def apply_model(model_fn, subset, language):
# assert language is None # to-do
# Load the model options
model_dir = path.split(model_fn)[0]
options_dict_fn = path.join(model_dir, "options_dict.pkl")
print("Reading:", options_dict_fn)
with open(options_dict_fn, "rb") as f:
options_dict = pickle.load(f)
# Load data
npz_fn = path.join(options_dict["data_dir"], subset + ".npz")
if language is not None:
if "buckeye" in npz_fn:
npz_fn = npz_fn.replace("buckeye", language)
elif "xitsonga" in npz_fn:
npz_fn = npz_fn.replace("xitsonga", language)
x_data, labels, lengths, keys, speakers = data_io.load_data_from_npz(
npz_fn)
# Truncate and limit dimensionality
data_io.trunc_and_limit_dim(x_data, lengths, options_dict["n_input"],
options_dict["max_length"])
# Build model
x = tf.placeholder(TF_DTYPE, [None, None, options_dict["n_input"]])
x_lengths = tf.placeholder(TF_ITYPE, [None])
model = build_model(x, x_lengths, options_dict)
# Embed data
batch_iterator = batching.SimpleIterator(x_data, len(x_data), False)
saver = tf.train.Saver()
with tf.Session() as session:
saver.restore(session, model_fn)
for batch_x_padded, batch_x_lengths in batch_iterator:
np_x = batch_x_padded
np_x_lengths = batch_x_lengths
np_z = session.run([model["encoding"]],
feed_dict={
x: np_x,
x_lengths: np_x_lengths
})[0]
# np_y = session.run(
# [y], feed_dict={a: np_x, a_lengths: np_x_lengths, b_lengths: np_x_lengths}
# )[0]
break # single batch
embed_dict = {}
for i, utt_key in enumerate([keys[i] for i in batch_iterator.indices]):
embed_dict[utt_key] = np_z[i]
return embed_dict
def train_cae(options_dict):
"""Train and save a CAE."""
# PRELIMINARY
assert (options_dict["train_tag"] != "rnd") or \
(options_dict["cae_n_epochs"] == 0), \
"random segment training only possible with AE (cae_n_epochs=0)"
print(datetime.now())
# Output directory
hasher = hashlib.md5(repr(sorted(options_dict.items())).encode("ascii"))
# hash_str = (
# datetime.now().strftime("%y%m%d.%Hh%M") + "." +
# # datetime.now().strftime("%y%m%d.%Hh%Mm%Ss") + "." +
# hasher.hexdigest()[:5]
# )
hash_str = hasher.hexdigest()[:10]
model_dir = path.join(
"models", path.split(options_dict["data_dir"])[-1] + "." +
options_dict["train_tag"],"cvae_model","70"
)
options_dict_fn = path.join(model_dir, "options_dict.pkl")
print("Model directory:", model_dir)
if not os.path.isdir(model_dir):
os.makedirs(model_dir)
print("Options:", options_dict)
# Random seeds
# random.seed(options_dict["rnd_seed"])
np.random.seed(options_dict["rnd_seed"])
tf.set_random_seed(options_dict["rnd_seed"])
# LOAD AND FORMAT DATA
# Training data
train_tag = options_dict["train_tag"]
min_length = None
if options_dict["train_tag"] == "rnd":
min_length = options_dict["min_length"]
train_tag = "all"
npz_fn = path.join(
options_dict["data_dir"], "train." + train_tag + ".npz"
)
##############################################
# train_x, train_labels, train_lengths, train_keys, train_speakers = (
# data_io.load_data_from_npz(npz_fn, min_length)
# )
##############################################
train_x, train_labels, train_lengths, train_keys = (data_io.load_data_from_npz(npz_fn, min_length))
##############################################
# Pretraining data (if specified)
pretrain_tag = options_dict["pretrain_tag"]
if options_dict["pretrain_tag"] is not None:
min_length = None
if options_dict["pretrain_tag"] == "rnd":
min_length = options_dict["min_length"]
pretrain_tag = "all"
npz_fn = path.join(
options_dict["data_dir"], "train." + pretrain_tag + ".npz"
)
(pretrain_x, pretrain_labels, pretrain_lengths, pretrain_keys) = data_io.load_data_from_npz(npz_fn, min_length)
# Validation data
if options_dict["use_test_for_val"]:
npz_fn = path.join(options_dict["data_dir"], "test.npz")
else:
npz_fn = path.join(options_dict["data_dir"], "val.npz")
val_x, val_labels, val_lengths, val_keys = (
data_io.load_data_from_npz(npz_fn)
)
# Convert training speakers, if speaker embeddings
if options_dict["d_speaker_embedding"] is not None:
train_speaker_set = set(train_speakers)
speaker_to_id = {}
id_to_speaker = {}
for i, speaker in enumerate(sorted(list(train_speaker_set))):
speaker_to_id[speaker] = i
id_to_speaker[i] = speaker
train_speaker_ids = []
for speaker in train_speakers:
train_speaker_ids.append(speaker_to_id[speaker])
train_speaker_ids = np.array(train_speaker_ids, dtype=NP_ITYPE)
options_dict["n_speakers"] = max(speaker_to_id.values()) + 1
# Truncate and limit dimensionality
max_length = options_dict["max_length"]
# d_frame = 13 # None
########################################################
d_frame = 108
########################################################
options_dict["n_input"] = d_frame
print("Limiting dimensionality:", d_frame)
print("Limiting length:", max_length)
data_io.trunc_and_limit_dim(train_x, train_lengths, d_frame, max_length)
if options_dict["pretrain_tag"] is not None:
data_io.trunc_and_limit_dim(
pretrain_x, pretrain_lengths, d_frame, max_length
)
data_io.trunc_and_limit_dim(val_x, val_lengths, d_frame, max_length)
# Get pairs
pair_list = batching.get_pair_list(train_labels)
# pair_list = batching.get_pair_list(train_labels, both_directions = False)
print("No. pairs:", int(len(pair_list)/2.0)) # pairs in both directions
# print("No. pairs:", len(pair_list))
# DEFINE MODEL
print(datetime.now())
print("Building model")
# Model filenames
pretrain_intermediate_model_fn = path.join(model_dir, "ae.tmp.ckpt")
pretrain_model_fn = path.join(model_dir, "ae.best_val.ckpt")
intermediate_model_fn = path.join(model_dir, "cvae.tmp.ckpt")
model_fn = path.join(model_dir, "cvae.best_val.ckpt")
# Model graph
a = tf.placeholder(TF_DTYPE, [None, None, options_dict["n_input"]])
a_lengths = tf.placeholder(TF_ITYPE, [None])
b = tf.placeholder(TF_DTYPE, [None, None, options_dict["n_input"]])
b_lengths = tf.placeholder(TF_ITYPE, [None])
network_dict = build_cae_from_options_dict(
a, a_lengths, b_lengths, options_dict
)
#####################################
z_mean = network_dict["z_mean"]
z_log_sigma_sq = network_dict["z_log_sigma_sq"]
z = network_dict["z"]
######################################
mask = network_dict["mask"]
# z = network_dict["z"]
y = network_dict["y"]
##### y: [n_data, n_sample, maxlength, d_frame]
if options_dict["d_speaker_embedding"] is not None:
speaker_id = network_dict["speaker_id"]
######################################
# # Reconstruction loss
# # tf.reduce_sum(mask, 1) get how the real leangth of a datapoint (length, n_frame)
# loss = tf.reduce_mean(
# tf.reduce_sum(tf.reduce_mean(tf.square(b - y), -1), -1) /
# tf.reduce_sum(mask, 1)
# ) # https://danijar.com/variable-sequence-lengths-in-tensorflow/
# # # Temp
# # alpha = 0.1
# # loss += alpha*tf.reduce_mean(
# # tf.reduce_sum(tf.reduce_mean(tf.square(a - y), -1), -1) /
# # tf.reduce_sum(mask, 1)
# # ) # temp
######################################
##### y: [n_data, n_sample, maxlength, d_frame]
b_cvae = tf.expand_dims(b, 1)
temp = tf.reduce_sum(tf.reduce_mean(tf.square(b_cvae - y), -1), -1) / tf.reduce_sum(mask, -1)
temp = tf.reduce_min(temp, -1)
reconstruction_loss = 1./(2*options_dict["sigma_sq"]) * tf.reduce_mean(temp)
# https://danijar.com/variable-sequence-lengths-in-tensorflow/
regularisation_loss = -0.5*tf.reduce_sum(
1 + z_log_sigma_sq - tf.square(z_mean) - tf.exp(z_log_sigma_sq), 1
)
loss = reconstruction_loss + tf.reduce_mean(regularisation_loss)
