yahboom
HomeLearn
Solution
Blog
Online Store
About Us
EN/中文
Jetson AGX Orin📖
01.Get started
  • 01.Learning Route
  • 02.Getting Started Quickly
02.Jetson basic course
  • 01.Jetson board introduction
  • 02.Flash new system to the onboard eMMC
  • 03.Flash new system to an NVMe SSD
  • 04.Install Jetpack environment
  • 05.Backup SSD system
  • 06.Restore factory image system
  • 07.SSD expansion
  • 08.Solid State Drive Speed Test
03.Basic setting course
  • 01.Basic knowledge
  • 02.Network configuration
  • 03.SSH remote login
  • 04.VNC remote control
  • 05.Remote file transfer
  • 06.Jtop tool
  • 07.Exchange space expansion
  • 08.Use VSCode
  • 09.Use VMware
  • 10. Change screen resolution
  • 11. Virtual Desktop
  • 12. Set root user password
  • 13. Configure sudo passwordless operation
  • 14. Managing custom services
  • 15. View system version information
04.GPIO control course
  • 01.GPIO description
  • 02.GPIO library installation
  • 03.GPIO reading
  • 04.GPIO output
  • 05.Serial communication
  • 06.I2C communication
05.OpenCV basic course
  • 01.Image reading
  • 02.Image saving
  • 03.Image modification
  • 04.Image scaling
  • 05.Image cropping
  • 06.Image translation
  • 07.Image mirroring
  • 08.Image grayscale conversion
  • 09.Image binarization
  • 10.Image edge detection
  • 11.Add line segments
  • 12.Add rectangles
  • 13.Add circular shape
  • 14.Add ellipses
  • 15.Add polygons
  • 16.Add Text
06.Vision basic course
  • 01.USB camera preview
  • 02.Use Jupyter Lab
  • 03.Use JetCam
07.Vision advanced course
01.Environment construction
  • 01.TensorFlow
  • 02.PyTorch
  • 03.Torchvision
02.DeepStream development
  • 01.DeepStream environment
  • 02.Four types of detection(traffic)
03.YOLO11 development
  • 01.YOLO environment construction
  • 02.CLI use
  • 03.Object detection
  • 04.Instance segmentation
  • 05.Pose estimation
  • 06.Image classification
  • 07.Oriented object detection
  • 08.Model conversion
  • 09.Dataset annotation
  • 10.Model training conversion
  • 11.Model prediction
04.Mediapipe development
  • 01.Mediapipe environment
  • 02.Hand detection
  • 03.Face detection
  • 04.Face effects
  • 05.3D object recognition
  • 06.Virtual brush
  • 07.Gesture recognition
  • 08.Finger control
  • 09.Posture estimation
  • 10.Face mask
  • 11.Overall detection
  • 12.Green scene removal(Human)
05.QR code recognition
  • 01.Barcode Recognition
  • 02.QR Code Recognition
  • 03.Machine Code Recognition
  • 04.Handwritten Digit Recognition
06.YOLO26 development
  • 01.YOLO Environment Setupnew
  • 02.CLI Usagenew
  • 03.Object Detectionnew
  • 04.Instance Segmentationnew
  • 05.Pose Estimationnew
  • 06.Image Classificationnew
  • 07.Oriented Bounding Boxes Object Detectionnew
  • 08.Model Conversionnew
  • 09.Dataset Annotationnew
  • 10.Model Training_Conversionnew
  • 11.Model Predictionnew
08.Docker basic course
  • 01.Docker introduction
  • 02.Docker installation
  • 03.Docker command
  • 04.Docker usage
  • 05.Docker interaction
09.ROS2-Humble
01.ROS2 basic course
  • 3.ROS2 development environment
  • 1.Introduction to ROS2
  • 2.ROS2 install Humble
  • 4.ROS2 workspace
  • 5.ROS2 function package
  • 6.ROS2 node
  • 7.ROS2 topic communication
  • 8.ROS2 service communication
  • 9.ROS2 action communication
  • 10.ROS2 custom interface message
  • 11.ROS2 parameter service case
  • 12.ROS2 meta-function package
  • 13.ROS2 distributed communication
  • 14.ROS2 DDS
  • 15.ROS2 time related API
  • 16.ROS2 common command tools
  • 17.ROS2 rviz2 use
  • 18.ROS2 rqt toolbox
  • 19.ROS2 Launch startup file configuration
  • 20.ROS2 recording and playback tool
  • 21.ROS2 URDF model
  • 22.ROS2 Gazebo simulation platform
  • 23.ROS2 TF2 coordinate transformation
02.ROS2 vision course
  • 01.Camera preview (USB)
  • 02.Camera calibration (USB)
  • 03.AR vision (USB)
10.Offline AI model course
Offline large model (Text Interaction)
  • 10.AI large model offline voice assistant
  • 1.Al large model environment deployment
  • 2.Install large model dialogue platform
  • 3.Meta Al Llama3.2 model
  • 4.Alibaba Cloud Qwen3 model
  • 5.Microsoft Phi-4-mini model
  • 6.DeepSeek DeepSeek-R1 model
  • 7.Alibaba Cloud Qwen2.5v visual multimodal large model
  • 8.Google Gemma3 visual multimodal large model
  • 9.Lava visual multimodal large model
  • 10.MiniCPM-V visual multimodal large model
  • 11.Multimodal visual understand application
  • 12.Multimodal text image application
  • 13.Multimodal video analysis application
  • 14.Multi module visual position application
  • 15.Multimodal table scanning application
  • 16.Multimodal autonomous agent application
Offline large model (Voice Interaction)
  • 0.Voice interaction hardware connection (ReadMe)
  • 1.Offline speech to text (ASR)
  • 2.Offline text to speech (TTS)
  • 3.AI large model voice interaction
  • 4.Multimodal visual understand speech interaction
  • 5.Multimodal text and graphics application
  • 6.Multimodal video analysis application
  • 7.Multi module visual position application
  • 8.Multimodal table scanning application
  • 9.Multimodal autonomous proxy application
11.Online AI model course
Online large model (Text Interaction)
  • 8.AI large model offline voice assistant
  • 1.OpenRouter large model API aggregation platform
  • 2.Multimodal visual understand application
  • 3.Multimodal visual localization application
  • 4.Multimodal table scanning application
  • 5.Multimodal autonomous proxy application
Online large model (Voice Interaction)
  • 0.Voice interaction hardware connection (ReadMe)
  • 1.Online speech to text (ASR)
  • 2.Online text to speech (TTS)
  • 3.AI large model voice interaction
  • 4.Multimodal visual understand speech interaction
  • 5.Multi module visual position application
  • 6.Multimodal table scanning application
  • 7.Multimodal autonomous proxy application
12.Offline Generative AI Applications
  • 01.Children's Storytelling Session
  • 02.Cooking Assistant
  • 03.Homework tutor
  • 04.Image to animation
13.OpenClaw Advanced Development
  • 00.OpenClaw Quick Start on Jetson Platform
  • 01.OpenClaw Installation
  • 02.OpenClaw Online Platform Deployment
  • 03.OpenClaw Local Model Deployment
  • 04.OpenClaw TUI Interaction
  • 05.OpenClaw WebChat Interaction
  • 06.OpenClaw Tools Introduction
  • 07.OpenClawGate Gateway User Manual
  • 08.OpenClaw Features Overview
  • 09.OpenClawClawHub Introduction (Skill Installation)
  • 10. OpenClaw Switching Models
  • 11.OpenClaw Application - File Management
  • 12.OpenClaw Application - Camera
  • 13.OpenClaw Application - Browser Control
  • 14.OpenClaw Application - Script Execution
  • 15.OpenClaw Application - AI Programming
  • 16.OpenClaw Application - Dedicated AI Assistant
  • 17.OpenClaw WhatsApp Plugin Application
Download
  • System_File
  • VM_File
  • Code
  • Tools
Download ZIP

