Porcupine SDK for Arduino boards - Mandarin language

Made in Vancouver, Canada by Picovoice

Porcupine is a highly-accurate and lightweight wake word engine. It enables building always-listening voice-enabled applications. It is

• using deep neural networks trained in real-world environments.
• compact and computationally-efficient. It is perfect for IoT.
• scalable. It can detect multiple always-listening voice commands with no added runtime footprint.
• self-service. Developers can train custom wake word models using Picovoice Console.

Compatibility

• Arduino Nano 33 BLE Sense

Dependency

• LibPrintf

AccessKey

The Porcupine SDK requires a valid AccessKey at initialization. AccessKeys act as your credentials when using Porcupine SDKs. You can create your AccessKey for free. Make sure to keep your AccessKey secret.

To obtain your AccessKey:

1. Login or Signup for a free account on the Picovoice Console.
2. Once logged in, go to the AccessKey tab to create one or use an existing AccessKey.

Integration

1. define all the necessary variables before setup():

#include <Porcupine_ZH.h>

#define MEMORY_BUFFER_SIZE ...
static uint8_t memory_buffer[MEMORY_BUFFER_SIZE] __attribute__((aligned(16));

static const char* ACCESS_KEY = ...; //AccessKey string obtained from [Picovoice Console](https://picovoice.ai/console/)

const uint8_t keyword_array[] = {...};
const int32_t keyword_model_sizes = sizeof(keyword_array);
const void *keyword_models = keyword_array;
static const float SENSITIVITY = 0.75f;

pv_porcupine_t *handle = NULL;

Sensitivity is the parameter that enables developers to trade miss rate for false alarm. It is a floating-point number within [0, 1]. A higher sensitivity reduces miss rate (false reject rate) at cost of increased false alarm rate.

handle is an instance of Porcupine runtime engine that detects utterances of wake phrase defined in keyword_array.

2. put the following code block inside setup() in order to initialize the Picovoice engine:

const pv_status_t status = pv_porcupine_init(
        ACCESS_KEY,
        MEMORY_BUFFER_SIZE,
        memory_buffer,
        1,
        &keyword_model_sizes,
        &keyword_models,
        &SENSITIVITY,
        &handle);

if (status != PV_STATUS_SUCCESS) {
    // error handling logic
}

Picovoice accepts single channel, 16-bit PCM audio. The sample rate can be retrieved using pv_sample_rate(). Picovoice accepts input audio in consecutive chunks (aka frames); the length of each frame can be retrieved using pv_porcupine_frame_length(). Inside the loop() function in the sketch, pass the recorded audio to the Picovoice engine:

const int16_t *pcm = pv_audio_rec_get_new_buffer()
int32_t keyword_index;
const pv_status_t status = pv_porcupine_process(handle, pcm, &keyword_index);
if (status != PV_STATUS_SUCCESS) {
    // error handling logic
}
if (keyword_index != -1) {
    // detection event logic/callback
}

Create Custom Wake Word

1. Compile and upload the Porcupine_ZH/GetUUID sketch from the File -> Examples menu. Copy the UUID of the board printed at the beginning of the session to the serial monitor.
2. Go to Picovoice Console to create models for Porcupine wake word engine.
3. Select Arm Cortex M as the platform when training the model.
4. Select your board type (Arduino Nano 33 BLE Sense) and provide the UUID of the chipset on the board.

The model is now being trained. You will be able to download it within a few hours.

Import the Custom Wake Word

1. Download your custom voice model(s) from Picovoice Console.
2. Decompress the zip file. The model for Porcupine wake word is located in two files: A binary .ppn file, and as a .h header file containing a C array version of the binary model.
3. Copy the contents of the array inside the .h header file and update update the DEFAULT_KEYWORD_ARRAY values in params.h.