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audio_deepdream_function.py
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from __future__ import print_function
import os
from io import BytesIO
import numpy as np
from functools import partial
import tensorflow as tf
import librosa
def deepdream_func(layer='mixed4d_3x3_bottleneck_pre_relu',channel=139,
path_to_audio='./audio/helix_drum_track.wav',iterations=8,octaves=8,sr=44100):
'''
Deep Dream Audio Effect.
=========================
Spectrogram is extracted with STFT and transformed/scaled to a RGB image matrix.
Deep dream image effect is then applied to the RGB spectrogram, flattened and rescaled
to 1D matrix. 1d matrix is resynthesized to audio using ISTFT & constant overlap-add is
used to add back in phase from original audio.
'''
audio_path = os.path.dirname(path_to_audio)
audio_filename = os.path.basename(path_to_audio)
audio_filename_new = os.path.join(audio_path,'dreamed_on_'+audio_filename)
model_fn = 'tensorflow_inception_graph.pb'
# creating TensorFlow session and loading the model
graph = tf.Graph()
sess = tf.InteractiveSession(graph=graph)
with tf.gfile.FastGFile(model_fn, 'rb') as f:
graph_def = tf.GraphDef()
graph_def.ParseFromString(f.read())
t_input = tf.placeholder(np.float32, name='input') # define the input tensor
imagenet_mean = 117.0
t_preprocessed = tf.expand_dims(t_input-imagenet_mean, 0)
tf.import_graph_def(graph_def, {'input':t_preprocessed})
# start with a gray image with a little noise
img_noise = np.random.uniform(size=(224,224,3)) + 100.0
def showarray(a, fmt='jpeg'):
a = np.uint8(np.clip(a, 0, 1)*255)
f = BytesIO()
PIL.Image.fromarray(a).save(f, fmt)
display(Image(data=f.getvalue()))
def visstd(a, s=0.1):
'''Normalize the image range for visualization'''
return (a-a.mean())/max(a.std(), 1e-4)*s + 0.5
def T(layer):
'''Helper for getting layer output tensor'''
return graph.get_tensor_by_name("import/%s:0"%layer)
def tffunc(*argtypes):
'''Helper that transforms TF-graph generating function into a regular one.
See "resize" function below.
'''
placeholders = list(map(tf.placeholder, argtypes)) # map all argtypes to a placeholder
def wrap(f):
out = f(*placeholders)
def wrapper(*args, **kw):
return out.eval(dict(zip(placeholders, args)), session=kw.get('session'))
return wrapper
return wrap
# Helper function that uses TF to resize an image
def resize(img, size):
img = tf.expand_dims(img, 0)
return tf.image.resize_bilinear(img, size)[0,:,:,:]
resize = tffunc(np.float32, np.int32)(resize)
def calc_grad_tiled(img, t_grad, tile_size=512):
'''Compute the value of tensor t_grad over the image in a tiled way.
Random shifts are applied to the image to blur tile boundaries over
multiple iterations.'''
sz = tile_size
h, w = img.shape[:2]
sx, sy = np.random.randint(sz, size=2)
img_shift = np.roll(np.roll(img, sx, axis=1), sy, axis=0)
grad = np.zeros_like(img)
for y in range(0, max(h-sz//2, sz),sz):
for x in range(0, max(w-sz//2, sz),sz):
sub = img_shift[y:y+sz,x:x+sz]
g = sess.run(t_grad, {t_input:sub})
grad[y:y+sz,x:x+sz] = g
return np.roll(np.roll(grad, -sx, 1), -sy, 0)
# start with a gray image with a little noise
img_noise = np.random.uniform(size=(224,224,3)) + 100.0
def render_deepdream(t_obj, img0=img_noise,
iter_n=10, step=1.5, octave_n=16, octave_scale=1.4):
t_obj_scaled = tf.mul(t_obj, tf.to_float(tf.log(t_obj) < .8*tf.reduce_max(t_obj)))
t_score = tf.reduce_mean(t_obj_scaled) # defining the optimization objective
t_grad = tf.gradients(t_score, t_input)[0]
# split the image into a number of octaves
img = img0.copy()
octaves = []
for i in range(octave_n-1):
hw = img.shape[:2]
lo = resize(img, np.int32(np.float32(hw)/octave_scale))
hi = img-resize(lo, hw)
img = lo
octaves.append(hi)
# generate details octave by octave
for octave in range(octave_n):
if octave>0:
hi = octaves[-octave]
img = resize(img, hi.shape[:2])+hi
for i in range(iter_n):
g = calc_grad_tiled(img, t_grad)
img += g*(step / (np.abs(g).mean()+1e-7))
#print('.',end = ' ')
#clear_output()
#showarray(img/255.0)
return img/255.0
def load_audio(path_to_audio, sr=44100, nfft=2048, hop=256):
'''
load the audio, do STFT, separate magnitude from phase.
returns STFT matrix and phase.
