def nptensor_to_midi(x_data, out_filename):
midi = MidiOutFile(out_filename)
config = nn_config.get_neural_net_configuration()
model_basename = config['model_basename']
hidden_dims = config['hidden_dimension_size']
num_dims = config['num_dims']
# non optional midi framework
midi.header(format=0, nTracks=1, division=10)
midi.start_of_track()
midi.tempo(500000)
# musical events
no = 0
vel = 0
last_event_time = 0
now_playing_note = []
num_seq_len = len(x_data)
time_table = [2, 5, 10, 15, 20, 30, 40]
for i in range(num_seq_len):
note_vector_index = np.argwhere(x_data[i] == 1)
if x_data[i][num_dims - 1] == 1:
no = 0
else:
for one_note in note_vector_index:
time = int(one_note / 720) #no, vel
vel = (int(one_note % 720 / 80) + 1) * 10
no = int(one_note) % 720 % 80 + 21
midi.update_time(last_event_time)
midi.note_on(channel=0, note=no, velocity=vel)
last_event_time = 0
now_playing_note.append([i, no, time_table[time]])
for now_note in now_playing_note:
if i - now_note[0] == now_note[2]:
midi.update_time(last_event_time)
midi.note_off(channel=0, note=now_note[1])
last_event_time = 0
del now_note
last_event_time += 1
# non optional midi framework
midi.update_time(0)
midi.end_of_track() # not optional!
midi.eof()
print('good')
recv_file = './test/' + 'veryveryvery.mp3'
recv_file_tmp = './test/' + 'received_music_tmp.mp3'
recv_file_wav = './test/' + 'received_music_wav.wav'
recv_file_mid = './test/' + 'received_music_mid.mid'
send_file = './test/' + 'sending_music.mp3'
send_file_wav = './test/' + 'sending_music_wav.wav'
send_file_mid = './test/' + 'sending_music_mid.mid'
x_file = './test/'
start_time = 00
end_time = 10
finish_time = 30
# Figure out how many frequencies we have in the data
# freq_space_dims = X_train.shape[2]
config = nn_config.get_neural_net_configuration()
model_basename = config['model_basename']
hidden_dims = config['hidden_dimension_size']
num_dims = config['num_dims']
# Creates a lstm network
model = network_utils.create_lstm_network(num_frequency_dimensions=num_dims,
num_hidden_dimensions=hidden_dims)
#2phase
#lame mp3 -> wav
sample_freq_str = "{0:.1f}".format(float(44100) / 1000.0)
# cmd = 'ffmpeg -i {0} -ar 44100 -ac 1 -c:a libmp3lame {1}'.format(recv_file, recv_file_tmp)
cmd = 'ffmpeg -i {0} -ss {1} -t {2} -ac 1 {3}'.format(recv_file, start_time, end_time - start_time, recv_file_tmp)