def train_nn(speech_data, speech_alignment):
print
print datetime.datetime.now()
print
random.seed(0)
try:
f = open(fetures_file_name, "rb")
crossvalid_x = np.load(f)
crossvalid_y = np.load(f)
train_x = np.load(f)
train_y = np.load(f)
tx_m = np.load(f)
tx_std = np.load(f)
f.close()
except IOError:
crossvalid_x, crossvalid_y, train_x, train_y, tx_m, tx_std = gen_features(
speech_data, speech_alignment)
input_size = train_x.shape[1] * (prev_frames + 1 + next_frames)
output_size = np.amax(train_y) + 1
print "The shape of non-multiplied training data: ", train_x.shape, train_y.shape
print "The shape of non-multiplied test data: ", crossvalid_x.shape, crossvalid_y.shape
# add prev, and last frames
tx_m = np.tile(tx_m, prev_frames + 1 + next_frames)
tx_std = np.tile(tx_std, prev_frames + 1 + next_frames)
print
print datetime.datetime.now()
print
print "prev_frames +1+ last_frames:", prev_frames, 1, next_frames
print "The shape of training data: ", train_x.shape, train_y.shape
print "The shape of test data: ", crossvalid_x.shape, crossvalid_y.shape
print "The shape of tx_m, tx_std: ", tx_m.shape, tx_std.shape
print "The output size: ", output_size
print
e = tffnn.TheanoFFNN(input_size,
hidden_units,
hidden_layers,
output_size,
hidden_activation=hact,
weight_l2=weight_l2,
training_set_x=train_x,
training_set_y=train_y,
prev_frames=prev_frames,
next_frames=next_frames,
amplify_center_frame=amplify_center_frame,
batch_size=batch_size)
e.set_input_norm(tx_m, tx_std)
dc_acc = []
dt_acc = []
epoch = 0
i_hidden_layers = hidden_layers
while True:
print
print '-' * 80
print 'Predictions'
print '-' * 80
predictions_y, gold_y = e.predict(crossvalid_x, batch_size,
prev_frames, next_frames,
crossvalid_y)
c_acc, c_sil = get_accuracy(gold_y, predictions_y)
predictions_y, gold_y = e.predict(train_x, batch_size, prev_frames,
next_frames, train_y)
t_acc, t_sil = get_accuracy(gold_y, predictions_y)
dc_acc.append(c_acc)
dt_acc.append(t_acc)
print
print "method, hact, max_frames, max_files, max_frames_per_segment, trim_segments, batch_size, max_epoch, " \
"hidden_units, hidden_layers, hidden_layers_add, prev_frames, next_frames, crossvalid_frames, usec0, usedelta, useacc, mel_banks_only "
print method, hact, max_frames, max_files, max_frames_per_segment, trim_segments, batch_size, max_epoch, \
hidden_units, hidden_layers, hidden_layers_add, prev_frames, next_frames, crossvalid_frames, usec0, usedelta, useacc, mel_banks_only
print "Epoch: %d" % (epoch, )
print
print "Cross-validation stats"
print "------------------------"
print "Epoch predictive accuracy: %0.4f" % c_acc
print "Last epoch accs:", ["%.4f" % x for x in dc_acc[-20:]]
print "Epoch sil bias: %0.2f" % c_sil
print
print "Training stats"
print "------------------------"
print "Epoch predictive accuracy: %0.4f" % t_acc
print "Last epoch accs:", ["%.4f" % x for x in dt_acc[-20:]]
print "Epoch sil bias: %0.2f" % t_sil
print
print "Best results"
print "------------------------"
print "Best iteration:", np.argmax(dc_acc)
print "Best iteration - cross-validation acc: %.4f" % dc_acc[np.argmax(
dc_acc)]
print "Best iteration - training acc: %.4f" % dt_acc[np.argmax(
