def process_single_file(row):
# row = index, Series
_, file = row
features = audiofile_to_input_vector(file.wav_filename, N_FEATURES, N_CONTEXT)
transcript = text_to_char_array(file.transcript, alphabet)
return features, len(features), transcript, len(transcript)
def find_transcripts(self, wav_file_path, visual_feature_json_path=None):
'''
Args:
wav_file_path: the filepath for your wav file.
visual_features: Visual features for video based speech recognition.
These will be required when the exported model is of AVSR type.
'''
if self.use_visual_features:
assert visual_feature_json_path is not None
source = np.array([(get_audio_visual_feature_vector(
wav_file_path, visual_feature_json_path,
NUM_MFCC_COEFF + NUM_VISUAL, N_CONTEXT))])
else:
source = np.array([
(audiofile_to_input_vector(wav_file_path, NUM_MFCC_COEFF,
N_CONTEXT))
])
source_len = np.array([(len(source[-1]))])
feed_dict = {self.input: source, self.input_len: source_len}
decoded = self.session.run(self.output, feed_dict)[0][0]
# session.run() will return shape = (1,1,X). X = Number of characters in the transcript
transcript = ndarray_to_text(decoded)
if self.use_spell_check:
transcript = correction(transcript)
return transcript
def _populate_batch_queue(self, session, coord):
'''
Queue thread routine.
'''
file_count = len(self._data_set.files)
index = -1
while not coord.should_stop():
index = self._data_set.next_index(index) % file_count
wav_file, transcript = self._data_set.files[index]
source = audiofile_to_input_vector(wav_file,
self._model_feeder.numcep,
self._model_feeder.numcontext)
source_len = len(source)
target = text_to_char_array(
transcript, self._alphabet) ## 이 부분을 diphone 형태로 받아오도록 수정
target_len = len(target)
if source_len < target_len:
raise ValueError(
'Error: Audio file {} is too short for transcription.'.
format(wav_file))
try:
session.run(self._enqueue_op,
feed_dict={
self._model_feeder.ph_x: source,
self._model_feeder.ph_x_length: source_len,
self._model_feeder.ph_y: target,
self._model_feeder.ph_y_length: target_len
})
except tf.errors.CancelledError:
return
def _populate_batch_queue(self, session, coord):
'''
Queue thread routine.
'''
file_count = len(self._data_set.files)
index = -1
while not coord.should_stop():
index = self._data_set.next_index(index) % file_count
wav_file, transcript = self._data_set.files[index]
source = audiofile_to_input_vector(wav_file,
self._model_feeder.numcep,
self._model_feeder.numcontext)
source_len = len(source)
target = text_to_char_array(transcript)
target_len = len(target)
try:
session.run(self._enqueue_op,
feed_dict={
self._model_feeder.ph_x: source,
self._model_feeder.ph_x_length: source_len,
self._model_feeder.ph_y: target,
self._model_feeder.ph_y_length: target_len
})
except tf.errors.CancelledError:
return
def _populate_batch_queue(self):
with self._graph.as_default():
while True:
n_steps = 0
sources = []
targets = []
for index, (txt_file,
wav_file) in enumerate(self._files_circular_list):
if index >= self._batch_size:
break
next_source = audiofile_to_input_vector(
wav_file, self._numcep, self._numcontext)
if n_steps < next_source.shape[0]:
n_steps = next_source.shape[0]
sources.append(next_source)
with open(txt_file) as open_txt_file:
targets.append(open_txt_file.read())
target = texts_to_sparse_tensor(targets)
for index, next_source in enumerate(sources):
npad = ((0, (n_steps - next_source.shape[0])), (0, 0))
sources[index] = np.pad(next_source,
pad_width=npad,
mode='constant')
source = np.array(sources)
self._batch_queue.put((source, target))
def do_single_file_inference(input_file_path):
with tf.Session(config=Config.session_config) as session:
inputs, outputs, layers = create_inference_graph(batch_size=1,
n_steps=-1)
# REVIEW josephz: Hack: print all layers here.
for i, l in enumerate(layers):
print("layer '{}': '{}'".format(i, l))
# Create a saver using variables from the above newly created graph
mapping = {
v.op.name: v
for v in tf.global_variables()
if not v.op.name.startswith('previous_state_')
}
saver = tf.train.Saver(mapping)
# Restore variables from training checkpoint
# TODO: This restores the most recent checkpoint, but if we use validation to counteract
# over-fitting, we may want to restore an earlier checkpoint.
checkpoint = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
if not checkpoint:
log_error(
'Checkpoint directory ({}) does not contain a valid checkpoint state.'
