diff --git a/0_speech/index.js b/0_speech/index.js
index ea36047..50918f8 100644
--- a/0_speech/index.js
+++ b/0_speech/index.js
@@ -106,7 +106,7 @@ function startConverting() {
transcript.replace("\n", "
");
if (event.results[i].isFinal) {
- finalTranscripts += transcript + "\n";
+ finalTranscripts += transcript.trim() + "\n";
} else {
// There are also shown the interim results and according to their "confidence" (the percentage of how much the word is correct) the color of each word could change
interimTranscripts += transcript;
diff --git a/1_pythoning/1-2_NLTKing.py b/1_pythoning/1-2_NLTKing.py
index 2cd8101..df1d084 100644
--- a/1_pythoning/1-2_NLTKing.py
+++ b/1_pythoning/1-2_NLTKing.py
@@ -2,53 +2,51 @@
# NLTK (Natural Language ToolKit) is a library for Natural Language Process.
-# We will use it to get the Part Of Speech (POS) of the speech-to-text results.
-#
+# We will use it to get the Part Of Speech (POS) of the speech-to-text results.
+#
# What does it mean?
#
# It works as grammar tagging: for instance, the sentence "Around the clouds"
-# would have this output:
-#
+# would have this output:
+#
# [('Around', 'IN'), ('the', 'DT'), ('clouds', 'NN')]
-#
+#
# 'IN' means 'preposition' - 'DT' means 'determiner' - 'NN' means 'noun, common, singular or mass'
-
-import time # to create delays :: for having a few seconds to check the console
+
import nltk # to use NLTK
+# to create delays :: for having a few seconds to check the console
+import time
- # Open the speech-to-text result :: downloaded from the web interface >>
+# Open the speech-to-text result :: downloaded from the web interface >>
-with open('../speech.txt','r') as speech: # let's import the text
+with open('../speech.txt', 'r') as speech: # let's import the text
text = speech.read() # and make python read it :)
print(text) # print it!
time.sleep(2) # check it in the console!
-
-text = text.replace('','').replace('\n','. ') # delete this from the results
+
+text = text.replace('',
+ '').replace('\n', '. ') # delete this from the results
tokens = nltk.word_tokenize(text) # Tokenize the words :: split each word
-pos = nltk.pos_tag(tokens) # Elaborate the Part of Speech! It will create an array, a list
+# Elaborate the Part of Speech! It will create an array, a list
+pos = nltk.pos_tag(tokens)
print(pos) # print the array!
time.sleep(2) # check it in the console!
+# To see all the POS tags, open the terminal and copy:
+#
+# python3
+# import nltk
+# nltk.help.upenn_tagset()
-
- # To see all the POS tags, open the terminal and copy:
- #
- # python3
- # import nltk
- # nltk.help.upenn_tagset()
-
-
-
-
- # start the layouting :: html + css + paged.js >>
- #
- # declare html :: we will fill it in the process with loops
- # declare the first part of the text for two html files with different CSS
+# start the layouting :: html + css + paged.js >>
+#
+# declare html :: we will fill it in the process with loops
+# declare the first part of the text for two html files with different CSS
html = ''
@@ -78,9 +76,8 @@ html2 = '''