Porque sempre na quinta palavra (5) a sincronização vermelha não é precisa como nas 4 palavras anteriores? corrija isso.

Mande o código completo e atualizado.

```python
import re
from datetime import datetime, timedelta
from PIL import Image, ImageDraw, ImageFont, ImageColor
import subprocess
import os
import multiprocessing
import math
import numpy as np

font_size = 80
image_size = (1280, 720)
background_color = (0, 0, 0)
font_color = (255, 0, 0)  # Red
highlight_color = (255, 255, 255)  # White
upper_case = True
fps = 20
words_per_line = 5
word_spacing = 22
outline_thickness = 1  # Outline thickness

def parse_time(time_str):
    match = re.match(r'(\d+):(\d+):(\d+)', time_str)
    if match:
        minutes, seconds, milliseconds = map(int, match.groups())
        time = timedelta(minutes=minutes, seconds=seconds, milliseconds=milliseconds)
        return time
    return timedelta(0)

def read_subtitles(filename):
    subtitles = []
    current_phrase = []
    word_count = 0

    with open(filename, 'r', encoding='utf-8') as file:
        for line in file:
            match = re.match(r'[(\d+:\d+:\d+)] (.+)', line.strip())
            if match:
                time_str, word = match.groups()
                
                is_end_of_sentence = word[0].isupper() and word.endswith('.')
                
                if (word[0].isupper() and word_count > 0 and not is_end_of_sentence) or (word_count > 0 and current_phrase[-1][1].endswith('.')):
                    subtitles.append(current_phrase)
                    current_phrase = []
                    word_count = 0
                
                current_phrase.append((parse_time(time_str), word))
                word_count += 1
                
                if word_count == words_per_line and not is_end_of_sentence:
                    subtitles.append(current_phrase)
                    current_phrase = []
                    word_count = 0

    if current_phrase:
        subtitles.append(current_phrase)

    return subtitles

def generate_frames_chunk(args):
    start_frame, end_frame, subtitles, output_dir, font, image_size, background_color, highlight_color, font_color, fps, upper_case, total_duration = args
    
    font_path = os.path.join(os.path.dirname(file), "impact.ttf")
    font = ImageFont.truetype(font_path, font_size)
    
    background_image = Image.open(r"C:\Users\lucas\OneDrive\Documentos\capa.jpg").convert("RGB")
    background_image = background_image.resize(image_size)

    offsets = [(-outline_thickness, -outline_thickness), 
               (-outline_thickness, outline_thickness), 
               (outline_thickness, -outline_thickness), 
               (outline_thickness, outline_thickness)]

    for frame_index in range(start_frame, end_frame):
        current_time = timedelta(seconds=frame_index / fps)
        image = background_image.copy()
        draw = ImageDraw.Draw(image)

        current_phrase = next(
            (phrase for phrase in subtitles if phrase[0][0] <= current_time <= phrase[-1][0] + timedelta(milliseconds=500)),
            None
        )

        if current_phrase:
            phrase_start_time = current_phrase[0][0]
            phrase_end_time = current_phrase[-1][0] + timedelta(milliseconds=500)
            phrase_duration = (phrase_end_time - phrase_start_time).total_seconds()
                
            current_word_index = next(
                (i for i, (time, ) in enumerate(current_phrase) if time > current_time),
                len(current_phrase)
            ) - 1

            if current_word_index < 0:
                current_word_index = 0  # Ajuste para evitar índices negativos

            current_word_start_time = current_phrase[current_word_index][0]

            # Calcular o tempo de início da próxima palavra
            if current_word_index + 1 < len(current_phrase):
                next_word_start_time = current_phrase[current_word_index + 1][0]
            else:
                # Procurar na próxima frase
                current_phrase_index = subtitles.index(current_phrase)
                if current_phrase_index + 1 < len(subtitles):
                    next_phrase = subtitles[current_phrase_index + 1]
                    next_word_start_time = next_phrase[0][0]
                else:
                    # Última palavra, usar a duração total
                    next_word_start_time = timedelta(seconds=total_duration)

            word_duration = max(0.001, (next_word_start_time - current_word_start_time).total_seconds())
            word_progress = min(1, max(0, (current_time - current_word_start_time).total_seconds() / word_duration))