.format(FLAGS.checkpoint_dir))
exit(1)
checkpoint_path = checkpoint.model_checkpoint_path
saver.restore(session, checkpoint_path)
session.run(outputs['initialize_state'])
features = audiofile_to_input_vector(input_file_path, Config.n_input,
Config.n_context)
num_strides = len(features) - (Config.n_context * 2)
# Create a view into the array with overlapping strides of size
# numcontext (past) + 1 (present) + numcontext (future)
window_size = 2 * Config.n_context + 1
features = np.lib.stride_tricks.as_strided(
features, (num_strides, window_size, Config.n_input),
(features.strides[0], features.strides[0], features.strides[1]),
writeable=False)
logits = session.run(outputs['outputs'],
feed_dict={
inputs['input']: [features],
inputs['input_lengths']: [num_strides],
})
logits = np.squeeze(logits)
scorer = Scorer(FLAGS.lm_alpha, FLAGS.lm_beta, FLAGS.lm_binary_path,
FLAGS.lm_trie_path, Config.alphabet)
decoded = ctc_beam_search_decoder(logits,
Config.alphabet,
FLAGS.beam_width,
scorer=scorer)
# Print highest probability result
print(decoded[0][1])
def process_single_file(row, numcep, numcontext, alphabet):
# row = index, Series
_, file = row
features = audiofile_to_input_vector(file.wav_filename, numcep, numcontext)
features_len = len(features) - 2*numcontext
transcript = text_to_char_array(file.transcript, alphabet)
if features_len < len(transcript):
raise ValueError('Error: Audio file {} is too short for transcription.'.format(file.wav_filename))
return features, features_len, transcript, len(transcript)
def process_single_file(row, numcep, numcontext, alphabet):
# row = index, Series
_, file = row
features = audiofile_to_input_vector(file.wav_filename, numcep, numcontext)
features_len = len(features) - 2*numcontext
transcript = text_to_char_array(file.transcript, alphabet)
if features_len < len(transcript):
raise ValueError('Error: Audio file {} is too short for transcription.'.format(file.wav_filename))
return features, features_len, transcript, len(transcript)
def main(_):
if not FLAGS.server:
print 'please specify server host:port'
return
if not FLAGS.file:
print 'pleace specify an audio file'
return
audio_waves = audiofile_to_input_vector(FLAGS.file, FLAGS.n_input,
FLAGS.n_context)
audio = np.array([audio_waves])
do_inference(FLAGS.server, audio)
def _compute_source_target(self):
txt_file = self._txt_files[0]
wav_file = path.splitext(txt_file)[0] + ".wav"
audio_waves = audiofile_to_input_vector(wav_file, self._numcep, self._numcontext)
with open(txt_file) as open_txt_file:
original = ' '.join(open_txt_file.read().strip().lower().split(' ')[2:]).replace('.', '')
target = text_to_char_array(original)
return audio_waves, len(audio_waves), target, len(target)
def _populate_batch_queue(self, session):
for txt_file, wav_file in self._files_circular_list:
source = audiofile_to_input_vector(wav_file, self._numcep, self._numcontext)
source_len = len(source)
with codecs.open(txt_file, encoding="utf-8") as open_txt_file:
target = unicodedata.normalize("NFKD", open_txt_file.read()).encode("ascii", "ignore")
target = text_to_char_array(target)
target_len = len(target)
session.run(self._enqueue_op, feed_dict={
self._x: source,
self._x_length: source_len,
self._y: target,
self._y_length: target_len})
def _populate_batch_queue(self, session):
for wav_file, transcript in self._indices():
source = audiofile_to_input_vector(wav_file, self._numcep, self._numcontext)
source_len = len(source)
target = text_to_char_array(transcript)
target_len = len(target)
try:
session.run(self._enqueue_op, feed_dict={
self._x: source,
self._x_length: source_len,
self._y: target,
self._y_length: target_len})
except tf.errors.CancelledError:
return
def pipeline(data):
data = data.head(2) #overfitting it for 2 file
#print train['transcript']
inputs_encoder = []
inputs_decoder = []
outputs_decoder = []
decoder_length = []
sequence_length = []
for ind, row in data.iterrows():
inputs_encoder.append(
audiofile_to_input_vector(row['wav_filename'], 26, 0))
inputs_decoder.append(
np.append([1], text_to_char_array(row['transcript'])))
outputs_decoder.append(
np.append(text_to_char_array(row['transcript']), [1]))
sequence_length.append(
audiofile_to_input_vector(row['wav_filename'], 26, 0).shape[0])
decoder_length.append(len(row['transcript']) + 1)
