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
liumaolin commited on
Commit ·
5ecb408
1
Parent(s): 9273b76
Add TTS generation error handling and `is_task_interrupted` helper function
Browse files- Log errors during TTS audio generation to improve debugging.
- Abstract task interruption check into `is_task_interrupted` method for better readability and reuse.
src/voice_dialogue/services/audio/generator.py
CHANGED
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@@ -6,6 +6,7 @@ from voice_dialogue.core.base import BaseThread
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from voice_dialogue.core.constants import dropped_audio_cache, user_still_speaking_event, voice_state_manager, \
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session_manager
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from voice_dialogue.models.voice_task import VoiceTask
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from .generators import tts_manager, BaseTTSConfig
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@@ -77,16 +78,35 @@ class TTSAudioGenerator(BaseThread):
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if answer_id in dropped_audio_cache:
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continue
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if
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continue
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if voice_task.session_id != session_manager.current_id:
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continue
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voice_task.tts_start_time = time.time()
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-
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voice_task.tts_generated_sentence_audio = tts_generated_sentence_audio
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voice_task.tts_end_time = time.time()
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# print(f'生成音频:{voice_task.answer_sentence}')
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self.audio_output_queue.put(voice_task)
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from voice_dialogue.core.constants import dropped_audio_cache, user_still_speaking_event, voice_state_manager, \
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session_manager
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from voice_dialogue.models.voice_task import VoiceTask
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from voice_dialogue.utils.logger import logger
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from .generators import tts_manager, BaseTTSConfig
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if answer_id in dropped_audio_cache:
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continue
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if self.is_task_interrupted(voice_task):
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continue
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if voice_task.session_id != session_manager.current_id:
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continue
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voice_task.tts_start_time = time.time()
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try:
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tts_generated_sentence_audio = self.tts_instance.synthesize(voice_task.answer_sentence)
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except Exception as e:
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logger.error(f"TTS 音频生成失败: {e}")
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voice_state_manager.reset_task_id()
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continue
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voice_task.tts_generated_sentence_audio = tts_generated_sentence_audio
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voice_task.tts_end_time = time.time()
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# print(f'生成音频:{voice_task.answer_sentence}')
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self.audio_output_queue.put(voice_task)
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def is_task_interrupted(self, voice_task: VoiceTask) -> bool:
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"""
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检查语音任务是否被中断
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Args:
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voice_task: 当前处理的语音任务
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Returns:
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bool: 如果任务被中断返回True,否则返回False
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"""
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return (voice_state_manager.interrupt_task_id and
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voice_task.id != voice_state_manager.interrupt_task_id)
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