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))
def model_train(freq_weighted):
logs_path = "tensorboard/" + strftime("%Y_%m_%d_%H_%M_%S", gmtime())
TESTING_MODE = True
data = h5py.File('%sdata%d' % (DIR, 0))['data'].value
np.append(data, h5py.File('%sdata%d' % (DIR, 1))['data'].value)
combined, clean, noise = zip(data)
combined = combined[0]
clean = clean[0]
noise = noise[0]
target = np.concatenate((clean, noise), axis=2)
num_data = len(combined)
random.seed(1)
dev_ix = set(random.sample(xrange(num_data), num_data / 5))
train_input = [s for i, s in enumerate(combined) if i not in dev_ix]
train_target = [s for i, s in enumerate(target) if i not in dev_ix]
dev_input = [s for i, s in enumerate(combined) if i in dev_ix]
dev_target = [s for i, s in enumerate(target) if i in dev_ix]
train_input_batch, train_target_batch = create_batch(
train_input, train_target, Config.batch_size)
dev_input_batch, dev_target_batch = create_batch(dev_input, dev_target,
Config.batch_size)
num_data = np.sum(len(batch) for batch in train_input_batch)
num_batches_per_epoch = int(math.ceil(num_data / Config.batch_size))
num_dev_data = np.sum(len(batch) for batch in dev_input_batch)
num_dev_batches_per_epoch = int(math.ceil(num_dev_data /
Config.batch_size))
with tf.Graph().as_default():
model = SeparationModel(freq_weighted=freq_weighted)
init = tf.global_variables_initializer()
saver = tf.train.Saver(tf.trainable_variables())
with tf.Session() as session:
session.run(init)
# if args.load_from_file is not None:
# new_saver = tf.train.import_meta_graph('%s.meta' % args.load_from_file, clear_devices=True)
# new_saver.restore(session, args.load_from_file)
train_writer = tf.summary.FileWriter(logs_path + '/train',
session.graph)
global_start = time.time()
step_ii = 0
for curr_epoch in range(Config.num_epochs):
total_train_cost = 0
total_train_examples = 0
start = time.time()
for batch in random.sample(range(num_batches_per_epoch),
num_batches_per_epoch):
cur_batch_size = len(train_target_batch[batch])
total_train_examples += cur_batch_size
_, batch_cost, summary = model.train_on_batch(
session,
train_input_batch[batch],
train_target_batch[batch],
train=True)
total_train_cost += batch_cost * cur_batch_size
train_writer.add_summary(summary, step_ii)
step_ii += 1
train_cost = total_train_cost / total_train_examples
num_dev_batches = len(dev_target_batch)
total_batch_cost = 0
total_batch_examples = 0
# val_batch_cost, _ = model.train_on_batch(session, dev_input_batch[0], dev_target_batch[0], train=False)
for batch in random.sample(range(num_dev_batches_per_epoch),
num_dev_batches_per_epoch):
cur_batch_size = len(dev_target_batch[batch])
total_batch_examples += cur_batch_size
_, _val_batch_cost, _ = model.train_on_batch(
session,
dev_input_batch[batch],
dev_target_batch[batch],
train=False)
total_batch_cost += cur_batch_size * _val_batch_cost
val_batch_cost = None
try:
val_batch_cost = total_batch_cost / total_batch_examples
except ZeroDivisionError:
val_batch_cost = 0
log = "Epoch {}/{}, train_cost = {:.3f}, val_cost = {:.3f}, time = {:.3f}"
print(
log.format(curr_epoch + 1, Config.num_epochs, train_cost,
val_batch_cost,
time.time() - start))
# if args.print_every is not None and (curr_epoch + 1) % args.print_every == 0:
# batch_ii = 0
# model.print_results(train_feature_minibatches[batch_ii], train_labels_minibatches[batch_ii])
if (curr_epoch + 1) % 10 == 0:
checkpoint_name = 'checkpoints/%dlayer_%flr_model' % (
Config.num_layers, Config.lr)
if freq_weighted:
checkpoint_name = checkpoint_name + '_freq_weighted'
saver.save(session,
checkpoint_name,
global_step=curr_epoch + 1)
def model_test(test_input):
test_rate, test_audio = wavfile.read(test_input)
clean_rate, clean_audio = wavfile.read(CLEAN_FILE)
noise_rate, noise_audio = wavfile.read(NOISE_FILE)
length = len(clean_audio)
noise_audio = noise_audio[:length]
clean_spec = stft.spectrogram(clean_audio)
noise_spec = stft.spectrogram(noise_audio)
test_spec = stft.spectrogram(test_audio)
reverted_clean = stft.ispectrogram(clean_spec)
reverted_noise = stft.ispectrogram(noise_spec)
test_data = np.array([test_spec.transpose() / 100000
]) # make data a batch of 1
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())
test_data_shape = np.shape(test_data)
dummy_target = np.zeros((test_data_shape[0], test_data_shape[1],
2 * test_data_shape[2]))
output, _, _ = model.train_on_batch(session,
test_data,
dummy_target,
train=False)
num_freq_bin = output.shape[2] / 2
clean_output = output[0, :, :num_freq_bin]
noise_output = output[0, :, num_freq_bin:]
clean_mask, noise_mask = create_mask(clean_output, noise_output)
clean_spec = createSpectrogram(
np.multiply(clean_mask.transpose(), test_spec),
test_spec.stft_settings)
noise_spec = createSpectrogram(
np.multiply(noise_mask.transpose(), test_spec),
test_spec.stft_settings)
clean_wav = stft.ispectrogram(clean_spec)
noise_wav = stft.ispectrogram(noise_spec)
sdr, sir, sar, _ = bss_eval_sources(
np.array([reverted_clean, reverted_noise]),
np.array([clean_wav, noise_wav]), False)
print(sdr, sir, sar)
writeWav('data/test_combined/output_clean.wav', 44100, clean_wav)
writeWav('data/test_combined/output_noise.wav', 44100, noise_wav)