loudness.py

import os
import soundfile as sd
import numpy as np
import matplotlib.pyplot as plt
import re


flacs = list()

for root, dir, files in os.walk('/Users/chabot/Dropbox/Music'):
    for file in files:
        if file.endswith('.flac'):
            flacs.append(os.path.join(root, file))

nplots = 5

for i in range(nplots):
    track = np.random.choice(flacs)
    print('Using track ', track)
    data, samplerate = sd.read(track)

    window = samplerate*3

    rms_t = list()
    peaks_t = list()
    for ch in range(data.shape[1]):
        raw_chunks = np.array_split(data[:, ch], data.shape[0]/window, axis=0)

        rms = [20*np.log10(np.sqrt(np.sum(chunk**2)/chunk.shape[0])) for chunk in raw_chunks]
        peaks = [20*np.log10(np.max(np.abs(chunk))) for chunk in raw_chunks]

        print('ch{} avg rms = {} avg peak = {}'.format(ch, np.mean(rms), np.mean(peaks)))

        rms_t.append(rms)
        peaks_t.append(peaks)

    # unpack ch0/ch1 and average
    rms_t = [(a + b)/2 for a, b in zip(rms_t[0], rms_t[1])]
    peaks_t = [(a + b)/2 for a, b in zip(peaks_t[0], peaks_t[1])]

    bins = np.linspace(-100, 0, 101)
    plt.subplot(nplots, 1, i+1)
    plt.hist(rms_t, bins, color='red', alpha=0.5, label='rms')
    plt.hist(peaks_t, bins, color='blue', alpha=0.5, label='peak')
    plt.ylim([0.5, 200])
    plt.yscale('log')
    ts = track.split('/')
    artist = ts[-3]
    album = ts[-2]
    song = re.findall(r"[\w\s']+", ts[-1])[-2]
    plt.title('{}, {}, {}'.format(artist, album, song), y=0.75, fontsize=10)
    plt.legend(loc='center left')

plt.xlabel('dB')
plt.show()