Welcome to Jetson AGX Orin repository

当製品の日本語版マニュアル資料は、こちらからご取得ください。

10.AI large model offline voice assistant

10.AI large model offline voice assistant

10.AI large model offline voice assistant1. Offline Voice Configuration2. Create a Startup Service (Systemd)2.1 Create a Startup Script2.2Creating the Systemd Service File2.3 Managing and Debugging Services

1. Offline Voice Configuration

Before setting up automatic startup, we must ensure that the program itself can operate independently in an offline state. This requires modifying the configuration file.

  1. Locate the Configuration File: In your project code, find and open the configuration file:

    config/yahboom.yaml

  2. Modify Configuration Parameters: Please check the following parameters in the file and ensure that their values ​​match those shown below. If the parameters do not exist, add them.

    ​x
    asr:                                    #Voice node parameters
      ros__parameters:
        VAD_MODE: 2                         #VAD sensitivity
        sample_rate: 16000                  #ASR audio sampling rate
        frame_duration_ms:  30              #VAD frame size in milliseconds
        use_oline_asr: False                #Whether to use online ASR recognition (True for online, False for offline)
        mic_serial_port: "/dev/ttyUSB0"     #Microphone serial port alias
        mic_index: 0                        #Microphone index
        language: 'en'                      #ASR language
        regional_setting : "international"  #international:International Edition  China:China internal version
    ​
    model_service:                          #Model server node parameters
      ros__parameters:
        language: 'en'                      #Large Model Interface Language
        useolinetts: False                   #Whether to use online speech synthesis (True to use online, False to use offline)
    ​
         # Large model configuration
        # llm_platform: 'ollama'              # Optional platforms: 'ollama', 'tongyi', 'spark', 'qianfan', 'openrouter'
        llm_platform: 'ollama'              # Currently selected large model platform
        regional_setting : "international" 
    ​
    • use_oline_asr and useolinetts must be set to False.
    • llm_platform must be set to ollama.