dc_acc)]
print
print datetime.datetime.now()
print
if epoch == np.argmax(dc_acc):
print
print "Saving the FFNN and TFFN models"
print
file_name = "model_voip/lid_nnt_%d_hu%d_hl%d_hla%d_pf%d_nf%d_acf_%.1f_mfr%d_mfl%d_mfps%d_ts%d_usec0%d_usedelta%d_useacc%d_mbo%d_bs%d" % \
(input_size, hidden_units, hidden_layers, hidden_layers_add,
prev_frames, next_frames, amplify_center_frame, max_frames, max_files, max_frames_per_segment,
trim_segments,
usec0, usedelta, useacc, mel_banks_only, batch_size)
nn = ffnn.FFNN()
for w, b in [
e.params[n:n + 2] for n in range(0, len(e.params), 2)
]:
nn.add_layer(w.get_value(), b.get_value())
nn.set_input_norm(tx_m, tx_std)
nn.save(file_name + ".ffnn")
e.save(file_name + ".tffnn")
if epoch == max_epoch:
break
epoch += 1
if epoch > 1 and epoch % 1 == 0:
if i_hidden_layers < hidden_layers + hidden_layers_add:
print
print '-' * 80
print 'Adding a hidden layer: ', i_hidden_layers + 1
print '-' * 80
e.add_hidden_layer(hidden_units)
i_hidden_layers += 1
print
print '-' * 80
print 'Training'
print '-' * 80
e.train(method=method,
learning_rate=learning_rate * learning_rate_decay /
(learning_rate_decay + epoch))
def train_nn(speech_data, speech_alignment):
vta = MLFMFCCOnlineAlignedArray(usec0=usec0,
n_last_frames=last_frames,
usedelta=usedelta,
useacc=useacc,
mel_banks_only=mel_banks_only)
sil_count = 0
speech_count = 0
for sd, sa in zip(speech_data, speech_alignment):
mlf_speech = load_mlf(sa, max_files, max_frames_per_segment)
vta.append_mlf(mlf_speech)
vta.append_trn(sd)
sil_count += mlf_speech.count_length('sil')
speech_count += mlf_speech.count_length('speech')
print "The length of sil segments: ", sil_count
print "The length of speech segments: ", speech_count
mfcc = vta.__iter__().next()
print "MFCC length:", len(mfcc[0])
input_size = len(mfcc[0])
e = theanets.Experiment(
theanets.Classifier,
layers=(input_size, hidden_units, hidden_units, hidden_units,
hidden_units, 2),
optimize="hf",
num_updates=30,
validate=1,
initial_lambda=0.1,
preconditioner=True if preconditioner else False,
hidden_dropouts=hidden_dropouts,
weight_l2=weight_l2,
batch_size=500,
)
random.seed(0)
print "Generating the cross-validation and train MFCC features"
crossvalid_x = []
crossvalid_y = []
train_x = []
train_y = []
i = 0
for frame, label in vta:
frame = frame - (10.0 if mel_banks_only else 0.0)
if i % (max_frames / 10) == 0:
print "Already processed: %.2f%% of data" % (100.0 * i /
max_frames)
if i > max_frames:
break
if random.random() < float(crossvalid_frames) / max_frames:
# sample validation (test) data
crossvalid_x.append(frame)
if label == "sil":
crossvalid_y.append(0)
else:
crossvalid_y.append(1)
else:
train_x.append(frame)
if label == "sil":
train_y.append(0)
else:
train_y.append(1)
i += 1
crossvalid = [
np.array(crossvalid_x),
np.array(crossvalid_y).astype('int32')
]
train = [np.array(train_x), np.array(train_y).astype('int32')]
print
print "The length of training data: ", len(train_x)
print "The length of test data: ", len(crossvalid_x)
print
dc_acc = deque(maxlen=20)
dt_acc = deque(maxlen=20)
epoch = 0
while True:
