def createSpectrogram(arr, orig):
x = SpectrogramArray(arr,
stft_settings={
'framelength': orig.stft_settings['framelength'],
'hopsize': orig.stft_settings['hopsize'],
'overlap': orig.stft_settings['overlap'],
'centered': orig.stft_settings['centered'],
'window': orig.stft_settings['window'],
'halved': orig.stft_settings['halved'],
'transform': orig.stft_settings['transform'],
'padding': orig.stft_settings['padding'],
'outlength': orig.stft_settings['outlength'],
})
return x
def model_batch_test():
test_batch = h5py.File('%stest_batch' % (DIR))
data = test_batch['data'].value
with open('%stest_settings.pkl' % (DIR), 'rb') as f:
settings = pickle.load(f)
# print(settings[:2])
combined, clean, noise = zip(data)
combined = combined[0]
clean = clean[0]
noise = noise[0]
target = np.concatenate((clean, noise), axis=2)
# test_rate, test_audio = wavfile.read('data/test_combined/combined.wav')
# test_spec = stft.spectrogram(test_audio)
combined_batch, target_batch = create_batch(combined, target, 50)
original_combined_batch = [
copy.deepcopy(batch) for batch in combined_batch
]
with tf.Graph().as_default():
model = SeparationModel()
saver = tf.train.Saver(tf.trainable_variables())
with tf.Session() as session:
ckpt = tf.train.get_checkpoint_state('checkpoints/')
if ckpt:
print("Reading model parameters from %s" %
ckpt.model_checkpoint_path)
saver.restore(session, ckpt.model_checkpoint_path)
else:
print("Created model with fresh parameters.")
session.run(tf.initialize_all_variables())
curr_mask_array = []
prev_mask_array = None
diff = float('inf')
iters = 0
while True:
iters += 1
output, _, _ = model.train_on_batch(session,
combined_batch[0],
target_batch[0],
train=False)
num_freq_bin = output.shape[2] / 2
clean_outputs = output[:, :, :num_freq_bin]
noise_outputs = output[:, :, num_freq_bin:]
# clean = [target[:,:num_freq_bin] for target in target_batch]
# noise = [target[:,num_freq_bin:] for target in target_batch]
num_outputs = len(clean_outputs)
results = []
for i in xrange(num_outputs):
orig_clean_output = clean_outputs[i]
orig_noise_output = noise_outputs[i]
stft_settings = copy.deepcopy(settings[i])
orig_length = stft_settings['orig_length']
stft_settings.pop('orig_length', None)
clean_output = orig_clean_output[-orig_length:]
noise_output = orig_noise_output[-orig_length:]
clean_mask, noise_mask = create_mask(
clean_output, noise_output)
orig_clean_mask, orig_noise_mask = create_mask(
orig_clean_output, orig_noise_output)
curr_mask_array.append(clean_mask)
# if i == 0:
# print clean_mask[10:20,10:20]
curr_mask_array.append(noise_mask)
clean_spec = createSpectrogram(
np.multiply(
clean_mask.transpose(), original_combined_batch[0]
[i][-orig_length:].transpose()), settings[i])
noise_spec = createSpectrogram(
np.multiply(
noise_mask.transpose(), original_combined_batch[0]
[i][-orig_length:].transpose()), settings[i])
# print '-' * 20
# print original_combined_batch[0][i]
# print '=' * 20
combined_batch[0][i] += np.multiply(
orig_clean_mask, original_combined_batch[0][i]) * 0.1
# print combined_batch[0][i]
# print '=' * 20
# print original_combined_batch[0][i]
# print '-' * 20
estimated_clean_wav = stft.ispectrogram(clean_spec)
estimated_noise_wav = stft.ispectrogram(noise_spec)
reference_clean_wav = stft.ispectrogram(
SpectrogramArray(clean[i][-orig_length:],
stft_settings).transpose())
reference_noise_wav = stft.ispectrogram(
SpectrogramArray(noise[i][-orig_length:],
stft_settings).transpose())
try:
sdr, sir, sar, _ = bss_eval_sources(
np.array(
[reference_clean_wav, reference_noise_wav]),
np.array(
[estimated_clean_wav, estimated_noise_wav]),
False)
results.append(
(sdr[0], sdr[1], sir[0], sir[1], sar[0], sar[1]))
# print('%f, %f, %f, %f, %f, %f' % (sdr[0], sdr[1], sir[0], sir[1], sar[0], sar[1]))
except ValueError:
print('error')
continue
break
# diff = 1
# if prev_mask_array is not None:
# # print curr_mask_array[0]
# # print prev_mask_array[0]
# diff = sum(np.sum(np.abs(curr_mask_array[i] - prev_mask_array[i])) for i in xrange(len(prev_mask_array)))
# print('Changes after iteration %d: %d' % (iters, diff))
# sdr_cleans, sdr_noises, sir_cleans, sir_noises, sar_cleans, sar_noises = zip(*results)
# print('Avg sdr_cleans: %f, sdr_noises: %f, sir_cleans: %f, sir_noises: %f, sar_cleans: %f, sar_noises: %f' % (np.mean(sdr_cleans), np.mean(sdr_noises), np.mean(sir_cleans), np.mean(sir_noises), np.mean(sar_cleans), np.mean(sar_noises)))
# prev_mask_array = [copy.deepcopy(mask[:,:]) for mask in curr_mask_array]
# if diff == 0:
# break
results_filename = '%sresults_%d_%f' % (
'data/results/', Config.num_layers, Config.lr)
# results_filename += 'freq_weighted'
with open(results_filename + '.csv', 'w+') as f:
for sdr_1, sdr_2, sir_1, sir_2, sar_1, sar_2 in results:
f.write('%f,%f,%f,%f,%f,%f\n' %
(sdr_1, sdr_2, sir_1, sir_2, sar_1, sar_2))