这个notebook展示了如何将一个json对象转换为可导入Anki的文件。重点在于Anki中NoteType的设计。内容上承接explore_all_in_one.ipynb。
《GRE核心词汇考法精析》、《GRE核心词汇助记与精练》以及从网上找到的《不择手段背单词》对应NoteType为GreWord。 《GRE高分必备短语搭配》对应NoteTpye为GrePhrase。
notebook执行完后，会自动生成两个脚本，名字参见变量file_name_greword，file_name_grephrase。单独运行两个脚本也可完成转换，只要有python就可使用。
子章节《处理发音文件》和《添加文件》，需要许多定制文件。所以没有导出到转换脚本。如果没有对应文件的话，直接运行这个notebook而会报错。所以如果只想得到无发音无笔记版本的导入文件，请运行那两个转换脚本。
转换出的Anki导入文件，名字参见变量output_file_GreWord，output_file_GrePhrase。
%run sync_to_file_magic_command.pyfile_name_greword = 'CreateAnkiImport_GreWord.py'
file_name_grephrase = 'CreateAnkiImport_GrePhrase.py'
configCreAnkiImpGreWord = file_name_greword
configCreAnkiImpGrePhrase = file_name_grephrase
configMyHelpers = 'my_helpers.py'%%sync_to_file $configMyHelpers
def custom_html_element(_str):
"""
convert the markdown notations in a string to html tags
currently, only two kinds of markdown notation exist in all the strings
** and *
"""
formatted_str = _str
# format double asterisk
match_double_asterisk_re = re.compile(u'\*\*(.*?)\*\*')
# replace **...** with <strong>...</strong>
#formatted_str = match_double_asterisk_re.sub(r'<strong>\1</strong>', formatted_str)
# replace **...** with <ins>...</ins>
formatted_str = match_double_asterisk_re.sub(r'<ins>\1</ins>', formatted_str)
# format single asterisk
# replace *...* with <i>...</i>
match_single_asterisk_re = re.compile(u'\*(.*?)\*')
formatted_str = match_single_asterisk_re.sub(r'<i>\1</i>', formatted_str)
return formatted_str%%sync_to_file $configMyHelpers
def is_file_and_json_load(file_name_str):
if os.path.isfile(file_name_str):
with codecs.open(file_name_str, 'r', encoding='utf-8') as f:
json_d = json.load(f)
return json_d%%sync_to_file $configCreAnkiImpGreWord $configCreAnkiImpGrePhrase -m o
# coding:utf-8
import json
import codecs
import os
from my_helpers import *# example
test_str = 'to **put an end to**(something planned or previously agreed to)'
print custom_html_element(test_str)
del test_strto <ins>put an end to</ins>(something planned or previously agreed to)
%%sync_to_file $configCreAnkiImpGreWord
file_name_new3000 = 'new3000_base_d.txt'
file_name_zhuji = 'zhuji_base_d.txt'
file_name_bzsdbdc = 'base_data\\bzsdbdc_dic.txt'
output_file_GreWord = 'AnkiImportData_GreWord.txt'
new3000_base_d = None
zhuji3000_base_d = None
bzsdbdc_data = None
new3000_base_d = is_file_and_json_load(file_name_new3000)
zhuji3000_base_d = is_file_and_json_load(file_name_zhuji)
bzsdbdc_data = is_file_and_json_load(file_name_bzsdbdc)%%sync_to_file $configCreAnkiImpGreWord
no_data_new3000 = new3000_base_d is None
no_data_zhuji = zhuji3000_base_d is None
no_data_bzsdbdc = bzsdbdc_data is None%%sync_to_file $configCreAnkiImpGreWord
def add_extra_fields():
if no_data_new3000:
print 'New3000 data file does not exists! Nothing can be done...'
return
iter_path = [('all','',True), ('key','usages',False),('all','',False)]
for word, usage_d in iter_through_general(new3000_base_d, iter_path):
usage_d['audio'] = ''
usage_d['mynotes'] = ''
for word_d in new3000_base_d.itervalues():
word_d['similar_form'] = ''add_extra_fields()# test
#pprint(new3000_base_d['abandon'])%%sync_to_file $configCreAnkiImpGreWord
def convert_to_GreWord():
if no_data_new3000:
print 'New3000 data file does not exists! Nothing can be done...'
return
if no_data_zhuji:
print 'No data of zhuji!'