predictions_y = e.network.predict(crossvalid_x)
c_acc, c_sil = get_accuracy(crossvalid_y, predictions_y)
predictions_y = e.network.predict(train_x)
t_acc, t_sil = get_accuracy(train_y, predictions_y)
print
print "max_frames, max_files, max_frames_per_segment, trim_segments, max_epoch, hidden_units, last_frames, crossvalid_frames, usec0, usedelta, useacc, mel_banks_only, preconditioner, hidden_dropouts, weight_l2"
print max_frames, max_files, max_frames_per_segment, trim_segments, max_epoch, hidden_units, last_frames, crossvalid_frames, usec0, usedelta, useacc, mel_banks_only, preconditioner, hidden_dropouts, weight_l2
print "Epoch: %d" % (epoch, )
print
print "Cross-validation stats"
print "------------------------"
print "Epoch predictive accuracy: %0.2f" % c_acc
print "Last epoch accs:", ["%.2f" % x for x in dc_acc]
print "Epoch sil bias: %0.2f" % c_sil
print
print "Training stats"
print "------------------------"
print "Epoch predictive accuracy: %0.2f" % t_acc
print "Last epoch accs:", ["%.2f" % x for x in dt_acc]
print "Epoch sil bias: %0.2f" % t_sil
if epoch == max_epoch:
break
epoch += 1
e.run(train, crossvalid)
dc_acc.append(c_acc)
dt_acc.append(t_acc)
nn = ffnn.FFNN()
for w, b in zip(e.network.weights, e.network.biases):
nn.add_layer(w.get_value(), b.get_value())
nn.save(file_name = "model_voip/vad_sds_mfcc_is%d_hu%d_lf%d_mfr%d_mfl%d_mfps%d_ts%d_usec0%d_usedelta%d_useacc%d_mbo%d.nn" % \
(input_size, hidden_units, last_frames, max_frames, max_files, max_frames_per_segment, trim_segments, usec0, usedelta, useacc, mel_banks_only))
def train_nn(speech_data, speech_alignment):
print
print datetime.datetime.now()
print
random.seed(0)
try:
f = open(fetures_file_name, "rb")
crossvalid_x = np.load(f)
crossvalid_y = np.load(f)
train_x = np.load(f)
train_y = np.load(f)
f.close()
except:
vta = MLFMFCCOnlineAlignedArray(usec0=usec0,
n_last_frames=last_frames,
usedelta=usedelta,
useacc=useacc,
mel_banks_only=mel_banks_only)
sil_count = 0
speech_count = 0
for sd, sa in zip(speech_data, speech_alignment):
mlf_speech = load_mlf(sa, max_files, max_frames_per_segment)
vta.append_mlf(mlf_speech)
vta.append_trn(sd)
sil_count += mlf_speech.count_length('sil')
speech_count += mlf_speech.count_length('speech')
print "The length of sil segments: ", sil_count
print "The length of speech segments: ", speech_count
mfcc = vta.__iter__().next()
print "MFCC length:", len(mfcc[0])
input_size = len(mfcc[0])
print "Generating the cross-validation and train MFCC features"
crossvalid_x = []
crossvalid_y = []
train_x = []
train_y = []
i = 0
for frame, label in vta:
# downcast
frame = frame.astype(np.float32)
# frame = frame - (10.0 if mel_banks_only else 0.0)
if i % (max_frames / 10) == 0:
print "Already processed: %.2f%% of data" % (100.0 * i /
max_frames)
if i > max_frames:
break
if random.random() < float(crossvalid_frames) / max_frames:
# sample validation (test) data
crossvalid_x.append(frame)
if label == "sil":
crossvalid_y.append(0)
else:
crossvalid_y.append(1)
else:
train_x.append(frame)
if label == "sil":
train_y.append(0)
else:
train_y.append(1)
i += 1
gc.collect()
crossvalid_x = np.array(crossvalid_x).astype(np.float32)
