from pydub import AudioSegment
from pydub.utils import make_chunks
from scipy.fftpack import fft
from scipy.io import wavfile # get the api
from scipy import arange
from python_speech_features import mfcc
from python_speech_features import delta
from python_speech_features import logfbank
import numpy as np
import glob
import os
import csv
from pydub import AudioSegment, scipy_effects

myaudio = AudioSegment.from_file("out.wav" , "wav")
chunk_length_ms = 2000 # pydub calculates in millisec
chunks = make_chunks(myaudio, chunk_length_ms) #Make chunks of one sec

csvfile = open("output.csv", "w")
writer = csv.writer(csvfile, delimiter=',')

#Export all of the individual chunks as wav files

for i, chunk in enumerate(chunks):
    song = chunk.set_channels(1)
    y = song.get_array_of_samples()
    y = np.array(y)
    
    fs = song.frame_rate
    Ts = 1.0/fs
    t = arange(0,1,Ts)
    n = len(y)
    k = arange(n)
    T = n/fs  # where fs is the sampling frequency
    frqLabel = k/T
    frq = frqLabel[range(n/2)]
    frq = frq[:6000]
    Y = fft(y)/n # calculate fourier transform (complex numbers list)
    Y = Y[range(n/2)]  # you only need half of the fft list (real signal symmetry)
    Y = Y[:6000]
    writer.writerow(abs(Y[::2]))
csvfile.close()