def run_predict(part_one=PART_ONE_RANGE, part_two=PART_TWO_RANGE):
'''Load pretrained model'''
loss_func = audio_loss(gamma=gamma_loss,
beta=beta_loss,
num_speaker=num_people)
AO_model = load_model(MODEL_PATH,
custom_objects={
"tf": tf,
'loss_func': loss_func
})
'''Load audio data'''
loaded_file = 0
for i, j in zip(range(part_one[0], part_one[1]),
range(part_two[0], part_two[1])):
try:
audio_data = np.load(dir_path_mix + "mix-%05d-%05d.npy" % (i, j))
loaded_file += 1
print(audio_data.shape)
'''check shape - first dim should be 298'''
audio_data = audio_data[:298]
if len(audio_data) < 298:
a = np.zeros((298, 257, 2))
a[:len(audio_data), :, :] = audio_data
audio_data = a
print(audio_data.shape)
mix_expand = np.expand_dims(audio_data, axis=0)
print(mix_expand.shape)
print("===== Completed processing audio =====")
'''Predict data'''
cRMs = AO_model.predict(mix_expand)
cRMs = cRMs[0]
print("===== Completed predicting cRMs =====")
'''Save output as wav'''
for k in range(num_people):
cRM = cRMs[:, :, :, k]
assert cRM.shape == (298, 257, 2)
F = utils.fast_icRM(audio_data, cRM)
T = utils.fast_istft(F, power=False)
filename = dir_path_pred + '%05d-%05d_pred_output%d.wav' % (
i, j, k)
wavfile.write(filename, 16000, T)
print("%05d-%05d_pred_output%d.wav created" % (i, j, k))
print("===== Completed saving output ===== \n")
except FileNotFoundError:
print('mix-%05d-%05d.npy is not found' % (i, j))
print('num of processed audio : %d' % loaded_file)
def run_predict(video_name=VIDEO_NAME):
'''Load audio data'''
audio_data = np.load('preprocessed-%s.npy' % video_name)
print(audio_data.shape)
# TODO: check shape - first dim should be 298
audio_data = audio_data[:298]
if len(audio_data) < 298:
a = np.zeros((298, 257, 2))
a[:len(audio_data), :, :] = audio_data
audio_data = a
print(audio_data.shape)
mix_expand = np.expand_dims(audio_data, axis=0)
print(mix_expand.shape)
'''Load visual data'''
face_embs = np.zeros((1, 75, 1, 1792, num_people))
print(face_embs.shape)
for i in range(num_people):
try:
# face_embs[1,:,:,:,i] = np.load(dir_path_face_embs+"%s_face_emb.npy"%single_idxs[i])
'''Currently does not use the correct face input for both speakers (uses the same images for both right now)'''
face_embs[0, :, :, :,
i] = np.load(dir_path_face_embs + "%s_face_emb_p%d.npy" %
(video_name, i))
except Exception as e:
print('No face embedding for speaker', i, "\n", e)
'''TODO: use Google Vision AI to find the face embedding for each speaker'''
# '''Load pretrained model'''
loss_func = audio_loss(gamma=gamma_loss,
beta=beta_loss,
num_speaker=num_people)
AV_model = load_model(MODEL_PATH,
custom_objects={
"tf": tf,
'loss_func': loss_func
})
# '''Predict data'''
cRMs = AV_model.predict([mix_expand, face_embs])
cRMs = cRMs[0]
# '''Save output as wav'''
for j in range(num_people):
cRM = cRMs[:, :, :, j]
assert cRM.shape == (298, 257, 2)
F = utils.fast_icRM(audio_data, cRM)
T = utils.fast_istft(F, power=False)
filename = dir_path_pred + 'pred_%s_output%d.wav' % (video_name, j)
wavfile.write(filename, 16000, T)
av_model = load_model(model_path, custom_objects={'tf': tf})
if num_gpu > 1:
parallel = ModelMGPU(av_model, num_gpu)
for line in test_file:
mix, single_idxs, face_emb = get_data_name(line, people, database,
face_emb)
mix_ex = np.expand_dims(mix, axis=0)
cRMs = parallel.predict([mix_ex, face_emb])
cRMs = cRMs[0]
prefix = ''
for idx in single_idxs:
prefix += idx + '-'
for i in range(len(cRMs)):
cRM = cRMs[:, :, :, i]
assert cRM.shape == (298, 257, 2)
F = utils.fast_icRM(mix, cRM)
T = utils.fase_istft(F, power=False)
filename = result_path + str(single_idxs[i]) + '.wav'
wavfile.write(filename, 16000, T)
if num_gpu <= 1:
for line in test_file:
mix, single_idxs, face_emb = get_data_name(line, people, database,
face_emb)
mix_ex = np.expand_dims(mix, axis=0)
cRMs = av_model.predict([mix_ex, face_emb])
cRMs = cRMs[0]
prefix = ''
for idx in single_idxs:
prefix += idx + '-'
for i in range(len(cRMs)):