gc.collect()
crossvalid_y = np.array(crossvalid_y).astype('int32')
gc.collect()
train_x = np.array(train_x).astype(np.float32)
gc.collect()
train_y = np.array(train_y).astype('int32')
gc.collect()
# normalise the data
tx_m = np.mean(train_x, axis=0)
tx_std = np.std(train_x, axis=0)
gc.collect()
crossvalid_x -= tx_m
gc.collect()
crossvalid_x /= tx_std
gc.collect()
train_x -= tx_m
gc.collect()
train_x /= tx_std
gc.collect()
print 'Saving data to:', fetures_file_name
f = open(fetures_file_name, "wb")
np.save(f, crossvalid_x)
np.save(f, crossvalid_y)
np.save(f, train_x)
np.save(f, train_y)
f.close()
print
print datetime.datetime.now()
print
print "The shape of training data: ", train_x.shape, train_y.shape
print "The shape of test data: ", crossvalid_x.shape, crossvalid_y.shape
print
input_size = train_x.shape[1]
e = ffnn.TheanoFFNN2(input_size,
hidden_units,
hidden_layers,
2,
hidden_activation=hact,
weight_l2=weight_l2)
dc_acc = []
dt_acc = []
epoch = 0
while True:
print
print '-' * 80
print 'Predictions'
print '-' * 80
predictions_y = e.predict(crossvalid_x, batch_size)
c_acc, c_sil = get_accuracy(crossvalid_y, predictions_y)
predictions_y = e.predict(train_x, batch_size)
t_acc, t_sil = get_accuracy(train_y, predictions_y)
dc_acc.append(c_acc)
dt_acc.append(t_acc)
print
print "method, hact, max_frames, max_files, max_frames_per_segment, trim_segments, batch_size, max_epoch, hidden_units, last_frames, crossvalid_frames, usec0, usedelta, useacc, mel_banks_only "
print method, hact, max_frames, max_files, max_frames_per_segment, trim_segments, batch_size, max_epoch, hidden_units, last_frames, crossvalid_frames, usec0, usedelta, useacc, mel_banks_only
print "Epoch: %d" % (epoch, )
print
print "Cross-validation stats"
print "------------------------"
print "Epoch predictive accuracy: %0.4f" % c_acc
print "Last epoch accs:", ["%.4f" % x for x in dc_acc[-20:]]
print "Epoch sil bias: %0.2f" % c_sil
print
print "Training stats"
print "------------------------"
print "Epoch predictive accuracy: %0.4f" % t_acc
print "Last epoch accs:", ["%.4f" % x for x in dt_acc[-20:]]
print "Epoch sil bias: %0.2f" % t_sil
print
print "Best results"
print "------------------------"
print "Best iteration:", np.argmax(dc_acc)
print "Best iteration - cross-validation acc: %.4f" % dc_acc[np.argmax(
dc_acc)]
print "Best iteration - training acc: %.4f" % dt_acc[np.argmax(
dc_acc)]
print
print datetime.datetime.now()
print
if epoch == np.argmax(dc_acc):
print
print "Saving the FFNN model"
print
nn = ffnn.FFNN()
for w, b in e.params:
nn.add_layer(w.get_value(), b.get_value())
nn.save(file_name = "model_voip/vad_sds_mfcc_is%d_hu%d_lf%d_mfr%d_mfl%d_mfps%d_ts%d_usec0%d_usedelta%d_useacc%d_mbo%d.nnt" % \
(input_size, hidden_units, last_frames, max_frames, max_files, max_frames_per_segment, trim_segments,
usec0, usedelta, useacc, mel_banks_only))
if epoch == max_epoch:
break
epoch += 1
print
print '-' * 80
print 'Training'
print '-' * 80
e.train(train_x,
train_y,
method=method,
learning_rate=learning_rate * learning_rate_decay /
(learning_rate_decay + epoch),
batch_size=batch_size)