Tim Sainburg timsainb

## make_audiobook.py
#!/usr/bin/env python3
"""
To use:
1. install/set-up the google cloud api and dependencies listed on https://github.com/GoogleCloudPlatform/python-docs-samples/tree/master/texttospeech/cloud-client
2. install pandoc and pypandoc, also tqdm
3. create and download a service_account.json ("Service account key") from https://console.cloud.google.com/apis/credentials
4. run GOOGLE_APPLICATION_CREDENTIALS=service_account.json python make_audiobook.py book_name.epub
"""
import re
import sys

## kernel_visualization.py
import numpy as np
import tensorflow as tf

# Layer name to inspect
layer_name = 'block3_conv1'

epochs = 100
step_size = 1.
filter_index = 0

## dtw_mse.py
# based on https://github.com/kylerbrown/ezdtw
# with modifications to be fully njit-able

import numpy as np
from numba import njit

@njit
def sqeuclidean(a, b):
    return np.sum((a - b)**2)

## unsupervisedpcfg.py
import numpy as np
import pandas as pd

from random import shuffle, choice
from scipy.stats import dirichlet
from scipy.misc import logsumexp
from nltk.tree import Tree

class UnsupervisedPCFG(object):
    '''unsupervised pcfg induction with EM

## Signal reconstruction from spectrograms.ipynb

      
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                kastnerkyle
                / Signal reconstruction from spectrograms.ipynb
            
            
              Created
              May 31, 2018 18:17
                — forked from carlthome/Signal reconstruction from spectrograms.ipynb
            
              
                Try to recover audio from filtered magnitudes when phase information has been lost.
              
          
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## blog_tensorflow_variational_auto_encoder.py
# Full example for my blog post at:
# https://danijar.com/building-variational-auto-encoders-in-tensorflow/

import numpy as np
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow.examples.tutorials.mnist import input_data

tfd = tf.contrib.distributions

## Attention.py

from keras import backend as K, initializers, regularizers, constraints
from keras.engine.topology import Layer


def dot_product(x, kernel):
    """
    Wrapper for dot product operation, in order to be compatible with both
    Theano and Tensorflow
    Args:

## rgb_to_hsv_np.py
#
# Copyright (c) 2016, Nikolay Polyarnyi
# All rights reserved.
#

import numpy as np


def rgb_to_hsv(rgb):
    """

## audio_tools.py
raise ValueError("DEPRECATED/FROZEN - see https://github.com/kastnerkyle/tools for the latest")

# License: BSD 3-clause
# Authors: Kyle Kastner
# Harvest, Cheaptrick, D4C, WORLD routines based on MATLAB code from M. Morise
# http://ml.cs.yamanashi.ac.jp/world/english/
# MGC code based on r9y9 (Ryuichi Yamamoto) MelGeneralizedCepstrums.jl
# Pieces also adapted from SPTK
from __future__ import division
import numpy as np

## mutual_info.py
'''
Non-parametric computation of entropy and mutual-information

Adapted by G Varoquaux for code created by R Brette, itself
from several papers (see in the code).

This code is maintained at https://github.com/mutualinfo/mutual_info
Please download the latest code there, to have improvements and
bug fixes.
	#!/usr/bin/env python3
	"""
	To use:
	1. install/set-up the google cloud api and dependencies listed on https://github.com/GoogleCloudPlatform/python-docs-samples/tree/master/texttospeech/cloud-client
	2. install pandoc and pypandoc, also tqdm
	3. create and download a service_account.json ("Service account key") from https://console.cloud.google.com/apis/credentials
	4. run GOOGLE_APPLICATION_CREDENTIALS=service_account.json python make_audiobook.py book_name.epub
	"""
	import re
	import sys
	import numpy as np
	import tensorflow as tf

	# Layer name to inspect
	layer_name = 'block3_conv1'

	epochs = 100
	step_size = 1.
	filter_index = 0
	# based on https://github.com/kylerbrown/ezdtw
	# with modifications to be fully njit-able

	import numpy as np
	from numba import njit

	@njit
	def sqeuclidean(a, b):
	return np.sum((a - b)**2)
	import numpy as np
	import pandas as pd

	from random import shuffle, choice
	from scipy.stats import dirichlet
	from scipy.misc import logsumexp
	from nltk.tree import Tree

	class UnsupervisedPCFG(object):
	'''unsupervised pcfg induction with EM
	# Full example for my blog post at:
	# https://danijar.com/building-variational-auto-encoders-in-tensorflow/

	import numpy as np
	import matplotlib.pyplot as plt
	import tensorflow as tf
	from tensorflow.examples.tutorials.mnist import input_data

	tfd = tf.contrib.distributions

	from keras import backend as K, initializers, regularizers, constraints
	from keras.engine.topology import Layer


	def dot_product(x, kernel):
	"""
	Wrapper for dot product operation, in order to be compatible with both
	Theano and Tensorflow
	Args:
	#
	# Copyright (c) 2016, Nikolay Polyarnyi
	# All rights reserved.
	#

	import numpy as np


	def rgb_to_hsv(rgb):
	"""
	raise ValueError("DEPRECATED/FROZEN - see https://github.com/kastnerkyle/tools for the latest")

	# License: BSD 3-clause
	# Authors: Kyle Kastner
	# Harvest, Cheaptrick, D4C, WORLD routines based on MATLAB code from M. Morise
	# http://ml.cs.yamanashi.ac.jp/world/english/
	# MGC code based on r9y9 (Ryuichi Yamamoto) MelGeneralizedCepstrums.jl
	# Pieces also adapted from SPTK
	from __future__ import division
	import numpy as np
	'''
	Non-parametric computation of entropy and mutual-information

	Adapted by G Varoquaux for code created by R Brette, itself
	from several papers (see in the code).

	This code is maintained at https://github.com/mutualinfo/mutual_info
	Please download the latest code there, to have improvements and
	bug fixes.