xt_decoder_input, xlen_decoder_input = helpers2.batch(inputs_decoder)
xt_encoder, xlen_encoder = helpers.batch(inputs_encoder)
xt_decoder_output, xlen_decoder_output = helpers2.batch(outputs_decoder)
sequence_length = np.asarray(sequence_length, dtype=np.int32)
decoder_length = np.asarray(decoder_length, dtype=np.int32)
#print inputs_encoder[1].shape
#print inputs_decoder[1].shape
#print xt_encoder.shape
#print xt_encoder.shape
#print xt_decoder_input.dtype
#print xt_decoder_output.shape
#fd={encoder_inputs_embedded:xt_encoder,seq_len_tensor:sequence_length,decoder_lengths:decoder_length,decoder_inputs:xt_decoder_input,decoder_targets:xt_decoder_output}
return ({
"A": xt_encoder,
"B": xt_decoder_input,
"C": sequence_length,
"D": decoder_length
}, xt_decoder_output)
def do_single_file_inference(input_file_path):
with tf.Session(config=Config.session_config) as session:
inputs, outputs, _ = create_inference_graph(batch_size=1, n_steps=-1)
# Create a saver using variables from the above newly created graph
mapping = {v.op.name: v for v in tf.global_variables() if not v.op.name.startswith('previous_state_')}
saver = tf.train.Saver(mapping)
# Restore variables from training checkpoint
# TODO: This restores the most recent checkpoint, but if we use validation to counteract
# over-fitting, we may want to restore an earlier checkpoint.
checkpoint = tf.train.get_checkpoint_state(FLAGS.checkpoint_dir)
if not checkpoint:
log_error('Checkpoint directory ({}) does not contain a valid checkpoint state.'.format(FLAGS.checkpoint_dir))
exit(1)
checkpoint_path = checkpoint.model_checkpoint_path
saver.restore(session, checkpoint_path)
session.run(outputs['initialize_state'])
features = audiofile_to_input_vector(input_file_path, Config.n_input, Config.n_context)
num_strides = len(features) - (Config.n_context * 2)
# Create a view into the array with overlapping strides of size
# numcontext (past) + 1 (present) + numcontext (future)
window_size = 2*Config.n_context+1
features = np.lib.stride_tricks.as_strided(
features,
(num_strides, window_size, Config.n_input),
(features.strides[0], features.strides[0], features.strides[1]),
writeable=False)
logits = session.run(outputs['outputs'], feed_dict = {
inputs['input']: [features],
inputs['input_lengths']: [num_strides],
})
logits = np.squeeze(logits)
scorer = Scorer(FLAGS.lm_alpha, FLAGS.lm_beta,
FLAGS.lm_binary_path, FLAGS.lm_trie_path,
Config.alphabet)
decoded = ctc_beam_search_decoder(logits, Config.alphabet, FLAGS.beam_width, scorer=scorer)
# Print highest probability result
print(decoded[0][1])
def _get_files_mfcc(wav_filenames):
# print('Processing MFCC...')
mfccs = []
lens = []
for audio_fname in wav_filenames:
this_mfcc = audiofile_to_input_vector(audio_fname, n_input, n_context)
if len(this_mfcc) != feature_len:
needlen = feature_len - len(this_mfcc)
a = ([[0 for x in range(feature_dim)] for y in range(needlen)])
this_mfcc = np.concatenate((this_mfcc, np.array(a)))
# print(this_mfcc.shape)
this_mfcc = np.reshape(this_mfcc, (feature_len, n_input, 1))
mfccs.append(this_mfcc)
lens.append(len(this_mfcc))
a_mfccs = np.array(mfccs) # shape, (batch, time_step_len, feature_len)
a_lens = np.array(lens) # shape, (batch, 1), value == time_step_len
# print('MFCCs shape', a_mfccs.shape, a_lens.shape)
return a_mfccs, a_lens
def do_inference(hostport, audio_file, server):
audio_waves = audiofile_to_input_vector(audio_file, FLAGS.n_input,
FLAGS.n_context)
audio = np.array([audio_waves])
host, port = hostport.split(':')
channel = implementations.insecure_channel(host, int(port))
stub = prediction_service_pb2.beta_create_PredictionService_stub(channel)
request = predict_pb2.PredictRequest()
request.model_spec.name = 'deepspeech'
request.inputs['input'].CopyFrom(tf.contrib.util.make_tensor_proto(audio))
event = threading.Event()
result_future = stub.Predict.future(request, 5.0) # 5 seconds
result_future.add_done_callback(_create_rpc_callback(event, server))
if event.is_set() != True:
event.wait()
def make_checkpoint(model_path, audio_path, save_path):
graph_def = GraphDef()
loaded = graph_def.ParseFromString(open(model_path, 'rb').read())
with tf.Graph().as_default() as graph:
new_input = tf.placeholder(tf.float32, [None, None, None],
name='new_input')
