def main(note_embedding_dim,
meta_embedding_dim,
num_layers,
lstm_hidden_size,
dropout_lstm,
linear_hidden_size,
batch_size,
num_epochs,
train,
num_iterations,
sequence_length_ticks):
dataset_manager = DatasetManager()
metadatas = [
FermataMetadata(),
TickMetadata(subdivision=4),
KeyMetadata()
]
chorale_dataset_kwargs = {
'voice_ids': [0, 1, 2, 3],
'metadatas': metadatas,
'sequences_size': 8,
'subdivision': 4
}
bach_chorales_dataset = dataset_manager.get_dataset(
name='bach_chorales',
**chorale_dataset_kwargs
)
dataset = bach_chorales_dataset
deepbach = DeepBach(
dataset=dataset,
note_embedding_dim=note_embedding_dim,
meta_embedding_dim=meta_embedding_dim,
num_layers=num_layers,
lstm_hidden_size=lstm_hidden_size,
dropout_lstm=dropout_lstm,
linear_hidden_size=linear_hidden_size
)
if train:
deepbach.train(batch_size=batch_size,
num_epochs=num_epochs)
else:
deepbach.load()
deepbach.cuda()
print('Generation')
score, tensor_chorale, tensor_metadata = deepbach.generation(
num_iterations=num_iterations,
length=sequence_length_ticks,
)
score.write('midi', fp = 'test.mid')
def init_app(note_embedding_dim, meta_embedding_dim, num_layers,
lstm_hidden_size, dropout_lstm, linear_hidden_size,
num_iterations, sequence_length_ticks, ticks_per_quarter, port):
global metadatas
global _sequence_length_ticks
global _num_iterations
global _ticks_per_quarter
global bach_chorales_dataset
_ticks_per_quarter = ticks_per_quarter
_sequence_length_ticks = sequence_length_ticks
_num_iterations = num_iterations
dataset_manager = DatasetManager()
chorale_dataset_kwargs = {
'voice_ids': [0, 1, 2, 3],
'metadatas': metadatas,
'sequences_size': 8,
'subdivision': 4
}
_bach_chorales_dataset: ChoraleDataset = dataset_manager.get_dataset(
name='bach_chorales', **chorale_dataset_kwargs)
bach_chorales_dataset = _bach_chorales_dataset
assert sequence_length_ticks % bach_chorales_dataset.subdivision == 0
global deepbach
deepbach = DeepBach(dataset=bach_chorales_dataset,
note_embedding_dim=note_embedding_dim,
meta_embedding_dim=meta_embedding_dim,
num_layers=num_layers,
lstm_hidden_size=lstm_hidden_size,
dropout_lstm=dropout_lstm,
linear_hidden_size=linear_hidden_size)
deepbach.load()
deepbach.cuda()
# launch the script
# use threaded=True to fix Chrome/Chromium engine hanging on requests
# [https://stackoverflow.com/a/30670626]
local_only = False
if local_only:
# accessible only locally:
app.run(threaded=True)
else:
# accessible from outside:
app.run(host='0.0.0.0', port=port, threaded=True)
def main(note_embedding_dim, meta_embedding_dim, num_layers, lstm_hidden_size,
dropout_lstm, linear_hidden_size, batch_size, num_epochs, train,
update, num_iterations, sequence_length_ticks, model_id,
include_transpositions, update_iterations, generations_per_iteration,
num_generations, score_chorales, write_scores):
print('step 1/3: prepare dataset')
dataset_manager = DatasetManager()
metadatas = [FermataMetadata(), TickMetadata(subdivision=4), KeyMetadata()]
chorale_dataset_kwargs = {
'voice_ids': [0, 1, 2, 3],
'metadatas': metadatas,
'sequences_size': 8,
'subdivision': 4,
'include_transpositions': include_transpositions,
}
bach_chorales_dataset: ChoraleDataset = dataset_manager.get_dataset(
name='bach_chorales', **chorale_dataset_kwargs)
dataset = bach_chorales_dataset
load_or_pickle_distributions(dataset)
print('step 2/3: prepare model')
print(f'Model ID: {model_id}')
deepbach = DeepBach(
dataset=dataset,
note_embedding_dim=note_embedding_dim,
meta_embedding_dim=meta_embedding_dim,
num_layers=num_layers,
lstm_hidden_size=lstm_hidden_size,
dropout_lstm=dropout_lstm,
linear_hidden_size=linear_hidden_size,
model_id=model_id,
)
if train:
print('step 2a/3: train base model')
deepbach.train(batch_size=batch_size,
num_epochs=num_epochs,
split=[0.85, 0.15])
else:
print('step 2a/3: load model')
deepbach.load()
deepbach.cuda()
if update:
print(
f'step 2b/3: update base model over {update_iterations} iterations'
)
thres = get_threshold('data/chorale_scores.csv', col=-1)
print(f'Threshold for selection: {thres}')
update_file = open('data/update_scores.csv', 'w')
reader = csv.writer(update_file)
reader.writerow(['iteration', 'chorale ID', 'score'])
for i in range(update_iterations):
print(f'----------- Iteration {i} -----------')
picked_chorales = []
num_picked_chorales = 0
ensure_dir(f'generations/{model_id}/{i}')
for j in tqdm(range(generations_per_iteration)):
chorale, tensor_chorale, tensor_metadata = deepbach.generation(
num_iterations=num_iterations,
sequence_length_ticks=sequence_length_ticks,
)
score = score_chorale(chorale, dataset)
# write data to csv file
reader.writerow([i, j,
score]) # iteration, generation #, score
# worst Bach chorale score rounded up to nearest .01
if score > thres:
