DeepLearning.AI/data_utils.py at master · techinAI/DeepLearning.AI · GitHub

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from music_utils import *

from preprocess import *

chords, abstract_grammars = get_musical_data('data/original_metheny.mid')

corpus, tones, tones_indices, indices_tones = get_corpus_data(abstract_grammars)

N_tones = len(set(corpus))

def load_music_utils():

chords, abstract_grammars = get_musical_data('data/original_metheny.mid')

corpus, tones, tones_indices, indices_tones = get_corpus_data(abstract_grammars)

N_tones = len(set(corpus))

X, Y, N_tones = data_processing(corpus, tones_indices, 60, 30)

return (X, Y, N_tones, indices_tones)

"""

def get_music(pred, indices_tones):

# this assumes that pred is a list of indices

predicted_tones = [indices_tones[p] for p in pred]

predicted_tones = np.array(predicted_tones).replace(' A',' C').replace(' X',' C')

# Pruning #1: smoothing measure

predicted_tones = prune_grammar(predicted_tones)

# Use predicted tones and current chords to generate sounds

sounds = unparse_grammar(predicted_tones, curr_chords)

# Pruning #2: removing repeated and too close together sounds

sounds = prune_notes(sounds)

# Quality assurance: clean up sounds

sounds = clean_up_notes(sounds)

# Print number of tones/notes in sounds

print('After pruning, generated %s sounds using the current chords' % (len([k for k in sounds if isinstance(k, note.Note)])))

# Insert sounds into the output stream

for m in sounds:

out_stream.insert(curr_offset + m.offset, m)

for mc in curr_chords:

out_stream.insert(curr_offset + mc.offset, mc)

curr_offset += 4.0

# Initialize tempo of the output stream with 130 bit per minute

out_stream.insert(0.0, tempo.MetronomeMark(number=130))

# Save audio stream to fine

mf = midi.translate.streamToMidiFile(out_stream)

mf.open("output/my_music.midi", 'wb')

mf.write()

print("Your generated music is saved in output/my_music.midi")

mf.close()

# Play the final stream through output (see 'play' lambda function above)

# play = lambda x: midi.realtime.StreamPlayer(x).play()

# play(out_stream)

return out_stream

"""

def generate_music(inference_model, corpus = corpus, abstract_grammars = abstract_grammars, tones = tones, tones_indices = tones_indices, indices_tones = indices_tones, T_y = 100, max_tries = 1000, diversity = 0.5):

Generates music using a model trained to learn musical patterns of a jazz soloist. Creates an audio stream

to save the music and play it.

Arguments:

model -- Keras model Instance, output of djmodel()

corpus -- musical corpus, list of 193 tones as strings (ex: 'C,0.333,<P1,d-5>')

abstract_grammars -- list of grammars, on element can be: 'S,0.250,<m2,P-4> C,0.250,<P4,m-2> A,0.250,<P4,m-2>'

tones -- set of unique tones, ex: 'A,0.250,<M2,d-4>' is one element of the set.

tones_indices -- a python dictionary mapping unique tone (ex: A,0.250,< m2,P-4 >) into their corresponding indices (0-77)

indices_tones -- a python dictionary mapping indices (0-77) into their corresponding unique tone (ex: A,0.250,< m2,P-4 >)

Tx -- integer, number of time-steps used at training time

temperature -- scalar value, defines how conservative/creative the model is when generating music

Returns:

predicted_tones -- python list containing predicted tones

# set up audio stream

out_stream = stream.Stream()

# Initialize chord variables

curr_offset = 0.0 # variable used to write sounds to the Stream.

num_chords = len(chords) # number of different set of chords

# Loop over all 18 set of chords. At each iteration generate a sequence of tones

# and use the current chords to convert it into actual sounds

for i in range(1, num_chords):

# Retrieve current chord from stream

curr_chords = stream.Voice()

# Loop over the chords of the current set of chords

for j in chords[i]:

# Add chord to the current chords with the adequate offset, no need to understand this

curr_chords.insert((j.offset % 4), j)

# Generate a sequence of tones using the model

_, predicted_tones = predict_and_sample(inference_model, Ty = Ty)

#### POST PROCESSING OF THE PREDICTED TONES ####

# We will consider "A" and "X" as "C" tones. It is a common choice.

predicted_tones = predicted_tones.replace(' A',' C').replace(' X',' C')

# Pruning #1: smoothing measure

predicted_tones = prune_grammar(predicted_tones)

# Use predicted tones and current chords to generate sounds

sounds = unparse_grammar(predicted_tones, curr_chords)

# Pruning #2: removing repeated and too close together sounds

sounds = prune_notes(sounds)

# Quality assurance: clean up sounds

sounds = clean_up_notes(sounds)

# Print number of tones/notes in sounds

print('After pruning, generated %s sounds using the current chords' % (len([k for k in sounds if isinstance(k, note.Note)])))

# Insert sounds into the output stream

for m in sounds:

out_stream.insert(curr_offset + m.offset, m)

for mc in curr_chords:

out_stream.insert(curr_offset + mc.offset, mc)

curr_offset += 4.0

# Initialize tempo of the output stream with 130 bit per minute

out_stream.insert(0.0, tempo.MetronomeMark(number=130))

# Save audio stream to fine

mf = midi.translate.streamToMidiFile(out_stream)

mf.open("output/my_music.midi", 'wb')

mf.write()

print("Your generated music is saved in output/my_music.midi")

mf.close()

# Play the final stream through output (see 'play' lambda function above)

# play = lambda x: midi.realtime.StreamPlayer(x).play()

# play(out_stream)

return out_stream

"""

Generates music using a model trained to learn musical patterns of a jazz soloist. Creates an audio stream

to save the music and play it.

