#!/usr/bin/env python
# processing.py -- various audio processing functions
# Copyright (C) 2008 MUSIC TECHNOLOGY GROUP (MTG)
# UNIVERSITAT POMPEU FABRA
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU Affero General Public License as
# published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU Affero General Public License for more details.
#
# You should have received a copy of the GNU Affero General Public License
# along with this program. If not, see .
#
# Authors:
# Bram de Jong
# 2012, Joar Wandborg
from PIL import Image, ImageDraw, ImageColor #@UnresolvedImport
from functools import partial
import math
import numpy
import os
import re
import signal
def get_sound_type(input_filename):
sound_type = os.path.splitext(input_filename.lower())[1].strip(".")
if sound_type == "fla":
sound_type = "flac"
elif sound_type == "aif":
sound_type = "aiff"
return sound_type
try:
import scikits.audiolab as audiolab
except ImportError:
print("WARNING: audiolab is not installed so wav2png will not work")
# Hack to prevent errors when uploading audio files. The issue is that
# scikits.audiolab does not support Python 3. By replacing it with a mock
# implementation here, we can accept audio files, but we won't get the nice
# waveform image.
import six
if six.PY3:
class MockSndfile(object):
def __init__(self, *args, **kwargs):
self.nframes = 0
self.channels = 1
self.samplerate = 44100
def read_frames(self, *args):
return []
def seek(self, *args):
return
def close(self):
return
import unittest.mock as mock
audiolab = mock.Mock()
audiolab.Sndfile = MockSndfile
import subprocess
class AudioProcessingException(Exception):
pass
class TestAudioFile(object):
"""A class that mimics audiolab.sndfile but generates noise instead of reading
a wave file. Additionally it can be told to have a "broken" header and thus crashing
in the middle of the file. Also useful for testing ultra-short files of 20 samples."""
def __init__(self, num_frames, has_broken_header=False):
self.seekpoint = 0
self.nframes = num_frames
self.samplerate = 44100
self.channels = 1
self.has_broken_header = has_broken_header
def seek(self, seekpoint):
self.seekpoint = seekpoint
def read_frames(self, frames_to_read):
if self.has_broken_header and self.seekpoint + frames_to_read > self.num_frames / 2:
raise RuntimeError()
num_frames_left = self.num_frames - self.seekpoint
will_read = num_frames_left if num_frames_left < frames_to_read else frames_to_read
self.seekpoint += will_read
return numpy.random.random(will_read)*2 - 1
def get_max_level(filename):
max_value = 0
buffer_size = 4096
audio_file = audiolab.Sndfile(filename, 'r')
n_samples_left = audio_file.nframes
while n_samples_left:
to_read = min(buffer_size, n_samples_left)
try:
samples = audio_file.read_frames(to_read)
except RuntimeError:
# this can happen with a broken header
break
# convert to mono by selecting left channel only
if audio_file.channels > 1:
samples = samples[:,0]
max_value = max(max_value, numpy.abs(samples).max())
n_samples_left -= to_read
audio_file.close()
return max_value
class AudioProcessor(object):
"""
The audio processor processes chunks of audio an calculates the spectrac centroid and the peak
samples in that chunk of audio.
"""
def __init__(self, input_filename, fft_size, window_function=numpy.hanning):
max_level = get_max_level(input_filename)
self.audio_file = audiolab.Sndfile(input_filename, 'r')
self.fft_size = fft_size
self.window = window_function(self.fft_size)
self.spectrum_range = None
self.lower = 100
self.higher = 22050
self.lower_log = math.log10(self.lower)
self.higher_log = math.log10(self.higher)
self.clip = lambda val, low, high: min(high, max(low, val))
# figure out what the maximum value is for an FFT doing the FFT of a DC signal
fft = numpy.fft.rfft(numpy.ones(fft_size) * self.window)
max_fft = (numpy.abs(fft)).max()
# set the scale to normalized audio and normalized FFT
self.scale = 1.0/max_level/max_fft if max_level > 0 else 1
def read(self, start, size, resize_if_less=False):
""" read size samples starting at start, if resize_if_less is True and less than size
samples are read, resize the array to size and fill with zeros """
# number of zeros to add to start and end of the buffer
add_to_start = 0
add_to_end = 0
if start < 0:
# the first FFT window starts centered around zero
if size + start <= 0:
return numpy.zeros(size) if resize_if_less else numpy.array([])
else:
self.audio_file.seek(0)
add_to_start = -start # remember: start is negative!
