User:Angeliki/2nd Trimester: Difference between revisions

Latest revision as of 18:52, 24 March 2018

OCR

Tesseract training

1. Install Tesseract
2. Recipe for training

Reading- Writing

Synopsis
Comparative essay

Reader

Mini reader

Reader#6
~~ From Tedious Tasks to Liberating Orality ~~

Cover made with Graphviz

Python scripts

The secrets of pocketsphinx

Acoustic model/training

what

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 <div style='
 width: 80%;
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    background-color: #FCFC03;
 '>
 == OCR ==
-=== Tesseract training: ===
+=== Tesseract training ===
 [[Install Tesseract 4.0-Ubuntu|1. Install Tesseract]]<br />
 . Recipe for training
 == Reading- Writing ==
-[[Synopsis_24-1-2018#Reading-_Angeliki|Synopsis]]
+[[Synopsis_24-1-2018#Reading-_Angeliki|Synopsis]]<br />
+[[Essay21Feb|Comparative essay]]
 == Reader ==
 [https://pzwiki.wdka.nl/mw-mediadesign/images/7/71/XPUB_reader_concept.pdf Mini reader]
-== Python scripts ==
+[[Reader#6/Angeliki|Reader#6<br />
-Python whisperer
-<syntaxhighlight lang="python" line='line'>
-import nltk
-import collections
-import random
-import sys
-from sys import stdin, stderr, stdout
-o = open("Synopsis_24012018.txt", 'r')
-original = o.read()
-tokens = nltk.word_tokenize(original)
-for noun in tokens:
-	noun = noun.lower()
-# print (tokens)
-v = open("nouns/91K nouns.txt")
-nouns = v.read()
-tokens_nouns = nltk.word_tokenize(nouns)
-# print (tokens_nouns)
-newnouns = []
-for word in tokens:
-	if word in tokens_nouns:
-		n=tokens_nouns.index(word)
-# 		# print (n)
-		newnouns.append(tokens_nouns[n])
-# 		# print (newnouns)
-filename = 'Audiosfera-2015-Westerkamp.txt'
-vocabulary = []
-vocabulary_size = 1000
-def read_input_text(filename):
-    txtfile = open(filename, 'r')
-    string = txtfile.read()
-    words = nltk.word_tokenize(string)
-    # print (words)
-    for word in words:
-    	word=word.lower()
-    	vocabulary.append(word)
-    # print('Data size:', len(vocabulary))
-read_input_text(filename)
-# print(vocabulary)
-newsynopsis = []
+ <big>''~~ From Tedious Tasks to Liberating Orality ~~''</big>]]
-for word in vocabulary:
-	if word in tokens_nouns:
-		newsynopsis.append(random.choice(newnouns))
-	else:
-		newsynopsis.append(word)
-print (" ".join(newsynopsis))
+Cover made with Graphviz
+== [[Angeliki/PROTOTYPING 2|Python scripts]] ==
-</syntaxhighlight>
+== The secrets of pocketsphinx ==
+=== Acoustic model/training ===
+what