def phones_for_word(find):
"""Get the CMUdict phones for a given word.
Because a given word might have more than one pronunciation in the
dictionary, this function returns a list of all possible pronunciations.
.. doctest::
>>> import pronouncing
>>> pronouncing.phones_for_word("permit")
['P ER0 M IH1 T', 'P ER1 M IH2 T']
:param find: a word to find in CMUdict.
:returns: a list of phone strings that correspond to that word.
"""
init_cmu()
found = lookup.get(find.lower(), [])
# If the requested word isn't in the CMU dictionary, fallback on g2p_en
# ML to attempt to determine prononuciation
if len(found) == 0:
found = [" ".join(g2p_en.g2p(find))]
return found
def headline_choice(headlines, query):
def levenshtein(s1, s2):
if len(s1) < len(s2):
return levenshtein(s2, s1)
if len(s2) == 0:
return len(s1)
previous_row = range(len(s2) + 1)
for i, c1 in enumerate(s1):
current_row = [i + 1]
for j, c2 in enumerate(s2):
insertions = previous_row[j + 1] + 1
deletions = current_row[j] + 1
substitutions = previous_row[j] + (c1 != c2)
current_row.append(min(insertions, deletions, substitutions))
previous_row = current_row
return previous_row[-1]
with g2p.Session():
search_query = ''.join(
[phoneme[:2] for phoneme in g2p.g2p(query) if phoneme != ' '])
headlines = [
''.join(
[phoneme[:2] for phoneme in g2p.g2p(text) if phoneme != ' '])
for text in headlines
]
chosen_index = 0
min_distance = len(query)
query = search_query
for index, t in enumerate(headlines):
edit_distances = []
if len(query) > len(t):
t, query = query, t
if len(query) == len(t):
t += ' '
for i in range(0, len(t) - len(query), 2):
substring = t[i:i + len(query)]
edit_distances.append(levenshtein(query, substring))
if min(edit_distances) < min_distance:
min_distance = min(edit_distances)
chosen_index = index
query = search_query
return chosen_index
def getViseme(grapheme):
phoneme = g2p(grapheme)
map = p2vmap()
viseme = []
vistring = ""
for k in phoneme:
if k in map:
viseme.append(map[k])
# prev=k
else:
viseme.append(-1)
viseme.append(-1)
for vis in viseme:
vistring = vistring + str(vis) + " "
print(vistring)
return vistring
def add_phoneme_features(df):
'''
Add phonmes as a column.
Use g2p_en (https://github.com/Kyubyong/g2p) to get the phonemes.
Input
- df: the pandas dataframe that has Transcript column
Output
- df: the pandas dataframe with the added column: phoneme
'''
def get_idx_of_numbers_in_sentence(sentence):
word_list = sentence.split()
idx_list = []
for i, word in enumerate(word_list):
if bool(re.search(r'\d', word)):
idx_list.append(i)
return idx_list
def split_sentence_by_number(sentence):
idx_of_numbers = get_idx_of_numbers_in_sentence(sentence)
word_list = sentence.split()
sentence_list = []
sub_sentence = ""
for i in range(len(word_list)):
if i in idx_of_numbers:
if len(sub_sentence) > 0:
sentence_list.append(sub_sentence.strip())
sub_sentence = ""
sentence_list.append(word_list[i])
else:
sub_sentence += ' ' + word_list[i]
sentence_list.append(sub_sentence.strip())
return sentence_list
past_sentence = ""
phoneme_list = []
with g2p.Session():
for i, sentence in enumerate(df['Transcript']):
if sentence != past_sentence:
splitted_sentence = split_sentence_by_number(sentence)
phonemes_of_sentence = []
for sub_sentence in splitted_sentence:
if bool(re.search(r'\d', sub_sentence)):
phonemes_of_sub_sentence = g2p.g2p(sub_sentence)
phonemes_of_sentence.append(phonemes_of_sub_sentence)
else:
phonemes_of_sub_sentence = g2p.g2p(sub_sentence)
phonemes_of_sub_sentence = ','.join(
phonemes_of_sub_sentence)
phonemes_of_sub_sentence = phonemes_of_sub_sentence.split(
', ,')
phonemes_of_sub_sentence = [
phonemes_chunk.split(',')
for phonemes_chunk in phonemes_of_sub_sentence
]
phonemes_of_sub_sentence = [
phonemes for phonemes in phonemes_of_sub_sentence
if phonemes != ['.']