######################################
optimizer = tf.train.AdamOptimizer(
learning_rate=options_dict["learning_rate"]
).minimize(loss)
######################################
# loss for autoencoder is different from that of cvae
y_ae = tf.reduce_mean(y, 1)
mask_ae = tf.reduce_mean(mask, 1)
loss_ae = tf.reduce_mean(
tf.reduce_sum(tf.reduce_mean(tf.square(b - y_ae), -1), -1) /
tf.reduce_sum(mask_ae, 1)
) # https://danijar.com/variable-sequence-lengths-in-tensorflow/
optimizer_ae = tf.train.AdamOptimizer(
learning_rate=options_dict["learning_rate"]
).minimize(loss_ae)
# AUTOENCODER PRETRAINING: TRAIN AND VALIDATE
print(datetime.now())
print("Pretraining model")
# Validation function
def samediff_val(normalise=True):
# Embed validation
np.random.seed(options_dict["rnd_seed"])
val_batch_iterator = batching.SimpleIterator(val_x, len(val_x), False)
labels = [val_labels[i] for i in val_batch_iterator.indices]
saver = tf.train.Saver()
with tf.Session() as session:
saver.restore(session, val_model_fn)
for batch_x_padded, batch_x_lengths in val_batch_iterator:
np_x = batch_x_padded
np_x_lengths = batch_x_lengths
# np_z = session.run(
# [z], feed_dict={a: np_x, a_lengths: np_x_lengths}
# )[0]
np_z = session.run(
[z_mean], feed_dict={a: np_x, a_lengths: np_x_lengths}
)[0]
# print(np_z)
break # single batch
embed_dict = {}
for i, utt_key in enumerate(
[val_keys[i] for i in val_batch_iterator.indices]):
embed_dict[utt_key] = np_z[i]
# Same-different
if normalise:
# print(np_z.shape)
np_z_normalised = (np_z - np_z.mean(axis=0))/np_z.std(axis=0)
distances = pdist(np_z_normalised, metric="cosine")
matches = samediff.generate_matches_array(labels)
ap, prb = samediff.average_precision(
distances[matches == True], distances[matches == False]
)
else:
distances = pdist(np_z, metric="cosine")
matches = samediff.generate_matches_array(labels)
ap, prb = samediff.average_precision(
distances[matches == True], distances[matches == False]
)
return [prb, -ap]
# Train AE
val_model_fn = pretrain_intermediate_model_fn
if options_dict["pretrain_tag"] is not None:
if options_dict["pretrain_tag"] == "rnd":
train_batch_iterator = batching.RandomSegmentsIterator(
pretrain_x, options_dict["ae_batch_size"],
options_dict["ae_n_buckets"], shuffle_every_epoch=True,
paired=True
)
else:
train_batch_iterator = batching.PairedBucketIterator(
pretrain_x, [(i, i) for i in range(len(pretrain_x))],
options_dict["ae_batch_size"], options_dict["ae_n_buckets"],
shuffle_every_epoch=True, speaker_ids=None if
options_dict["d_speaker_embedding"] is None else
train_speaker_ids
)
else:
if options_dict["train_tag"] == "rnd":
train_batch_iterator = batching.RandomSegmentsIterator(
train_x, options_dict["ae_batch_size"],
options_dict["ae_n_buckets"], shuffle_every_epoch=True,
paired=True
)
else:
train_batch_iterator = batching.PairedBucketIterator(
train_x, [(i, i) for i in range(len(train_x))],
options_dict["ae_batch_size"], options_dict["ae_n_buckets"],
shuffle_every_epoch=True, speaker_ids=None if
options_dict["d_speaker_embedding"] is None else
train_speaker_ids
)
if options_dict["d_speaker_embedding"] is None:
ae_record_dict = training.train_fixed_epochs_external_val(
options_dict["ae_n_epochs"], optimizer_ae, loss_ae, train_batch_iterator,
[a, a_lengths, b, b_lengths], samediff_val,
save_model_fn=pretrain_intermediate_model_fn,
save_best_val_model_fn=pretrain_model_fn,
n_val_interval=options_dict["ae_n_val_interval"]
)
else:
ae_record_dict = training.train_fixed_epochs_external_val(
options_dict["ae_n_epochs"], optimizer_ae, loss_ae, train_batch_iterator,
[a, a_lengths, b, b_lengths, speaker_id], samediff_val,
save_model_fn=pretrain_intermediate_model_fn,
save_best_val_model_fn=pretrain_model_fn,
n_val_interval=options_dict["ae_n_val_interval"]
)
# CORRESPONDENCE TRAINING: TRAIN AND VALIDATE
if options_dict["cae_n_epochs"] > 0:
print("Training model")
cae_pretrain_model_fn = pretrain_model_fn
if options_dict["pretrain_usefinal"]:
cae_pretrain_model_fn = pretrain_intermediate_model_fn
if options_dict["ae_n_epochs"] == 0:
cae_pretrain_model_fn = None
# Train CAE
val_model_fn = intermediate_model_fn
##########################################################
train_batch_iterator = batching.PairedBucketIterator(
train_x, pair_list, batch_size=options_dict["cae_batch_size"],
n_buckets=options_dict["cae_n_buckets"], shuffle_every_epoch=True,
speaker_ids=None if options_dict["d_speaker_embedding"] is None
else train_speaker_ids
)
##########################################################
# train_batch_iterator = batching.PairedBucketIterator(
# train_x, [(i, i) for i in range(len(train_x))], batch_size=options_dict["cae_batch_size"],
# n_buckets=options_dict["cae_n_buckets"], shuffle_every_epoch=True,
# speaker_ids=None if options_dict["d_speaker_embedding"] is None
# else train_speaker_ids
# )
##########################################################
if options_dict["d_speaker_embedding"] is None:
cae_record_dict = training.train_fixed_epochs_external_val(
options_dict["cae_n_epochs"], optimizer, loss,
train_batch_iterator, [a, a_lengths, b, b_lengths],
samediff_val, save_model_fn=intermediate_model_fn,
save_best_val_model_fn=model_fn,
n_val_interval=options_dict["cae_n_val_interval"],
# load_model_fn=cae_pretrain_model_fn
load_model_fn=path.join(
"models", path.split(options_dict["data_dir"])[-1] + "." +
options_dict["train_tag"],"pretrain_ae","50", "ae.best_val.ckpt"
))
else:
cae_record_dict = training.train_fixed_epochs_external_val(
options_dict["cae_n_epochs"], optimizer, loss,
train_batch_iterator, [a, a_lengths, b, b_lengths, speaker_id],
samediff_val, save_model_fn=intermediate_model_fn,
save_best_val_model_fn=model_fn,
n_val_interval=options_dict["cae_n_val_interval"],
load_model_fn=cae_pretrain_model_fn
)
# Save record
record_dict_fn = path.join(model_dir, "record_dict.pkl")
print("Writing:", record_dict_fn)
with open(record_dict_fn, "wb") as f:
pickle.dump(ae_record_dict, f, -1)
if options_dict["cae_n_epochs"] > 0:
pickle.dump(cae_record_dict, f, -1)
# Save options_dict
options_dict_fn = path.join(model_dir, "options_dict.pkl")
print("Writing:", options_dict_fn)
with open(options_dict_fn, "wb") as f:
pickle.dump(options_dict, f, -1)
# FINAL EXTRINSIC EVALUATION
print ("Performing final validation")
if options_dict["cae_n_epochs"] == 0:
if options_dict["extrinsic_usefinal"]:
val_model_fn = pretrain_intermediate_model_fn
else:
val_model_fn = pretrain_model_fn
else:
if options_dict["extrinsic_usefinal"]:
val_model_fn = intermediate_model_fn
else:
val_model_fn = model_fn
prb, ap = samediff_val(normalise=False)
ap = -ap
prb_normalised, ap_normalised = samediff_val(normalise=True)
ap_normalised = -ap_normalised
print("Validation AP:", ap)
print("Validation AP with normalisation:", ap_normalised)
ap_fn = path.join(model_dir, "val_ap.txt")
print("Writing:", ap_fn)
with open(ap_fn, "w") as f:
f.write(str(ap) + "\n")
f.write(str(ap_normalised) + "\n")
print("Validation model:", val_model_fn)
print(datetime.now())
def apply_model(model_fn, language, subset, segtag):
# Load the model options
model_dir = path.split(model_fn)[0]
options_dict_fn = path.join(model_dir, "options_dict.pkl")
print("Reading:", options_dict_fn)
with open(options_dict_fn, "rb") as f:
options_dict = pickle.load(f)
# Load data and intervals
npz_fn = path.join("data", language, subset + ".npz")
