def model_train(freq_weighted):
logs_path = "tensorboard/" + strftime("%Y_%m_%d_%H_%M_%S", gmtime()) + model_name
with tf.Graph().as_default():
train_inputs, train_targets = prepare_data(True)
model = SeparationModel(freq_weighted=False) # don't use freq_weighted for now
model.run_on_batch(train_inputs, train_targets)
init = tf.group(tf.initialize_all_variables(), tf.initialize_local_variables())
saver = tf.train.Saver()
with tf.Session() as session:
ckpt = tf.train.get_checkpoint_state('checkpoints/')
if ckpt:
print("Reading model parameters from %s" % ckpt.model_checkpoint_path)
session.run(tf.initialize_local_variables())
saver.restore(session, ckpt.model_checkpoint_path)
else:
session.run(init)
train_writer = tf.summary.FileWriter(logs_path + '/train', session.graph)
global_start = time.time()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=session, coord=coord)
print('num trainable parameters: %s' % (np.sum([np.prod(v.get_shape().as_list()) for v in tf.trainable_variables()])))
step_ii = 0
try:
while not coord.should_stop():
start = time.time()
step_ii += 1
output, batch_cost, masked_loss, summary, optimizer = session.run([model.output, model.loss, model.masked_loss, model.merged_summary_op, model.optimizer])
# total_train_cost += batch_cost * curr_batch_size
train_writer.add_summary(summary, step_ii)
duration = time.time() - start
if step_ii % 10 == 0:
print('Step %d: loss = %.5f masked_loss = %.5f (%.3f sec)' % (step_ii, batch_cost, masked_loss, duration))
if step_ii % 500 == 0:
checkpoint_name = 'checkpoints/' + model_name
saver.save(session, checkpoint_name, global_step=model.global_step)
except tf.errors.OutOfRangeError:
print('Done Training for %d epochs, %d steps' % (Config.num_epochs, step_ii))
finally:
coord.request_stop()
coord.join(threads)
def model_test():
with tf.Graph().as_default():
train_inputs, train_targets = prepare_data(False)
model = SeparationModel(
freq_weighted=False) # don't use freq_weighted for now
model.run_on_batch(train_inputs, train_targets)
print(train_inputs.get_shape())
init = tf.group(tf.initialize_all_variables(),
tf.initialize_local_variables())
saver = tf.train.Saver()
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)
session.run(tf.initialize_local_variables())
else:
session.run(init)
global_start = time.time()
coord = tf.train.Coordinator()
threads = tf.train.start_queue_runners(sess=session, coord=coord)
soft_masked_results = []
print('num trainable parameters: %s' % (np.sum([
np.prod(v.get_shape().as_list())
for v in tf.trainable_variables()
])))
try:
step_ii = 0
while not coord.should_stop():
start = time.time()
soft_masked_output, batch_cost, masked_loss, summary, target, mixed_spec = session.run(
[
model.soft_masked_output, model.loss,
model.masked_loss, model.merged_summary_op,
model.target, model.input
])
step_ii += 1
duration = time.time() - start
print(
'Step %d: loss = %.5f masked_loss = %.5f (%.3f sec)' %
(step_ii, batch_cost, masked_loss, duration))
soft_song_masked, soft_voice_masked = tf.split(
soft_masked_output,
[Config.num_freq_bins, Config.num_freq_bins],
axis=1)
# soft_song_masked *= stats[1][0]
# soft_song_masked += stats[0][0]
# soft_voice_masked *= stats[1][1]
# soft_voice_masked += stats[0][1]
song_target, voice_target = tf.split(
target, [Config.num_freq_bins, Config.num_freq_bins],
axis=1)
# song_target *= stats[1][0]
# song_target += stats[0][0]
# voice_target *= stats[1][1]
# voice_target += stats[0][1]
mixed_spec = mixed_spec[:, :, 1]
# mixed_spec *= stats[1][2]
# mixed_spec += stats[0][2]
result_wav_dir = 'data/results'
mixed_audio = create_audio_from_spectrogram(mixed_spec)
writeWav(
os.path.join(result_wav_dir,
'mixed%d.wav' % (step_ii)),
Config.sample_rate, mixed_audio)
soft_song_masked_audio = create_audio_from_spectrogram(
soft_song_masked)
soft_voice_masked_audio = create_audio_from_spectrogram(
soft_voice_masked)
writeWav(
os.path.join(result_wav_dir,
'soft_song_masked%d.wav' % (step_ii)),
Config.sample_rate, soft_song_masked_audio)
writeWav(
os.path.join(result_wav_dir,
'soft_voice_masked%d.wav' % (step_ii)),
Config.sample_rate, soft_voice_masked_audio)
song_target_audio = create_audio_from_spectrogram(
song_target)
voice_target_audio = create_audio_from_spectrogram(
voice_target)
writeWav(
os.path.join(result_wav_dir,
'song_target%d.wav' % (step_ii)),
Config.sample_rate, song_target_audio)
writeWav(
os.path.join(result_wav_dir,
'voice_target%d.wav' % (step_ii)),
Config.sample_rate, voice_target_audio)
# soft_sdr, soft_sir, soft_sar, _ = bss_eval_sources(np.array([song_target_audio, voice_target_audio]), np.array([soft_song_masked_audio, soft_voice_masked_audio]), False)
soft_gnsdr, soft_gsir, soft_gsar = bss_eval_global(
mixed_audio, song_target_audio, voice_target_audio,
soft_song_masked_audio, soft_voice_masked_audio)
# masked_results.append([soft_sdr[0], soft_sdr[1], soft_sir[0], soft_sir[1], soft_sar[0],soft_sar[1]])
print(soft_gnsdr[0], soft_gnsdr[1], soft_gsir[0],
soft_gsir[1], soft_gsar[0], soft_gsar[1])
soft_masked_results.append([
soft_gnsdr[0], soft_gnsdr[1], soft_gsir[0],
soft_gsir[1], soft_gsar[0], soft_gsar[1]
])
except tf.errors.OutOfRangeError:
soft_masked_results = np.asarray(soft_masked_results)
print(np.mean(soft_masked_results, axis=0))
finally:
coord.request_stop()
coord.join(threads)