if no_data_bzsdbdc:
print 'No data of bzsdbdc!'
output_list = []
None_repr = u''
join_by_line_break = u'<br>'.join
replace_with_br = lambda _str: _str.replace('\n', '<br>')
tag_pos_prefix = ' in_'
for word in new3000_base_d:
# new 3000 part
"""
the structure of a word of new3000_base_d.txt
{'phon': u"[\u02cc\xe6d'l\u026ab]",
'pos': (1, 6),
'usages': [{'ants': u'\u53cd\u3000considered, planned, premeditated, rehearsed \u9884\u5148\u8ba1\u5212\u7684',
'ants_d': {'cn': u'\u9884\u5148\u8ba1\u5212\u7684',
'en': u'considered, planned, premeditated, rehearsed ',
'en_cn': u'considered, planned, premeditated, rehearsed \u9884\u5148\u8ba1\u5212\u7684'},
'der': '',
'examples': u'content...',
'en': u'not bad for an ad-lib comedy routine',
'en_cn': u'content...'},
'exp': u'*adj.* \u5373\u5174\u7684\uff1amade or done **without previous thought or preparation**',
'exp_d': {'cn': u'\u5373\u5174\u7684',
'en': u'made or done **without previous thought or preparation**',
'en_cn': u'\u5373\u5174\u7684\uff1amade or done **without previous thought or preparation**'},
'ph_symbl': u"[\u02cc\xe6d'l\u026ab]",
'pspeech': u'adj.',
'syns': u'content...'}
"""
one_new3000_word_d = new3000_base_d[word]
word_pos_L, word_pos_U = one_new3000_word_d['pos']
word_pos = u'L' + unicode(word_pos_L) + u' U' + unicode(word_pos_U)
num_usages = len(one_new3000_word_d['usages'])
usages_tag = unicode(num_usages) + u'_usage'
for usage_index, usage in enumerate(one_new3000_word_d['usages']):
word_phs = usage['ph_symbl']
word_tags = usages_tag + tag_pos_prefix + 'zaiyaoniming3000'
if not no_data_zhuji:
if word in zhuji3000_base_d:
word_tags += tag_pos_prefix + 'zhuji3000'
if not no_data_bzsdbdc:
if word in bzsdbdc_data:
word_tags += tag_pos_prefix + 'bzsdbdc'
usage_index = unicode(usage_index+1)
word_uid = unicode(word) + usage_index
ph_symbl = usage['ph_symbl']
word_Audio = usage['audio']
pspeech = usage['pspeech']
exp_en = usage['exp_d']['en']
exp_cn = usage['exp_d']['cn']
exp_en_cn = usage['exp_d']['en_cn']
# combine other explanation
#usage_index_l = range(num_usages)
#usage_index_l.remove(usage_index)
#exp_other = ['**考法%d**:'%(i+1) + one_new3000_word_d['usages'][i]['exp_d']['en_cn'] +'\n' for i in usage_index_l]
# use word_block_str as all explanation
exp_all = one_new3000_word_d['word_block_str']
examples_en = usage['examples_d']['en']
examples_cn = usage['examples_d']['cn']
examples_en_cn = usage['examples_d']['en_cn']
examples_others = ''
ants_en = usage['ants_d']['en']
ants_cn = usage['ants_d']['cn']
ants_en_cn = usage['ants_d']['en_cn']
syns = usage['syns']
# der from the book zaiyaoniming3000
der_new3000 = usage['der']
# bzsdbdc part
how_to_mem_bzsdbdc = None_repr
if not no_data_bzsdbdc:
if word in bzsdbdc_data:
how_to_mem_bzsdbdc = bzsdbdc_data[word]['combined']
# zhuji3000 part
how_to_mem_zhuji3000, eytma_gr, eytma_gr_exp, eytma_cognates = None_repr, None_repr, None_repr, None_repr
'''
the structure of a word of zhuji3000_base_d
{'content': u'[\u6839] per- [through] + vad [go] + -e [v.], go through, \u904d\u5e03 \u2192 vt. \u5f25\u6f2b\uff0c\u5145\u6ee1\n',
'ety': 'vad, vag, ced',
'etyma_cognates_l': u'pervade, evasive, extravagant, vague, cessation, incessant',