to_read = size + start
if to_read > self.audio_file.nframes:
add_to_end = to_read - self.audio_file.nframes
to_read = self.audio_file.nframes
else:
self.audio_file.seek(start)
to_read = size
if start + to_read >= self.audio_file.nframes:
to_read = self.audio_file.nframes - start
add_to_end = size - to_read
try:
samples = self.audio_file.read_frames(to_read)
except RuntimeError:
# this can happen for wave files with broken headers...
return numpy.zeros(size) if resize_if_less else numpy.zeros(2)
# convert to mono by selecting left channel only
if self.audio_file.channels > 1:
samples = samples[:,0]
if resize_if_less and (add_to_start > 0 or add_to_end > 0):
if add_to_start > 0:
samples = numpy.concatenate((numpy.zeros(add_to_start), samples), axis=1)
if add_to_end > 0:
samples = numpy.resize(samples, size)
samples[size - add_to_end:] = 0
return samples
def spectral_centroid(self, seek_point, spec_range=110.0):
""" starting at seek_point read fft_size samples, and calculate the spectral centroid """
samples = self.read(seek_point - self.fft_size/2, self.fft_size, True)
samples *= self.window
fft = numpy.fft.rfft(samples)
spectrum = self.scale * numpy.abs(fft) # normalized abs(FFT) between 0 and 1
length = numpy.float64(spectrum.shape[0])
# scale the db spectrum from [- spec_range db ... 0 db] > [0..1]
db_spectrum = ((20*(numpy.log10(spectrum + 1e-60))).clip(-spec_range, 0.0) + spec_range)/spec_range
energy = spectrum.sum()
spectral_centroid = 0
if energy > 1e-60:
# calculate the spectral centroid
if self.spectrum_range == None:
self.spectrum_range = numpy.arange(length)
spectral_centroid = (spectrum * self.spectrum_range).sum() / (energy * (length - 1)) * self.audio_file.samplerate * 0.5
# clip > log10 > scale between 0 and 1
spectral_centroid = (math.log10(self.clip(spectral_centroid, self.lower, self.higher)) - self.lower_log) / (self.higher_log - self.lower_log)
return (spectral_centroid, db_spectrum)
def peaks(self, start_seek, end_seek):
""" read all samples between start_seek and end_seek, then find the minimum and maximum peak
in that range. Returns that pair in the order they were found. So if min was found first,
it returns (min, max) else the other way around. """