]
phonemes_of_sentence += phonemes_of_sub_sentence
past_sentence = sentence
phonemes_len = len(phonemes_of_sentence)
count = 0
assert len(sentence.split()) == phonemes_len
phoneme_list.append(phonemes_of_sentence[count])
count = (count + 1) % phonemes_len
df['phoneme'] = phoneme_list
return df
def my_route():
# page = request.args.get('page', default = 1, type = int)
word = request.args.get('word', default='*', type=str)
phenomes = g2p(word)
return str(phenomes)
labels = np.array([[1, 3, 5], [2, 4, 6], [0, 0, 0]])
print(labels[None, ...])
print(labels[None, :])
_whitespace_re = re.compile(r"\s+")
print(re.sub(_whitespace_re, " ", "nihao ma wo hen hao"))
text = "nihaoma wo {B IY}henhao a "
print(len(text))
_curly_re = re.compile(r"(.*?)\{(.+?)\}(.*)")
print(_curly_re.match(text))
from g2p_en import g2p
g2p = g2p.G2p()
print(g2p('Ｂ'))
# print("==========="*2)
test = "ABCDEFGHIJKLMNOPQRSTUVWXYZabcdefghijklmnopqrstuvwxyz"
for i in test:
print(g2p(i))
print(g2p('tv'))
print(g2p('ad'))
# print(g2p('fetch'))
# print(g2p('cake'))
# print(g2p('age'))
# print(g2p('banana'))
# print(g2p('ABCDEFG'))
#
# texts = ""
# with open("/home/wangyl/test.txt") as text_file:
def prepare_txt_dict():
speakers = vctk.available_speakers
td = vctk.TranscriptionDataSource(hp.vctk_path, speakers=speakers)
transcriptions = td.collect_files()
wav_paths = vctk.WavFileDataSource(hp.vctk_path,
speakers=speakers).collect_files()
executor = ProcessPoolExecutor(max_workers=cpu_count())
futures = list()
save_name_list = list()
if not os.path.exists("processed"):
os.mkdir("processed")
for ind in range(len(wav_paths)):
savename = os.path.basename(
wav_paths[ind])[0:len(os.path.basename(wav_paths[ind])) -
4] + ".txt"
savename = os.path.join("processed", savename)
save_name_list.append(savename)
# print(savename)
print("Get Name Done.")
lists_P = list()
with g2p.Session():
for i, text in enumerate(transcriptions):
list_not_alpha = list()
for ind, ele in enumerate(text):
if (not ele.isalpha()) and (ele != ' '):
list_not_alpha.append(ind)
# print(list_not_alpha)
cnt = 0
for ind in list_not_alpha:
text = delete_alpha_str(text, ind - cnt)
cnt = cnt + 1
# print(text + "######")
# os.path.basename(wav_paths[ind])[0:len(os.path.basename(wav_paths[ind]))-4]
# print(os.path.basename(wav_paths[ind])[0:len(os.path.basename(wav_paths[ind]))-4])
list_P = g2p.g2p(text)
# print("...")
# prepare_txt(savename, text)
# futures.append(executor.submit(partial(prepare_txt, save_name_list[ind], list_P)))
lists_P.append(list_P)
if i % 100 == 0:
print(i)
print("Get P Done.")
for ind, list_P in enumerate(lists_P):
futures.append(
executor.submit(partial(prepare_txt, save_name_list[ind], list_P)))
print("Prepare Done.")
words_dict = dict()
for future in futures:
# print(future.result())
words_dict.update(future.result())
# print(word_P_dict)
with open("words_dict.txt", "w") as f:
for key in words_dict:
temp_str_P = str()
for P in words_dict[key]:
temp_str_P = temp_str_P + P + " "
str_write = key + " " + temp_str_P
f.write(str_write + "\n")