'etyma_group_explanation': u'group explanation content',
'phon': u"[p\u0259r've\u026ad]",
'pos': u'6, 7',
'summary': u'summary content',
'word': u'pervade'}
'''
if not no_data_zhuji:
if word in zhuji3000_base_d:
how_to_mem_zhuji3000 = zhuji3000_base_d[word]['content']
eytma_gr = zhuji3000_base_d[word]['ety']
eytma_gr_exp = zhuji3000_base_d[word]['etyma_group_explanation']
eytma_cognates = zhuji3000_base_d[word]['etyma_cognates_l']
# extra fields
mynotes = usage['mynotes']
similar_form = one_new3000_word_d['similar_form']
"""
Anki GreWord Structure
word_uid word usage_index ph_symbl word_audio pspeech mynotes
exp_en exp_cn exp_en_cn exp_all
examples_en examples_cn examples_encn examples_others
ants_en ants_cn ants_encn
syns der_new3000
how_to_mem_bzsdbdc how_to_mem_zhuji3000
etyma_group etyma_group_exp etyma_cognates
position similar_form tags
"""
one_line = [word_uid, word, usage_index, ph_symbl, word_Audio, pspeech, mynotes,
exp_en, exp_cn, exp_en_cn, exp_all,
examples_en, examples_cn, examples_en_cn, examples_others,
ants_en, ants_cn, ants_en_cn] +\
[syns, der_new3000, how_to_mem_bzsdbdc, how_to_mem_zhuji3000,
eytma_gr, eytma_gr_exp, eytma_cognates, word_pos, similar_form, word_tags]
for index, _str in enumerate(one_line):
_str = replace_with_br(collapse_blank_line(_str).strip(' \n'))
one_line[index] = custom_html_element(_str)
output_list.append(one_line)
output_list.sort(key=lambda x: x[0])
return output_list上面的函数构建了基本的Anki导入文件。现在还需要将发音文件的指针添加进去。
如果是更新原有的note，那么还需要将原有note的mynotes字段取出来，放到output_list的对应位置。
所以先不执行下面的函数。等到数据补充齐全后再运行。
%%sync_to_file $configCreAnkiImpGreWord
def main():
add_field_audio_and_mynotes()
output_list = convert_to_GreWord()
if output_list is None:
return
with codecs.open(output_file_GreWord, 'w', encoding='utf-8') as f:
for one_line in output_list:
one_string = u'\t'.join(one_line) + '\n'
f.write(one_string)
del output_list%%sync_to_file $configCreAnkiImpGreWord -p
if __name__ == '__main__':
main()Anki中，添加发音文件的语法是[sound:发音文件指针]。发音文件指针即发音文件的文件名。所有相关文件必须放在Anki自己的collection.media文件夹里。所以路径应该使用相对引用。
接下来，从各个发音库抽取文件指针，并且将相应文件拷贝到Anki的collection.media文件夹下，同时将指针添加到new3000_base_d中。
print new3000_base_d['addict']['usages'][0].keys()[u'exp_d', u'pspeech', u'ph_symbl', u'ants_d', u'der', u'ants', 'mynotes', u'examples', u'examples_d', u'exp', 'audio', u'syns']
path_to_pron = [('all','',True), ('key','usages',False), ('all','',True),('key','ph_symbl',False)]
pre_word_pron = None
multi_pron_word_set = set()
for word, usage_index, word_pron in iter_through_general(new3000_base_d, deepcopy(path_to_pron)):
if usage_index > 0:
if word_pron != pre_word_pron:
multi_pron_word_set.add(word)
else:
pre_word_pron = word_pronprint multi_pron_word_setset([u'incarnate', u'articulate', u'appropriate', u'incense', u'subordinate', u'animate', u'surmise', u'content', u'duplicate', u'escort', u'moderate', u'compliment', u'entrance', u'intimate', u'addict', u'compound', u'aggregate', u'discharge', u'diffuse', u'convert', u'elaborate', u'exploit', u'contract', u'project', u'initiate', u'ally', u'alloy', u'intrigue'])
dsl格式的Longman Pronunciation Dictionary 3rd Ed.
