Bregman lib error

[Pipe-Runner]

The Chromagram function used in chapter 5 for K-Means classification returns an error when fed an audio file.
TypeError: 'float' object cannot be interpreted as an index

[zoldaten]

this error from Bregman lib that doesnt work with lastest numpy.
try to install from here for python3 https://github.com/pkmital/BregmanToolkit
Then change in features_base.py:
num_frames = 1000 #len(self.x)

try this code first:

from bregman.suite import *

p = default_feature_params()
import os
path=r'./audio_dataset/'
os.chdir(path)

audio_file = ("amen.wav")
print(audio_file)
F = Features(audio_file, p)
imagesc(F.X,dbscale=True)
#title=title('Default constant-Q spectrogram')

F.inverse(F.X, pvoc=True) # invert features to audio
play(balance_signal(F.x_hat),F.sample_rate)

p['feature']='stft'
p['nfft']=1024
p['wfft']=512
p['nhop']=256
F = Features(audio_file, p)
imagesc(F.X,dbscale=True)
title('Wide-band spectrogram')

F.inverse(F.X) # invert features to audio
play(balance_signal(F.x_hat),F.sample_rate)

tuts = get_tutorials()
execfile(tuts[0])

execfile(tuts[1])

[zoldaten]

To fix the issue with python2 - BinRoot/BregmanToolkit@f7b924b

[zoldaten]

import tensorflow as tf
import numpy as np
from bregman.suite import *
import os

path=r'./audio_dataset/'
os.chdir(path)
print(os.getcwd())

k = 2
max_iterations = 100

filenames = tf.train.match_filenames_once('*.wav')
count_num_files = tf.size(filenames)
#print(count_num_files)
init = (tf.global_variables_initializer(), tf.local_variables_initializer())

filename_queue = tf.train.string_input_producer(filenames)
reader = tf.WholeFileReader()
filename, file_contents = reader.read(filename_queue)

chromo = tf.placeholder(tf.float32)
max_freqs = tf.argmax(chromo, 0)

def get_next_chromogram(sess):
audio_file = sess.run(filename)
F = Chromagram(audio_file, nfft=16384, wfft=8192, nhop=2205)
return F.X, audio_file

def extract_feature_vector(sess, chromo_data):
num_features, num_samples = np.shape(chromo_data)
freq_vals = sess.run(max_freqs, feed_dict={chromo: chromo_data})
hist, bins = np.histogram(freq_vals, bins=range(num_features + 1))
normalized_hist = hist.astype(float) / num_samples
return normalized_hist

def get_dataset(sess):
num_files = sess.run(count_num_files)
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(coord=coord)
xs = list()
names = list()
plt.figure()
for _ in range(num_files):
chromo_data, filename = get_next_chromogram(sess)

    plt.subplot(1, 2, 1)
    plt.imshow(chromo_data, cmap='Greys', interpolation='nearest')
    plt.title('Visualization of Sound Spectrum')

    plt.subplot(1, 2, 2)
    freq_vals = sess.run(max_freqs, feed_dict={chromo: chromo_data})
    plt.hist(freq_vals)
    plt.title('Histogram of Notes')
    plt.xlabel('Musical Note')
    plt.ylabel('Count')
    plt.savefig('{}.png'.format(filename))
    plt.clf()

    plt.clf()
    names.append(filename)
    x = extract_feature_vector(sess, chromo_data)
    xs.append(x)
xs = np.asmatrix(xs)
return xs, names

def initial_cluster_centroids(X, k):
return X[0:k, :]

def assign_cluster(X, centroids):
expanded_vectors = tf.expand_dims(X, 0)
expanded_centroids = tf.expand_dims(centroids, 1)
distances = tf.reduce_sum(tf.square(tf.subtract(expanded_vectors, expanded_centroids)), 2)
mins = tf.argmin(distances, 0)
return mins

def recompute_centroids(X, Y):
sums = tf.unsorted_segment_sum(X, Y, k)
counts = tf.unsorted_segment_sum(tf.ones_like(X), Y, k)
return sums / counts

with tf.Session() as sess:
sess.run(init)
X, names = get_dataset(sess)
centroids = initial_cluster_centroids(X, k)
i, converged = 0, False
while not converged and i < max_iterations:
i += 1
Y = assign_cluster(X, centroids)
centroids = sess.run(recompute_centroids(X, Y))
print(zip(sess.run(Y), names))