notes?=?[]
for?file?in?self.songs:
????print("Parsing?%s"?%?file)
????try:
????????midi?=?converter.parse(file)
????except?IndexError?as?e:
????????print(f"Could?not?parse?{file}")
????????print(e)
????????continue
????notes_to_parse?=?None
????try:??
????????s2?=?instrument.partitionByInstrument(midi)
????????notes_to_parse?=?s2.parts[0].recurse()
????except:?
????????notes_to_parse?=?midi.flat.notes
????prev_offset?=?0.0
????for?element?in?notes_to_parse:
????????if?isinstance(element,?note.Note)?or?isinstance(element,?chord.Chord):
????????????duration?=?element.duration.quarterLength
????????????if?isinstance(element,?note.Note):
????????????????name?=?element.pitch
????????????elif?isinstance(element,?chord.Chord):
????????????????name?=?".".join(str(n)?for?n?in?element.normalOrder)
????????????notes.append(f"{name}${duration}")
???????????rest_notes?=?int((element.offset?-?prev_offset)?/?TIMESTEP?-?1)
????????????for?_?in?range(0,?rest_notes):
????????????????notes.append("NULL")
????????prev_offset?=?element.offset
with?open("notes/"?+?self.model_name,?"wb")?as?filepath:
????pickle.dump(notes,?filepath)

def?prepare_sequences(self,?notes,?n_vocab):
????#?获取所有pitch?名称
????pitchnames?=?sorted(set(item?for?item?in?notes))
????#?创建一个字典来映射音高到整数
????note_to_int?=?dict((note,?number?+?1)?for?number,?note?in?enumerate(pitchnames))
????note_to_int["NULL"]?=?0
????network_input?=?[]
????network_output?=?[]
????for?i?in?range(0,?len(notes)?-?SEQUENCE_LEN,?1):
????????sequence_in?=?notes[i?:?i?+?SEQUENCE_LEN]
????????sequence_out?=?notes[i?+?SEQUENCE_LEN]
????????network_input.append([note_to_int[char]?for?char?in?sequence_in])
????????network_output.append(note_to_int[sequence_out])
????n_patterns?=?len(network_input)
????network_input?=?numpy.reshape(network_input,?(n_patterns,?SEQUENCE_LEN,?1))
????network_input?=?network_input?/?float(n_vocab)
????print(network_output)
????network_output?=?np_utils.to_categorical(network_output)
????return?(network_input,?network_output)

def?train(self,?network_input,?network_output):
????????"""?train?the?neural?network?"""
????????filepath?=?(
????????????self.model_name?+?"-weights-improvement-{epoch:02d}-{loss:.4f}-bigger.hdf5"
????????)
????????checkpoint?=?ModelCheckpoint(
????????????filepath,?monitor="loss",?verbose=0,?save_best_only=True,?mode="min"
????????)
????????callbacks_list?=?[checkpoint]
????????self.model.fit(
????????????network_input,
????????????network_output,
????????????epochs=self.epochs,
????????????batch_size=64,
????????????callbacks=callbacks_list,
????????)
def?create_network(network_input,?n_vocab):
????print("Input?shape?",?network_input.shape)
????print("Output?shape?",?n_vocab)
????"""?create?the?structure?of?the?neural?network?"""
????model?=?Sequential()
????model.add(
????????Bidirectional(
????????????LSTM(512,?return_sequences=True),
????????????input_shape=(network_input.shape[1],?network_input.shape[2]),
????????)
????)
????model.add(Dropout(0.3))
????model.add(Bidirectional(LSTM(512)))
????model.add(Dense(n_vocab))
????model.add(Activation("softmax"))
????model.compile(loss="categorical_crossentropy",?optimizer="rmsprop")
????return?model

