Picovoice Shepherd - STM32F769I-DISCO

The STM32F769 discovery kit is a development board featuring an Arm Cortex-M7 microcontroller. It comes with 2MB of on-chip flash memory and 512KB of RAM. The Cortex-M7 core operates at up to 216 MHz.

Picovoice Shepherd is the first no-code platform for building voice interfaces on microcontrollers. It enables creating voice experiences similar to Alexa that run entirely on microcontrollers. Picovoice Shepherd accelerates prototyping, mitigates technical risks, and shortens time to market. Paired with Picovoice Console users can deploy custom voice models into microcontrollers instantly.

Requirements

• Python 3

Compatibility

• Linux (x86_64)
• macOS (x86_64)
• Windows (x86_64)

Installation

Install the Picovoice Shepherd:

pip3 install pvshepherd

Note for macOS

Install Python using either the official installer or Homebrew. Shepherd cannot run using the Python shipped with macOS. If using the Homebrew Python, make sure that /usr/local/bin is in the PATH variable before installing Shepherd.

Note for Windows

The default Python installation options do not add it to the Windows PATH variable. To fix the issue, refer to this link.

Usage

Run the following command from the terminal:

pvshepherd

Upload the Picovoice Firmware

First, connect the board to the computer using a MICRO-USB Type-B cable. STM32F769 discovery has two USB ports on it; Make sure to use the correct one as shown in the figure below:

Select STM32F769I-DISCO on the first page.

Press the Upload Firmware button and wait for the operation to complete. One complete, the LCD on the board updates.

Upload The Default Models

The unique universal identifier (UUID) of Microcontroller on the board is at the top. You need this UUID to create custom models using Picovoice Console. It is also displayed on the board's LCD. For now, let's continue with the default models. Upload the default voice models to the board by pressing Use Default Models.

Test the Default Models

The board is ready. It has started processing the audio input from the microphone in real-time. It writes to the Shepherd console and on-board LCD when the Picovoice engine detects utterances of the given wake word and follow-on voice commands. Say:

Picovoice, turn the lights on

Picovoice will detect the occurrence of the default wake word ("Picovoice"), and then determines the intent from the follow-on spoken command:

{
   is_understood : True,
   intent : changeColor,
   slots {
     location : living room,
     color : purple,
   }
 }

The Show Context button opens a new window and lists all the available voice commands.

The volume and CPU usage are on the top left. The inference sensitivity of the engines can be changed on the fly. The sensitivity parameter controls the tradeoff between the miss rate and false alarm. A higher sensitivity reduces the miss rate (false reject rate) at the cost of increased false alarm rate.

Audio Debugging

You can record and save the audio fed to the Picovoice from Shepherd. Go to the Audio debugging tab and click on the Record Audio button.

Create Custom Models

1. Go back to the Upload Model page and copy the UUID to the clipboard using the Copy button.
2. Go to Picovoice Console to create models for Porcupine wake word engine and Rhino Speech-to-Intent engine.
3. Select Arm Cortex-M as the platform when training the model.
4. Select STM32 as the board type and provide the UUID of the chipset on the board.

Upload the Custom Models

1. Download your custom voice model(s) from Picovoice Console.
2. Decompress the zip file. The model file is either .ppn for Porcupine wake word or .rhn for Rhino Speech-to-Intent.
3. Go to the Upload Model page and select the models.
4. Press the Upload button.

Demo Project

Additionally, there is a demo project on the Picovoice GitHub repository for the STM32F769 discovery board to ease integrating the designed voice interface into projects.