Functions — github.com/dhruvsharma1992/Music-generation-cRNN-GAN

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Functions113 in github.com/dhruvsharma1992/Music-generation-cRNN-GAN

⨍ Functions 113◇ Types & classes 4

↓ 15 callersFunctiondata_type

()

Conditional GAN/rnn_gan.py:213

↓ 14 callersFunctiondata_type

()

rnn_gan.py:206

↓ 8 callersFunctionlinear

(inp, output_dim, scope=None, stddev=1.0, reuse_scope=False)

rnn_gan.py:222

↓ 7 callersFunctionlinear

(inp, output_dim, scope=None, stddev=1.0, reuse_scope=False)

Conditional GAN/rnn_gan.py:229

↓ 6 callersFunctionmake_rnn_cell

Makes a RNN cell from the given hyperparameters. Args: rnn_layer_sizes: A list of integer sizes (in units) for each layer of the RNN. dropo

rnn_gan.py:149

↓ 6 callersFunctionmake_rnn_cell

Makes a RNN cell from the given hyperparameters. Args: rnn_layer_sizes: A list of integer sizes (in units) for each layer of the RNN. dropo

Conditional GAN/rnn_gan.py:156

↓ 6 callersFunctiontone_to_freq

returns the frequency of a tone. formulas from * https://en.wikipedia.org/wiki/MIDI_Tuning_Standard * https://en.wikipedia.org/

music_data_utils.py:1381

↓ 6 callersFunctiontone_to_freq

returns the frequency of a tone. formulas from * https://en.wikipedia.org/wiki/MIDI_Tuning_Standard * https://en.wikipedia.org/

Conditional GAN/music_data_utils.py:707

↓ 5 callersFunctiontone_to_freq

returns the frequency of a tone. formulas from * https://en.wikipedia.org/wiki/MIDI_Tuning_Standard * https://en.wikipedia.org/

midi_statistics.py:367

↓ 5 callersFunctiontone_to_freq

returns the frequency of a tone. formulas from * https://en.wikipedia.org/wiki/MIDI_Tuning_Standard * https://en.wikipedia.org/

Conditional GAN/midi_statistics.py:367

↓ 4 callersMethodget_midi_pattern

get_midi_pattern takes a song in internal representation (a tensor of dimensions [songlength, self.num_song_features]). the three values

Conditional GAN/music_data_utils.py:576

↓ 4 callersFunctionrun_epoch

Runs the model on the given data.

rnn_gan.py:561

↓ 4 callersFunctionrun_epoch

Runs the model on the given data.

Conditional GAN/rnn_gan.py:539

↓ 3 callersMethoddiscriminator

(self, inputs, is_training, msg='')

Conditional GAN/rnn_gan.py:451

↓ 3 callersFunctionget_all_stats

(midi_pattern)

midi_statistics.py:398

↓ 3 callersFunctionget_all_stats

(midi_pattern)

Conditional GAN/midi_statistics.py:398

↓ 3 callersMethodget_batch

get_batch() returns a batch from self.songs, as a pair of tensors (genrecomposer, song_data). The first tensor is a tensor o

music_data_utils.py:1144

↓ 3 callersMethodget_batch

get_batch() returns a batch from self.songs, as a pair of tensors (genrecomposer, song_data). The first tensor is a tensor o

Conditional GAN/music_data_utils.py:456

↓ 3 callersMethodget_midi_pattern

get_midi_pattern takes a song in internal representation (a tensor of dimensions [songlength, self.num_song_features]). the three values

music_data_utils.py:1250

↓ 3 callersMethodsave_data

save_data takes a filename and a song in internal representation (a tensor of dimensions [songlength, 3]). the three values are length,

music_data_utils.py:1365

↓ 3 callersMethodsave_midi_pattern

(self, filename, midi_pattern)

Conditional GAN/music_data_utils.py:687

↓ 2 callersMethoddiscriminator

(self, inputs, is_training, msg='')

rnn_gan.py:454

↓ 2 callersFunctionget_abs_ticks

(midi_pattern)

midi_statistics.py:135

↓ 2 callersFunctionget_abs_ticks

(midi_pattern)

Conditional GAN/midi_statistics.py:135

↓ 2 callersFunctionget_midi_pattern

(filename)

midi_statistics.py:263

↓ 2 callersFunctionget_midi_pattern

(filename)

Conditional GAN/midi_statistics.py:263

↓ 2 callersFunctiononehot

(i, length)

music_data_utils.py:1412

↓ 2 callersMethodread_one_file

(self, path, filename, pace_events)

music_data_utils.py:1026

↓ 2 callersMethodread_one_file

(self, path, filename, pace_events)

Conditional GAN/music_data_utils.py:338

↓ 2 callersFunctionsample

Samples from the generative model.

rnn_gan.py:658

↓ 2 callersFunctionsample

Samples from the generative model.

