speech_classifier_gui.py

from gui import *
from distances import *
from nearest_neighbor_classifier import *
import sounddevice as sd
import numpy as np
import time

sd.default.samplerate = 8000
sd.default.channels = 1
points = []
labels = []

distance = dtw(L2_vector(L2_scalar))

def start_recording(maximum_duration, for_classify):
    def internal():
        if (not for_classify) or len(points)>0:
            global waveform, start_time
            message("")
            waveform = sd.rec(maximum_duration*sd.default.samplerate)
            start_time = time.time()
    return internal

def stop_recording():
    global waveform
    actual_time = time.time()-start_time
    sd.stop()
    samples = min(int(actual_time*sd.default.samplerate), len(waveform))
    waveform = waveform[0:samples, 0]
    get_axes().clear()
    spectrum, freqs, t, im = get_axes().specgram(waveform,
                                                 Fs=sd.default.samplerate)
    redraw()
    sd.play(waveform)
    time.sleep(float(len(waveform))/sd.default.samplerate)
    return np.transpose(spectrum)

def clear_command():
    global points, labels
    points = []
    labels = []
    message("")
    get_axes().clear()
    redraw()

def dog_command():
    message("")
    points.append(stop_recording())
    labels.append("Dog")

def cat_command():
    message("")
    points.append(stop_recording())
    labels.append("Cat")

def classify_command():
    message("")
    if len(points)==0:
        message("No data")
    else:
        message(classify(stop_recording(), distance, points, labels))

add_button(0, 0, "Clear", clear_command, nothing)
add_button(0, 1, "Dog", start_recording(10, False), dog_command)
add_button(0, 2, "Cat", start_recording(10, False), cat_command)
add_button(0, 3, "Classify", start_recording(10, True), classify_command)
add_button(0, 4, "Exit", done, nothing)
message = add_message(1, 0, 5)
start_variable_size_matplotlib(7, 7, 2, 5)