关于处理dsl的基本知识，参考Full Text Search in GoldenDict
import gzipfile_pronunciation = 'D:\Eudict\dsl\En-En_Longman_Pronunciation3\En-En-Longman_Pronunciation.dsl.dz'
dsl_str = gzip.open(file_pronunciation, mode='r').read().decode('utf-16')
print dsl_str[100:400]sh"
A
[m1][b]A, a[/b] [i] name of letter[/i] [p]BrE[/p] [s]uk_ld44a.wav[/s] [p]AmE[/p] [s]us_l3a-2.wav[/s] [c mediumblue]eɪ[/c][/m]
[m1]▷ [b]A's, As, a's[/b] [c mediumblue]eɪz[/c][i] —Communications code name:[/i][c darkmagenta] Alfa[/c][/m]
[m1]▶[b][c blue]ˌ[/c]A[c blue]ˈ[/c]1[c blue]◂[/c], [c
match_word_fun = lambda word: re.search('^(%s)[ \t]*$(.*?)(?=^[^ \t])'%word, dsl_str, re.M|re.S)
findall_word_fun = lambda word: re.findall('^(%s)[ \t]*$(.*?)(?=^[^ \t])'%word, dsl_str, re.M|re.S)
match_us_pron_re = re.compile('\[s\](us.*?)\[/s\]')有的单词，其下属派生词，以▷标识，也有自己的音标。这部分中可能出现斜体字，以[i]..[/i]标识。只有主释义单词后面的斜体才是音标。
match_pspeech_re = re.compile('^[ \t]*?\[m1\]\[b\].*?\[/b\] \[i\] ([a-z, ]+).*?\[/i\]', re.M)# test
def unit_test():
result = match_word_fun('content')
result_str = result.group()
print result_str
print 'All pronunciation files: ', match_us_pron_re.findall(result_str)
print 'All part of speech: ', match_pspeech_re.findall(result_str)
#unit_test()
del unit_testextract_word_block_re = re.compile(ur'^([a-z-]+)[ \t]*$(.*?)(?=^[^ \t])', re.M|re.S|re.I)# test
#extract_word_block_re.findall(dsl_str[0:5000])dsl_pron_d = {}
for one_match_obj in extract_word_block_re.finditer(dsl_str):
word = one_match_obj.group(1)
if word in dsl_pron_d:
print '%s already exists!'%word
one_word_d = {}
word_block = one_match_obj.group(2)
one_word_d['word_block'] = word_block
one_word_d['pspeech_l'] = match_pspeech_re.findall(word_block)
one_word_d['ph_symbol_l'] = match_us_pron_re.findall(word_block)
if word in multi_pron_word_set:
#print 'check pspeech'
#print word, one_word_d['pspeech_l']
pass
dsl_pron_d[word] = one_word_d# example
iter_print(dsl_pron_d['content']) word_block
{{Roman}}I{{/Roman}}
[m1][b]con|tent[/b] [i] adjective, verb, noun 'contentment'[/i] [p]BrE[/p] [s]uk_ld44content.wav[/s] [p]AmE[/p] [s]us_l3content2.wav[/s] [c mediumblue]kən |ˈtent[/c] [p]§[/p]\ [sub]([/sub]ˌ[sub])[/sub]kɒn-[/m]
[m1]▷ [b]con|tented[/b] [c mediumblue]ˈtent ɪd[/c] -əd [p]AmE[/p]\ [c mediumblue]ˈten[i]t̬[/i] əd[/c][/m]
[m1]▷ [b]con|tenting[/b] [c mediumblue]ˈtent ɪŋ[/c] [p]AmE[/p]\ [c mediumblue]ˈten[i]t̬[/i] ɪŋ[/c][/m]
[m1]▷ [b]con|tents[/b] [c mediumblue]ˈten[i]t[/i]s[/c][/m]
{{Roman}}II{{/Roman}}
[m1][b]content[/b] [i] noun 'matter contained'[/i] [p]BrE[/p] [s]uk_content2.wav[/s] [p]AmE[/p] [s]us_l3content.wav[/s] [c mediumblue]ˈkɒn tent[/c] [p]AmE[/p]\ [c mediumblue]ˈkɑːn-[/c][/m]
[m1]▷ [b]content|s[/b] [c mediumblue]s[/c][/m]
ph_symbol_l
0
us_l3content2.wav
1
us_l3content.wav
pspeech_l
0
adjective, verb, noun
1
noun
def summary_pspeech():
#dsl
dsl_pspeech_set = set()
for word, word_d in dsl_pron_d.iteritems():
dsl_pspeech_l = word_d['pspeech_l']
for pspeech in dsl_pspeech_l:
dsl_pspeech_set.add(pspeech)
# new3000
new3000_pspeech_set = set()
path_to_pspeech = path_to_pron = [('all','',True), ('key','usages',False), ('all','',False),('key','pspeech',False)]
for word, pspeech in iter_through_general(new3000_base_d, path_to_pspeech):
for sub_pspeech in pspeech.split('/'):
new3000_pspeech_set.add(sub_pspeech)
stripped_pspeech = pspeech.strip('.')