# Load the saved .pb into the current graph to let us grab
# access to the weights.
logits, = tf.import_graph_def(
graph_def,
input_map={'input_node:0': new_input},
return_elements=['logits:0'],
name='newname',
op_dict=None,
producer_op_list=None
)
# Now let's dump these weights into a new copy of the network.
with tf.Session(graph=graph) as sess:
# Sample sentence, to make sure we've done it right
mfcc = audiofile_to_input_vector(audio_path, 26, 9)
# Okay, so this is ugly again.
# We just want it to not crash.
tf.app.flags.FLAGS.alphabet_config_path = \
os.path.join(os.path.dirname(__file__), 'DeepSpeech/data/alphabet.txt')
DeepSpeech.initialize_globals()
logits2 = DeepSpeech.BiRNN(new_input, [len(mfcc)], [0]*10)
# Here's where all the work happens. Copy the variables
# over from the .pb to the session object.
for var in tf.global_variables():
sess.run(var.assign(sess.run('newname/'+var.name)))
# Test to make sure we did it right.
res = (sess.run(logits, {new_input: [mfcc],
'newname/input_lengths:0': [len(mfcc)]}).flatten())
res2 = (sess.run(logits2, {new_input: [mfcc]})).flatten()
print('This value should be small', np.sum(np.abs(res - res2)))
# And finally save the constructed session.
saver = tf.train.Saver()
saver.save(sess, save_path)
def _populate_batch_queue(self, session):
for txt_file, wav_file in self._indices():
source = audiofile_to_input_vector(wav_file, self._numcep, self._numcontext)
source_len = len(source)
with codecs.open(txt_file, encoding="utf-8") as open_txt_file:
# We need to do the encode-decode dance here because encode
# returns a bytes() object on Python 3, and text_to_char_array
# expects a string.
target = unicodedata.normalize("NFKD", open_txt_file.read()) \
.encode("ascii", "ignore") \
.decode("ascii", "ignore")
target = text_to_char_array(target)
target_len = len(target)
try:
session.run(self._enqueue_op, feed_dict={
self._x: source,
self._x_length: source_len,
self._y: target,
self._y_length: target_len})
except tf.errors.CancelledError:
return
def _populate_batch_queue(self, session):
for txt_file, wav_file in self._files_circular_list:
if self._coord.should_stop():
return
source = audiofile_to_input_vector(wav_file, self._numcep,
self._numcontext)
source_len = len(source)
with codecs.open(txt_file, encoding="utf-8") as open_txt_file:
target = unicodedata.normalize("NFKD", open_txt_file.read())
target = text_to_char_array(target)
target_len = len(target)
try:
session.run(self._enqueue_op,
feed_dict={
self._x: source,
self._x_length: source_len,
self._y: target,
self._y_length: target_len
})
except tf.errors.CancelledError:
return
def _populate_batch_queue(self, session, coord):
'''
Queue thread routine.
'''
file_count = len(self._data_set.files)
index = -1
while not coord.should_stop():
index = self._data_set.next_index(index) % file_count
wav_file, transcript = self._data_set.files[index]
source = audiofile_to_input_vector(wav_file, self._model_feeder.numcep, self._model_feeder.numcontext)
source_len = len(source)
target = text_to_char_array(transcript, self._alphabet)
target_len = len(target)
if source_len < target_len:
raise ValueError('Error: Audio file {} is too short for transcription.'.format(wav_file))
try:
session.run(self._enqueue_op, feed_dict={ self._model_feeder.ph_x: source,
self._model_feeder.ph_x_length: source_len,
self._model_feeder.ph_y: target,
self._model_feeder.ph_y_length: target_len })
except tf.errors.CancelledError:
return
with tf.Graph().as_default() as graph:
new_input = tf.placeholder(tf.float32, [None, None, None],
name="new_input")
# Load the saved .pb into the current graph to let us grab
# access to the weights.
logits, = tf.import_graph_def(graph_def,
input_map={"input_node:0": new_input},
return_elements=['logits:0'],
name="newname",
op_dict=None,
producer_op_list=None)
# Now let's dump these weights into a new copy of the network.
with tf.Session(graph=graph) as sess:
# Sample sentetnce, to make sure we've done it right
mfcc = audiofile_to_input_vector(wav_file, 26, 9)
