from ulab import numpy as np
from ulab import scipy as sp
SIZE = 512//4
raw_buf = None
fb = image.Image(SIZE+50, SIZE, image.RGB565, copy_to_fb=True)
audio.init(channels=2, frequency=16000, gain=24, highpass=0.9883)
def audio_callback(buf):
# NOTE: do Not call any function that allocates memory.
global raw_buf
if (raw_buf == None):
raw_buf = buf
# Start audio streaming
audio.start_streaming(audio_callback)
def draw_fft(img, fft_buf):
fft_buf = (fft_buf / max(fft_buf)) * SIZE
fft_buf = np.log10(fft_buf + 1) * 20
color = (0xFF, 0x0F, 0x00)
for i in range(0, SIZE):
img.draw_line(i, SIZE, i, SIZE-int(fft_buf[i]), color, 1)
def draw_audio_bar(img, level, offset):
blk_size = SIZE//10
color = (0xFF, 0x00, 0xF0)
blk_space = (blk_size//4)
for i in range(0, int(round(level/10))):
fb.draw_rectangle(SIZE+offset, SIZE - ((i+1)*blk_size) + blk_space, 20, blk_size - blk_space, color, 1, True)
import audio, time, tf, micro_speech, pyb
labels = ['Silence', 'Unknown', 'Yes', 'No']
led_red = pyb.LED(1)
led_green = pyb.LED(2)
model = tf.load('/model.tflite')
speech = micro_speech.MicroSpeech()
audio.init(channels=1, frequency=16000, gain=24, highpass=0.9883)
# Start audio streaming
audio.start_streaming(speech.audio_callback)
while (True):
# Run micro-speech without a timeout and filter detections by label index.
idx = speech.listen(model, timeout=0, threshold=0.78, filter=[2, 3])
led = led_green if idx == 2 else led_red
print(labels[idx])
for i in range(0, 4):
led.on()
time.sleep_ms(25)
led.off()
time.sleep_ms(25)
# Stop streaming
audio.stop_streaming()