CremaPrecomputer.py

import crema
import librosa
import sys
import warnings
import numpy as np
import scipy.io as sio
import subprocess
from multiprocessing import Pool as PPool
from SongStructure import *
from SalamiExperiments import *
model = crema.models.chord.ChordModel()

def compute_crema(num):
    filename = "%s/%i/audio.mp3"%(AUDIO_DIR, num)
    print("Doing crema on %s"%filename)
    matfilename = "%s_crema.mat"%filename
    subprocess.call([FFMPEG_BINARY, "-i", filename, "-ar", "44100", "-ac", "1", "%s.wav"%filename])
    _, y44100 = sio.wavfile.read("%s.wav"%filename)
    y44100 = y44100/2.0**15
    os.remove("%s.wav"%filename)

    data = model.outputs(y=y44100, sr=44100)
    sio.savemat(matfilename, data)

def compute_all_crema(NThreads = 12):
    """
    Precompute all crema features in the SALAMI dataset
    """
    # Disable inconsistent hierarchy warnings
    if not sys.warnoptions:
        warnings.simplefilter("ignore")
    songnums = [int(s) for s in os.listdir(AUDIO_DIR)]
    if NThreads > -1:
        parpool = PPool(NThreads)
        parpool.map(compute_crema, (songnums))
    else:
        for num in songnums:
            compute_crema(num)

if __name__ == '__main__':
    compute_all_crema(-1)