def evaluate(hyper_params):
if hyper_params["test_dataset_dirs"] is None:
logging.fatal("Setting test_dataset_dirs in config file is mandatory for evaluation mode")
return
# Load the test set data
data_processor = dataprocessor.DataProcessor(hyper_params["test_dataset_dirs"])
test_set = data_processor.get_dataset()
logging.info("Using %d size of test set", len(test_set))
if len(test_set) == 0:
logging.fatal("No files in test set during an evaluation mode")
return
with tf.Session() as sess:
# create model
model = AcousticModel(hyper_params["num_layers"], hyper_params["hidden_size"], hyper_params["batch_size"],
hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"],
hyper_params["input_dim"], hyper_params["batch_normalization"],
hyper_params["char_map_length"])
model.create_forward_rnn()
model.initialize(sess)
model.restore(sess, hyper_params["checkpoint_dir"] + "/acoustic/")
wer, cer = model.evaluate_full(sess, test_set, hyper_params["max_input_seq_length"],
hyper_params["signal_processing"], hyper_params["char_map"])
print("Resulting WER : {0:.3g} %".format(wer))
print("Resulting CER : {0:.3g} %".format(cer))
return
def record_and_write(audio_processor, hyper_params):
import pyaudio
_CHUNK = hyper_params["max_input_seq_length"]
_SR = 22050
p = pyaudio.PyAudio()
with tf.Session() as sess:
# create model
model = AcousticModel(hyper_params["num_layers"], hyper_params["hidden_size"], 1,
hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"],
hyper_params["input_dim"], hyper_params["batch_normalization"],
hyper_params["char_map_length"])
model.create_forward_rnn()
model.initialize(sess)
model.restore(sess, hyper_params["checkpoint_dir"] + "/acoustic/")
# Create stream of listening
stream = p.open(format=pyaudio.paInt16, channels=1, rate=_SR, input=True, frames_per_buffer=_CHUNK)
print("NOW RECORDING...")
while True:
data = stream.read(_CHUNK)
data = np.fromstring(data)
feat_vec, original_feat_vec_length = audio_processor.process_signal(data, _SR)
(a, b) = feat_vec.shape
feat_vec = feat_vec.reshape((a, 1, b))
predictions = model.process_input(sess, feat_vec, [original_feat_vec_length])
result = [dataprocessor.DataProcessor.get_labels_str(hyper_params["char_map"], prediction)
for prediction in predictions]
print(result, end="")
def record_and_write(audio_processor, hyper_params):
import pyaudio
_CHUNK = hyper_params["max_input_seq_length"]
_SR = 22050
p = pyaudio.PyAudio()
with tf.Session() as sess:
# create model
model = AcousticModel(hyper_params["num_layers"], hyper_params["hidden_size"], 1,
hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"],
hyper_params["input_dim"], hyper_params["batch_normalization"],
language=hyper_params["language"])
model.create_forward_rnn()
model.initialize(sess)
model.restore(sess, hyper_params["checkpoint_dir"])
# Create stream of listening
stream = p.open(format=pyaudio.paInt16, channels=1, rate=_SR, input=True, frames_per_buffer=_CHUNK)
print("NOW RECORDING...")
while True:
data = stream.read(_CHUNK)
data = np.fromstring(data)
feat_vec, original_feat_vec_length = audio_processor.process_signal(data, _SR)
(a, b) = feat_vec.shape
feat_vec = feat_vec.reshape((a, 1, b))
result = model.process_input(sess, feat_vec, [original_feat_vec_length])
print(result, end="")
def process_file(audio_processor, hyper_params, file):
feat_vec, original_feat_vec_length = audio_processor.process_audio_file(file)
if original_feat_vec_length > hyper_params["max_input_seq_length"]:
logging.warning("File too long")
return
elif original_feat_vec_length < hyper_params["max_input_seq_length"]:
# Pad the feat_vec with zeros
pad_length = hyper_params["max_input_seq_length"] - original_feat_vec_length
padding = np.zeros((pad_length, hyper_params["input_dim"]), dtype=np.float)
feat_vec = np.concatenate((feat_vec, padding), 0)
with tf.Session() as sess:
# create model
model = AcousticModel(hyper_params["num_layers"], hyper_params["hidden_size"], 1,
hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"],
hyper_params["input_dim"], hyper_params["batch_normalization"],
hyper_params["char_map_length"])
model.create_forward_rnn()
model.initialize(sess)
model.restore(sess, hyper_params["checkpoint_dir"] + "/acoustic/")
(a, b) = feat_vec.shape
feat_vec = feat_vec.reshape((a, 1, b))
predictions = model.process_input(sess, feat_vec, [original_feat_vec_length])
transcribed_text = [dataprocessor.DataProcessor.get_labels_str(hyper_params["char_map"], prediction)