if word in dsl_pron_d:
for dsl_pspeech in dsl_pron_d[word]['pspeech_l']:
if dsl_pspeech.startswith(stripped_pspeech):
break
else:
if len(dsl_pron_d[word]['ph_symbol_l']) > 1:
#print 'pspeech of %s in new3000 not match with dsl'%word
# a lot!
pass
print dsl_pspeech_set
print new3000_pspeech_set# summary_pspeech()dsl_pron_n中的有效词性类别：adjective verb pronoun preposition adverb
所以，只要看看dsl_pron_n中的词性是不是以new3000_base_d中的开头就可以。
将西欧字符转换为普通字符，即éï转为ei
def check_pron_in_new3000_and_dsl(word, print_only_bad_result = True):
word_converted = word.replace(u'é', 'e').replace(u'ï', 'i').split('/')[0]
return_message_l = []
not_found = False
if not (word_converted in dsl_pron_d):
return_message_l.append('**%s** not found in dsl'%word)
not_found = True
else:
pron_in_dsl_l = dsl_pron_d[word_converted]['ph_symbol_l']
pspeech_in_dsl_l = dsl_pron_d[word_converted]['pspeech_l']
pron_new3000_l = []
pspeech_new3000_l = []
for usage_d in new3000_base_d[word]['usages']:
pron_new3000_l.append(usage_d['ph_symbl'])
pspeech_new3000_l.append(usage_d['pspeech'])
diff_pron_new3000_set = set(pron_new3000_l)
if len(pron_in_dsl_l) < len(diff_pron_new3000_set):
message = '**%s** in dsl has less pron'%word
message += '\n' + str(len(pron_in_dsl_l)) + ', ' + str(len(diff_pron_new3000_set))
message += '\n' + ','.join(pron_in_dsl_l)
message += '\n' + ','.join(pron_new3000_l)
return_message_l.append(message)
else:
if not print_only_bad_result:
return_message_l.append('**%s** in dsl has enough pron'%word)
return '\n'.join(return_message_l), not_foundresult_l = []
not_found_word_l = []
for word in new3000_base_d.iterkeys():
message_str, not_found = check_pron_in_new3000_and_dsl(word)
if message_str != '':
result_l.append(message_str)
if not_found:
not_found_word_l.append(word)
if word in multi_pron_word_set:
print 'Warning! **%s** in multi_pron_word_set'%wordwith codecs.open('temp_check_pron_log.txt', 'w', encoding='utf-8') as f:
json.dump(result_l, f, indent=5)
json.dump(not_found_word_l, f, indent=2)print '%d words not found'%len(not_found_word_l)153 words not found
虽然还有153个没找到，但注意到，多音词都在其中。
从网上找的韦氏发音库，网址：http://blog.emagic.org.cn/content/i1931.html
ed2k链接
ed2k://|file|%E9%9F%A6%E6%B0%8F%E5%B8%B8%E7%94%A8%E5%8D%95%E8%AF%8D%E8%AF%AD%E9%9F%B3%E5%BA%93.rar|315458082|88b70fe90a6658cec689352f66a7af6c|h=4rblspftuskt5gfvmpbnfkdvhi2ey3fn|/
path_of_media_source = 'D:\\mvoice\\'
word_list_file = 'word_list.txt'media_path_dict = {}
match_word = r'([a-z1-9 ~]+)\.mp3'
match_word_re = re.compile(match_word, re.I|re.M)
with codecs.open(path_of_media_source + word_list_file, encoding='utf-8') as f:
for line in f:
result = match_word_re.search(line)
if not (result is None):
media_path_dict[result.group(1)] = line.strip()
else:
#print line
passprint media_path_dict['habit']D:\mvoice\h\habit.mp3
count = 0
still_not_found_word_l = []
for word in not_found_word_l:
word_converted = word.replace(u'é', 'e').replace(u'ï', 'i').split('/')[0]