print(f'Picked chorale {j} with score {score}')
picked_chorales.append(chorale)
num_picked_chorales += 1
chorale.write('midi', f'generations/{model_id}/{i}/c{j}.mid')
print(
f'Number of picked chorales for iteration {i}: {num_picked_chorales}'
)
if num_picked_chorales == 0:
continue
all_datasets.update({
f'generated_chorales_{i}': {
'dataset_class_name': ChoraleDataset,
'corpus_it_gen':
GeneratedChoraleIteratorGen(picked_chorales)
}
})
generated_dataset: ChoraleDataset = dataset_manager.get_dataset(
name=f'generated_chorales_{i}',
index2note_dicts=dataset.index2note_dicts,
note2index_dicts=dataset.note2index_dicts,
voice_ranges=dataset.voice_ranges,
**chorale_dataset_kwargs)
deepbach.dataset = generated_dataset
deepbach.train(
batch_size=batch_size,
num_epochs=2,
split=[1, 0], # use all selected chorales for training
early_stopping=False)
# generate chorales
if score_chorales:
chorale_scores = {}
print('Scoring real chorales')
for chorale_id, chorale in tqdm(enumerate(dataset.iterator_gen()),
total=num_generations):
score = score_chorale(chorale, dataset)
chorale_scores[chorale_id] = score
if chorale_id == num_generations:
break
# write scores to file
if write_scores:
with open('data/chorale_scores.csv', 'w') as chorale_file:
reader = csv.writer(chorale_file)
reader.writerow(['', 'score'] + list(weights.keys()))
for id, value in chorale_scores.items():
reader.writerow([id, value])
if num_generations != 0:
generation_scores = {}
print('Generating and scoring generated chorales')
ensure_dir(f'generations/{model_id}')
for i in range(num_generations):
chorale, tensor_chorale, tensor_metadata = deepbach.generation(
num_iterations=num_iterations,
sequence_length_ticks=sequence_length_ticks,
)
chorale.write('midi', f'generations/{model_id}/c{i}.mid')
score = score_chorale(chorale, dataset)
generation_scores[i] = score
# write scores to file
if write_scores:
with open(f'data/model{model_id}_scores.csv',
'w') as generation_file:
reader = csv.writer(generation_file)
reader.writerow(['', 'score'] + list(weights.keys()))
for id, value in generation_scores.items():
reader.writerow([id, value])
class Predictor(cog.Predictor):
def setup(self):
"""Load the model"""
# music21.environment.set("musicxmlPath", "/bin/true")
note_embedding_dim = 20
meta_embedding_dim = 20
num_layers = 2
lstm_hidden_size = 256
dropout_lstm = 0.5
linear_hidden_size = 256
batch_size = 256
num_epochs = 5
train = False
num_iterations = 500
sequence_length_ticks = 64
dataset_manager = DatasetManager()
metadatas = [FermataMetadata(), TickMetadata(subdivision=4), KeyMetadata()]
chorale_dataset_kwargs = {
"voice_ids": [0, 1, 2, 3],
"metadatas": metadatas,
"sequences_size": 8,
"subdivision": 4,
}
bach_chorales_dataset: ChoraleDataset = dataset_manager.get_dataset(
name="bach_chorales", **chorale_dataset_kwargs
)
dataset = bach_chorales_dataset
self.deepbach = DeepBach(
dataset=dataset,
note_embedding_dim=note_embedding_dim,
meta_embedding_dim=meta_embedding_dim,
num_layers=num_layers,
lstm_hidden_size=lstm_hidden_size,
dropout_lstm=dropout_lstm,
linear_hidden_size=linear_hidden_size,
)
self.deepbach.load()
# load fluidsynth fo rmidi 2 audio conversion
self.fs = FluidSynth()
# self.converter = music21.converter.parse('path_to_musicxml.xml')
@cog.input(
"num_iterations",
type=int,
help="Number of parallel pseudo-Gibbs sampling iterations",
default=500,
)
@cog.input(
"sequence_length_ticks",
type=int,
min=16,
help="Length of the generated chorale (in ticks)",
default=64,
)
@cog.input(
"output_type",
type=str,
help="Output representation type: can be audio or midi",
default="audio",
options=["midi", "audio"],
)
@cog.input("seed", type=int, default=-1, help="Random seed, -1 for random")
def predict(self, num_iterations, sequence_length_ticks, output_type, seed):
"""Score Generation"""
if seed >= 0:
random.seed(seed)
np.random.seed(seed)
torch.use_deterministic_algorithms(True)
torch.manual_seed(seed)
score, tensor_chorale, tensor_metadata = self.deepbach.generation(
num_iterations=num_iterations,
sequence_length_ticks=sequence_length_ticks,
)
if output_type == "audio":
output_path_wav = Path(tempfile.mkdtemp()) / "output.wav"
output_path_mp3 = Path(tempfile.mkdtemp()) / "output.mp3"
midi_score = score.write("midi")
self.fs.midi_to_audio(midi_score, str(output_path_wav))
subprocess.check_output(
[
"ffmpeg",
"-i",
str(output_path_wav),
"-af",
"silenceremove=1:0:-50dB,aformat=dblp,areverse,silenceremove=1:0:-50dB,aformat=dblp,areverse", # strip silence
str(output_path_mp3),
],
)
return output_path_mp3
elif output_type == "midi":
output_path_midi = Path(tempfile.mkdtemp()) / "output.mid"
score.write("midi", fp=output_path_midi)
return output_path_midi