def is_hendecasyllable(syllables, special_tokens):
syllables = [
prettify_text(s, special_tokens).strip() for s in syllables
if s not in special_tokens.values()
]
if len(syllables) > 9:
return get_last_tonedsyl_index(syllables, special_tokens) == 9
else:
return False
def get_well_formed_rhymes(toned_verses_syls, synalepha):
verses = [ ''.join(verse_syls) for verse_syls in toned_verses_syls]
generated_canto = ''.join(verses)
generated_canto = prettify_text(generated_canto, special_tokens)
generated_canto_list = generated_canto.split("\n")
generated_canto_list = [line.strip() for line in generated_canto_list if line != 'CANTO']
generated_canto = "\n".join(generated_canto_list)
n_rhymes = 0
correct_rhymes = 0
generated_canto = generated_canto.replace('\n\n', '\n').strip()
generated_canto_list = generated_canto.split("\n")
triplets = []
l = None
for i, verse in enumerate(generated_canto_list):
if i % 3 == 0:
if l:
triplets.append('\n'.join(l))
l = [verse]
else:
l.append(verse)
triplets.append('\n'.join(l))
for i in range(0,len(triplets)-1,1):
t1 = triplets[i].split('\n')
t2 = triplets[i+1].split('\n')
if i==0:
n_rhymes+=1
if is_rhyme(t1[0], t1[2]):
correct_rhymes+=1
n_rhymes+=1
if i == len(triplets)-2 and len(t2) <3 :
if is_rhyme(t1[1], t2[0]):
correct_rhymes+=1
else:
if is_rhyme(t1[1], t2[0]):
correct_rhymes+=1/3
if is_rhyme(t2[0], t2[2]):
correct_rhymes+=1/3
if is_rhyme(t1[1], t2[2]):
correct_rhymes+=1/3
return correct_rhymes/n_rhymes
def _apply_synalepha(syllables, special_tokens):
syllables_cleaned = [
prettify_text(s, special_tokens).strip() for s in syllables
if s not in special_tokens.values()
]
if len(syllables_cleaned) <= 9:
return syllables
# SMARAGLIATA
vowels = "ÁÀAaàáÉÈEeèéIÍÌiíìOoóòÚÙUuúùHh'"
n_synalepha = 0
i = 1
while i < (len(syllables) - 1):
if syllables[i] == special_tokens['WORD_SEP']:
pre_syl = syllables[i - 1]
next_syl = syllables[i + 1]
if pre_syl[-1] in vowels and next_syl[0] in vowels:
i += 1
n_synalepha += 1
i += 1
last_tonedrv_index = get_last_tonedsyl_index(syllables, special_tokens)
n_synalepha_needed = last_tonedrv_index - 9
n_synalepha_to_apply = min(n_synalepha_needed, n_synalepha)
result = [syllables[0]]
i = 1
n_synalepha_applied = 0
while i < (len(syllables) - 1):
if syllables[i] == special_tokens['WORD_SEP']:
pre_syl = syllables[i - 1]
next_syl = syllables[i + 1]
if pre_syl[-1] in vowels and next_syl[0] in vowels:
if n_synalepha_applied < n_synalepha_to_apply:
result.append(result[-1] + syllables[i] + next_syl)
del result[-2]
n_synalepha_applied += 1
i += 1
else:
result.append(syllables[i])
else:
result.append(syllables[i])
else:
result.append(syllables[i])
i += 1
result.append(syllables[-1])
return result
def get_last_tonedsyl_index(syllables, special_tokens):
# syllables only without <word_sep> token
syllables = [
prettify_text(s, special_tokens).strip() for s in syllables
if s not in special_tokens.values()
]
syllables_rev = syllables[::-1]
for i, syl in enumerate(syllables_rev):
if is_toned_syl(syl):
return len(syllables_rev) - i - 1
sys.path.append(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
from dante_by_tonedrev_syl.text_processing import clean_comedy, prettify_text, special_tokens, remove_all_punctuation
from dante_by_tonedrev_syl.tone import ToneTagger
working_dir = os.path.join(
os.path.dirname(os.path.dirname(os.path.abspath(__file__))),
'dante_by_tonedrev_syl')
divine_comedy_file = os.path.join(os.path.dirname(working_dir),
"divina_commedia",
"divina_commedia_accent_UTF-8.txt")
with open(divine_comedy_file, "r") as f:
divine_comedy = f.read()
divine_comedy = clean_comedy(divine_comedy, special_tokens)
divine_comedy = prettify_text(divine_comedy, special_tokens)
#divine_comedy = remove_all_punctuation(divine_comedy)
tone_tagger = ToneTagger()
print("\nMODEL: {}\n".format(tone_tagger.model_filename))
divine_comedy_words = divine_comedy.split()[:10] + []
for w in divine_comedy_words:
print(tone_tagger.tone(w), flush=True, end=' ')
