diff --git a/markov_chain.py b/markov_chain.py index 5ba6df7f60ca3763f3e7eb6db94bc223aab2d0b7..88b8cc9f1e739a9c802007f1cd785bb3ecdfa754 100644 --- a/markov_chain.py +++ b/markov_chain.py @@ -1,5 +1,12 @@ + import os -from mido import MidiFile +# from mido import MidiFile +import mido +import music21 +import numpy as np +import pandas as pd +from music21 import * +from mido import Message, MidiFile, MidiTrack #number of notes to be used for prediction window = 3 @@ -18,18 +25,228 @@ def abs_paths(dir): for dir_path,_,filenames in os.walk(dir): for f in filenames: yield os.path.abspath(os.path.join(dir_path, f)) -def pitch_to_int(): - #TODO: look at this later - pass -if not os.path.exists('tracks'): - # os.mkdir('tracks') - i = 0 - #Parse midi files into tracks folder - for path in abs_paths('data'): - print(path) - mid = MidiFile(path) - for i, track in enumerate(mid.tracks): - print('Track {}: {}'.format(i, track.name)) - for message in track: - print(message) - break \ No newline at end of file +def pitch_to_int(nameWithOctave): + # letter names with corresponding values + letter_dict = {'C':0,'D':2,'E':4,'F':5,'G':7,'A':9,'B':11} + # parse characters from strings + chars = list(nameWithOctave) + # convert octave number to corresponding midi value + octave = 12*(int(chars[-1])+1) + # select value from letter_dict using first character + note = letter_dict[chars[0]] + # set accidental value + accidental = 0 + # does accidental exist? + if not len(chars)==2: + # increase (sharp) or decrease (flat) value by one + accidental = 1 if chars[1]=='#' else -1 + # return sum of these numbers, middle C(4) == 60 + return octave + note + accidental +def generate_notes(): + df_notes = pd.read_csv('prepared.csv') + print(df_notes.shape) + # define arrays for generated notes and durations + gen_notes = [] + gen_durations = [] + # define note and duration feature columns based on names + features = df_notes.columns[:-2] + note_features = [s for s in features if "note" in s] + duration_features = [s for s in features if "duration" in s] + # define target columns + note_target = df_notes.columns[-2] + duration_target = df_notes.columns[-1] + + # sample random row from dataframe and define start notes and durations + initial_sample = df_notes.sample() + start_notes = list(initial_sample[note_features].values[0]) + start_durations = list(initial_sample[duration_features].values[0]) + # append starting notes and durations to gen arrays + for note in start_notes: + gen_notes.append(int(note)) + for duration in start_durations: + gen_durations.append(duration) + + for i in range(num_notes) : + rows = df_notes + for i in range(window-1): + rows = rows.loc[df_notes[note_features[i]] == start_notes[i]] + rows = rows.loc[df_notes[duration_features[i]]== start_durations[i]] + + #This gives the same effect as probability. + # We effectively sample from a list which might have more than 1 C note, Hence increasing its probability + #Sometime, The start_notes and durations could be selected in such a way that we cannot generate any further notes uptill num_notes, + #This means there maybe some combinations of notes such as 76,68 which are not there in the dataset and hence cannot be sampled. + #In such cases, the only way about it would be to reset the start notes, because we cannot sample from an empty row + #So here we check if any rows which we ta + if len(rows): + next_sample = rows.sample() + next_note = next_sample[note_target].values[0] + next_duration = next_sample[duration_target].values[0] + gen_notes.append(int(next_note)) + gen_durations.append(next_duration) + + start_notes.pop() + start_durations.pop() + + start_notes.append(next_note) + start_durations.append(next_duration) + else: + #Received empty row + # print("Exiting!!!!!!") + #restarting again to get new start notes + return [],[] + + # print(rows[note_target].value_counts(normalize=True)) + # print(rows[duration_target].value_counts(normalize=True)) + + return gen_notes, gen_durations + +#MAIN FUNCTION +if __name__=="__main__": + # https://stackoverflow.com/questions/49462107/how-can-i-get-all-piano-parts-from-a-music21-score + if not os.path.exists('tracks'): + os.mkdir('tracks') + i = 0 + #Parse midi files into tracks folder + for path in abs_paths('data'): + print(path) + # mid = MidiFile(path) + piece = converter.parse(path) + print(list(piece.parts)) + for part in piece.parts: + part_notes = [] + #get all note messages from all tracks + for event in part: + if getattr(event, 'isNote', None) and event.isNote: + print('note in {}'.format(part)) + + #check if note is in accepted length + #convert string to numerical value + if event.quarterLength in accepeted_lengths: + part_notes.append([pitch_to_int(event.nameWithOctave), event.quarterLength]) + if not len(part_notes) == 0: + np.save('tracks/{}.npy'.format(i), np.array(part_notes)) + i+=1 + print('Number of tracks parsed: {}'.format(i)) + if not os.path.exists('prepared.csv'): + columns = [] + for i in range(window): + columns.append('note' + str(i)) + columns.append('duration' + str(i)) + df_notes = pd.DataFrame(columns=columns) + # append segments from each track as rows to dataframe + for path in abs_paths('tracks'): + notes = np.load(path) + for i in range(len(notes)-window): + # take every x notes and durations + segment = notes[i:i+window].flatten() + # make into pd.Series row + row = pd.Series(segment, index=df_notes.columns) + # append row to dataframe + df_notes = df_notes.append(row, ignore_index=True) + # export + df_notes.to_csv('prepared.csv', index=False) + success = False + gen_notes =[] + gen_durations =[] + + #Retry mechanism + while len(gen_notes), ] +/cs/home/va35/Documents/SMG/data/chopin/chpn-p6.mid +[, ] +note in +note in +note in +note in +note in +note in +note in +note in +note in +note in +note in +note in +note in +note in +note in +note in +note in +note in +note in +note in +note in +note in +note in +note in +note in +note in +note in +note in +note 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