--- /dev/null
+#!/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 <http://www.gnu.org/licenses/>.
+#
+# Authors:
+# Bram de Jong <bram.dejong at domain.com where domain in gmail>
+# 2012, Joar Wandborg <first name at last name dot se>
+
+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"
+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<duration>[\d\.]+).*", stdout)
+ if m != None:
+ duration = float(m.group("duration"))
+
+ channels = 0
+ m = re.match(r".*#channels (?P<channels>\d+).*", stdout)
+ if m != None:
+ channels = float(m.group("channels"))
+
+ samplerate = 0
+ m = re.match(r".*#samplerate (?P<samplerate>\d+).*", stdout)
+ if m != None:
+ samplerate = float(m.group("samplerate"))
+
+ bitdepth = None
+ m = re.match(r".*#bitdepth (?P<bitdepth>\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
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