Conditional GAN/rnn_gan.py:642

↓ 2 callersMethodsave_data

save_data takes a filename and a song in internal representation (a tensor of dimensions [songlength, 3]). the three values are length,

Conditional GAN/music_data_utils.py:691

↓ 2 callersMethodsave_midi_pattern

(self, filename, midi_pattern)

music_data_utils.py:1361

↓ 2 callersFunctiontone_to_tone_name

Midi to tone name (octave: -5): 0: C 1: C# 2: D 3: D# 4: E 5: F 6: F# 7: G 8: G# 9: A 10: A# 11: B One oct

midi_statistics.py:335

↓ 2 callersFunctiontone_to_tone_name

Midi to tone name (octave: -5): 0: C 1: C# 2: D 3: D# 4: E 5: F 6: F# 7: G 8: G# 9: A 10: A# 11: B One oct

Conditional GAN/midi_statistics.py:335

↓ 1 callersMethodassign_lr

(self, session, lr_value)

rnn_gan.py:520

↓ 1 callersMethodassign_lr

(self, session, lr_value)

Conditional GAN/rnn_gan.py:494

↓ 1 callersFunctioncents_to_pitchwheel_units

(cents)

music_data_utils.py:1409

↓ 1 callersFunctioncents_to_pitchwheel_units

(cents)

Conditional GAN/music_data_utils.py:735

↓ 1 callersFunctiondetect_beat

returns a dict of statistics, keys: [scale_distribution,

midi_statistics.py:93

↓ 1 callersFunctiondetect_beat

returns a dict of statistics, keys: [scale_distribution,

Conditional GAN/midi_statistics.py:93

↓ 1 callersMethoddownload_midi_data

download_midi_data will download a number of midi files, linked from the html pages specified in the sources dict, into datadir. There will b

music_data_utils.py:696

↓ 1 callersFunctionfreq_to_tone

returns a dict d where d['tone'] is the base tone in midi standard d['cents'] is the cents to make the tone into the exact-ish frequency

music_data_utils.py:1391

↓ 1 callersFunctionfreq_to_tone

returns a dict d where d['tone'] is the base tone in midi standard d['cents'] is the cents to make the tone into the exact-ish frequency

Conditional GAN/music_data_utils.py:717

↓ 1 callersMethodgenerate_chords

generate_chords generates synthetic songs with either major or minor chords in a chosen scale. returns a list of tuples, [genre, compose

music_data_utils.py:779

↓ 1 callersMethodgenerate_chords

generate_chords generates synthetic songs with either major or minor chords in a chosen scale. returns a list of tuples, [genre, compose

Conditional GAN/music_data_utils.py:153

↓ 1 callersFunctionget_gnuplot_line

(midi_patterns, i, showheader=True)

midi_statistics.py:452

↓ 1 callersFunctionget_gnuplot_line

(midi_patterns, i, showheader=True)

Conditional GAN/midi_statistics.py:452

↓ 1 callersFunctionget_intensities

returns a dict of statistics, keys: [scale_distribution,

midi_statistics.py:241

↓ 1 callersFunctionget_intensities

returns a dict of statistics, keys: [scale_distribution,

Conditional GAN/midi_statistics.py:241

↓ 1 callersMethodget_num_meta_features

(self)

music_data_utils.py:1247

↓ 1 callersMethodget_num_meta_features

(self)

Conditional GAN/music_data_utils.py:572

↓ 1 callersMethodget_num_song_features

(self)

music_data_utils.py:1245

↓ 1 callersMethodget_num_song_features

(self)

Conditional GAN/music_data_utils.py:570

↓ 1 callersFunctionget_polyphony_score

returns a fraction of the noteon events in midi_pattern that are polyphonous (several notes occurring at the same time). Here, two note on even

midi_statistics.py:175

↓ 1 callersFunctionget_polyphony_score

returns a fraction of the noteon events in midi_pattern that are polyphonous (several notes occurring at the same time). Here, two note on even

Conditional GAN/midi_statistics.py:175

↓ 1 callersFunctionget_tones

returns a dict of statistics, keys: [scale_distribution,

midi_statistics.py:74

↓ 1 callersFunctionget_tones

returns a dict of statistics, keys: [scale_distribution,

Conditional GAN/midi_statistics.py:74

↓ 1 callersFunctionget_top_k_intervals

returns a fraction of the noteon events in midi_pattern that are polyphonous (several notes occurring at the same time). Here, two note on even

midi_statistics.py:152

↓ 1 callersFunctionget_top_k_intervals

returns a fraction of the noteon events in midi_pattern that are polyphonous (several notes occurring at the same time). Here, two note on even