            full_text = ' '.join(word for , word in current_phrase)
            if upper_case:
                full_text = full_text.upper()
            
            lines = wrap_text(full_text, font, image_size[0] * 0.9)
            y = (image_size[1] - len(lines) * font_size) // 2
            current_word_count = 0

            for line in lines:
                x = (image_size[0] - font.getlength(line)) // 2
                words = line.split()
                for word_index, word in enumerate(words):
                    if current_word_count < current_word_index:
                        # Palavras anteriores: totalmente vermelhas
                        color = font_color
                        draw_word(draw, x, y, word, font, color, offsets)
                    elif current_word_count == current_word_index:
                        # Palavra atual: aplicar gradiente
                        word_width = font.getlength(word)
                        gradient_image = create_gradient_word(word, font, word_width, font_size, font_color, highlight_color, word_progress)
                        image.paste(gradient_image, (int(x), int(y)), gradient_image)
                    else:
                        # Próximas palavras: totalmente brancas
                        color = highlight_color
                        draw_word(draw, x, y, word, font, color, offsets)
                    
                    x += font.getlength(word + ' ')
                    current_word_count += 1

                y += font_size

        frame_filename = os.path.join(output_dir, f"frame{frame_index:04d}.jpg")
        image.save(frame_filename)

def create_gradient_word(word, font, width, height, start_color, end_color, progress):
    word_image = Image.new('RGBA', (int(width), height), (0, 0, 0, 0))
    word_draw = ImageDraw.Draw(word_image)

    for i in range(int(width)):
        char_progress = i / width
        if char_progress < progress:
            color = start_color
        else:
            color = end_color
        word_draw.line([(i, 0), (i, height)], fill=color)

    # Aplicar máscara de texto
    mask = Image.new('L', (int(width), height), 0)
    mask_draw = ImageDraw.Draw(mask)
    mask_draw.text((0, 0), word, font=font, fill=255)
    word_image.putalpha(mask)

    return word_image

def draw_word(draw, x, y, word, font, color, offsets):
    for offset_x, offset_y in offsets:
        draw.text((x + offset_x, y + offset_y), word, font=font, fill=(0, 0, 0))
    draw.text((x, y), word, font=font, fill=color)

def wrap_text(text, font, max_width):
    words = text.split()
    lines = []
    current_line = []
    current_width = 0

    for word in words:
        word_width = font.getlength(word)
        if current_width + word_width <= max_width:
            current_line.append(word)
            current_width += word_width + word_spacing
        else:
            if current_line:
                lines.append(' '.join(current_line))
            current_line = [word]
            current_width = word_width + word_spacing

    if current_line:
        lines.append(' '.join(current_line))

    return lines

def generate_frames_parallel(subtitles, output_dir):
    font_path = os.path.join(os.path.dirname(file), "impact.ttf")
    font = ImageFont.truetype(font_path, font_size)
    total_duration = subtitles[-1][-1][0].total_seconds() + 0.5  # Tempo total do vídeo
    
    num_frames = int(total_duration * fps) + 1
    
    num_processes = multiprocessing.cpu_count()
    chunk_size = num_frames // num_processes + 1

    with multiprocessing.Pool() as pool:
        args = [
            (i * chunk_size, min((i + 1) * chunk_size, num_frames), subtitles, output_dir, font, image_size, background_color, highlight_color, font_color, fps, upper_case, total_duration)
            for i in range(num_processes)
        ]
        pool.map(generate_frames_chunk, args)

def create_video(frame_folder, output_video, framerate, audio_file):
    frame_pattern = os.path.join(frame_folder, 'frame%04d.jpg')
    command = [
        'ffmpeg',
        '-y',
        '-framerate', str(framerate),
        '-i', frame_pattern,
        '-i', audio_file,
        '-c:v', 'h264_nvenc',
        '-preset', 'slow',
        '-crf', '23',
        '-c:a', 'aac',
        '-strict', 'experimental',
        '-shortest',
        output_video
    ]
    subprocess.run(command, check=True)

if name == "main":
    subtitles = read_subtitles('legenda.srt')
    output_dir = 'frames'
    os.makedirs(output_dir, exist_ok=True)

    generate_frames_parallel(subtitles, output_dir)
    create_video(output_dir, 'output_video.mp4', fps, 'audio.wav')
```