def get_data(self, audio_file, transcripts_file):
encoder_input_data = load_pickle_data(audio_file)
(decoder_input_data,
decoder_target_data) = load_pickle_data(transcripts_file)
data = self._generate_timestep_dict(encoder_input_data,
decoder_input_data,
decoder_target_data)
return data
def plot_train_loss_acc(model_hist_path, word_level):
# Load history
train_hist = load_pickle_data(model_hist_path)
if word_level:
title = "mots"
else:
title = "caractères"
# Prepare paths
accuracy_plot_path = model_hist_path.split(".pkl")[0] + "acc.png"
loss_plot_path = model_hist_path.split(".pkl")[0] + "loss.png"
# Plot and save accuracy
plt.plot(train_hist["accuracy"])
plt.title("Évolution de l'exactitude pour la reconnaissance basée " +
title)
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
plt.legend(['train accuracy'], loc='upper left')
plt.savefig(accuracy_plot_path)
# plt.show()
plt.clf()
# Plot and save loss
plt.plot(train_hist["loss"], "r")
plt.title(
"Évolution de la fonction d'erreur pour la reconnaissance basée " +
title)
plt.xlabel("Epochs")
plt.ylabel("Loss")
plt.legend(['train loss'], loc='upper left')
plt.savefig(loss_plot_path)
# plt.show()
plt.clf()
def on_epoch_end(self, epoch, logs=None):
# Saving training history
# Check if directory exists
directory_path = settings.TRAINED_MODELS_PATH + self.model_name
if not file_exists(directory_path):
create_dir(directory_path)
# Word level history
if self.word_level:
hist_path = settings.TRAINED_MODELS_PATH + self.model_name + "/" + self.model_name + "word.pkl"
average_accuracy = 0
if file_exists(hist_path):
acc_loss_history = load_pickle_data(hist_path)
else:
acc_loss_history = dict()
acc_loss_history["accuracy"] = []
acc_loss_history["loss"] = []
# Average accuracy
for i in range(0, 6):
accuracy = "decoder_dense" + str(i) + "_acc"
average_accuracy += logs[accuracy]
average_accuracy = float(average_accuracy) / float(6)
acc_loss_history["accuracy"].append(average_accuracy)
acc_loss_history["loss"].append(logs["loss"])
# Character level history
else:
hist_path = settings.TRAINED_MODELS_PATH + self.model_name + "/" + self.model_name + "char.pkl"
if file_exists(hist_path):
acc_loss_history = load_pickle_data(hist_path)
else:
acc_loss_history = dict()
acc_loss_history["accuracy"] = []
acc_loss_history["loss"] = []
acc_loss_history["accuracy"].append(logs["acc"])
acc_loss_history["loss"].append(logs["loss"])
generate_pickle_file(acc_loss_history, hist_path)
plot_train_loss_acc(hist_path, self.word_level)
self.model.save(self.model_path)
def _get_train_test_data(train_ratio=0.8, padding=False):
"""
Splits dataset into train and test according to a ratio
:param train_ratio: float
:param padding: Boolean
:return: List of InputAudio, List of InputAudio
"""
if padding is False:
data = load_pickle_data(PICKLE_PAD_FILE_PATH)
else:
data = load_pickle_data(PICKLE_PAD_FILE_PATH)
train_length = int(len(data) * train_ratio)
train_data = []
test_data = []
for i, audio_sample in enumerate(data):
if i <= train_length:
train_data.append(audio_sample)
else:
test_data.append(audio_sample)
return train_data, test_data
def __init__(self, model_path, latent_dim):
self.model = models.load_model(model_path)
self.encoder_states = None
self.latent_dim = latent_dim
# Getting dataset and training information
general_info = load_pickle_data(
settings.DATASET_WORD_INFERENCE_INFORMATION_PATH)
settings.MFCC_FEATURES_LENGTH = general_info[0]
settings.TOTAL_SAMPLES_NUMBER = general_info[1]