start_seq_verse = syls_verse_list[start_idx:index_eov]
generated_text, generated_text_no_tone = generate_text(model_rhyme,
model_verse,
special_tokens,
vocab_size_rhyme,
vocab_size_verse,
syl2idx_rhyme,
idx2syl_rhyme,
syl2idx_verse,
idx2syl_verse,
SEQ_LENGTH_RHYME,
SEQ_LENGTH_VERSE,
start_seq_rhyme,
start_seq_verse,
temperature=1.0)
#print(prettify_text(generated_text, special_tokens))
with open(output_file, "w") as f:
f.write(prettify_text(generated_text_no_tone, special_tokens))
with open(raw_output_file, "w") as f:
f.write(generated_text_no_tone)
with open(output_toned_file, "w") as f:
f.write(prettify_text(generated_text, special_tokens))
with open(raw_output_toned_file, "w") as f:
f.write(generated_text)
def generate_text(model_rhyme,
model_verse,
special_tokens,
vocab_size_rhyme,
vocab_size_verse,
syl2idx_rhyme,
idx2syl_rhyme,
syl2idx_verse,
idx2syl_verse,
seq_length_rhyme,
seq_length_verse,
start_seq_rhyme,
start_seq_verse,
temperature=1.0):
seq_text_rhyme = start_seq_rhyme
seq_text_verse = start_seq_verse
generated_text_list = []
model_rhyme.reset_states()
model_verse.reset_states()
end_of_canto = False
while not end_of_canto:
# and generated_text_list.count(special_tokens['END_OF_VERSO']) < 10:
# and generated_text_list.count(special_tokens['END_OF_TERZINA']) < 45 \
next_syl_rhyme = ''
end_verse_list = []
structure_list = []
while not end_of_canto and next_syl_rhyme != special_tokens[
'END_OF_VERSO']:
seq_text_rhyme = seq_text_rhyme[-seq_length_rhyme:]
sequence_rhyme = [
syl2idx_rhyme[syl]
for syl in seq_text_rhyme[-seq_length_rhyme:]
]
sequence_rhyme = tf.keras.preprocessing.sequence.pad_sequences(
[sequence_rhyme], maxlen=seq_length_rhyme)
x_rhyme = np.array(sequence_rhyme, dtype='int64')
prediction_rhyme = model_rhyme.predict(x_rhyme, verbose=0)
prediction_rhyme = tf.squeeze(prediction_rhyme, 0)[-1]
prediction_rhyme = prediction_rhyme / temperature
prediction_rhyme = prediction_rhyme.numpy()
# index_rhyme = np.random.choice(len(prediction_rhyme), size=1, p=prediction_rhyme)[0]
index_rhyme = np.argmax(prediction_rhyme)
next_syl_rhyme = idx2syl_rhyme[index_rhyme]
seq_text_rhyme.append(next_syl_rhyme)
if next_syl_rhyme in special_tokens.values(
) and next_syl_rhyme != special_tokens['END_OF_VERSO']:
structure_list.append(next_syl_rhyme)
else:
end_verse_list.append(next_syl_rhyme)
if next_syl_rhyme == special_tokens['END_OF_CANTO']:
end_of_canto = True
generated_text_list += structure_list
reverse_rhyme_list = end_verse_list[::-1]
## seq_text_verse += structure_list
seq_text_verse += reverse_rhyme_list
next_syl_verse = ''
rest_revese_verse_list = []
while not end_of_canto and next_syl_verse != special_tokens[
'END_OF_VERSO']:
seq_text_verse = seq_text_verse[-seq_length_verse:]
sequence_verse = [
syl2idx_verse[syl]
for syl in seq_text_verse[-seq_length_verse:]
]
sequence_verse = tf.keras.preprocessing.sequence.pad_sequences(
[sequence_verse], maxlen=seq_length_verse)
x_verse = np.array(sequence_verse, dtype='int64')
prediction_verse = model_verse.predict(x_verse, verbose=0)
prediction_verse = tf.squeeze(prediction_verse, 0)[-1]
prediction_verse = prediction_verse / temperature
prediction_verse = prediction_verse.numpy()
index_verse = np.random.choice(len(prediction_verse),
size=1,
p=prediction_verse)[0]
next_syl_verse = idx2syl_verse[index_verse]
if next_syl_verse != special_tokens['END_OF_VERSO']:
seq_text_verse.append(next_syl_verse)
rest_revese_verse_list.append(next_syl_verse)
whole_verse_list = rest_revese_verse_list[::-1] + end_verse_list
generated_text_list += whole_verse_list
print(prettify_text(''.join(structure_list), special_tokens),
end='',
flush=True)
# print(prettify_text(''.join(whole_verse_list), special_tokens), end='', flush=True)
print(prettify_text(
''.join(remove_tone(whole_verse_list, special_tokens)),
special_tokens),
end='',
flush=True)
generated_text_no_tone_list = remove_tone(generated_text_list,
special_tokens)
return ''.join(generated_text_list), ''.join(generated_text_no_tone_list)