Arguments:

model -- Keras model Instance, output of djmodel()

corpus -- musical corpus, list of 193 tones as strings (ex: 'C,0.333,<P1,d-5>')

abstract_grammars -- list of grammars, on element can be: 'S,0.250,<m2,P-4> C,0.250,<P4,m-2> A,0.250,<P4,m-2>'

tones -- set of unique tones, ex: 'A,0.250,<M2,d-4>' is one element of the set.

tones_indices -- a python dictionary mapping unique tone (ex: A,0.250,< m2,P-4 >) into their corresponding indices (0-77)

indices_tones -- a python dictionary mapping indices (0-77) into their corresponding unique tone (ex: A,0.250,< m2,P-4 >)

Tx -- integer, number of time-steps used at training time

temperature -- scalar value, defines how conservative/creative the model is when generating music

Returns:

predicted_tones -- python list containing predicted tones

"""

# set up audio stream

out_stream = stream.Stream()

# Initialize chord variables

curr_offset = 0.0 # variable used to write sounds to the Stream.

num_chords = len(chords) # number of different set of chords

# Loop over all 18 set of chords. At each iteration generate a sequence of tones

# and use the current chords to convert it into actual sounds

for i in range(1, num_chords):

# Retrieve current chord from stream

curr_chords = stream.Voice()

# Loop over the chords of the current set of chords

for j in chords[i]:

# Add chord to the current chords with the adequate offset, no need to understand this

curr_chords.insert((j.offset % 4), j)

# Generate a sequence of tones using the model

_, indices = predict_and_sample(inference_model, Ty = 100)

indices = list(indices.squeeze())

pred = [indices_tones[p] for p in indices]

predicted_tones = 'C,0.25 '

for k in range(len(pred) - 1):

predicted_tones += pred[k] + ' '

predicted_tones += pred[-1]

#### POST PROCESSING OF THE PREDICTED TONES ####

# We will consider "A" and "X" as "C" tones. It is a common choice.

predicted_tones = predicted_tones.replace(' A',' C').replace(' X',' C')

# Pruning #1: smoothing measure

predicted_tones = prune_grammar(predicted_tones)

# Use predicted tones and current chords to generate sounds

sounds = unparse_grammar(predicted_tones, curr_chords)

# Pruning #2: removing repeated and too close together sounds

sounds = prune_notes(sounds)

# Quality assurance: clean up sounds

sounds = clean_up_notes(sounds)

# Print number of tones/notes in sounds

print('After pruning, generated %s sounds using the current chords' % (len([k for k in sounds if isinstance(k, note.Note)])))

# Insert sounds into the output stream

for m in sounds:

out_stream.insert(curr_offset + m.offset, m)

for mc in curr_chords:

out_stream.insert(curr_offset + mc.offset, mc)

curr_offset += 4.0

# Initialize tempo of the output stream with 130 bit per minute

out_stream.insert(0.0, tempo.MetronomeMark(number=130))

# Save audio stream to fine

mf = midi.translate.streamToMidiFile(out_stream)

mf.open("output/my_music.midi", 'wb')

mf.write()

print("Your generated music is saved in output/my_music.midi")

mf.close()

# Play the final stream through output (see 'play' lambda function above)

# play = lambda x: midi.realtime.StreamPlayer(x).play()

# play(out_stream)

return out_stream

def predict_and_sample(inference_model, x_initializer, a_initializer,

c_initializer, Ty = 100):

"""

Predicts the next value of values using the inference model.

Arguments:

inference_model -- Keras model instance for inference time

x_initializer -- numpy array of shape (1, 1, 78), one-hot vector initializing the values generation

a_initializer -- numpy array of shape (1, n_a), initializing the hidden state of the LSTM_cell

c_initializer -- numpy array of shape (1, n_a), initializing the cell state of the LSTM_cel

Ty -- length of the sequence you'd like to generate.

Returns:

results -- numpy-array of shape (Ty, 78), matrix of one-hot vectors representing the values generated

indices -- numpy-array of shape (Ty, 1), matrix of indices representing the values generated

"""

### START CODE HERE ###

pred = inference_model.predict([x_initializer, a_initializer, c_initializer])

indices = np.argmax(pred, axis = -1)

results = to_categorical(indices, num_classes=78)

### END CODE HERE ###

return results, indices

DeepLearning.AI/data_utils.py at master · techinAI/DeepLearning.AI · GitHub

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