# larger blocksizes are faster but take more mem...
# Aha, Watson, a clue, a tradeof!
block_size = 4096
max_index = -1
max_value = -1
min_index = -1
min_value = 1
if start_seek < 0:
start_seek = 0
if end_seek > self.audio_file.nframes:
end_seek = self.audio_file.nframes
if end_seek <= start_seek:
samples = self.read(start_seek, 1)
return (samples[0], samples[0])
if block_size > end_seek - start_seek:
block_size = end_seek - start_seek
for i in range(start_seek, end_seek, block_size):
samples = self.read(i, block_size)
local_max_index = numpy.argmax(samples)
local_max_value = samples[local_max_index]
if local_max_value > max_value:
max_value = local_max_value
max_index = local_max_index
local_min_index = numpy.argmin(samples)
local_min_value = samples[local_min_index]
if local_min_value < min_value:
min_value = local_min_value
min_index = local_min_index
return (min_value, max_value) if min_index < max_index else (max_value, min_value)
def interpolate_colors(colors, flat=False, num_colors=256):
""" given a list of colors, create a larger list of colors interpolating
the first one. If flatten is True a list of numers will be returned. If
False, a list of (r,g,b) tuples. num_colors is the number of colors wanted
in the final list """
palette = []
for i in range(num_colors):
index = (i * (len(colors) - 1))/(num_colors - 1.0)
index_int = int(index)
alpha = index - float(index_int)
if alpha > 0:
r = (1.0 - alpha) * colors[index_int][0] + alpha * colors[index_int + 1][0]
g = (1.0 - alpha) * colors[index_int][1] + alpha * colors[index_int + 1][1]
b = (1.0 - alpha) * colors[index_int][2] + alpha * colors[index_int + 1][2]
else:
r = (1.0 - alpha) * colors[index_int][0]
g = (1.0 - alpha) * colors[index_int][1]
b = (1.0 - alpha) * colors[index_int][2]
if flat:
palette.extend((int(r), int(g), int(b)))
else:
palette.append((int(r), int(g), int(b)))
return palette
def desaturate(rgb, amount):
"""
desaturate colors by amount
amount == 0, no change
amount == 1, grey
"""
luminosity = sum(rgb) / 3.0
desat = lambda color: color - amount * (color - luminosity)
return tuple(map(int, map(desat, rgb)))
class WaveformImage(object):
"""
Given peaks and spectral centroids from the AudioProcessor, this class will construct
a wavefile image which can be saved as PNG.
"""
def __init__(self, image_width, image_height, palette=1):
if image_height % 2 == 0:
raise AudioProcessingException("Height should be uneven: images look much better at uneven height")
if palette == 1:
background_color = (0,0,0)
colors = [
(50,0,200),
(0,220,80),
(255,224,0),
(255,70,0),
]
elif palette == 2:
background_color = (0,0,0)
colors = [self.color_from_value(value/29.0) for value in range(0,30)]
elif palette == 3:
background_color = (213, 217, 221)
colors = map( partial(desaturate, amount=0.7), [
(50,0,200),
(0,220,80),
(255,224,0),
])
elif palette == 4:
background_color = (213, 217, 221)
colors = map( partial(desaturate, amount=0.8), [self.color_from_value(value/29.0) for value in range(0,30)])
self.image = Image.new("RGB", (image_width, image_height), background_color)
self.image_width = image_width
self.image_height = image_height
self.draw = ImageDraw.Draw(self.image)
self.previous_x, self.previous_y = None, None
self.color_lookup = interpolate_colors(colors)
self.pix = self.image.load()
def color_from_value(self, value):
""" given a value between 0 and 1, return an (r,g,b) tuple """
return ImageColor.getrgb("hsl(%d,%d%%,%d%%)" % (int( (1.0 - value) * 360 ), 80, 50))
def draw_peaks(self, x, peaks, spectral_centroid):
""" draw 2 peaks at x using the spectral_centroid for color """
y1 = self.image_height * 0.5 - peaks[0] * (self.image_height - 4) * 0.5
y2 = self.image_height * 0.5 - peaks[1] * (self.image_height - 4) * 0.5
line_color = self.color_lookup[int(spectral_centroid*255.0)]
if self.previous_y != None:
self.draw.line([self.previous_x, self.previous_y, x, y1, x, y2], line_color)
else:
self.draw.line([x, y1, x, y2], line_color)
self.previous_x, self.previous_y = x, y2
self.draw_anti_aliased_pixels(x, y1, y2, line_color)
def draw_anti_aliased_pixels(self, x, y1, y2, color):
""" vertical anti-aliasing at y1 and y2 """
y_max = max(y1, y2)
y_max_int = int(y_max)
alpha = y_max - y_max_int
if alpha > 0.0 and alpha < 1.0 and y_max_int + 1 < self.image_height:
current_pix = self.pix[x, y_max_int + 1]
r = int((1-alpha)*current_pix[0] + alpha*color[0])
g = int((1-alpha)*current_pix[1] + alpha*color[1])
b = int((1-alpha)*current_pix[2] + alpha*color[2])
self.pix[x, y_max_int + 1] = (r,g,b)
y_min = min(y1, y2)
y_min_int = int(y_min)
alpha = 1.0 - (y_min - y_min_int)
if alpha > 0.0 and alpha < 1.0 and y_min_int - 1 >= 0:
current_pix = self.pix[x, y_min_int - 1]
r = int((1-alpha)*current_pix[0] + alpha*color[0])
g = int((1-alpha)*current_pix[1] + alpha*color[1])
b = int((1-alpha)*current_pix[2] + alpha*color[2])
self.pix[x, y_min_int - 1] = (r,g,b)
def save(self, filename):
# draw a zero "zero" line
a = 25
for x in range(self.image_width):
self.pix[x, self.image_height/2] = tuple(map(lambda p: p+a, self.pix[x, self.image_height/2]))
self.image.save(filename)
class SpectrogramImage(object):
"""
Given spectra from the AudioProcessor, this class will construct a wavefile image which
can be saved as PNG.
