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
- 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
- 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
File size: 3,177 Bytes
d08a15b e0f42b2 d08a15b 4e071d3 d846f85 4e071d3 afd6c89 d08a15b | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 | """
命令行参数处理模块
提供命令行参数解析和配置功能
"""
import argparse
from voice_dialogue.config.speaker_config import update_argument_parser_speaker_choices
def create_argument_parser():
"""创建命令行参数解析器"""
# 动态获取可用说话人列表
available_speakers = update_argument_parser_speaker_choices()
parser = argparse.ArgumentParser(
description="VoiceDialogue - 语音对话系统",
formatter_class=argparse.RawDescriptionHelpFormatter,
epilog=f"""
示例用法:
# 启动命令行模式(默认)
python main.py
# 启动命令行模式并指定参数
python main.py --mode cli --language zh --speaker 沈逸
# 启动API服务器
python main.py --mode api
# 启动API服务器并指定端口
python main.py --mode api --port 9000
# 启动API服务器并启用热重载(开发模式)
python main.py --mode api --port 8000 --reload
支持的说话人:
{', '.join(available_speakers)}
"""
)
# 运行模式选择
parser.add_argument(
'--mode', '-m',
choices=['cli', 'api'],
default='cli',
help='运行模式: cli=命令行模式, api=API服务器模式 (默认: cli)'
)
parser.add_argument(
'--debug',
action='store_true',
default=False,
help='启动debug模式'
)
# 命令行模式参数
cli_group = parser.add_argument_group('命令行模式参数')
cli_group.add_argument(
'--language', '-l',
choices=['zh', 'en'],
default='zh',
help='用户语言: zh=中文, en=英文 (默认: zh)'
)
cli_group.add_argument(
'--speaker', '-s',
choices=available_speakers,
default='沈逸' if '沈逸' in available_speakers else (available_speakers[0] if available_speakers else '沈逸'),
help='TTS说话人 (默认: 沈逸)'
)
cli_group.add_argument(
'--disable-echo-cancellation',
action='store_true',
default=False,
help='禁用回声消除功能 (默认: 不禁用)'
)
cli_group.add_argument(
'--input-device', '-i',
type=int,
default=None,
metavar='INDEX',
help='指定输入设备索引(如外置麦克风阵列)。多通道会自动降混为单声道;'
'指定后回声消除依赖设备硬件。用 --list-audio-devices 查看可用索引。'
)
cli_group.add_argument(
'--list-audio-devices',
action='store_true',
default=False,
help='列出可用的音频输入设备及其索引后退出'
)
# API服务器模式参数
api_group = parser.add_argument_group('API服务器模式参数')
api_group.add_argument(
'--host',
default='0.0.0.0',
help='服务器主机地址 (默认: 0.0.0.0)'
)
api_group.add_argument(
'--port', '-p',
type=int,
default=8000,
help='服务器端口 (默认: 8000)'
)
api_group.add_argument(
'--reload',
action='store_true',
help='启用热重载(开发模式)'
)
return parser
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