if word_converted in media_path_dict:
count += 1
#print 'found', word
else:
still_not_found_word_l.append(word)
print 'found %d of %d'%(count, len(not_found_word_l))found 57 of 153
使用朗文当代第5版的mdx和mdd文件
使用插件 https://bitbucket.org/xwang/mdict-analysis
from readmdict import MDX, MDD
from bs4 import BeautifulSoup
file_to_longman_mdx = "D:\Eudict\Frequent\Longman Dictionary of Contemporary English.mdx"
mdx = MDX(file_to_longman_mdx)
longman_mdx_iter = mdx.items()
longman_in_new3000_d = {}
for word, word_block in longman_mdx_iter:
if word in new3000_base_d:
longman_in_new3000_d[word] = word_block
print 'In longman, found %d words of new3000 (%d in total)'%(len(longman_in_new3000_d), len(new3000_base_d))In longman, found 2954 words of new3000 (3145 in total)
抽取音频地址
# this is the pattern we gonna use
soup = BeautifulSoup(longman_in_new3000_d['abandon'],"lxml")
print soup.find_all(href=re.compile('sound.*?US'))[0]['href'][8:]US_abandon1.spx
count = 0
still_still_not_found_word_l = []
longman_found_word_d = {}
for word in still_not_found_word_l:
founded = False
word_converted = word.replace(u'é', 'e').replace(u'ï', 'i').split('/')[0]
if word_converted in longman_in_new3000_d:
soup = BeautifulSoup(longman_in_new3000_d[word_converted],"lxml")
find_result = soup.find_all(href=re.compile('sound.*?US'))
if len(find_result) != 0:
count += 1
#print word
founded = True
longman_found_word_d[word] = find_result[0]['href'][8:]
if not founded:
still_still_not_found_word_l.append(word)
print 'found %d of %d'%(count, len(still_not_found_word_l))found 52 of 96
# example
longman_found_word_d['ingratiating']'US_ingratiating.spx'
# unzip the mdd mdx file.
# Warning! This take a lot of time. I have already unpacked it, so commend the next line
#! python readmdict.py -x "D:\Eudict\Frequent\Longman Dictionary of Contemporary English.mdx"import shutilanki_media_collection = os.path.expanduser('~\\Documents\\Anki\\xiaohang\\collection.media')
dsl_source_media_path = 'D:\Eudict\dsl\En-En_Longman_Pronunciation3\En-En-Longman_Pronunciation.dsl.dz.files'
longman_source_media_path = 'D:\Eudict\Frequent\data'def add_audio_pointer(word):
word_converted = word.replace(u'é', 'e').replace(u'ï', 'i').split('/')[0]
word_d = new3000_base_d[word]
for usage_d in word_d['usages']:
usage_d['audio'] = ''
source_audio_file_name = None
first_pspeech_match_obj = re.search('^([a-z]+)\.', usage_d['pspeech'])
if first_pspeech_match_obj is None:
print '%s has no pspeech'%word
new3000_pspeech = ''
else:
new3000_pspeech = first_pspeech_match_obj.group(1)
if new3000_pspeech in ['vt', 'vi']:
new3000_pspeech = 'v'
new_audio_pointer_without_ext = word_converted + '_' + new3000_pspeech
new_audio_file_name_without_ext = anki_media_collection + '\\' + new_audio_pointer_without_ext
new_audio_pointer_without_ext = '[sound:' + new_audio_pointer_without_ext
existed = False
for file_ext in ['.wav', '.mp3', '.spx']:
if os.path.isfile(new_audio_file_name_without_ext + file_ext):