    This setting will make everything offline.

  3. Save the file and recompile the project to apply the changes:

    xxxxxxxxxx
    cd ~/yahboom_ws
    colcon build
    source install/setup.bash

After completing this step, the program is already a purely offline voice service.

2. Create a Startup Service (Systemd)

Now, we will create a systemd service to automatically run largemodel_control.launch.py at system startup.

2.1 Create a Startup Script

To ensure systemd can properly load the ROS2 environment, it's best to create a simple bash script to encapsulate our startup commands.

  1. Create the script file:

    In the directory (~/yahboom_ws/src/largemodel/), create a file named start_largemodel.sh.

    xxxxxxxxxx
    vim ~/yahboom_ws/src/largemodel/start_largemodel.sh
  2. Write script content: Copy and paste the following content into the script file.

    xxxxxxxxxx
    #!/bin/bash
    ​
    # Source ROS2 Humble environment
    source /opt/ros/humble/setup.bash
    ​
    # SOurce Yahboom workspace environment
    source /home/jetson/yahboom_ws/install/setup.bash
    ​
    #Start the largemodel control script
    ros2 launch largemodel largemodel_control.launch.py

    IMPORTANT: Please make sure to replace /home/sunrise/ in the script with your own user home directory path.

  3. Save and exit

  4. Give the script execution permissions:

    xxxxxxxxxx
    chmod +x ~/yahboom_ws/src/largemodel/start_largemodel.sh

2.2Creating the Systemd Service File

This is the most crucial step. We'll tell the system that we have a new service to manage.

  1. Create service file: You will need sudo privileges to create this file.

    xxxxxxxxxx
    sudo vim /etc/systemd/system/largemodel.service
  2. Write service configuration: Copy and paste the following content into the service file.

    xxxxxxxxxx
    [Unit]
    Description=Robot Service
    After=network.target sound.target graphical.target multi-user.target
    Wants=network.target sound.target graphical.target multi-user.target
    ​
    [Service]
    Type=simple
    User=sunrise
    Group=sunrise
    Environment=DISPLAY=:0
    Environment=XDG_RUNTIME_DIR=/run/user/1000
    Environment=PULSE_SERVER=unix:/run/user/1000/pulse/native
    SupplementaryGroups=audio video
    ExecStartPre=/bin/sleep 10
    ExecStart=/home/sunrise/yahboom_ws/src/largemodel/start_largemodel.sh
    Restart=on-failure
    StandardOutput=journal
    StandardError=journal
    ​
    [Install]
    WantedBy=multi-user.target
    • The path in ExecStart is exactly the same as the path to the startup script to be executed.
  3. Save and exit.

2.3 Managing and Debugging Services

Now that your service has been created, we need to have systemd load it and configure it to start automatically at boot.

  1. Reload the systemd daemon so that it reads our newly created service file:

    xxxxxxxxxx
    sudo systemctl daemon-reload
  2. Set the service to start automatically at boot:

    xxxxxxxxxx
    sudo systemctl enable largemodel.service
  3. Start the service immediately:

    xxxxxxxxxx
    sudo systemctl start largemodel.service
  4. Check service status: This is the most important command to verify that the service is running successfully.

    xxxxxxxxxx
    sudo systemctl status largemodel.service
    • If you see Active: active (running), congratulations, the service has started successfully!
    • If the status is failed or something else, proceed to the next step for debugging.
  5. View service log (required for debugging): If the service fails to start, you can use the following command to view all real-time logs generated by the ros2 launch command, which is crucial for locating the problem.

    xxxxxxxxxx
    journalctl -u largemodel.service -f

After completing all the above steps, the purely offline largemodel voice service will be automatically started every time you turn on your phone.

AR vision (USB)
Al large model environment deployment

COMPANY

  • About us
  • Team Style
  • Blog

LEARNING & EXPLORATION

  • Tutorials
  • Edge AI hardware
  • Educational robots
  • Embedded intelligent hardware

WHERE TO BUY

  • Yahboom Online Store
  • Amazon
  • Alibaba
  • AliExpress
  • Mercado

TECHNOLOGY SUPPORT

  • Email: support@yahboom.com

BE A DISTRIBUTOR

  • Email: sales@yahboom.com
QR Code

Copyright © 2017-2026 Yahboom. All Rights Reserved.

EN/中文