x_data, labels, lengths, keys, speakers = data_io.load_data_from_npz(
npz_fn
)
seglist_fn = path.join(
"data", language, "search.seglist." + segtag + ".pkl"
)
print("Reading:", seglist_fn)
with open(seglist_fn, "rb") as f:
seglist_dict = pickle.load(f)
seglists = [seglist_dict[i] for i in keys]
print("No. utterances:", len(x_data))
n_intervals = sum([len(i) for i in seglists])
print("No. intervals:", n_intervals)
# assert False
# print("Reading:", input_npz_fn)
# features_dict = np.load(input_npz_fn)
# seglist_fn = path.join(
# "data", language, "search.seglist." + segtag + ".pkl"
# )
# print("Reading:", seglist_fn)
# with open(seglist_fn, "rb") as f:
# seglist_dict = pickle.load(f)
# utterances = sorted(features_dict.keys())
# input_sequences = [features_dict[i] for i in utterances]
# seglists = [seglist_dict[i] for i in utterances]
# print("No. utterances:", len(input_sequences))
# n_intervals = sum([len(i) for i in seglists])
# print("No. intervals:", n_intervals)
# if "cnn" in options_dict["script"]:
# assert False, "to-do"
# else: # rnn
# print("No. utterances:", len(input_sequences))
# n_intervals = sum([len(i) for i in seglists])
# print("No. intervals:", n_intervals)
# # Load data
# npz_fn = path.join("data", language, subset + ".npz")
# x_data, labels, lengths, keys, speakers = data_io.load_data_from_npz(
# npz_fn
# )
if "cnn" in options_dict["script"]:
assert False, "to-do"
# Pad and flatten data
x_data, _ = data_io.pad_sequences(
x_data, options_dict["max_length"], True
)
x_data = np.transpose(x_data, (0, 2, 1))
x_data = x_data.reshape((-1, options_dict["d_in"]))
# Build model
x = tf.placeholder(TF_DTYPE, [None, options_dict["d_in"]])
model = build_model(x, None, options_dict)
# Embed data
batch_iterator = batching.LabelledIterator(
x_data, None, x_data.shape[0], False
)
saver = tf.train.Saver()
with tf.Session() as session:
saver.restore(session, model_fn)
for batch_x in batch_iterator:
np_z = session.run(
[model["encoding"]], feed_dict={x: batch_x})[0]
break # single batch
else: # rnn
# Truncate and limit dimensionality
data_io.trunc_and_limit_dim(
x_data, lengths, options_dict["n_input"], None
)
class DenseBatchFeedIterator(object):
def __init__(self, input_sequences, seglists):
self.input_sequences = input_sequences
self.n_input = self.input_sequences[0].shape[-1]
self.seglists = seglists
def __iter__(self):
for i_utt in range(len(self.input_sequences)):
# Get intervals
seglist = self.seglists[i_utt]
input_sequence = self.input_sequences[i_utt]
# Get segments for intervals
segments = []
for i, j in seglist:
segments.append(input_sequence[i:j, :])
batch_x_lengths = [i.shape[0] for i in segments]
# Pad to maximum length in batch
batch_x_padded = np.zeros(
(len(batch_x_lengths), np.max(batch_x_lengths),
self.n_input), dtype=NP_DTYPE
)
for i, length in enumerate(batch_x_lengths):
seq = segments[i]
batch_x_padded[i, :length, :] = seq
yield (batch_x_padded, batch_x_lengths)
batch_iterator = DenseBatchFeedIterator(x_data, seglists)
# Build model
x = tf.placeholder(TF_DTYPE, [None, None, options_dict["n_input"]])
x_lengths = tf.placeholder(TF_ITYPE, [None])
model = build_model(x, x_lengths, options_dict)
# Embed data
# batch_iterator = batching.SimpleIterator(x_data, len(x_data), False)
saver = tf.train.Saver()
n_outputs = 0
embed_dict = {}
with tf.Session() as session:
saver.restore(session, model_fn)
# print(datetime.now())
print(
"Applying model to segments ({} iterations):".format(
len(x_data))
)
for i_batch, (batch_x_padded, batch_x_lengths) in \
tqdm(enumerate(batch_iterator)):
cur_output = session.run(
[model["encoding"]], feed_dict={x: batch_x_padded,
x_lengths: batch_x_lengths}
)[0]
utt_key = keys[i_batch]
seglist = seglists[i_batch]
embeddings = []
for i in range(cur_output.shape[0]):
embeddings.append(cur_output[i, :])
n_outputs += 1
embed_dict[utt_key] = np.array(embeddings)
# print(datetime.now())
# for batch_x_padded, batch_x_lengths in batch_iterator:
# np_x = batch_x_padded
# np_x_lengths = batch_x_lengths
# np_z = session.run(
# [model["encoding"]], feed_dict={x: np_x, x_lengths:
# np_x_lengths}
# )[0]
# break # single batch
print("Processed {} out of {} inputs".format(n_outputs, n_intervals))
return embed_dict
def train_rnn(options_dict):
"""Train and save a Siamese triplets model."""
# PRELIMINARY
print(datetime.now())
# Output directory
hasher = hashlib.md5(repr(sorted(options_dict.items())).encode("ascii"))
hash_str = hasher.hexdigest()[:10]
model_dir = path.join(
"models", options_dict["train_lang"] + "." + options_dict["train_tag"],
options_dict["script"], hash_str)
options_dict_fn = path.join(model_dir, "options_dict.pkl")
print("Model directory:", model_dir)
if not os.path.isdir(model_dir):
os.makedirs(model_dir)
print("Options:", options_dict)
# Random seeds
random.seed(options_dict["rnd_seed"])
np.random.seed(options_dict["rnd_seed"])
tf.set_random_seed(options_dict["rnd_seed"])
# LOAD AND FORMAT DATA
# Training data
if "+" in options_dict["train_lang"]:
train_languages = options_dict["train_lang"].split("+")
train_x = []
train_labels = []
train_lengths = []
train_keys = []
train_speakers = []
for cur_lang in train_languages:
cur_npz_fn = path.join(
"data", cur_lang,
"train." + options_dict["train_tag"] + ".npz")
(cur_train_x, cur_train_labels, cur_train_lengths, cur_train_keys,
cur_train_speakers) = data_io.load_data_from_npz(
cur_npz_fn, None)
(cur_train_x, cur_train_labels, cur_train_lengths, cur_train_keys,
cur_train_speakers) = data_io.filter_data(
cur_train_x,
cur_train_labels,
cur_train_lengths,
cur_train_keys,
cur_train_speakers,
n_min_tokens_per_type=options_dict["n_min_tokens_per_type"],
n_max_types=options_dict["n_max_types"],
n_max_tokens=options_dict["n_max_tokens"],
n_max_tokens_per_type=options_dict["n_max_tokens_per_type"],
)
train_x.extend(cur_train_x)
train_labels.extend(cur_train_labels)
train_lengths.extend(cur_train_lengths)
train_keys.extend(cur_train_keys)
train_speakers.extend(cur_train_speakers)
print("Total no. items:", len(train_labels))
else:
npz_fn = path.join("data", options_dict["train_lang"],
"train." + options_dict["train_tag"] + ".npz")
train_x, train_labels, train_lengths, train_keys, train_speakers = (
data_io.load_data_from_npz(npz_fn, None))
train_x, train_labels, train_lengths, train_keys, train_speakers = (
data_io.filter_data(
train_x,
train_labels,
train_lengths,
train_keys,
train_speakers,
n_min_tokens_per_type=options_dict["n_min_tokens_per_type"],
n_max_types=options_dict["n_max_types"],
n_max_tokens=options_dict["n_max_tokens"],
n_max_tokens_per_type=options_dict["n_max_tokens_per_type"],
))
# Convert training labels to integers
train_label_set = list(set(train_labels))
label_to_id = {}
for i, label in enumerate(sorted(train_label_set)):
label_to_id[label] = i
train_y = []
for label in train_labels:
train_y.append(label_to_id[label])
train_y = np.array(train_y, dtype=NP_ITYPE)
options_dict["n_classes"] = len(label_to_id)
print("Total no. classes:", options_dict["n_classes"])
# Validation data
if options_dict["val_lang"] is not None:
npz_fn = path.join("data", options_dict["val_lang"], "val.npz")
val_x, val_labels, val_lengths, val_keys, val_speakers = (
data_io.load_data_from_npz(npz_fn))
# Truncate and limit dimensionality
max_length = options_dict["max_length"]
d_frame = 13 # None