"""
def __init__(self, image_width, image_height, fft_size):
self.image_width = image_width
self.image_height = image_height
self.fft_size = fft_size
self.image = Image.new("RGBA", (image_height, image_width))
colors = [
(0, 0, 0, 0),
(58/4, 68/4, 65/4, 255),
(80/2, 100/2, 153/2, 255),
(90, 180, 100, 255),
(224, 224, 44, 255),
(255, 60, 30, 255),
(255, 255, 255, 255)
]
self.palette = interpolate_colors(colors)
# generate the lookup which translates y-coordinate to fft-bin
self.y_to_bin = []
f_min = 100.0
f_max = 22050.0
y_min = math.log10(f_min)
y_max = math.log10(f_max)
for y in range(self.image_height):
freq = math.pow(10.0, y_min + y / (image_height - 1.0) *(y_max - y_min))
bin = freq / 22050.0 * (self.fft_size/2 + 1)
if bin < self.fft_size/2:
alpha = bin - int(bin)
self.y_to_bin.append((int(bin), alpha * 255))
# this is a bit strange, but using image.load()[x,y] = ... is
# a lot slower than using image.putadata and then rotating the image
# so we store all the pixels in an array and then create the image when saving
self.pixels = []
def draw_spectrum(self, x, spectrum):
# for all frequencies, draw the pixels
for (index, alpha) in self.y_to_bin:
self.pixels.append( self.palette[int((255.0-alpha) * spectrum[index] + alpha * spectrum[index + 1])] )
# if the FFT is too small to fill up the image, fill with black to the top
for y in range(len(self.y_to_bin), self.image_height): #@UnusedVariable
self.pixels.append(self.palette[0])
def save(self, filename, quality=80):
assert filename.lower().endswith(".jpg")
self.image.putdata(self.pixels)
self.image.transpose(Image.ROTATE_90).save(filename, quality=quality)
def create_wave_images(input_filename, output_filename_w, output_filename_s, image_width, image_height, fft_size, progress_callback=None):
"""
Utility function for creating both wavefile and spectrum images from an audio input file.
