test.py

import pyaudio
import os
import wave
import librosa
import numpy as np
from sys import byteorder
from array import array
from struct import pack


THRESHOLD = 500
CHUNK_SIZE = 1024
FORMAT = pyaudio.paInt16
RATE = 16000

SILENCE = 30

def is_silent(snd_data):
    "Returns 'True' if below the 'silent' threshold"
    return max(snd_data) < THRESHOLD

def normalize(snd_data):
    "Average the volume out"
    MAXIMUM = 16384
    times = float(MAXIMUM)/max(abs(i) for i in snd_data)

    r = array('h')
    for i in snd_data:
        r.append(int(i*times))
    return r

def trim(snd_data):
    "Trim the blank spots at the start and end"
    def _trim(snd_data):
        snd_started = False
        r = array('h')

        for i in snd_data:
            if not snd_started and abs(i)>THRESHOLD:
                snd_started = True
                r.append(i)

            elif snd_started:
                r.append(i)
        return r

    # Trim to the left
    snd_data = _trim(snd_data)

    # Trim to the right
    snd_data.reverse()
    snd_data = _trim(snd_data)
    snd_data.reverse()
    return snd_data

def add_silence(snd_data, seconds):
    "Add silence to the start and end of 'snd_data' of length 'seconds' (float)"
    r = array('h', [0 for i in range(int(seconds*RATE))])
    r.extend(snd_data)
    r.extend([0 for i in range(int(seconds*RATE))])
    return r

def record():
    """
    Record a word or words from the microphone and 
    return the data as an array of signed shorts.
    Normalizes the audio, trims silence from the 
    start and end, and pads with 0.5 seconds of 
    blank sound to make sure VLC et al can play 
    it without getting chopped off.
    """
    p = pyaudio.PyAudio()
    stream = p.open(format=FORMAT, channels=1, rate=RATE,
        input=True, output=True,
        frames_per_buffer=CHUNK_SIZE)

    num_silent = 0
    snd_started = False

    r = array('h')

    while 1:
        # little endian, signed short
        snd_data = array('h', stream.read(CHUNK_SIZE))
        if byteorder == 'big':
            snd_data.byteswap()
        r.extend(snd_data)

        silent = is_silent(snd_data)

        if silent and snd_started:
            num_silent += 1
        elif not silent and not snd_started:
            snd_started = True

        if snd_started and num_silent > SILENCE:
            break

    sample_width = p.get_sample_size(FORMAT)
    stream.stop_stream()
    stream.close()
    p.terminate()

    r = normalize(r)
    r = trim(r)
    r = add_silence(r, 0.5)
    return sample_width, r

def record_to_file(path):
    "Records from the microphone and outputs the resulting data to 'path'"
    sample_width, data = record()
    data = pack('<' + ('h'*len(data)), *data)

    wf = wave.open(path, 'wb')
    wf.setnchannels(1)
    wf.setsampwidth(sample_width)
    wf.setframerate(RATE)
    wf.writeframes(data)
    wf.close()



def extract_feature(file_name, **kwargs):
    """
    Extract feature from audio file `file_name`
        Features supported:
            - MFCC (mfcc)
            - Chroma (chroma)
            - MEL Spectrogram Frequency (mel)
            - Contrast (contrast)
            - Tonnetz (tonnetz)
        e.g:
        `features = extract_feature(path, mel=True, mfcc=True)`
    """
    mfcc = kwargs.get("mfcc")
    chroma = kwargs.get("chroma")
    mel = kwargs.get("mel")
    contrast = kwargs.get("contrast")
    tonnetz = kwargs.get("tonnetz")
    X, sample_rate = librosa.core.load(file_name)
    if chroma or contrast:
        stft = np.abs(librosa.stft(X))
    result = np.array([])
    if mfcc:
        mfccs = np.mean(librosa.feature.mfcc(y=X, sr=sample_rate, n_mfcc=40).T, axis=0)
        result = np.hstack((result, mfccs))
    if chroma:
        chroma = np.mean(librosa.feature.chroma_stft(S=stft, sr=sample_rate).T,axis=0)
        result = np.hstack((result, chroma))
    if mel:
        mel = np.mean(librosa.feature.melspectrogram(y=X, sr=sample_rate).T,axis=0)
        result = np.hstack((result, mel))
    if contrast:
        contrast = np.mean(librosa.feature.spectral_contrast(S=stft, sr=sample_rate).T,axis=0)
        result = np.hstack((result, contrast))
    if tonnetz:
        tonnetz = np.mean(librosa.feature.tonnetz(y=librosa.effects.harmonic(X), sr=sample_rate).T,axis=0)
        result = np.hstack((result, tonnetz))
    return result


if __name__ == "__main__":
    # load the saved model (after training)
    # model = pickle.load(open("result/mlp_classifier.model", "rb"))
    from utils import load_data, split_data, create_model
    import argparse
    parser = argparse.ArgumentParser(description="""Gender recognition script, this will load the model you trained, 
                                    and perform inference on a sample you provide (either using your voice or a file)""")
    parser.add_argument("-f", "--file", help="The path to the file, preferred to be in WAV format")
    args = parser.parse_args()
    file = args.file
    # construct the model
    model = create_model()
    # load the saved/trained weights
    model.load_weights("results/model.h5")
    if not file or not os.path.isfile(file):
        # if file not provided, or it doesn't exist, use your voice
        print("Please talk")
        # put the file name here
        file = "test.wav"
        # record the file (start talking)
        record_to_file(file)
    # extract features and reshape it
    features = extract_feature(file, mel=True).reshape(1, -1)
    # predict the gender!
    male_prob = model.predict(features)[0][0]
    female_prob = 1 - male_prob
    gender = "male" if male_prob > female_prob else "female"
    # show the result!
    print("Result:", gender)
    print(f"Probabilities:     Male: {male_prob*100:.2f}%    Female: {female_prob*100:.2f}%")