options_dict["n_input"] = d_frame
print("Limiting dimensionality:", d_frame)
print("Limiting length:", max_length)
data_io.trunc_and_limit_dim(train_x, train_lengths, d_frame, max_length)
if options_dict["val_lang"] is not None:
data_io.trunc_and_limit_dim(val_x, val_lengths, d_frame, max_length)
# DEFINE MODEL
print(datetime.now())
print("Building model")
# Model filenames
intermediate_model_fn = path.join(model_dir, "rnn.tmp.ckpt")
model_fn = path.join(model_dir, "rnn.best_val.ckpt")
# Model graph
x = tf.placeholder(TF_DTYPE, [None, None, options_dict["n_input"]])
x_lengths = tf.placeholder(TF_ITYPE, [None])
y = tf.placeholder(TF_ITYPE, [None])
network_dict = build_rnn_from_options_dict(x, x_lengths, options_dict)
encoding = network_dict["encoding"]
output = network_dict["output"]
# Cross entropy loss
loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y,
logits=output))
optimizer = tf.train.AdamOptimizer(
learning_rate=options_dict["learning_rate"]).minimize(loss)
# Save options_dict
options_dict_fn = path.join(model_dir, "options_dict.pkl")
print("Writing:", options_dict_fn)
with open(options_dict_fn, "wb") as f:
pickle.dump(options_dict, f, -1)
# TRAIN AND VALIDATE
print(datetime.now())
print("Training model")
# Validation function
def samediff_val(normalise=True):
# Embed validation
np.random.seed(options_dict["rnd_seed"])
val_batch_iterator = batching.SimpleIterator(val_x, len(val_x), False)
labels = [val_labels[i] for i in val_batch_iterator.indices]
speakers = [val_speakers[i] for i in val_batch_iterator.indices]
saver = tf.train.Saver()
with tf.Session() as session:
saver.restore(session, val_model_fn)
for batch_x_padded, batch_x_lengths in val_batch_iterator:
np_x = batch_x_padded
np_x_lengths = batch_x_lengths
np_z = session.run([encoding],
feed_dict={
x: np_x,
x_lengths: np_x_lengths
})[0]
break # single batch
embed_dict = {}
for i, utt_key in enumerate(
[val_keys[i] for i in val_batch_iterator.indices]):
embed_dict[utt_key] = np_z[i]
# Same-different
if normalise:
np_z_normalised = (np_z - np_z.mean(axis=0)) / np_z.std(axis=0)
distances = pdist(np_z_normalised, metric="cosine")
else:
distances = pdist(np_z, metric="cosine")
# matches = samediff.generate_matches_array(labels)
# ap, prb = samediff.average_precision(
# distances[matches == True], distances[matches == False]
# )
word_matches = samediff.generate_matches_array(labels)
speaker_matches = samediff.generate_matches_array(speakers)
sw_ap, sw_prb, swdp_ap, swdp_prb = samediff.average_precision_swdp(
distances[np.logical_and(word_matches, speaker_matches)],
distances[np.logical_and(word_matches, speaker_matches == False)],
distances[word_matches == False])
# return [sw_prb, -sw_ap, swdp_prb, -swdp_ap]
return [swdp_prb, -swdp_ap]
# Train RNN
val_model_fn = intermediate_model_fn
train_batch_iterator = batching.LabelledBucketIterator(
train_x,
train_y,
options_dict["batch_size"],
n_buckets=options_dict["n_buckets"],
shuffle_every_epoch=True)
if options_dict["val_lang"] is None:
record_dict = training.train_fixed_epochs(
options_dict["n_epochs"],
optimizer,
loss,
train_batch_iterator, [x, x_lengths, y],
save_model_fn=intermediate_model_fn)
else:
record_dict = training.train_fixed_epochs_external_val(
options_dict["n_epochs"],
optimizer,
loss,
train_batch_iterator, [x, x_lengths, y],
samediff_val,
save_model_fn=intermediate_model_fn,
save_best_val_model_fn=model_fn,
n_val_interval=options_dict["n_val_interval"])
# Save record
record_dict_fn = path.join(model_dir, "record_dict.pkl")
print("Writing:", record_dict_fn)
with open(record_dict_fn, "wb") as f:
pickle.dump(record_dict, f, -1)
# FINAL EXTRINSIC EVALUATION
if options_dict["val_lang"] is not None:
print("Performing final validation")
if options_dict["extrinsic_usefinal"]:
val_model_fn = intermediate_model_fn
else:
val_model_fn = model_fn
# sw_prb, sw_ap, swdp_prb, swdp_ap = samediff_val(normalise=False)
swdp_prb, swdp_ap = samediff_val(normalise=False)
# sw_ap = -sw_ap
swdp_ap = -swdp_ap
# (sw_prb_normalised, sw_ap_normalised, swdp_prb_normalised,
# swdp_ap_normalised) = samediff_val(normalise=True)
swdp_prb_normalised, swdp_ap_normalised = samediff_val(normalise=True)
# sw_ap_normalised = -sw_ap_normalised
swdp_ap_normalised = -swdp_ap_normalised
print("Validation SWDP AP:", swdp_ap)
print("Validation SWDP AP with normalisation:", swdp_ap_normalised)
ap_fn = path.join(model_dir, "val_ap.txt")
print("Writing:", ap_fn)
with open(ap_fn, "w") as f:
f.write(str(swdp_ap) + "\n")
f.write(str(swdp_ap_normalised) + "\n")
print("Validation model:", val_model_fn)
print(datetime.now())
def apply_model(model_fn, subset, batch_size=None):
# assert language is None # to-do
# Load the model options
model_dir = path.split(model_fn)[0]
options_dict_fn = path.join(model_dir, "options_dict.pkl")
print("Reading:", options_dict_fn)
with open(options_dict_fn, "rb") as f:
options_dict = pickle.load(f)
# Load data
npz_fn = path.join(options_dict["data_dir"], subset + ".npz")
# if language is not None:
# if "buckeye" in npz_fn:
# npz_fn = npz_fn.replace("buckeye", language)
# elif "xitsonga" in npz_fn:
# npz_fn = npz_fn.replace("xitsonga", language)
x_data, labels, lengths, keys = data_io.load_data_from_npz(npz_fn)
if "cnn" in options_dict["script"]:
# Pad and flatten data
x_data, _ = data_io.pad_sequences(x_data, options_dict["max_length"],
True)
x_data = np.transpose(x_data, (0, 2, 1))
x_data = x_data.reshape((-1, options_dict["d_in"]))
# Build model
x = tf.placeholder(TF_DTYPE, [None, options_dict["d_in"]])
model = build_model(x, None, options_dict)
# Embed data
if batch_size is None:
batch_iterator = batching.LabelledIterator(x_data, None,
x_data.shape[0], False)
saver = tf.train.Saver()
with tf.Session() as session:
saver.restore(session, model_fn)
for batch_x in batch_iterator:
np_z = session.run([model["encoding"]],
feed_dict={x: batch_x})[0]
break # single batch
else:
assert False, "need to implement"
else: # rnn
# Truncate and limit dimensionality
data_io.trunc_and_limit_dim(x_data, lengths, options_dict["n_input"],
options_dict["max_length"])
# Build model
x = tf.placeholder(TF_DTYPE, [None, None, options_dict["n_input"]])
x_lengths = tf.placeholder(TF_ITYPE, [None])
model = build_model(x, x_lengths, options_dict)
# Embed data
if batch_size is None:
batch_iterator = batching.SimpleIterator(x_data, len(x_data),
False)
saver = tf.train.Saver()
with tf.Session() as session:
saver.restore(session, model_fn)
for batch_x_padded, batch_x_lengths in batch_iterator:
np_x = batch_x_padded
np_x_lengths = batch_x_lengths
np_z = session.run([model["encoding"]],
feed_dict={
x: np_x,
x_lengths: np_x_lengths
})[0]
break # single batch
else:
batch_iterator = batching.SimpleIterator(x_data, batch_size, False)
saver = tf.train.Saver()
with tf.Session() as session:
saver.restore(session, model_fn)
np_z = []
for batch_x_padded, batch_x_lengths in batch_iterator:
np_x = batch_x_padded
np_x_lengths = batch_x_lengths
cur_np_z = session.run([model["encoding"]],
feed_dict={
x: np_x,
x_lengths: np_x_lengths
})[0]
print("!", cur_np_z.shape)
np_z.append(cur_np_z)
np_z = np.vstack(np_z)
print("!", np_z.shape)
embed_dict = {}
for i, utt_key in enumerate([keys[i] for i in batch_iterator.indices]):
embed_dict[utt_key] = np_z[i]
return embed_dict
def train_vae(options_dict):
"""Train and save a VAE."""
