def tempos():
global TEMPO
global SONGNAME
r = request.json
if (type(r) == dict):
r_json = r
else:
r_json = json.loads(r)
t1 = r_json['tempo_1']
t2 = r_json['tempo_2']
tempo_1 = np.asarray(t1)
tempo_2 = np.asarray(t2)
TEMPO = np.array([tempo_1, tempo_2])
song1 = path + SONGNAME[0] # heyjude
song2 = path + SONGNAME[1] # someonelikeyou
m, c, tempo = model.load_midi(song1, song2, UNIT_LEN)
m_seq, c_seq, z = model.interp_sample(vae, m, c, INTERP_NUM, RHYTHM_THRESHOLD)
response_pickled = numpy2json(m_seq, c_seq, z, TEMPO, SONGNAME, RHYTHM_THRESHOLD)
return Response(response=response_pickled, status=200, mimetype="application/json")
def theta():
global RHYTHM_THRESHOLD
global SONGNAME
r = request.json
if (type(r) == dict):
r_json = r
else:
r_json = json.loads(r)
theta_temp = r_json['theta']
theta = np.float(theta_temp)
with torch.no_grad():
RHYTHM_THRESHOLD = theta
song1 = path + SONGNAME[0] # heyjude
song2 = path + SONGNAME[1] # someonelikeyou
# print('song1:',song1)
# print('song2:',song2)
m, c, tempo = model.load_midi(song1, song2, UNIT_LEN)
m_seq, c_seq, z = model.interp_sample(vae, m, c, INTERP_NUM, RHYTHM_THRESHOLD)
response_pickled = numpy2json(m_seq, c_seq, z, TEMPO, SONGNAME, RHYTHM_THRESHOLD)
return Response(response=response_pickled, status=200, mimetype="application/json")
def static():
with torch.no_grad():
global UNIT_LEN
global INTERP_NUM
global TOTAL_LEN
global path
global RHYTHM_THRESHOLD
global TEMPO
global SONGNAME
song1 = path + songfiles[1] # heyjude
song2 = path + songfiles[2] # someonelikeyou
# print('song1:',song1)
# print('song2:',song2)
m, c, tempo = model.load_midi(song1, song2, UNIT_LEN)
m_seq, c_seq = model.interp_sample(vae, m, c, INTERP_NUM,
RHYTHM_THRESHOLD)
TEMPO = tempo
SONGNAME = np.array([songfiles[1], songfiles[2]])
response_pickled = numpy2json(m_seq, c_seq, TEMPO, SONGNAME,
RHYTHM_THRESHOLD)
return Response(response=response_pickled,
status=200,
mimetype="application/json")
def static_twosong(s1, s2, num):
with torch.no_grad():
global UNIT_LEN
global INTERP_NUM
global TOTAL_LEN
global path
global RHYTHM_THRESHOLD
global TEMPO
global SONGNAME
INTERP_NUM = num # number of interp group
# TOTAL_LEN = (INTERP_NUM + 2)*4 # number of group * 4bar = total bars
song1 = path + songfiles[int(s1)]
song2 = path + songfiles[int(s2)]
# print('song1:',song1)
# print('song2:',song2)
m, c, tempo = model.load_midi(song1, song2, UNIT_LEN)
# print(m.shape)
# print(c.shape)
# print(tempo.shape)
m_seq, c_seq, z = model.interp_sample(vae, m, c, INTERP_NUM, RHYTHM_THRESHOLD)
print("z", np.shape(z))
TEMPO = tempo
SONGNAME = np.array([songfiles[int(s1)],songfiles[int(s2)]])
response_pickled = numpy2json(m_seq, c_seq, z, TEMPO, SONGNAME, RHYTHM_THRESHOLD)
return Response(response=response_pickled, status=200, mimetype="application/json")
def melody_chord():
r = request.json
if (type(r) == dict):
r_json = r
else:
r_json = json.loads(r)
m1 = r_json['m_seq_1']
c1 = r_json['c_seq_1']
m2 = r_json['m_seq_2']
c2 = r_json['c_seq_2']
m_seq1 = np.asarray(m1).astype(int)
c_seq1 = np.asarray(c1)
m_seq2 = np.asarray(m2).astype(int)
c_seq2 = np.asarray(c2)
with torch.no_grad():
m, c = model.load_seq(m_seq1, c_seq1, m_seq2, c_seq2)
m_seq, c_seq = model.interp_sample(vae, m, c, INTERP_NUM,
RHYTHM_THRESHOLD)
response_pickled = numpy2json(m_seq, c_seq, TEMPO, SONGNAME,
RHYTHM_THRESHOLD)
return Response(response=response_pickled,
status=200,
mimetype="application/json")