"""
processor = AudioProcessor(input_filename, fft_size, numpy.hanning)
samples_per_pixel = processor.audio_file.nframes / float(image_width)
waveform = WaveformImage(image_width, image_height)
spectrogram = SpectrogramImage(image_width, image_height, fft_size)
for x in range(image_width):
if progress_callback and x % (image_width/10) == 0:
progress_callback((x*100)/image_width)
seek_point = int(x * samples_per_pixel)
next_seek_point = int((x + 1) * samples_per_pixel)
(spectral_centroid, db_spectrum) = processor.spectral_centroid(seek_point)
peaks = processor.peaks(seek_point, next_seek_point)
waveform.draw_peaks(x, peaks, spectral_centroid)
spectrogram.draw_spectrum(x, db_spectrum)
if progress_callback:
progress_callback(100)
waveform.save(output_filename_w)
spectrogram.save(output_filename_s)
class NoSpaceLeftException(Exception):
pass
def convert_to_pcm(input_filename, output_filename):
"""
converts any audio file type to pcm audio
"""
if not os.path.exists(input_filename):
raise AudioProcessingException("file %s does not exist" % input_filename)
sound_type = get_sound_type(input_filename)
if sound_type == "mp3":
cmd = ["lame", "--decode", input_filename, output_filename]
elif sound_type == "ogg":
cmd = ["oggdec", input_filename, "-o", output_filename]
elif sound_type == "flac":
cmd = ["flac", "-f", "-d", "-s", "-o", output_filename, input_filename]
else:
return False
process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
(stdout, stderr) = process.communicate()
if process.returncode != 0 or not os.path.exists(output_filename):
if "No space left on device" in stderr + " " + stdout:
raise NoSpaceLeftException
raise AudioProcessingException("failed converting to pcm data:\n" + " ".join(cmd) + "\n" + stderr + "\n" + stdout)
return True
def stereofy_and_find_info(stereofy_executble_path, input_filename, output_filename):
"""
converts a pcm wave file to two channel, 16 bit integer
"""
if not os.path.exists(input_filename):
raise AudioProcessingException("file %s does not exist" % input_filename)
cmd = [stereofy_executble_path, "--input", input_filename, "--output", output_filename]
process = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
(stdout, stderr) = process.communicate()
if process.returncode != 0 or not os.path.exists(output_filename):
if "No space left on device" in stderr + " " + stdout:
raise NoSpaceLeftException
raise AudioProcessingException("failed calling stereofy data:\n" + " ".join(cmd) + "\n" + stderr + "\n" + stdout)
stdout = (stdout + " " + stderr).replace("\n", " ")
duration = 0
m = re.match(r".*#duration (?P[\d\.]+).*", stdout)
if m != None:
duration = float(m.group("duration"))
channels = 0
m = re.match(r".*#channels (?P\d+).*", stdout)
if m != None:
channels = float(m.group("channels"))
samplerate = 0
m = re.match(r".*#samplerate (?P\d+).*", stdout)
if m != None:
samplerate = float(m.group("samplerate"))
bitdepth = None
m = re.match(r".*#bitdepth (?P\d+).*", stdout)
if m != None:
bitdepth = float(m.group("bitdepth"))
bitrate = (os.path.getsize(input_filename) * 8.0) / 1024.0 / duration if duration > 0 else 0
return dict(duration=duration, channels=channels, samplerate=samplerate, bitrate=bitrate, bitdepth=bitdepth)
def convert_to_mp3(input_filename, output_filename, quality=70):
"""
converts the incoming wave file to a mp3 file
"""
if not os.path.exists(input_filename):
raise AudioProcessingException("file %s does not exist" % input_filename)
command = ["lame", "--silent", "--abr", str(quality), input_filename, output_filename]
process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
(stdout, stderr) = process.communicate()
if process.returncode != 0 or not os.path.exists(output_filename):
raise AudioProcessingException(stdout)
def convert_to_ogg(input_filename, output_filename, quality=1):
"""
converts the incoming wave file to n ogg file
"""
if not os.path.exists(input_filename):
raise AudioProcessingException("file %s does not exist" % input_filename)
command = ["oggenc", "-q", str(quality), input_filename, "-o", output_filename]
process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
(stdout, stderr) = process.communicate()
if process.returncode != 0 or not os.path.exists(output_filename):
raise AudioProcessingException(stdout)
def convert_using_ffmpeg(input_filename, output_filename):
"""
converts the incoming wave file to stereo pcm using fffmpeg
"""
TIMEOUT = 3 * 60
def alarm_handler(signum, frame):
raise AudioProcessingException("timeout while waiting for ffmpeg")
if not os.path.exists(input_filename):
raise AudioProcessingException("file %s does not exist" % input_filename)
command = ["ffmpeg", "-y", "-i", input_filename, "-ac","1","-acodec", "pcm_s16le", "-ar", "44100", output_filename]
process = subprocess.Popen(command, stdout=subprocess.PIPE, stderr=subprocess.PIPE)
signal.signal(signal.SIGALRM,alarm_handler)
signal.alarm(TIMEOUT)
(stdout, stderr) = process.communicate()
signal.alarm(0)
if process.returncode != 0 or not os.path.exists(output_filename):
raise AudioProcessingException(stdout)