# PRELIMINARY
print(datetime.now())
# Output directory
hasher = hashlib.md5(repr(sorted(options_dict.items())).encode("ascii"))
hash_str = hasher.hexdigest()[:10]
# model_dir = path.join(
# "models", path.split(options_dict["data_dir"])[-1] + "." +
# options_dict["train_tag"], options_dict["script"], hash_str
# )
model_dir = path.join(
"models",
path.split(options_dict["data_dir"])[-1] + "." +
options_dict["train_tag"], "vae_model", "70")
options_dict_fn = path.join(model_dir, "options_dict.pkl")
print("Model directory:", model_dir)
if not os.path.isdir(model_dir):
os.makedirs(model_dir)
print("Options:", options_dict)
# Random seeds
np.random.seed(options_dict["rnd_seed"])
tf.set_random_seed(options_dict["rnd_seed"])
# LOAD AND FORMAT DATA
# Training data
train_tag = options_dict["train_tag"]
min_length = None
if options_dict["train_tag"] == "rnd":
min_length = options_dict["min_length"]
train_tag = "all"
npz_fn = path.join(options_dict["data_dir"], "train." + train_tag + ".npz")
train_x, train_labels, train_lengths, train_keys = (
data_io.load_data_from_npz(npz_fn, min_length))
# Validation data
if options_dict["use_test_for_val"]:
npz_fn = path.join(options_dict["data_dir"], "test.npz")
else:
npz_fn = path.join(options_dict["data_dir"], "val.npz")
val_x, val_labels, val_lengths, val_keys = (
data_io.load_data_from_npz(npz_fn))
# Truncate and limit dimensionality
max_length = options_dict["max_length"]
d_frame = 108 # None
options_dict["n_input"] = d_frame
print("Limiting dimensionality:", d_frame)
print("Limiting length:", max_length)
data_io.trunc_and_limit_dim(train_x, train_lengths, d_frame, max_length)
data_io.trunc_and_limit_dim(val_x, val_lengths, d_frame, max_length)
# DEFINE MODEL
print(datetime.now())
print("Building model")
# Model filenames
intermediate_model_fn = path.join(model_dir, "vae.tmp.ckpt")
model_fn = path.join(model_dir, "vae.best_val.ckpt")
# Model graph
x = tf.placeholder(TF_DTYPE, [None, None, options_dict["n_input"]])
x_lengths = tf.placeholder(TF_ITYPE, [None])
network_dict = build_vae_from_options_dict(x, x_lengths, options_dict)
encoder_states = network_dict["encoder_states"]
vae = network_dict["latent_layer"]
z_mean = vae["z_mean"]
z_log_sigma_sq = vae["z_log_sigma_sq"]
z = vae["z"]
y = network_dict["decoder_output"]
mask = network_dict["mask"]
# VAE loss
# reconstruction_loss = tf.reduce_mean(
# tf.reduce_sum(tf.reduce_mean(tf.square(x - y), -1), -1) /
# tf.reduce_sum(mask, 1)
# ) # https://danijar.com/variable-sequence-lengths-in-tensorflow/
# loss = tflego.vae_loss_gaussian(
# x, y, options_dict["sigma_sq"], z_mean, z_log_sigma_sq,
# reconstruction_loss=reconstruction_loss
# )
reconstruction_loss = 1. / (2 * options_dict["sigma_sq"]) * tf.reduce_mean(
tf.reduce_sum(tf.reduce_mean(tf.square(x - y), -1), -1) /
tf.reduce_sum(mask, 1)
) # https://danijar.com/variable-sequence-lengths-in-tensorflow/
regularisation_loss = -0.5 * tf.reduce_sum(
1 + z_log_sigma_sq - tf.square(z_mean) - tf.exp(z_log_sigma_sq), 1)
loss = reconstruction_loss + tf.reduce_mean(regularisation_loss)
# loss = tflego.vae_loss_gaussian(
# x, y, options_dict["sigma_sq"], z_mean, z_log_sigma_sq,
# reconstruction_loss=reconstruction_loss
# )
optimizer = tf.train.AdamOptimizer(
learning_rate=options_dict["learning_rate"]).minimize(loss)
# TRAIN AND VALIDATE
print(datetime.now())
print("Training model")
# Validation function
def samediff_val(normalise=False):
# Embed validation
np.random.seed(options_dict["rnd_seed"])
val_batch_iterator = batching.SimpleIterator(val_x, len(val_x), False)
labels = [val_labels[i] for i in val_batch_iterator.indices]
saver = tf.train.Saver()
with tf.Session() as session:
saver.restore(session, val_model_fn)
for batch_x_padded, batch_x_lengths in val_batch_iterator:
np_x = batch_x_padded
np_x_lengths = batch_x_lengths
np_z = session.run([z_mean],
feed_dict={
x: np_x,
x_lengths: np_x_lengths
})[0]
break # single batch
embed_dict = {}
for i, utt_key in enumerate(
[val_keys[i] for i in val_batch_iterator.indices]):
embed_dict[utt_key] = np_z[i]
# Same-different
if normalise:
np_z_normalised = (np_z - np_z.mean(axis=0)) / np_z.std(axis=0)
distances = pdist(np_z_normalised, metric="cosine")
matches = samediff.generate_matches_array(labels)
ap, prb = samediff.average_precision(distances[matches == True],
distances[matches == False])
else:
distances = pdist(np_z, metric="cosine")
matches = samediff.generate_matches_array(labels)
ap, prb = samediff.average_precision(distances[matches == True],
distances[matches == False])
return [prb, -ap]
# Train VAE
val_model_fn = intermediate_model_fn
if options_dict["train_tag"] == "rnd":
train_batch_iterator = batching.RandomSegmentsIterator(
train_x,
options_dict["batch_size"],
options_dict["n_buckets"],
shuffle_every_epoch=True)
else:
train_batch_iterator = batching.SimpleBucketIterator(
train_x,
options_dict["batch_size"],
options_dict["n_buckets"],
shuffle_every_epoch=True)
record_dict = training.train_fixed_epochs_external_val(
options_dict["n_epochs"],
optimizer,
loss,
train_batch_iterator, [x, x_lengths],
samediff_val,
save_model_fn=intermediate_model_fn,
save_best_val_model_fn=model_fn,
n_val_interval=options_dict["n_val_interval"])
# Save record
record_dict_fn = path.join(model_dir, "record_dict.pkl")
print("Writing:", record_dict_fn)
with open(record_dict_fn, "wb") as f:
pickle.dump(record_dict, f, -1)
# Save options_dict
options_dict_fn = path.join(model_dir, "options_dict.pkl")
print("Writing:" + options_dict_fn)
with open(options_dict_fn, "wb") as f:
pickle.dump(options_dict, f, -1)
# FINAL EXTRINSIC EVALUATION
print("Performing final validation")
if options_dict["extrinsic_usefinal"]:
val_model_fn = intermediate_model_fn
else:
val_model_fn = model_fn
prb, ap = samediff_val(normalise=False)
ap = -ap
prb_normalised, ap_normalised = samediff_val(normalise=True)
ap_normalised = -ap_normalised
print("Validation AP:", ap)
print("Validation AP with normalisation:", ap_normalised)
ap_fn = path.join(model_dir, "val_ap.txt")
print("Writing:", ap_fn)
with open(ap_fn, "w") as f:
f.write(str(ap) + "\n")
f.write(str(ap_normalised) + "\n")
print("Validation model:", val_model_fn)
print(datetime.now())
def apply_model(model_fn, npz_fn):
# Load the model options
model_dir = path.split(model_fn)[0]
options_dict_fn = path.join(model_dir, "options_dict.pkl")
print("Reading:", options_dict_fn)
with open(options_dict_fn, "rb") as f:
options_dict = pickle.load(f)
# Load data
x_data, labels, lengths, keys, speakers = data_io.load_data_from_npz(
npz_fn)
if "cnn" in options_dict["script"]:
# Pad and flatten data
x_data, _ = data_io.pad_sequences(x_data, options_dict["max_length"],
True)
x_data = np.transpose(x_data, (0, 2, 1))
x_data = x_data.reshape((-1, options_dict["d_in"]))
# Build model
x = tf.placeholder(TF_DTYPE, [None, options_dict["d_in"]])
model = build_model(x, None, options_dict)
# Embed data
batch_iterator = batching.LabelledIterator(x_data, None,
x_data.shape[0], False)
saver = tf.train.Saver()
with tf.Session() as session:
saver.restore(session, model_fn)
for batch_x in batch_iterator:
np_z = session.run([model["encoding"]], feed_dict={x:
batch_x})[0]
break # single batch
else: # rnn
# Truncate and limit dimensionality
data_io.trunc_and_limit_dim(x_data, lengths, options_dict["n_input"],