'''
y, sr = librosa.load(path_to_audio, sr)
# do the stft
y_stft = librosa.core.stft(y, n_fft = nfft, hop_length = hop, center=True)
# Separate the magnitude and phase
y_stft_mag1, y_stft_ang = librosa.magphase(y_stft)
return y_stft_mag1, y_stft_ang
def scale_spect_to_RGB(y_stft_mag1, nonlin = 1.0/8.0):
# scale the spectrogram such that its values correspond to 0-255 (16-bit rgb amplitude)
y_stft_mag = np.power(y_stft_mag1, nonlin)
y_stft_mag = np.flipud((1 - y_stft_mag/y_stft_mag.max()))
# create a 3-layer matrix and copy scaled spectrogram to each layer
y_stft_mag_rgb = np.zeros([y_stft_mag.shape[0], y_stft_mag.shape[1], 3])
y_stft_mag_rgb[:, :, 0] = y_stft_mag
y_stft_mag_rgb[:, :, 1] = y_stft_mag
y_stft_mag_rgb[:, :, 2] = y_stft_mag
# get log mag spectrogram
og_spectrogram = librosa.display.specshow(data=np.log(np.abs(y_stft_mag1)), sr=sr, x_axis='time', y_axis='log')
return y_stft_mag_rgb, y_stft_logmag_spect
def deep_dream_step(y_stft_mag_rgb):
img = 255*y_stft_mag_rgb
dream_stft_rgb = render_deepdream(T(layer)[:,:,:,channel], img, iter_n=iterations, octave_n=octaves)
return dream_stft_rgb
def reverse_image_processing_on_stft_rgb(dream_stft_rgb):
# undo processing to bring the image back from 0-255 to original scale
deepdream_out = np.flipud(dream_stft_rgb)
deepdream_out = (1 - deepdream_out) * y_stft_mag.max()
deepdream_out = np.power(deepdream_out, 1/nonlin)
# flatten the three channels and normalize over number of channels
dream_stft_mag_rgb_orig = np.sum(deepdream_out, axis=2) / 3.0
# show the new log-spectrogram
dream_stft_logmag_spect = librosa.display.specshow(np.log(np.abs(deepdream_out)), sr=sr, x_axis='time', y_axis='log')
return dream_stft_mag_rgb_orig, dream_stft_logmag_spect
def resynthesize_spectrogram(y_stft_ang, dream_stft_mag_rgb_orig, nfft=2048, hop=256):
# add back in the original phase and resynthesizewith istft
dream_stft_mag_rgb = dream_stft_mag_rgb_orig.copy()
dream_stft_mag_rgb = dream_stft_mag_rgb * y_stft_ang
deepdream_audio_out = librosa.core.istft(deepdream_out, hop_length=hop, win_length=nfft, center=True)
return deepdream_audio_out
def save_audio_to_disk(deepdream_audio_out, audio_filename_new, sr):
librosa.output.write_wav(os.path.join(audio_filename_new), deepdream_audio_out, sr)
# main function execution
y_stft_mag1, y_stft_ang = load_audio(path_to_audio, sr=44100, nfft=2048, hop=256)
y_stft_mag_rgb, y_stft_logmag_spect = scale_spect_to_RGB(y_stft_mag1, nonlin = 1.0/8.0)
dream_stft_rgb = deep_dream_step(y_stft_mag_rgb)
dream_stft_mag_rgb_orig, dream_stft_logmag_spect = reverse_image_processing_on_stft_rgb(dream_stft_rgb)
deepdream_audio_out = resynthesize_spectrogram(y_stft_ang, dream_stft_mag_rgb_orig, nfft=2048, hop=256)
save_audio_to_disk(deepdream_audio_out, audio_filename_new, sr)
return og_spectrogram_img, og_spectrogram, dream_spec_img, dream_spectrogram, deepdream_audio_out
deepdream_func(layer,channel,path_to_audio,iterations,octaves)