settings.WORD_SET = general_info[2]
settings.LONGEST_WORD_LENGTH = general_info[3]
settings.CHARACTER_SET = general_info[4]
settings.WORD_TARGET_LENGTH = general_info[5]
self.encoder_model = None
self.decoder_model = None
self.get_encoder_decoder_baseline()
def _get_train_test_data_partition(dataset_path, train_ratio=0.8):
"""
Splits dataset into train and test according to a ratio
:param train_ratio: float
:param padding: Boolean
:return: List of InputAudio, List of InputAudio
"""
data = load_pickle_data(dataset_path)
train_length = int(len(data) * train_ratio)
train_data = []
test_data = []
for i, audio_sample in enumerate(data):
if i <= train_length:
train_data.append(audio_sample)
else:
test_data.append(audio_sample)
return train_data, test_data
def bench(size, bench_size, filename):
if os.path.isfile(filename):
open("../benchmarks/pid", "w+").write(str(os.getpid()))
else:
con, meta = connect('ganga', 'ganga', 'jobs')
JOBS, BLOBS = create_tables(con, meta)
jobs, blobs = utils.load_pickle_data(size=size)
add_jobs_batch(con, JOBS=JOBS, jobs=jobs, batch_size=batch_size)
add_blobs_batch(con, BLOBS=BLOBS, blobs=blobs, batch_size=batch_size)
query_jobs_all(con, "jobs")
query_blobs_all(con, "blobs")
if not os.path.isdir("../benchmarks"):
os.makedirs("../benchmarks")
json.dump(utils.TIMES, open(filename, "w+"))
open("../benchmarks/pid", "w+").write(str(os.getpid()))
def bench(size, batch_size, filename):
if os.path.isfile(filename):
open("../benchmarks/pid", "w+").write(str(os.getpid()))
else:
jobs, blobs = utils.load_pickle_data(size=size)
print("starting to conniet")
session, _ = connect_cassandra()
session = create_tables_cassandra(session)
add_jobs_cassandra(session, jobs=jobs, batch_size=batch_size)
add_blobs_cassandra(session, blobs=blobs, batch_size=batch_size)
# query_jobs_all_cassandra(session, "JOB")
# query_blobs_all_cassandra(session, "BLOB")
if not os.path.isdir("../benchmarks"):
os.makedirs("../benchmarks")
json.dump(utils.TIMES, open(filename, "w+"))
open("../benchmarks/pid", "w+").write(str(os.getpid()))
def __init__(self, word_level, architecture, latent_dim):
model_name = "architecture" + str(architecture)
if word_level:
model_path = settings.TRAINED_MODELS_PATH + model_name + "/" + model_name + "word.h5"
else:
model_path = settings.TRAINED_MODELS_PATH + model_name + "/" + model_name + "char.h5"
print(model_path)
self.model = models.load_model(model_path)
self.encoder_states = None
self.latent_dim = latent_dim
general_info = load_pickle_data(settings.DATASET_CHAR_INFERENCE_INFORMATION_PATH)
settings.MFCC_FEATURES_LENGTH = general_info[0]
settings.CHARACTER_SET = general_info[2]
self.encoder_model = None
self.decoder_model = None
if architecture == 6:
self._get_encoder_decoder_model_baseline()
else:
self._get_encoder_decoder_model_cnn()
def upload_dataset_partition(train_ratio=0.95, word_level=False, partitions=8):
"""
Generate :
train ==> encoder inputs, decoder inputs, decoder target
test ==> encoder inputs, decoder inputs, decoder target
:return: Tuple, Tuple
"""
print("PREPARING PARTITIONED DATASET")
if empty_directory(
settings.AUDIO_CHAR_SPLIT_TRAIN_PATH) or empty_directory(
settings.AUDIO_WORD_SPLIT_TRAIN_PATH):
get_dataset_information(word_level, train_ratio=train_ratio)
list_datasets = get_files(settings.PICKLE_PARTITIONS_PATH)
for dataset_number, dataset_file in enumerate(list_datasets):
# Generate directories
if word_level:
path = settings.AUDIO_WORD_SPLIT_TRAIN_PATH + "dataset" + str(