options_dict["max_length"])
# Build model
x = tf.placeholder(TF_DTYPE, [None, None, options_dict["n_input"]])
x_lengths = tf.placeholder(TF_ITYPE, [None])
model = build_model(x, x_lengths, options_dict)
# Embed data
batch_iterator = batching.SimpleIterator(x_data, len(x_data), False)
saver = tf.train.Saver()
with tf.Session() as session:
saver.restore(session, model_fn)
for batch_x_padded, batch_x_lengths in batch_iterator:
np_x = batch_x_padded
np_x_lengths = batch_x_lengths
np_z = session.run([model["encoding"]],
feed_dict={
x: np_x,
x_lengths: np_x_lengths
})[0]
break # single batch
embed_dict = {}
for i, utt_key in enumerate([keys[i] for i in batch_iterator.indices]):
embed_dict[utt_key] = np_z[i]
return embed_dict
def train_cae(options_dict):
"""Train and save a CAE."""
# PRELIMINARY
assert (options_dict["train_tag"] != "rnd") or \
(options_dict["cae_n_epochs"] == 0), \
"random segment training only possible with AE (cae_n_epochs=0)"
print(datetime.now())
# Output directory
hasher = hashlib.md5(repr(sorted(options_dict.items())).encode("ascii"))
hash_str = hasher.hexdigest()[:10]
model_dir = path.join(
"models", options_dict["train_lang"] + "." + options_dict["train_tag"],
options_dict["script"], hash_str)
options_dict_fn = path.join(model_dir, "options_dict.pkl")
print("Model directory:", model_dir)
if not os.path.isdir(model_dir):
os.makedirs(model_dir)
print("Options:", options_dict)
# Random seeds
random.seed(options_dict["rnd_seed"])
np.random.seed(options_dict["rnd_seed"])
tf.set_random_seed(options_dict["rnd_seed"])
# LOAD AND FORMAT DATA
# Training data
train_tag = options_dict["train_tag"]
min_length = None
if options_dict["train_tag"] == "rnd":
min_length = options_dict["min_length"]
train_tag = "all"
if "+" in options_dict["train_lang"]:
train_x = []
train_labels = []
train_lengths = []
train_keys = []
train_speakers = []
train_languages = []
for cur_lang in options_dict["train_lang"].split("+"):
cur_npz_fn = path.join("data", cur_lang,
"train." + train_tag + ".npz")
(cur_train_x, cur_train_labels, cur_train_lengths, cur_train_keys,
cur_train_speakers) = data_io.load_data_from_npz(
cur_npz_fn, min_length)
(cur_train_x, cur_train_labels, cur_train_lengths, cur_train_keys,
cur_train_speakers) = data_io.filter_data(
cur_train_x,
cur_train_labels,
cur_train_lengths,
cur_train_keys,
cur_train_speakers,
n_min_tokens_per_type=options_dict["n_min_tokens_per_type"],
n_max_types=options_dict["n_max_types"],
n_max_tokens=options_dict["n_max_tokens"],
n_max_tokens_per_type=options_dict["n_max_tokens_per_type"],
)
train_x.extend(cur_train_x)
train_labels.extend(cur_train_labels)
train_lengths.extend(cur_train_lengths)
train_keys.extend(cur_train_keys)
train_speakers.extend(cur_train_speakers)
train_languages.extend([cur_lang] * len(cur_train_speakers))
print("Total no. items:", len(train_labels))
else:
npz_fn = path.join("data", options_dict["train_lang"],
"train." + train_tag + ".npz")
train_x, train_labels, train_lengths, train_keys, train_speakers = (
data_io.load_data_from_npz(npz_fn, min_length))
train_x, train_labels, train_lengths, train_keys, train_speakers = (
data_io.filter_data(
train_x,
train_labels,
train_lengths,
train_keys,
train_speakers,
n_min_tokens_per_type=options_dict["n_min_tokens_per_type"],
n_max_types=options_dict["n_max_types"],
n_max_tokens=options_dict["n_max_tokens"],
n_max_tokens_per_type=options_dict["n_max_tokens_per_type"],
))
# Pretraining data (if specified)
pretrain_tag = options_dict["pretrain_tag"]
if options_dict["pretrain_tag"] is not None:
min_length = None
if options_dict["pretrain_tag"] == "rnd":
min_length = options_dict["min_length"]
pretrain_tag = "all"
npz_fn = path.join("data", options_dict["train_lang"],
"train." + pretrain_tag + ".npz")
(pretrain_x, pretrain_labels, pretrain_lengths, pretrain_keys,
pretrain_speakers) = data_io.load_data_from_npz(npz_fn, min_length)
# Validation data
if options_dict["val_lang"] is not None:
npz_fn = path.join("data", options_dict["val_lang"], "val.npz")
val_x, val_labels, val_lengths, val_keys, val_speakers = (
data_io.load_data_from_npz(npz_fn))
# # Convert training speakers, if speaker embeddings
# # To-do: Untested
# if options_dict["d_speaker_embedding"] is not None:
# train_speaker_set = set(train_speakers)
# speaker_to_id = {}
# id_to_speaker = {}
# for i, speaker in enumerate(sorted(list(train_speaker_set))):
# speaker_to_id[speaker] = i
# id_to_speaker[i] = speaker
# train_speaker_ids = []
# for speaker in train_speakers:
# train_speaker_ids.append(speaker_to_id[speaker])
# train_speaker_ids = np.array(train_speaker_ids, dtype=NP_ITYPE)
# options_dict["n_speakers"] = max(speaker_to_id.values()) + 1
# Convert training languages to integers, if language embeddings
if options_dict["d_language_embedding"] is not None:
train_language_set = set(train_languages)
language_to_id = {}
id_to_lang = {}
for i, lang in enumerate(sorted(list(train_language_set))):
language_to_id[lang] = i
id_to_lang[i] = lang
train_language_ids = []
for lang in train_languages:
train_language_ids.append(language_to_id[lang])
train_language_ids = np.array(train_language_ids, dtype=NP_ITYPE)
options_dict["n_languages"] = max(language_to_id.values()) + 1
# Truncate and limit dimensionality
max_length = options_dict["max_length"]
d_frame = 13 # None
options_dict["n_input"] = d_frame
print("Limiting dimensionality:", d_frame)
print("Limiting length:", max_length)
data_io.trunc_and_limit_dim(train_x, train_lengths, d_frame, max_length)
if options_dict["pretrain_tag"] is not None:
data_io.trunc_and_limit_dim(pretrain_x, pretrain_lengths, d_frame,
max_length)
if options_dict["val_lang"] is not None:
data_io.trunc_and_limit_dim(val_x, val_lengths, d_frame, max_length)
# Get pairs
pair_list = batching.get_pair_list(train_labels,
both_directions=True,
n_max_pairs=options_dict["n_max_pairs"])
print("No. pairs:", int(len(pair_list) / 2.0)) # pairs in both directions
# DEFINE MODEL
print(datetime.now())
print("Building model")
# Model filenames
pretrain_intermediate_model_fn = path.join(model_dir, "ae.tmp.ckpt")
pretrain_model_fn = path.join(model_dir, "ae.best_val.ckpt")
intermediate_model_fn = path.join(model_dir, "cae.tmp.ckpt")
model_fn = path.join(model_dir, "cae.best_val.ckpt")
# Model graph
a = tf.placeholder(TF_DTYPE, [None, None, options_dict["n_input"]])
a_lengths = tf.placeholder(TF_ITYPE, [None])
b = tf.placeholder(TF_DTYPE, [None, None, options_dict["n_input"]])
b_lengths = tf.placeholder(TF_ITYPE, [None])
network_dict = build_cae_from_options_dict(a, a_lengths, b_lengths,
options_dict)
mask = network_dict["mask"]
z = network_dict["z"]
y = network_dict["y"]
# if options_dict["d_speaker_embedding"] is not None:
# speaker_id = network_dict["speaker_id"]
if options_dict["d_language_embedding"] is not None:
language_id = network_dict["language_id"]
# Reconstruction loss
loss = tf.reduce_mean(
tf.reduce_sum(tf.reduce_mean(tf.square(b - y), -1), -1) /
tf.reduce_sum(mask, 1)
) # https://danijar.com/variable-sequence-lengths-in-tensorflow/
optimizer = tf.train.AdamOptimizer(
learning_rate=options_dict["learning_rate"]).minimize(loss)
# Save options_dict
options_dict_fn = path.join(model_dir, "options_dict.pkl")
print("Writing:", options_dict_fn)
with open(options_dict_fn, "wb") as f:
pickle.dump(options_dict, f, -1)
