者"""
        return list(cls._registry.keys())

	from abc import ABC, abstractmethod
	from typing import Tuple

	import numpy as np

	from ..models.base import BaseTTSConfig


	class TTSInterface(ABC):
	"""TTS服务的抽象接口"""

	def __init__(self, config: BaseTTSConfig):
	self.config = config
	self._is_ready = False

	@abstractmethod
	def setup(self, **kwargs) -> None:
	"""
	初始化TTS服务

	Args:
	**kwargs: 额外的初始化参数
	"""
	pass

	@abstractmethod
	def warmup(self, warmup_steps: int = 1) -> None:
	"""
	预热TTS引擎

	Args:
	warmup_steps: 预热步数
	"""
	pass

	@abstractmethod
	def synthesize(self, text: str, **kwargs) -> Tuple[np.ndarray, int]:
	"""
	将文本转换为语音

	Args:
	text: 要转换的文本
	**kwargs: 额外的合成参数

	Returns:
	Tuple[np.ndarray, int]: (音频数据, 采样率)
	"""
	pass

	@property
	def is_ready(self) -> bool:
	"""
	检查TTS服务是否准备就绪

	Returns:
	bool: 是否准备就绪
	"""
	return self._is_ready

	@is_ready.setter
	def is_ready(self, value: bool):
	self._is_ready = value

	def get_config(self) -> BaseTTSConfig:
	"""获取当前配置"""
	return self.config


	class TTSFactory:
	"""TTS工厂类，用于创建不同的TTS实现"""

	_registry = {}

	@classmethod
	def register(cls, provider_name: str, tts_class):
	"""注册TTS提供者"""
	cls._registry[provider_name] = tts_class

	@classmethod
	def create(cls, config: BaseTTSConfig) -> TTSInterface:
	"""
	根据配置创建TTS实例

	Args:
	config: TTS配置

	Returns:
	TTSInterface: TTS实例

	Raises:
	ValueError: 不支持的TTS提供者
	"""
	provider = config.provider.value
	if provider not in cls._registry:
	raise ValueError(f"不支持的TTS提供者: {provider}")

	tts_class = cls._registry[provider]
	return tts_class(config)

	@classmethod
	def list_providers(cls):
	"""列出所有已注册的TTS提供者"""
	return list(cls._registry.keys())