[ACCEPTED]-Any Python password-generators that are readable and pronounceable?-passwords

If you're really just looking for something 13 "better than I can make up" and "pronounceable," then 12 maybe just use random.sample() to pull from a list of consonant-vowel-consonant 11 pseudosyllables:

import string
import itertools
import random

initial_consonants = (set(string.ascii_lowercase) - set('aeiou')
                      # remove those easily confused with others
                      - set('qxc')
                      # add some crunchy clusters
                      | set(['bl', 'br', 'cl', 'cr', 'dr', 'fl',
                             'fr', 'gl', 'gr', 'pl', 'pr', 'sk',
                             'sl', 'sm', 'sn', 'sp', 'st', 'str',
                             'sw', 'tr'])
                      )

final_consonants = (set(string.ascii_lowercase) - set('aeiou')
                    # confusable
                    - set('qxcsj')
                    # crunchy clusters
                    | set(['ct', 'ft', 'mp', 'nd', 'ng', 'nk', 'nt',
                           'pt', 'sk', 'sp', 'ss', 'st'])
                    )

vowels = 'aeiou' # we'll keep this simple

# each syllable is consonant-vowel-consonant "pronounceable"
syllables = map(''.join, itertools.product(initial_consonants, 
                                           vowels, 
                                           final_consonants))

# you could trow in number combinations, maybe capitalized versions... 

def gibberish(wordcount, wordlist=syllables):
    return ' '.join(random.sample(wordlist, wordcount))

Then you just choose a suitably 10 large number of "words":

>>> len(syllables)
5320
>>> gibberish(4)
'nong fromp glosk zunt'
>>> gibberish(5)
'samp nuv fog blew grig'
>>> gibberish(10)
'strot fray hag sting skask stim grun prug spaf mond'

My statistics 9 are a little fuzzy, but this may be enough 8 for non-NSA purposes. Note that random.sample() operates 7 without replacement. I should also point 6 out that if a malicious party was aware 5 you were using this method, it would be 4 vulnerable to a dictionary attack. A pinch 3 of salt would help with that.

Update: For those interested, an 2 updated and fork-able version of this is 1 available at https://github.com/greghaskins/gibberish.

You can create a simple Markov text generator and then train 3 it with a list of common/pronounceable words.

Some 2 time ago I wrote a simple generator for 1 fun. Here it is:

#! /usr/bin/python

from cStringIO import StringIO
from sys import argv
import random

USAGE="usage: ./markov.py input_file"
END_TAG='<end>'
SEPARATOR='\n'

def append(model,token, target):
    if token not in model:
        model[token]=[]
    model[token].append(target)

def add_to_model(model,word, end_tag=END_TAG):
    append(model,'',word[:2])
    for i in xrange(len(word)-2):
        append(model, word[i:i+2],word[i+2])
    append(model,word[-2:],end_tag)

def generate(model, end_tag=END_TAG):
    ret=''
    while True:
        cur=random.choice(model[ret[-2:]])
        if cur==end_tag:
            break
        else:
            ret+=cur
    return ret

if __name__=='__main__':
    if len(argv)>1:
        data=file(argv[1],'r').read().split(SEPARATOR)
        model={}
        for word in data:
            add_to_model(model,word)
        print generate(model)
    else:
        print USAGE

I'm a big fan of the xkcd password generator. Very customizable, pip 3 installable, and the "acrostic" feature 2 provides a nice way to give users a memory 1 clue for their generated word set.

I like Simon Sapin's version: http://exyr.org/2011/random-pronounceable-passwords/

0

I guess the project I worked on is applicable. I 5 learned a Markov Model from over 14 million 4 passwords (from the RockYou.com password 3 dump), and created artificial passwords 2 that way. The blog post (and accompanying 1 code) are here. Some sampled artificial passwords:

• tablester111
• genny0
• mikk92
• lvingree10633769
• bubuzzarap71666
• isamistilloro13020
• dunl0velyiristalecasia4799