# print 'existed!'
existed = True
usage_d['audio'] = new_audio_pointer_without_ext + file_ext + ']'
break
if existed:
continue
if word_converted in dsl_pron_d:
dsl_word_d = dsl_pron_d[word_converted]
if word in multi_pron_word_set:
# check pspeech
for index, dsl_pspeech in enumerate(dsl_word_d['pspeech_l']):
for dsl_sub_pspeech in dsl_pspeech.split(','):
if dsl_sub_pspeech.strip().startswith(new3000_pspeech):
source_audio_file_name = dsl_source_media_path + '\\' + dsl_word_d['ph_symbol_l'][index]
break
else:
print 'no match of pspeech, word %s'%word
print dsl_word_d['pspeech_l'], new3000_pspeech
pass
else:
# use the first audio pointer
source_audio_file_name = dsl_source_media_path + '\\' + dsl_word_d['ph_symbol_l'][0]
if not (source_audio_file_name is None):
new_audio_pointer = new_audio_pointer_without_ext + '.wav]'
new_audio_file_name = new_audio_file_name_without_ext + '.wav'
else:
# the not found word
if word_converted in media_path_dict:
# try webster
source_audio_file_name = media_path_dict[word_converted]
new_audio_pointer = new_audio_pointer_without_ext + '.mp3]'
new_audio_file_name = new_audio_file_name_without_ext + '.mp3'
elif word in longman_found_word_d:
# try longman
source_audio_file_name = longman_source_media_path + '\\' + longman_found_word_d[word]
new_audio_pointer = new_audio_pointer_without_ext + '.spx]'
new_audio_file_name = new_audio_file_name_without_ext + '.spx'
if not (source_audio_file_name is None):
usage_d['audio'] = new_audio_pointer
shutil.copy(source_audio_file_name, new_audio_file_name)for word in new3000_base_d:
add_audio_pointer(word)# example
word = 'compendium'
for index, usage_d in enumerate(new3000_base_d[word]['usages']):
print usage_d['audio'][sound:compendium_n.mp3]
[sound:compendium_n.mp3]
到这里，电脑上已经可以发音了。但手机只支持mp3格式，所以要将collection.media中的wav和spx转换为mp3。
使用pydub+ffmpeg
参考Pydub
from pydub import AudioSegment
import globdef convert_to_mp3():
owd = os.getcwd()
os.chdir(anki_media_collection)
extension_list = ('*.wav', '*.spx')
for extension in extension_list:
for audio in glob.glob(extension):
mp3_filename = os.path.splitext(os.path.basename(audio))[0] + '.mp3'
if not os.path.isfile(mp3_filename):
AudioSegment.from_file(audio).export(mp3_filename, format='mp3')
os.chdir(owd)convert_to_mp3()def modify_audio_pointer():
path_to_usage_d = path_to_pron = [('all','',False), ('key','usages',False), ('all','',False)]
for usage_d, in iter_through_general(new3000_base_d, path_to_usage_d):
old_audio_name = usage_d['audio']
if old_audio_name != '':
new_audio_name = os.path.splitext(os.path.basename(old_audio_name))[0] + '.mp3]'
usage_d['audio'] = new_audio_namemodify_audio_pointer()# test
#iter_print(new3000_base_d['chaperone'])old_anki_GreWord_file_name = 'old_anki_greword.txt'def add_mynotes():
if not os.path.isfile(old_anki_GreWord_file_name):
return
old_data_line_l = codecs_open_r_utf8(old_anki_GreWord_file_name).split('\n')
for line in old_data_line_l:
field_l = line.split('\t')
word = field_l[1]
usage_index = int(field_l[2])
my_note = field_l[6]
if my_note != '':
new3000_base_d[word]['usages'][usage_index-1]['mynotes'] = my_noteadd_mynotes()第一遍背诵时，由于不会精确的记忆词形，经常出现各种误识别。所以加入一个环节，根据Wagner–Fischer算法自动生成形近词，并将形近词的简单中文释义添加到其后。计算单词的Levenshtein distance时，使用的别人写好的算法，并没做优化，所以可能慢一些。另外，也没有缓存距离，即计算完A、B间的距离后，B、A间还会再计算一次。
由于ipython执行multiprocess有bug，multiprocessing提到：
This means that some examples, such as the Pool examples will not work in the interactive interpreter.