for prediction in predictions]
print(transcribed_text[0])
def evaluate(hyper_params):
if hyper_params["test_dataset_dirs"] is None:
logging.fatal("Setting test_dataset_dirs in config file is mandatory for evaluation mode")
return
# Load the test set data
data_processor = dataprocessor.DataProcessor(hyper_params["test_dataset_dirs"])
test_set = data_processor.get_dataset()
logging.info("Using %d size of test set", len(test_set))
if len(test_set) == 0:
logging.fatal("No files in test set during an evaluation mode")
return
with tf.Session() as sess:
# create model
model = AcousticModel(hyper_params["num_layers"], hyper_params["hidden_size"], hyper_params["batch_size"],
hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"],
hyper_params["input_dim"], hyper_params["batch_normalization"],
language=hyper_params["language"])
model.create_forward_rnn()
model.initialize(sess)
model.restore(sess, hyper_params["checkpoint_dir"])
wer, cer = model.evaluate_full(sess, test_set, hyper_params["max_input_seq_length"],
hyper_params["signal_processing"])
print("Resulting WER : {0:.3g} %".format(wer))
print("Resulting CER : {0:.3g} %".format(cer))
return
def process_file(audio_processor, hyper_params, file):
feat_vec, original_feat_vec_length = audio_processor.process_audio_file(file)
if original_feat_vec_length > hyper_params["max_input_seq_length"]:
logging.warning("File too long")
return
elif original_feat_vec_length < hyper_params["max_input_seq_length"]:
# Pad the feat_vec with zeros
pad_length = hyper_params["max_input_seq_length"] - original_feat_vec_length
padding = np.zeros((pad_length, hyper_params["input_dim"]), dtype=np.float)
feat_vec = np.concatenate((feat_vec, padding), 0)
with tf.Session() as sess:
# create model
model = AcousticModel(hyper_params["num_layers"], hyper_params["hidden_size"], 1,
hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"],
hyper_params["input_dim"], hyper_params["batch_normalization"],
hyper_params["char_map_length"])
model.create_forward_rnn()
model.initialize(sess)
model.restore(sess, hyper_params["checkpoint_dir"] + "/acoustic/")
(a, b) = feat_vec.shape
feat_vec = feat_vec.reshape((a, 1, b))
predictions = model.process_input(sess, feat_vec, [original_feat_vec_length])
transcribed_text = [dataprocessor.DataProcessor.get_labels_str(hyper_params["char_map"], prediction)
for prediction in predictions]
print(transcribed_text[0])
def build_acoustic_training_rnn(is_chief, is_ditributed, sess, hyper_params,
prog_params, train_set, test_set):
model = AcousticModel(
hyper_params["num_layers"], hyper_params["hidden_size"],
hyper_params["batch_size"], hyper_params["max_input_seq_length"],
hyper_params["max_target_seq_length"], hyper_params["input_dim"],
hyper_params["batch_normalization"], hyper_params["char_map_length"])
# Create a Dataset from the train_set and the test_set
train_dataset = model.build_dataset(train_set, hyper_params["batch_size"],
hyper_params["max_input_seq_length"],
hyper_params["max_target_seq_length"],
hyper_params["signal_processing"],
hyper_params["char_map"])
train_dataset = train_dataset.shuffle(10, reshuffle_each_iteration=True)
v_iterator = None
if test_set is []:
t_iterator = model.add_dataset_input(train_dataset)
else:
test_dataset = model.build_dataset(
test_set, hyper_params["batch_size"],
hyper_params["max_input_seq_length"],
hyper_params["max_target_seq_length"],
hyper_params["signal_processing"], hyper_params["char_map"])
# Build the input stream from the different datasets
t_iterator, v_iterator = model.add_datasets_input(
train_dataset, test_dataset)
# Create the model
#tensorboard_dir
model.create_training_rnn(is_chief,
is_ditributed,
hyper_params["dropout_input_keep_prob"],
hyper_params["dropout_output_keep_prob"],
hyper_params["grad_clip"],
hyper_params["learning_rate"],
hyper_params["lr_decay_factor"],
use_iterator=True)
model.add_tensorboard(sess, prog_params["train_dir"],
prog_params["timeline"])
sv = None
if is_ditributed:
init_op = tf.global_variables_initializer()
sv = tf.train.Supervisor(is_chief=is_chief,
logdir=prog_params["train_dir"],
init_op=init_op,
recovery_wait_secs=1,
summary_op=None,
global_step=model.global_step)
model.supervisor = sv
else:
model.initialize(sess)
model.restore(sess, prog_params["train_dir"])
# Override the learning rate if given on the command line
if prog_params["learn_rate"] is not None:
model.set_learning_rate(sess, prog_params["learn_rate"])