# Okay, so this is ugly again.
# We just want it to not crash.
tf.app.flags.FLAGS.alphabet_config_path = alphabet_file
DeepSpeech.initialize_globals()
logits2 = DeepSpeech.BiRNN(new_input, [len(mfcc)], [0] * 10)
# Here's where all the work happens. Copy the variables
# over from the .pb to the session object.
for var in tf.global_variables():
sess.run(var.assign(sess.run('newname/' + var.name)))
# Test to make sure we did it right.
res = (sess.run(logits, {
new_input: [mfcc],
# Open tf.Session.
with tf.Session(graph=graph) as sess:
# Extract graph node names.
tf.import_graph_def(graph_def, name='')
graph_nodes = [n for n in graph_def.node]
names = []
for i, t in enumerate(graph_nodes):
names.append(t.name)
print("graph_node: '{:03d}' -- '{}'".format(i, t.name))
# Prepare audio input data.
input_file_path = '/home/josephz/GoogleDrive/University/UW/2018-19/CSE481I/singing-style-transfer' \
'/src/data/aligned/one_last_time/one_last_time_original_30s.wav'
features = audiofile_to_input_vector(input_file_path,
Config.n_input,
Config.n_context)
num_strides = len(features) - (Config.n_context * 2)
# Create a view into the array with overlapping strides of size
# numcontext (past) + 1 (present) + numcontext (future)
window_size = 2 * Config.n_context + 1
features = np.lib.stride_tricks.as_strided(
features, (num_strides, window_size, Config.n_input),
(features.strides[0], features.strides[0],
features.strides[1]),
writeable=False)
# Prepare graph nodes for inference.
# Prepare input nodes.
# initialize_state = graph.get_tensor_by_name('initialize_state:0')
input_node = graph.get_tensor_by_name('input_node:0')
name="new_input")
# Load the saved .pb into the current graph to let us grab
# access to the weights.
logits, = tf.import_graph_def(
graph_def,
input_map={"input_node:0": new_input},
return_elements=['logits:0'],
name="newname",
op_dict=None,
producer_op_list=None
)
# Now let's dump these weights into a new copy of the network.
with tf.Session(graph=graph) as sess:
# Sample sentetnce, to make sure we've done it right
mfcc = audiofile_to_input_vector("sample.wav", 26, 9)
# Okay, so this is ugly again.
# We just want it to not crash.
tf.app.flags.FLAGS.alphabet_config_path = "DeepSpeech/data/alphabet.txt"
# Make it stop complaining
tf.app.flags.FLAGS.decoder_library_path = "."
DeepSpeech.initialize_globals()
logits2 = DeepSpeech.BiRNN(new_input, [len(mfcc)], [0]*10)
# Here's where all the work happens. Copy the variables
# over from the .pb to the session object.
for var in tf.global_variables():
sess.run(var.assign(sess.run('newname/'+var.name)))
# Test to make sure we did it right.
def _maybe_convert_set(source_dir, target_dir, mode, datasets):
rows = []
remove_alphabets = set('۱١٢۳٣٤٥٦٧۷۸٨٩۹٠۰0123456789٪éàçèáâïóöúﺠپچﭽ')
for dataset in datasets:
for subdir, dirs, files in os.walk(source_dir + '/' + dataset + '/' + mode):
# for audio_filename in sorted(glob.iglob(corpus_dir + "/" + '/**/*.' + ext, recursive=True)):
for file in files:
if file.endswith('.txt'):
filepath = path.abspath(subdir + '/' + file).split('.')[:-1][0]
if path.exists(filepath + '.txt') and path.exists(filepath + '.wav'):
with open(filepath + '.txt', 'r') as readfile:
for transcript in readfile.readlines():
features_len = audiofile_to_input_vector(filepath + '.wav', numcep=26, numcontext=9, compute_len=True, model='deepspeech_2')
if(features_len > 100 and len(transcript) > 2):
if ('english' in language):
if ('tedlium' in dataset):
if (len(transcript) >= 7 and len(transcript) <= transcript_len and features_len > (len(transcript) * transcript_features_ratio)):
rows.append((filepath + '.wav', path.getsize(filepath + '.wav'), transcript))
# if (features_len <= (len(transcript) * transcript_features_ratio)):
# print('Error: Audio file {} is too short for transcription.'.format(filepath + '.wav') + " -- " + str(features_len) + " < " + str(len(transcript)))
elif ('tidigits' in dataset):