dataset_number) + "/"
if not file_exists(path):
os.mkdir(path)
path = settings.AUDIO_WORD_SPLIT_TEST_PATH + "dataset" + str(
dataset_number) + "/"
if not file_exists(path):
os.mkdir(path)
path = settings.TRANSCRIPTS_ENCODING_WORD_SPLIT_TRAIN_PATH + "dataset" + str(
dataset_number) + "/"
if not file_exists(path):
os.mkdir(path)
path = settings.TRANSCRIPTS_ENCODING_WORD_SPLIT_TEST_PATH + "dataset" + str(
dataset_number) + "/"
if not file_exists(path):
os.mkdir(path)
else:
path = settings.AUDIO_CHAR_SPLIT_TRAIN_PATH + "dataset" + str(
dataset_number) + "/"
if not file_exists(path):
os.mkdir(path)
path = settings.AUDIO_CHAR_SPLIT_TEST_PATH + "dataset" + str(
dataset_number) + "/"
if not file_exists(path):
os.mkdir(path)
path = settings.TRANSCRIPTS_ENCODING_CHAR_SPLIT_TRAIN_PATH + "dataset" + str(
dataset_number) + "/"
if not file_exists(path):
os.mkdir(path)
path = settings.TRANSCRIPTS_ENCODING_CHAR_SPLIT_TEST_PATH + "dataset" + str(
dataset_number) + "/"
if not file_exists(path):
os.mkdir(path)
# Upload train and test data, the train ration is 0.8 and can be modified through ration param
train_data, test_data = _get_train_test_data_partition(
dataset_path=dataset_file, train_ratio=train_ratio)
if word_level:
train_audio, train_transcripts = _get_audio_transcripts_word_level(
train_data)
# train_audio, train_transcripts = print_suspicious_characters(train_data)
test_audio, test_transcripts = _get_audio_transcripts_word_level(
test_data)
else:
train_audio, train_transcripts = _get_audio_transcripts_character_level(
train_data)
# train_audio, train_transcripts = print_suspicious_characters(train_data)
test_audio, test_transcripts = _get_audio_transcripts_character_level(
test_data)
_generate_spllited_encoder_input_data_partition(
train_audio,
word_level=word_level,
dataset_number=dataset_number,
partitions=partitions)
# train_encoder_input = _get_encoder_input_data(audio_data=train_audio)
_generate_spllited_encoder_input_data_partition(
test_audio,
word_level=word_level,
dataset_number=dataset_number,
test=True,
partitions=partitions)
generate_decoder_input_target(transcripts=train_transcripts,
word_level=word_level,
partitions=partitions,
dataset_number=dataset_number)
generate_decoder_input_target(transcripts=test_transcripts,
word_level=word_level,
partitions=partitions,
dataset_number=dataset_number,
test=True)
else:
if not file_exists(settings.DATASET_CHAR_INFORMATION_PATH):
get_dataset_information(0, train_ratio=train_ratio)
if not file_exists(settings.DATASET_WORD_INFORMATION_PATH):
get_dataset_information(1, train_ratio=train_ratio)
if word_level:
general_info = load_pickle_data(
settings.DATASET_WORD_INFORMATION_PATH)
settings.MFCC_FEATURES_LENGTH = general_info[0]
settings.TOTAL_SAMPLES_NUMBER = general_info[1]
settings.WORD_SET = general_info[2]
settings.LONGEST_WORD_LENGTH = general_info[3]
settings.CHARACTER_SET = general_info[4]
settings.WORD_TARGET_LENGTH = general_info[5]
else:
general_info = load_pickle_data(
settings.DATASET_CHAR_INFORMATION_PATH)
settings.MFCC_FEATURES_LENGTH = general_info[0]
settings.TOTAL_SAMPLES_NUMBER = general_info[1]
settings.CHARACTER_SET = general_info[2]
print(settings.CHARACTER_SET)
def get_test_data(self, audio_file, transcripts_file):
encoder_input_data = load_pickle_data(audio_file)
(decoder_input_data,
decoder_target_data) = load_pickle_data(transcripts_file)
return encoder_input_data, decoder_input_data, decoder_target_data