def?get_start():
????#?pick?a?random?sequence?from?the?input?as?a?starting?point?for?the?prediction
????start?=?numpy.random.randint(0,?len(network_input)?-?1)
????pattern?=?network_input[start]
????prediction_output?=?[]
????return?pattern,?prediction_output
#?generate?verse?1
verse1_pattern,?verse1_prediction_output?=?get_start()
for?note_index?in?range(4?*?SEQUENCE_LEN):
????prediction_input?=?numpy.reshape(
????????verse1_pattern,?(1,?len(verse1_pattern),?1)
????)
????prediction_input?=?prediction_input?/?float(n_vocab)
????prediction?=?model.predict(prediction_input,?verbose=0)
????index?=?numpy.argmax(prediction)
????print("index",?index)
????result?=?int_to_note[index]
????verse1_prediction_output.append(result)
????verse1_pattern.append(index)
????verse1_pattern?=?verse1_pattern[1?:?len(verse1_pattern)]
#?generate?verse?2
verse2_pattern?=?verse1_pattern
verse2_prediction_output?=?[]
for?note_index?in?range(4?*?SEQUENCE_LEN):
????prediction_input?=?numpy.reshape(
????????verse2_pattern,?(1,?len(verse2_pattern),?1)
????)
????prediction_input?=?prediction_input?/?float(n_vocab)
????prediction?=?model.predict(prediction_input,?verbose=0)
????index?=?numpy.argmax(prediction)
????print("index",?index)
????result?=?int_to_note[index]
????verse2_prediction_output.append(result)
????verse2_pattern.append(index)
????verse2_pattern?=?verse2_pattern[1?:?len(verse2_pattern)]
#?generate?chorus
chorus_pattern,?chorus_prediction_output?=?get_start()
for?note_index?in?range(4?*?SEQUENCE_LEN):
????prediction_input?=?numpy.reshape(
????????chorus_pattern,?(1,?len(chorus_pattern),?1)
????)
????prediction_input?=?prediction_input?/?float(n_vocab)
????prediction?=?model.predict(prediction_input,?verbose=0)
????index?=?numpy.argmax(prediction)
????print("index",?index)
????result?=?int_to_note[index]
????chorus_prediction_output.append(result)
????chorus_pattern.append(index)
????chorus_pattern?=?chorus_pattern[1?:?len(chorus_pattern)]
#?generate?bridge
bridge_pattern,?bridge_prediction_output?=?get_start()
for?note_index?in?range(4?*?SEQUENCE_LEN):
????prediction_input?=?numpy.reshape(
????????bridge_pattern,?(1,?len(bridge_pattern),?1)
????)
????prediction_input?=?prediction_input?/?float(n_vocab)
????prediction?=?model.predict(prediction_input,?verbose=0)
????index?=?numpy.argmax(prediction)
????print("index",?index)
????result?=?int_to_note[index]
????bridge_prediction_output.append(result)
????bridge_pattern.append(index)
????bridge_pattern?=?bridge_pattern[1?:?len(bridge_pattern)]
return?(
????verse1_prediction_output
????+?chorus_prediction_output
????+?verse2_prediction_output
????+?chorus_prediction_output
????+?bridge_prediction_output
????+?chorus_prediction_output
)

for?pattern?in?prediction_output:
????if?"$"?in?pattern:
????????pattern,?dur?=?pattern.split("$")
????????if?"/"?in?dur:
????????????a,?b?=?dur.split("/")
????????????dur?=?float(a)?/?float(b)
????????else:
????????????dur?=?float(dur)
????#?pattern?is?a?chord
????if?("."?in?pattern)?or?pattern.isdigit():
????????notes_in_chord?=?pattern.split(".")
????????notes?=?[]
????????for?current_note?in?notes_in_chord:
????????????new_note?=?note.Note(int(current_note))
????????????new_note.storedInstrument?=?instrument.Piano()
????????????notes.append(new_note)
????????new_chord?=?chord.Chord(notes)
????????new_chord.offset?=?offset
????????new_chord.duration?=?duration.Duration(dur)
????????output_notes.append(new_chord)
????#?pattern?is?a?rest
????elif?pattern?is?"NULL":
????????offset?+=?TIMESTEP
????#?pattern?is?a?note
????else:
????????new_note?=?note.Note(pattern)
????????new_note.offset?=?offset
????????new_note.storedInstrument?=?instrument.Piano()
????????new_note.duration?=?duration.Duration(dur)
????????output_notes.append(new_note)
????#?增加每次迭代的偏移量，这样笔记就不会堆积
????offset?+=?TIMESTEP
midi_stream?=?stream.Stream(output_notes)
output_file?=?os.path.basename(self.weights)?+?".mid"
print("output?to?"?+?output_file)
midi_stream.write("midi",?fp=output_file)