if (len(transcript) <= transcript_len and features_len > (len(transcript) * transcript_features_ratio)):
rows.append((filepath + '.wav', path.getsize(filepath + '.wav'), transcript))
# if (features_len <= len(transcript)):
# print('Error: Audio file {} is too short for transcription.'.format(filepath + '.wav') + " -- " + str(features_len) + " < " + str(len(transcript)))
elif ('voxforge' in dataset):
if (len(transcript) >= 2 and len(transcript) <= transcript_len and features_len > (len(transcript) * transcript_features_ratio)):
rows.append((filepath + '.wav', path.getsize(filepath + '.wav'), transcript))
# if (features_len <= len(transcript)):
# print('Error: Audio file {} is too short for transcription.'.format(filepath + '.wav') + " -- " + str(features_len) + " < " + str(len(transcript)))
elif ('vctk' in dataset):
if (len(transcript) >= 2 and len(transcript) <= transcript_len and features_len > (len(transcript) * transcript_features_ratio)):
rows.append((filepath + '.wav', path.getsize(filepath + '.wav'), transcript))
# if (features_len <= (len(transcript) * transcript_features_ratio)):
# print('Error: Audio file {} is too short for transcription.'.format(filepath + '.wav') + " -- " + str(features_len) + " < " + str(len(transcript)))
elif ('common_voice' in dataset):
if (len(transcript) >= 8 and len(transcript) <= transcript_len and features_len > (len(transcript) * transcript_features_ratio)):
rows.append((filepath + '.wav', path.getsize(filepath + '.wav'), transcript))
# if (features_len <= (len(transcript) * transcript_features_ratio)):
# print('Error: Audio file {} is too short for transcription.'.format(filepath + '.wav') + " -- " + str(features_len) + " < " + str(len(transcript)))
elif (len(transcript) >= 5 and len(transcript) <= transcript_len and features_len > (len(transcript) * transcript_features_ratio)):
rows.append((filepath + '.wav', path.getsize(filepath + '.wav'), transcript))
# if (features_len <= len(transcript)):
# print('Error: Audio file {} is too short for transcription.'.format(filepath + '.wav') + " -- " + str(features_len) + " < " + str(len(transcript)))
else:
transcript = transcript.replace('آ', 'آ')
transcript = transcript.replace('ﻻ', 'لا')
transcript = transcript.replace('ﻵ', 'لآ')
transcript = transcript.replace('ﻷ', 'لأ')
transcript = transcript.replace('ﻹ', 'لإ')
transcript = transcript.replace('ﺇ', 'إ')
transcript = transcript.replace('ک', 'ك')
transcript = transcript.replace('ی', 'ى')
transcript = transcript.replace('', ' ')
transcript = transcript.replace('', ' ')
# remove diacritics
transcript = transcript.replace('ً', '')
transcript = transcript.replace('ٍ', '')
transcript = transcript.replace('ٌ', '')
transcript = transcript.replace('ْ', '')
# normalization
transcript = transcript.replace('َ', '')
transcript = transcript.replace('ِ', '')
transcript = transcript.replace('ُ', '')
transcript = transcript.replace('ّ', '')
transcript = transcript.replace('ؤ', 'ؤ')
transcript = transcript.replace('ئ', 'ىٔ')
transcript = transcript.replace('أ', 'أ')
if not any((c in remove_alphabets) for c in transcript):
if ('ksu' in dataset):
if (len(transcript) >= 2 and len(transcript) <= transcript_len and features_len > (len(transcript) * transcript_features_ratio)):
rows.append((filepath + '.wav', path.getsize(filepath + '.wav'), transcript))
# if (features_len <= (len(transcript) * transcript_features_ratio)):
# print('Error: Audio file {} is too short for transcription.'.format(filepath + '.wav') + " -- " + str(features_len) + " < " + str(len(transcript)))
else:
if (len(transcript) >= 2 and len(transcript) <= transcript_len and features_len > (len(transcript) * transcript_features_ratio)):
rows.append((filepath + '.wav', path.getsize(filepath + '.wav'), transcript))
# if (features_len <= (len(transcript) * transcript_features_ratio)):
# print('Error: Audio file {} is too short for transcription.'.format(filepath + '.wav') + " -- " + str(features_len) + " < " + str(len(transcript)))