# AUTOENCODER PRETRAINING: TRAIN AND VALIDATE
print(datetime.now())
print("Pretraining model")
# Validation function
def samediff_val(normalise=True):
# Embed validation
np.random.seed(options_dict["rnd_seed"])
val_batch_iterator = batching.SimpleIterator(val_x, len(val_x), False)
labels = [val_labels[i] for i in val_batch_iterator.indices]
speakers = [val_speakers[i] for i in val_batch_iterator.indices]
saver = tf.train.Saver()
with tf.Session() as session:
saver.restore(session, val_model_fn)
for batch_x_padded, batch_x_lengths in val_batch_iterator:
np_x = batch_x_padded
np_x_lengths = batch_x_lengths
np_z = session.run([z],
feed_dict={
a: np_x,
a_lengths: np_x_lengths
})[0]
break # single batch
embed_dict = {}
for i, utt_key in enumerate(
[val_keys[i] for i in val_batch_iterator.indices]):
embed_dict[utt_key] = np_z[i]
# Same-different
if normalise:
np_z_normalised = (np_z - np_z.mean(axis=0)) / np_z.std(axis=0)
distances = pdist(np_z_normalised, metric="cosine")
else:
distances = pdist(np_z, metric="cosine")
# matches = samediff.generate_matches_array(labels)
# ap, prb = samediff.average_precision(
# distances[matches == True], distances[matches == False]
# )
word_matches = samediff.generate_matches_array(labels)
speaker_matches = samediff.generate_matches_array(speakers)
sw_ap, sw_prb, swdp_ap, swdp_prb = samediff.average_precision_swdp(
distances[np.logical_and(word_matches, speaker_matches)],
distances[np.logical_and(word_matches, speaker_matches == False)],
distances[word_matches == False])
# return [sw_prb, -sw_ap, swdp_prb, -swdp_ap]
return [swdp_prb, -swdp_ap]
# Train AE
val_model_fn = pretrain_intermediate_model_fn
if options_dict["pretrain_tag"] is not None:
if options_dict["pretrain_tag"] == "rnd":
train_batch_iterator = batching.RandomSegmentsIterator(
pretrain_x,
options_dict["ae_batch_size"],
options_dict["ae_n_buckets"],
shuffle_every_epoch=True,
paired=True)
else:
train_batch_iterator = batching.PairedBucketIterator(
pretrain_x, [(i, i) for i in range(len(pretrain_x))],
options_dict["ae_batch_size"],
options_dict["ae_n_buckets"],
shuffle_every_epoch=True,
language_ids=None
if options_dict["d_language_embedding"] is None else
train_language_ids,
flip_output=options_dict["flip_output"])
else:
if options_dict["train_tag"] == "rnd":
train_batch_iterator = batching.RandomSegmentsIterator(
train_x,
options_dict["ae_batch_size"],
options_dict["ae_n_buckets"],
shuffle_every_epoch=True,
paired=True)
else:
train_batch_iterator = batching.PairedBucketIterator(
train_x, [(i, i) for i in range(len(train_x))],
options_dict["ae_batch_size"],
options_dict["ae_n_buckets"],
shuffle_every_epoch=True,
language_ids=None
if options_dict["d_language_embedding"] is None else
train_language_ids,
flip_output=options_dict["flip_output"])
if options_dict["d_language_embedding"] is None:
if options_dict["val_lang"] is None:
ae_record_dict = training.train_fixed_epochs(
options_dict["ae_n_epochs"],
optimizer,
loss,
train_batch_iterator, [a, a_lengths, b, b_lengths],
save_model_fn=pretrain_intermediate_model_fn)
else:
ae_record_dict = training.train_fixed_epochs_external_val(
options_dict["ae_n_epochs"],
optimizer,
loss,
train_batch_iterator, [a, a_lengths, b, b_lengths],
samediff_val,
save_model_fn=pretrain_intermediate_model_fn,
save_best_val_model_fn=pretrain_model_fn,
n_val_interval=options_dict["ae_n_val_interval"])
else:
if options_dict["val_lang"] is None:
ae_record_dict = training.train_fixed_epochs(
options_dict["ae_n_epochs"],
optimizer,
loss,
train_batch_iterator,
[a, a_lengths, b, b_lengths, language_id],
save_model_fn=pretrain_intermediate_model_fn)
else:
ae_record_dict = training.train_fixed_epochs_external_val(
options_dict["ae_n_epochs"],
optimizer,
loss,
train_batch_iterator,
[a, a_lengths, b, b_lengths, language_id],
samediff_val,
save_model_fn=pretrain_intermediate_model_fn,
save_best_val_model_fn=pretrain_model_fn,
n_val_interval=options_dict["ae_n_val_interval"])
# CORRESPONDENCE TRAINING: TRAIN AND VALIDATE
if options_dict["cae_n_epochs"] > 0:
print("Training model")
cae_pretrain_model_fn = pretrain_model_fn
if (options_dict["pretrain_usefinal"]
or options_dict["val_lang"] is None):
cae_pretrain_model_fn = pretrain_intermediate_model_fn
if options_dict["ae_n_epochs"] == 0:
cae_pretrain_model_fn = None
# Train CAE
val_model_fn = intermediate_model_fn
train_batch_iterator = batching.PairedBucketIterator(
train_x,
pair_list,
batch_size=options_dict["cae_batch_size"],
n_buckets=options_dict["cae_n_buckets"],
shuffle_every_epoch=True,
language_ids=None if options_dict["d_language_embedding"] is None
else train_language_ids,
flip_output=options_dict["flip_output"])
if options_dict["d_language_embedding"] is None:
if options_dict["val_lang"] is None:
cae_record_dict = training.train_fixed_epochs(
options_dict["cae_n_epochs"],
optimizer,
loss,
train_batch_iterator, [a, a_lengths, b, b_lengths],
save_model_fn=intermediate_model_fn,
load_model_fn=cae_pretrain_model_fn)
else:
cae_record_dict = training.train_fixed_epochs_external_val(
options_dict["cae_n_epochs"],
optimizer,
loss,
train_batch_iterator, [a, a_lengths, b, b_lengths],
samediff_val,
save_model_fn=intermediate_model_fn,
save_best_val_model_fn=model_fn,
n_val_interval=options_dict["cae_n_val_interval"],
load_model_fn=cae_pretrain_model_fn)
else:
if options_dict["val_lang"] is None:
cae_record_dict = training.train_fixed_epochs(
options_dict["cae_n_epochs"],
optimizer,
loss,
train_batch_iterator,
[a, a_lengths, b, b_lengths, language_id],
samediff_val,
save_model_fn=intermediate_model_fn,
load_model_fn=cae_pretrain_model_fn)
else:
cae_record_dict = training.train_fixed_epochs_external_val(
options_dict["cae_n_epochs"],
optimizer,
loss,
train_batch_iterator,
[a, a_lengths, b, b_lengths, language_id],
samediff_val,
save_model_fn=intermediate_model_fn,
save_best_val_model_fn=model_fn,
n_val_interval=options_dict["cae_n_val_interval"],
load_model_fn=cae_pretrain_model_fn)
# Save record
record_dict_fn = path.join(model_dir, "record_dict.pkl")
print("Writing:", record_dict_fn)
with open(record_dict_fn, "wb") as f:
pickle.dump(ae_record_dict, f, -1)
if options_dict["cae_n_epochs"] > 0:
pickle.dump(cae_record_dict, f, -1)
# FINAL EXTRINSIC EVALUATION
if options_dict["val_lang"] is not None:
print("Performing final validation")
if options_dict["cae_n_epochs"] == 0:
if options_dict["extrinsic_usefinal"]:
val_model_fn = pretrain_intermediate_model_fn
else:
val_model_fn = pretrain_model_fn
else:
if options_dict["extrinsic_usefinal"]:
val_model_fn = intermediate_model_fn
else:
val_model_fn = model_fn
# sw_prb, sw_ap, swdp_prb, swdp_ap = samediff_val(normalise=False)
swdp_prb, swdp_ap = samediff_val(normalise=False)
# sw_ap = -sw_ap
swdp_ap = -swdp_ap
swdp_prb_normalised, swdp_ap_normalised = samediff_val(normalise=True)
# sw_ap_normalised = -sw_ap_normalised
swdp_ap_normalised = -swdp_ap_normalised
print("Validation SWDP AP:", swdp_ap)
print("Validation SWDP AP with normalisation:", swdp_ap_normalised)
ap_fn = path.join(model_dir, "val_ap.txt")
print("Writing:", ap_fn)
with open(ap_fn, "w") as f:
f.write(str(swdp_ap) + "\n")
f.write(str(swdp_ap_normalised) + "\n")
print("Validation model:", val_model_fn)
print(datetime.now())
def train_siamese(options_dict):
"""Train and save a Siamese triplets model."""
