File size: 4,537 Bytes
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import nltk
from nltk.tokenize import word_tokenize
import phonemizer
from phonemizer.backend import EspeakBackend
import numpy as np
class TextProcessor:
def __init__(self):
# Initialize phonemizer with English backend
self.backend = EspeakBackend('en-us')
def process(self, text):
"""
Process text into phonemes with duration and stress markers for singing
Args:
text (str): Input text to be processed
Returns:
tuple: (phonemes, durations, stress_markers)
"""
# Clean text
text = self._clean_text(text)
# Tokenize
tokens = word_tokenize(text)
# Get phonemes
phonemes = self._text_to_phonemes(text)
# Estimate durations
durations = self._estimate_durations(tokens, phonemes)
# Mark stress for singing emphasis
stress_markers = self._mark_stress(tokens, phonemes)
return phonemes, durations, stress_markers
def _clean_text(self, text):
"""Clean and normalize text"""
# Convert to lowercase
text = text.lower()
# Remove extra whitespace
text = re.sub(r'\s+', ' ', text).strip()
# Remove special characters but keep punctuation important for phrasing
text = re.sub(r'[^a-z0-9\s.,!?\'"-]', '', text)
return text
def _text_to_phonemes(self, text):
"""Convert text to phoneme sequence"""
phonemes = self.backend.phonemize([text], strip=True)[0]
# Clean up phoneme representation
phonemes = re.sub(r'\s+', ' ', phonemes).strip()
return phonemes
def _estimate_durations(self, tokens, phonemes):
"""Estimate phoneme durations for singing"""
# Split phonemes into list
phoneme_list = phonemes.split()
# Default duration (in seconds) for each phoneme
base_duration = 0.1
# Assign longer durations to vowels and certain consonants
durations = []
for p in phoneme_list:
# Vowels get longer duration
if re.search(r'[aeiou]', p):
durations.append(base_duration * 2)
# Certain consonants get medium duration
elif re.search(r'[lrmnw]', p):
durations.append(base_duration * 1.5)
# Other phonemes get standard duration
else:
durations.append(base_duration)
# Adjust for punctuation (create pauses)
for i, token in enumerate(tokens):
if token in ['.', ',', '!', '?', ';', ':']:
# Add a pause duration at the end of sentences or phrases
durations.append(base_duration * 3 if token in ['.', '!', '?'] else base_duration * 1.5)
return durations
def _mark_stress(self, tokens, phonemes):
"""Mark which phonemes should be stressed in singing"""
# Simple heuristic: mark first syllable of content words
stress_markers = np.zeros(len(phonemes.split()))
# POS tagging to identify content words
tagged = nltk.pos_tag(tokens)
content_word_indices = []
for i, (word, tag) in enumerate(tagged):
# Content words: nouns, verbs, adjectives, adverbs
if tag.startswith(('N', 'V', 'J', 'R')) and len(word) > 2:
content_word_indices.append(i)
# Estimate phoneme positions for content words and mark stress
phoneme_idx = 0
word_idx = 0
phoneme_list = phonemes.split()
# This is a simplified approach - in practice, you'd need
# a more sophisticated alignment between words and phonemes
for i, word in enumerate(tokens):
if i in content_word_indices:
# Mark the first vowel phoneme of this word
word_phonemes = len(word) # This is an approximation
for j in range(word_phonemes):
if phoneme_idx + j < len(phoneme_list):
phon = phoneme_list[phoneme_idx + j]
if re.search(r'[aeiou]', phon):
stress_markers[phoneme_idx + j] = 1
break
phoneme_idx += len(word) # Approximate phoneme position
return stress_markers |