# # if path.exists(target_dir + '/dataset.csv'):
# samples = []
# with open(source_csv) as source_csv_file:
# reader = csv.DictReader(source_csv_file)
# for row in reader:
# samples.append((row['filename'], row['text']))
#
# # Mutable counters for the concurrent embedded routine
# counter = { 'all': 0, 'too_short': 0, 'too_long': 0 }
# lock = RLock()
# num_samples = len(samples)
# rows = []
#
# def one_sample(sample):
# mp3_filename = path.join(*(sample[0].split('/')))
# mp3_filename = path.join(extracted_dir, mp3_filename)
# # Storing wav files next to the mp3 ones - just with a different suffix
# wav_filename = path.splitext(mp3_filename)[0] + ".wav"
# _maybe_convert_wav(mp3_filename, wav_filename)
# frames = int(subprocess.check_output(['soxi', '-s', wav_filename], stderr=subprocess.STDOUT))
# file_size = path.getsize(wav_filename)
# with lock:
# if int(frames/SAMPLE_RATE*1000/10/2) < len(str(sample[1])):
# # Excluding samples that are too short to fit the transcript
# counter['too_short'] += 1
# elif frames/SAMPLE_RATE > MAX_SECS:
# # Excluding very long samples to keep a reasonable batch-size
# counter['too_long'] += 1
# else:
# # This one is good - keep it for the target CSV
# rows.append((wav_filename, file_size, sample[1]))
# counter['all'] += 1
#
# print('Importing mp3 files...')
# pool = Pool(cpu_count())
# bar = progressbar.ProgressBar(max_value=num_samples, widgets=SIMPLE_BAR)
# for i, _ in enumerate(pool.imap_unordered(one_sample, samples), start=1):
# bar.update(i)
# bar.update(num_samples)
# pool.close()
# pool.join()
#
# print('Writing "%s"...' % target_csv)
# if ('english' in language):
# dict_ = {}
rows.sort(key=lambda item: int(item[1]))
with open(target_dir + '/' + mode + '.csv', 'w') as target_csv_file:
writer = csv.DictWriter(target_csv_file, fieldnames=FIELDNAMES)
writer.writeheader()
# bar = progressbar.ProgressBar(max_value=len(rows), widgets=SIMPLE_BAR)
for filename, file_size, transcript in rows:
include = True
transcript = transcript.lower()
transcript = apos_re.sub('', transcript) # remove qoutes
transcript = transcript.replace('-', ' ')
# transcript = ''.join(['-'.join(c for c in s if c not in punctuationList) for s in transcript])
transcript = transcript.replace(' ', ' ').replace('\r', ' ').replace('\n', ' ').replace('\t', ' ').replace(
'_', ' ').lower().strip()
for c in transcript:
if (c in punctuationList):
include = False
break
if (include):
writer.writerow({'wav_filename': filename, 'wav_filesize': file_size, 'transcript': transcript})
def do_single_file_inference(checkpoint_dir, input_file_path, layer_wanted,
softmax_wanted, save_filename, save_folder,
stride_size_s, win_size_s, fea_format,
csv_format):
with tf.Session(config=Config.session_config) as session:
inputs, outputs, _ = create_inference_graph(
batch_size=1,
n_steps=-1,
layer_wanted=layer_wanted,
softmax_applied=softmax_wanted)
# Create a saver using variables from the above newly created graph
mapping = {
v.op.name: v
for v in tf.global_variables()
if not v.op.name.startswith('previous_state_')
}
saver = tf.train.Saver(mapping)
# Restore variables from training checkpoint
checkpoint = tf.train.get_checkpoint_state(checkpoint_dir)
if not checkpoint:
log_error(
'Checkpoint directory ({}) does not contain a valid checkpoint state.'
.format(checkpoint_dir))
exit(1)
checkpoint_path = checkpoint.model_checkpoint_path
saver.restore(session, checkpoint_path)
session.run(outputs['initialize_state'])
# transformation of the audio file
features = audiofile_to_input_vector(input_file_path, Config.n_input,
Config.n_context)
#print(features.shape)
num_strides = len(features) - (Config.n_context * 2)
# Create a view into the array with overlapping strides of size
# numcontext (past) + 1 (present) + numcontext (future)
window_size = 2 * Config.n_context + 1
features = np.lib.stride_tricks.as_strided(
features, (num_strides, window_size, Config.n_input),
(features.strides[0], features.strides[0], features.strides[1]),
writeable=False)
# This is not the logits but the ouput of the layer wanted
logits = session.run(outputs['outputs'],
feed_dict={
inputs['input']: [features],
inputs['input_lengths']: [num_strides],
})
logits = np.squeeze(logits)
if fea_format:
write_fea_file(logits,
save_folder,
save_filename,
stride_size_s=stride_size_s,
win_len_s=win_size_s)