return sv, model, t_iterator, v_iterator
def build_acoustic_training_rnn(sess, hyper_params, prog_params, train_set,
test_set):
model = AcousticModel(
hyper_params["num_layers"], hyper_params["hidden_size"],
hyper_params["batch_size"], hyper_params["max_input_seq_length"],
hyper_params["max_target_seq_length"], hyper_params["input_dim"],
hyper_params["batch_normalization"], hyper_params["char_map_length"])
# Create a Dataset from the train_set and the test_set
train_dataset = model.build_dataset(train_set, hyper_params["batch_size"],
hyper_params["max_input_seq_length"],
hyper_params["max_target_seq_length"],
hyper_params["signal_processing"],
hyper_params["char_map"])
v_iterator = None
if test_set is []:
t_iterator = model.add_dataset_input(train_dataset)
sess.run(t_iterator.initializer)
else:
test_dataset = model.build_dataset(
test_set, hyper_params["batch_size"],
hyper_params["max_input_seq_length"],
hyper_params["max_target_seq_length"],
hyper_params["signal_processing"], hyper_params["char_map"])
# Build the input stream from the different datasets
t_iterator, v_iterator = model.add_datasets_input(
train_dataset, test_dataset)
sess.run(t_iterator.initializer)
sess.run(v_iterator.initializer)
# Create the model
model.create_training_rnn(hyper_params["dropout_input_keep_prob"],
hyper_params["dropout_output_keep_prob"],
hyper_params["grad_clip"],
hyper_params["learning_rate"],
hyper_params["lr_decay_factor"],
use_iterator=True)
model.add_tensorboard(sess, hyper_params["tensorboard_dir"],
prog_params["tb_name"], prog_params["timeline"])
model.initialize(sess)
model.restore(sess, hyper_params["checkpoint_dir"] + "/acoustic/")
# Override the learning rate if given on the command line
if prog_params["learn_rate"] is not None:
model.set_learning_rate(sess, prog_params["learn_rate"])
return model, t_iterator, v_iterator
def process_file(audio_processor, hyper_params, file):
feat_vec, original_feat_vec_length = audio_processor.process_audio_file(file)
if original_feat_vec_length > hyper_params["max_input_seq_length"]:
logging.warning("File too long")
return
with tf.Session() as sess:
# create model
model = AcousticModel(hyper_params["num_layers"], hyper_params["hidden_size"], 1,
hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"],
hyper_params["input_dim"], hyper_params["batch_normalization"],
language=hyper_params["language"])
model.create_forward_rnn()
model.initialize(sess)
model.restore(sess, hyper_params["checkpoint_dir"])
(a, b) = feat_vec.shape
feat_vec = feat_vec.reshape((a, 1, b))
transcribed_text = model.process_input(sess, feat_vec, [original_feat_vec_length])
print(transcribed_text[0])
def build_acoustic_training_rnn(sess, hyper_params, prog_params, train_set, test_set):
model = AcousticModel(hyper_params["num_layers"], hyper_params["hidden_size"], hyper_params["batch_size"],
hyper_params["max_input_seq_length"], hyper_params["max_target_seq_length"],
hyper_params["input_dim"], hyper_params["batch_normalization"],
hyper_params["char_map_length"])
# Create a Dataset from the train_set and the test_set
train_dataset = model.build_dataset(train_set, hyper_params["batch_size"], hyper_params["max_input_seq_length"],
hyper_params["max_target_seq_length"], hyper_params["signal_processing"],
hyper_params["char_map"])
v_iterator = None
if test_set is []:
t_iterator = model.add_dataset_input(train_dataset)
sess.run(t_iterator.initializer)
else:
test_dataset = model.build_dataset(test_set, hyper_params["batch_size"], hyper_params["max_input_seq_length"],
hyper_params["max_target_seq_length"], hyper_params["signal_processing"],
hyper_params["char_map"])
# Build the input stream from the different datasets
t_iterator, v_iterator = model.add_datasets_input(train_dataset, test_dataset)
sess.run(t_iterator.initializer)
sess.run(v_iterator.initializer)
# Create the model
model.create_training_rnn(hyper_params["dropout_input_keep_prob"], hyper_params["dropout_output_keep_prob"],
hyper_params["grad_clip"], hyper_params["learning_rate"],
hyper_params["lr_decay_factor"], use_iterator=True)
model.add_tensorboard(sess, hyper_params["tensorboard_dir"], prog_params["tb_name"], prog_params["timeline"])
model.initialize(sess)
model.restore(sess, hyper_params["checkpoint_dir"] + "/acoustic/")
# Override the learning rate if given on the command line
if prog_params["learn_rate"] is not None:
model.set_learning_rate(sess, prog_params["learn_rate"])
return model, t_iterator, v_iterator