Conditional GAN/midi_statistics.py:152

↓ 1 callersFunctionmain

()

midi_statistics.py:470

↓ 1 callersFunctionmain

()

music_data_utils.py:1419

↓ 1 callersFunctionmain

()

Conditional GAN/midi_statistics.py:470

↓ 1 callersFunctionmain

()

Conditional GAN/music_data_utils.py:745

↓ 1 callersFunctionmax_likelihood_scale

(tones)

midi_statistics.py:358

↓ 1 callersFunctionmax_likelihood_scale

(tones)

Conditional GAN/midi_statistics.py:358

↓ 1 callersFunctionminibatch

Borrowed from http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/

rnn_gan.py:234

↓ 1 callersFunctionminibatch

Borrowed from http://blog.aylien.com/introduction-generative-adversarial-networks-code-tensorflow/

Conditional GAN/rnn_gan.py:241

↓ 1 callersFunctiononehot

(i, length)

Conditional GAN/music_data_utils.py:738

↓ 1 callersMethodread_data

read_data takes a datadir with genre subdirs, and composer subsubdirs containing midi files, reads them into training data for an rnn-gan mod

music_data_utils.py:902

↓ 1 callersMethodread_data

read_data takes a datadir with genre subdirs, and composer subsubdirs containing midi files, reads them into training data for an rnn-gan mod

Conditional GAN/music_data_utils.py:276

↓ 1 callersFunctionrepetitions

(tones)

midi_statistics.py:316

↓ 1 callersFunctionrepetitions

(tones)

Conditional GAN/midi_statistics.py:316

↓ 1 callersFunctionrestore_flags

(save_if_none_found=True)

rnn_gan.py:181

↓ 1 callersFunctionrestore_flags

(save_if_none_found=True)

Conditional GAN/rnn_gan.py:188

↓ 1 callersMethodrewind

(self, part='train')

music_data_utils.py:1141

↓ 1 callersMethodrewind

(self, part='train')

Conditional GAN/music_data_utils.py:453

↓ 1 callersFunctiontones_to_scales

Midi to tone name (octave: -5): 0: C 1: C# 2: D 3: D# 4: E 5: F 6: F# 7: G 8: G# 9: A 10: A# 11: B Melodic

midi_statistics.py:270

↓ 1 callersFunctiontones_to_scales

Midi to tone name (octave: -5): 0: C 1: C# 2: D 3: D# 4: E 5: F 6: F# 7: G 8: G# 9: A 10: A# 11: B Melodic

Conditional GAN/midi_statistics.py:270

Method__init__

(self, is_training, num_song_features=None, num_meta_features=None)

rnn_gan.py:264

Method__init__

(self, datadir, select_validation_percentage, select_test_percentage, works_per_composer=None, pace_events=Fal

music_data_utils.py:680

Method__init__

(self, is_training, num_song_features=None, num_meta_features=None)

Conditional GAN/rnn_gan.py:271

Method__init__

(self, datadir, select_validation_percentage, select_test_percentage, genres, works_per_composer=None, pace_ev

Conditional GAN/music_data_utils.py:53

Functioncents_to_pitchwheel_units

(cents)

midi_statistics.py:395

Functioncents_to_pitchwheel_units

(cents)

Conditional GAN/midi_statistics.py:395

Conditional GAN/rnn_gan.py:522

Methoddownload_midi_data

download_midi_data will download a number of midi files, linked from the html pages specified in the sources dict, into datadir. There will b

Conditional GAN/music_data_utils.py:70

Conditional GAN/rnn_gan.py:526

Functionfreq_to_tone

returns a dict d where d['tone'] is the base tone in midi standard d['cents'] is the cents to make the tone into the exact-ish frequency

midi_statistics.py:377

Functionfreq_to_tone

returns a dict d where d['tone'] is the base tone in midi standard d['cents'] is the cents to make the tone into the exact-ish frequency

Conditional GAN/midi_statistics.py:377

Methodgenerated_features

(self)

rnn_gan.py:524

Methodgenerated_features

(self)

Conditional GAN/rnn_gan.py:498

Functionget_rhythm_stats

returns a dict of statistics, keys: [scale_distribution,

midi_statistics.py:208

Functionget_rhythm_stats

returns a dict of statistics, keys: [scale_distribution,

Conditional GAN/midi_statistics.py:208

Conditional GAN/rnn_gan.py:518

Conditional GAN/rnn_gan.py:506

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