if csv_format:
np.savetxt(save_folder + '/' + save_filename + '.csv',
logits,
delimiter=',')
def main(_):
start = stopwatch()
initialize_globals()
if len(FLAGS.one_shot_infer):
#load the frozen graph as in train(...) or as in https://blog.metaflow.fr/tensorflow-how-to-freeze-a-model-and-serve-it-with-a-python-api-d4f3596b3adc
with tf.gfile.FastGFile("../../models/output_graph.pb", 'rb') as fin:
graph_def = tf.GraphDef()
graph_def.ParseFromString(fin.read())
with tf.Graph().as_default() as pretrained_model:
tf.import_graph_def(graph_def, name="pretrained_")
"""
for op in pretrained_model.get_operations():
print(op.name)
"""
# print("------------***-------------")
# https://stackoverflow.com/questions/36883949/in-tensorflow-get-the-names-of-all-the-tensors-in-a-graph?utm_medium=organic&utm_source=google_rich_qa&utm_campaign=google_rich_qa
lstTensors = [op.values() for op in pretrained_model.get_operations()]
input_node = lstTensors[0]
input_lengths = lstTensors[1]
output_node = lstTensors[-1]
"""
print("input node name: ")
print(input_node[0].name)
print("input node shape: ")
print(input_node[0].shape)#V IMP: shape of input node is [x,y,z] where x = batch_size. For one shot infer, batch_size = 1
print("input lengths name: ")
print(input_lengths[0].name)
print("input lengths shape: ")
print(input_lengths[0].shape) #V IMP: shape of input_lengths node is [x,y] where x = batch_size. For one shot infer, batch_size = 1
print("output node name: ")
print(output_node[0].name)
print("output node shape: ")
print(output_node[0].shape)
"""
# do_single_file_inference(FLAGS.one_shot_infer)
# print("n_input = "+repr(n_input))
# print("n_context = "+repr(n_context))
mfcc = audiofile_to_input_vector(FLAGS.one_shot_infer, n_input,
n_context)
# print(mfcc.shape)
# output_node = pretrained_model.get_tensor_by_name(pretrained_model.get_operations()[-1].name)
batch_size = 1
with tf.Session(graph=pretrained_model) as sess:
output = sess.run(
output_node,
feed_dict={
input_node:
[mfcc.reshape((batch_size, mfcc.shape[0], mfcc.shape[1]))],
input_lengths:
[np.array(len(mfcc)).reshape((batch_size, ))]
})
# print(output)
text = ndarray_to_text(output[0][0][0], alphabet)
print("\n\nResult:")
print(text)
else:
print(
"Correct usage: python3 _this.py --one_shot_infer <<path-of-input-wav-file>>"
)
delta = stopwatch(start)
print("Net execution time including loading of the graph = " +
format_duration(delta))
result = np.asarray([
SPACE_INDEX if xt == SPACE_TOKEN else ord(xt) - FIRST_INDEX
for xt in result
])
return result
train = pd.read_csv('./real_batch/clean-test_dev-combined.csv')
train = train.head(3) #overfitting it for 2 file
print train.shape
inputs_encoder = []
inputs_decoder = []
outputs_decoder = []
for ind, row in train.iterrows():
inputs_encoder.append(audiofile_to_input_vector(row['wav_filename'], 26,
0))
for ind, row in train.iterrows():
inputs_decoder.append(np.append([0],
text_to_char_array(row['transcript'])))
for ind, row in train.iterrows():
outputs_decoder.append(
np.append(text_to_char_array(row['transcript']), [0]))
xt_decoder_input, xlen_decoder_input = helpers2.batch(inputs_decoder)
xt_encoder, xlen_encoder = helpers.batch(inputs_encoder)
xt_decoder_output, xlen_decoder_output = helpers2.batch(outputs_decoder)
new_input = tf.placeholder(tf.float32, [None, None, None],
name="new_input")
# Load the saved .pb into the current graph to let us grab
# access to the weights.
logits, = tf.import_graph_def(graph_def,
input_map={"input_node:0": new_input},
return_elements=['logits:0'],
name="newname",
op_dict=None,
producer_op_list=None)
# Now let's dump these weights into a new copy of the network.
with tf.Session(graph=graph) as sess:
# Sample sentetnce, to make sure we've done it right
# TODO i've substitutes this file
mfcc = audiofile_to_input_vector("LDC93S1.wav", 26, 9)
# Okay, so this is ugly again.
# We just want it to not crash.
tf.app.flags.FLAGS.alphabet_config_path = "DeepSpeech/data/alphabet.txt"
DeepSpeech.initialize_globals()
logits2 = DeepSpeech.BiRNN(new_input, [len(mfcc)], [0] * 10)
# Here's where all the work happens. Copy the variables
# over from the .pb to the session object.
for var in tf.global_variables():
sess.run(var.assign(sess.run('newname/' + var.name)))
# Test to make sure we did it right.
res = (sess.run(logits, {
new_input: [mfcc],
