Output as *.mid file parameters for a generations of a chorale of length 100

[shyamalschandra]

Hi,

I tried the following command after installing all the dependencies and the DeepBach code:

python3 deepBach.py -l 100 -o deepBach.mid

Here is the error I get:

Traceback (most recent call last):
  File "deepBach.py", line 847, in <module>
    main()
  File "deepBach.py", line 775, in main
    voice_ids=[0, 1, 2, 3])
  File "/Users/shyamalsuhanachandra/DeepBach/data_utils.py", line 582, in initialization
    metadatas=metadatas)
  File "/Users/shyamalsuhanachandra/DeepBach/data_utils.py", line 247, in make_dataset
    pickle.dump(dataset, open(dataset_name, 'wb'), pickle.HIGHEST_PROTOCOL)
FileNotFoundError: [Errno 2] No such file or directory: 'datasets/raw_dataset/bach_dataset.pickle'
I see the following code inside deepBach.py:

It seems that the dataset/raw_dataset/back_dataset.pickle is missing. Will you be providing that file in the future? If there is no file, it uses the constant BACH_DATASET instead and fails.

    if output_file:
        mf = midi.translate.music21ObjectToMidiFile(score)
        mf.open(output_file, 'wb')
        mf.write()
        mf.close()
        print("File " + output_file + " written")

I checked out the indexed_chorale_to_score function from data_utils library before this snippet of code above. The code for that function is the following:

def indexed_chorale_to_score(seq, pickled_dataset):
    _, _, _, index2notes, note2indexes, _ = pickle.load(open(pickled_dataset, 'rb'))
    num_pitches = list(map(len, index2notes))
    slur_indexes = list(map(lambda d: d[SLUR_SYMBOL], note2indexes))

    score = stream.Score()
    for voice_index, v in enumerate(seq):
        part = stream.Part(id='part' + str(voice_index))
        dur = 0
        f = note.Rest()
        for k, n in enumerate(v):
            # if it is a played note
            if not n == slur_indexes[voice_index]:
                # add previous note
                if dur > 0:
                    f.duration = duration.Duration(dur / SUBDIVISION)
                    part.append(f)

                dur = 1
                f = standard_note(index2notes[voice_index][n])
            else:
                dur += 1
        # add last note
        f.duration = duration.Duration(dur / SUBDIVISION)
        part.append(f)
        score.insert(part)
    return score

There is also a function that converts the score into midi that uses the output of the previous function for the score variable on line 55 of deepBach.py. Do I need to make any mods?

def music21ObjectToMidiFile(music21Object):
    '''
    Either calls streamToMidiFile on the music21Object or
    puts a copy of that object into a Stream (so as
    not to change activeSites, etc.) and calls streamToMidiFile on
    that object.
    '''
    classes = music21Object.classes
    if 'Stream' in classes:
        return streamToMidiFile(music21Object)
    else:
        m21ObjectCopy = copy.deepcopy(music21Object)
        s = stream.Stream()
        s.insert(0, m21ObjectCopy)
        return streamToMidiFile(s) 

What should I do? Can you provide the pretrained dataset or a dataset of MIDIs?

[Ghadjeres]

Default dataset is the Bach Chorales dataset obtained from music21 by corpus.getBachChorales function.

For the moment, create the 'datasets/raw_dataset' folders in DeepBach folder. Calling the program should preprocess, pickle and save the Bach Chorales dataset at location BACH_DATASET

Pickled datasets as well as pretrained models will be available soon

[shyamalschandra]

I couldn't find the Bach Chorales dataset from music21. Do you have the links?

[Ghadjeres]

Please see function music21.corpus.getBachChorales(fileExtensions='xml')
http://web.mit.edu/music21/doc/moduleReference/moduleCorpus.html

[bzamecnik]

Note that the pre-trained model and pre-processed dataset is already available for download:

See the README. Get it from Dropbox manually or have it downloaded/extracted by a script:

./download_pretrained_data.sh

Since pre-processing all the chorales from music21 takes about an hour this might be good for a quick start.