所以，具体程序参见脚本add_similar_word.py。该脚本生成similar_word.txt，里面有需要的数据。
with open('similar_word.txt') as f:
similar_word_l = json.load(f)similar_word_d = {pair[0]:pair[1] for pair in similar_word_l}def add_similar_word_to_new3000_base_d():
for word in similar_word_d:
new3000_base_d[word]['similar_form'] = similar_word_d[word]greword_import_data_l = convert_to_GreWord()
with codecs.open(output_file_GreWord, 'w', encoding='utf-8') as f:
for one_line in greword_import_data_l:
one_string = u'\t'.join(one_line) + '\n'
f.write(one_string)# test
#iter_print(new3000_base_d['hike'])%%sync_to_file $configCreAnkiImpGrePhrase
file_name_duanyu = 'duanyu_base_d.txt'
duanyu_base_d = is_file_and_json_load(file_name_duanyu)
output_file_GrePhrase = 'AnkiImportData_GrePhrase.txt'print 'The structure of duanyu_base_d'
pprint(duanyu_base_d['under one\'s control1'])The structure of duanyu_base_d
{u'cn_exp': u'\u5728\u2026\u2026\u7684\u63a7\u5236\u4e4b\u4e0b',
u'en_exp': u'If something is **under** your **control**, you have the **power to make** all the important **decisions** about the way that it is run.',
u'example': u'The current protest doesn\u2019t look likely to be brought under government\u2019s control any time soon.',
u'gre_example_cn': u'\u5f53\u5fb7\u514b\u8428\u65af\u5dde\u8fd8\u5904\u4e8e\u58a8\u897f\u54e5\u7684\u7ba1\u8f96\u4e2d\u65f6\uff0c\u5c3d\u7ba1\u58a8\u897f\u54e5\u653f\u5e9c\u6781\u529b\u529d\u963b\u6765\u81ea\u7f8e\u56fd\u7684\u79fb\u6c11\uff0c\u5fb7\u5dde\u7684\u4eba\u53e3\u8fd8\u662f\u7ffb\u4e86\u4e24\u756a\u3002',
u'gre_example_en': u'While Texas was under Mexican control, the population of Texas quadrupled, in spite of the fact that Mexico discouraged immigration from the United States.',
u'phrase': u"under one's control",
u'pos': 7,
u'usage_index': u'1'}
%%sync_to_file $configCreAnkiImpGrePhrase
def convert_to_GrePhrase():
with codecs.open(output_file_GrePhrase, 'w', encoding='utf-8') as f:
my_notes = ''
for phrase_uid, phrase_dict in duanyu_base_d.iteritems():
one_line = [phrase_uid, phrase_dict['phrase'], phrase_dict['usage_index'], my_notes,
phrase_dict['en_exp'], phrase_dict['cn_exp'],
phrase_dict['example'], phrase_dict['gre_example_cn'],
phrase_dict['gre_example_en']]
one_line = '\t'.join(one_line) + '\n'
f.write(one_line)convert_to_GrePhrase()%%sync_to_file $file_name_grephrase -p
if __name__ == '__main__':
if not (duanyu_base_d is None):
convert_to_GrePhrase()! jupyter nbconvert anki_import.ipynb --to markdown
! jupyter nbconvert anki_import.ipynb -- to html[NbConvertApp] WARNING | Collisions detected in jupyter_nbconvert_config.py and jupyter_nbconvert_config.json config files. jupyter_nbconvert_config.json has higher priority: {
"Exporter": {
"template_path": "['.', 'C:\\\\Users\\\\xiaohang\\\\AppData\\\\Roaming\\\\jupyter\\\\templates'] ignored, using [u'C:\\\\Users\\\\xiaohang\\\\AppData\\\\Roaming\\\\jupyter\\\\templates']"
}
}
C:\Users\xiaohang\Anaconda\lib\site-packages\IPython\nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead.
"You should import from ipython_nbconvert instead.", ShimWarning)
[NbConvertApp] Converting notebook anki_import.ipynb to markdown
[NbConvertApp] Writing 30598 bytes to anki_import.md
[NbConvertApp] WARNING | Collisions detected in jupyter_nbconvert_config.py and jupyter_nbconvert_config.json config files. jupyter_nbconvert_config.json has higher priority: {
"Exporter": {
"template_path": "['.', 'C:\\\\Users\\\\xiaohang\\\\AppData\\\\Roaming\\\\jupyter\\\\templates'] ignored, using [u'C:\\\\Users\\\\xiaohang\\\\AppData\\\\Roaming\\\\jupyter\\\\templates']"
}
}
[NbConvertApp] WARNING | pattern u'to' matched no files
[NbConvertApp] WARNING | pattern u'html' matched no files
C:\Users\xiaohang\Anaconda\lib\site-packages\IPython\nbconvert.py:13: ShimWarning: The `IPython.nbconvert` package has been deprecated. You should import from ipython_nbconvert instead.
"You should import from ipython_nbconvert instead.", ShimWarning)
[NbConvertApp] Converting notebook anki_import.ipynb to html
[NbConvertApp] Writing 298264 bytes to anki_import.html