# PRELIMINARY
print(datetime.now())
# Output directory
hasher = hashlib.md5(repr(sorted(options_dict.items())).encode("ascii"))
hash_str = hasher.hexdigest()[:10]
model_dir = path.join(
"models",
path.split(options_dict["data_dir"])[-1] + "." +
options_dict["train_tag"], options_dict["script"], hash_str)
options_dict_fn = path.join(model_dir, "options_dict.pkl")
print("Model directory:", model_dir)
if not os.path.isdir(model_dir):
os.makedirs(model_dir)
print("Options:", options_dict)
# Random seeds
np.random.seed(options_dict["rnd_seed"])
tf.set_random_seed(options_dict["rnd_seed"])
# LOAD AND FORMAT DATA
# Training data
train_tag = options_dict["train_tag"]
npz_fn = path.join(options_dict["data_dir"], "train." + train_tag + ".npz")
train_x, train_labels, train_lengths, train_keys = (
data_io.load_data_from_npz(npz_fn, None))
# Convert training labels to integers
train_label_set = list(set(train_labels))
label_to_id = {}
for i, label in enumerate(sorted(train_label_set)):
label_to_id[label] = i
train_y = []
for label in train_labels:
train_y.append(label_to_id[label])
train_y = np.array(train_y, dtype=NP_ITYPE)
# Validation data
if options_dict["use_test_for_val"]:
npz_fn = path.join(options_dict["data_dir"], "test.npz")
else:
npz_fn = path.join(options_dict["data_dir"], "val.npz")
val_x, val_labels, val_lengths, val_keys = data_io.load_data_from_npz(
npz_fn)
# Truncate and limit dimensionality
max_length = options_dict["max_length"]
d_frame = 13 # None
options_dict["n_input"] = d_frame
print("Limiting dimensionality:", d_frame)
print("Limiting length:", max_length)
data_io.trunc_and_limit_dim(train_x, train_lengths, d_frame, max_length)
data_io.trunc_and_limit_dim(val_x, val_lengths, d_frame, max_length)
# DEFINE MODEL
print(datetime.now())
print("Building model")
# Model filenames
intermediate_model_fn = path.join(model_dir, "siamese.tmp.ckpt")
model_fn = path.join(model_dir, "siamese.best_val.ckpt")
# Model graph
x = tf.placeholder(TF_DTYPE, [None, None, options_dict["n_input"]])
x_lengths = tf.placeholder(TF_ITYPE, [None])
y = tf.placeholder(TF_ITYPE, [None])
network_dict = build_siamese_from_options_dict(x, x_lengths, options_dict)
output = network_dict["output"]
# Semi-hard triplets loss
loss = tf.contrib.losses.metric_learning.triplet_semihard_loss(
labels=y, embeddings=output, margin=options_dict["margin"])
optimizer = tf.train.AdamOptimizer(
learning_rate=options_dict["learning_rate"]).minimize(loss)
# TRAIN AND VALIDATE
print(datetime.now())
print("Training model")
# Validation function
def samediff_val(normalise=False):
# Embed validation
np.random.seed(options_dict["rnd_seed"])
val_batch_iterator = batching.SimpleIterator(val_x, len(val_x), False)
labels = [val_labels[i] for i in val_batch_iterator.indices]
saver = tf.train.Saver()
with tf.Session() as session:
saver.restore(session, val_model_fn)
for batch_x_padded, batch_x_lengths in val_batch_iterator:
np_x = batch_x_padded
np_x_lengths = batch_x_lengths
np_z = session.run([output],
feed_dict={
x: np_x,
x_lengths: np_x_lengths
})[0]
break # single batch
embed_dict = {}
for i, utt_key in enumerate(
[val_keys[i] for i in val_batch_iterator.indices]):
embed_dict[utt_key] = np_z[i]
# Same-different
if normalise:
np_z_normalised = (np_z - np_z.mean(axis=0)) / np_z.std(axis=0)
distances = pdist(np_z_normalised, metric="cosine")
matches = samediff.generate_matches_array(labels)
ap, prb = samediff.average_precision(distances[matches == True],
distances[matches == False])
else:
distances = pdist(np_z, metric="cosine")
matches = samediff.generate_matches_array(labels)
ap, prb = samediff.average_precision(distances[matches == True],
distances[matches == False])
return [prb, -ap]
# Train Siamese model
val_model_fn = intermediate_model_fn
train_batch_iterator = batching.LabelledBucketIterator(
train_x,
train_y,
options_dict["batch_size"],
n_buckets=options_dict["n_buckets"],
shuffle_every_epoch=True)
record_dict = training.train_fixed_epochs_external_val(
options_dict["n_epochs"],
optimizer,
loss,
train_batch_iterator, [x, x_lengths, y],
samediff_val,
save_model_fn=intermediate_model_fn,
save_best_val_model_fn=model_fn,
n_val_interval=options_dict["n_val_interval"])
# Save record
record_dict_fn = path.join(model_dir, "record_dict.pkl")
print("Writing:", record_dict_fn)
with open(record_dict_fn, "wb") as f:
pickle.dump(record_dict, f, -1)
# Save options_dict
options_dict_fn = path.join(model_dir, "options_dict.pkl")
print("Writing:" + options_dict_fn)
with open(options_dict_fn, "wb") as f:
pickle.dump(options_dict, f, -1)
# FINAL EXTRINSIC EVALUATION
print("Performing final validation")
if options_dict["extrinsic_usefinal"]:
val_model_fn = intermediate_model_fn
else:
val_model_fn = model_fn
prb, ap = samediff_val(normalise=False)
ap = -ap
prb_normalised, ap_normalised = samediff_val(normalise=True)
ap_normalised = -ap_normalised
print("Validation AP:", ap)
print("Validation AP with normalisation:", ap_normalised)
ap_fn = path.join(model_dir, "val_ap.txt")
print("Writing:", ap_fn)
with open(ap_fn, "w") as f:
f.write(str(ap) + "\n")
f.write(str(ap_normalised) + "\n")
print("Validation model:", val_model_fn)
print(datetime.now())
