text_to_video_single.py

#Video Story Creator - Full single code
#by ruslanmv.com

# Step 1 - Libraries Installation
!nvidia-smi
!pip install min-dalle
!pip install gradio -q
!pip install transformers torch requests moviepy huggingface_hub opencv-python
!pip install moviepy
!pip install imageio-ffmpeg
!pip install imageio==2.4.1
!apt install imagemagick
!cat /etc/ImageMagick-6/policy.xml | sed 's/none/read,write/g'> /etc/ImageMagick-6/policy.xml
!pip install gTTS
!pip install mutagen
#We reset the runtime
#exit()

# Step 2 - Importing Libraries
from moviepy.editor import *
from PIL import Image
from transformers import AutoTokenizer, AutoModelForSeq2SeqLM,pipeline
import requests
import gradio as gr
import torch
import re
import os
import sys
from huggingface_hub import snapshot_download
import base64
import io
import cv2
import argparse
import os
from PIL import Image
from min_dalle import MinDalle
import torch
from PIL import Image, ImageDraw, ImageFont
import textwrap
from mutagen.mp3 import MP3
# Import the required module for text
# to speech conversion
from gtts import gTTS
from IPython.display import Audio
from IPython.display import display
from pydub import AudioSegment
from os import getcwd
import glob
import nltk
from IPython.display import HTML
from base64 import b64encode

nltk.download('punkt')
# Libraries
tokenizer = AutoTokenizer.from_pretrained("sshleifer/distilbart-cnn-12-6")
model = AutoModelForSeq2SeqLM.from_pretrained("sshleifer/distilbart-cnn-12-6")

# Step 3- Creation of the application
text ='Once, there was a girl called Laura who went to the supermarket to buy the ingredients to make a cake. Because today is her birthday and her friends come to her house and help her to prepare the cake.'
inputs = tokenizer(text, 
                  max_length=1024, 
                  truncation=True,
                  return_tensors="pt")
  
summary_ids = model.generate(inputs["input_ids"])
summary = tokenizer.batch_decode(summary_ids, 
                                skip_special_tokens=True, 
                                clean_up_tokenization_spaces=False)
plot = list(summary[0].split('.'))
def save_image(image: Image.Image, path: str):
    if os.path.isdir(path):
        path = os.path.join(path, 'generated.png')
    elif not path.endswith('.png'):
        path += '.png'
    print("saving image to", path)
    image.save(path)
    return image
def generate_image(
    is_mega: bool,
    text: str,
    seed: int,
    grid_size: int,
    top_k: int,
    image_path: str,
    models_root: str,
    fp16: bool,
):
    model = MinDalle(
        is_mega=is_mega, 
        models_root=models_root,
        is_reusable=False,
        is_verbose=True,
        dtype=torch.float16 if fp16 else torch.float32
    )

    image = model.generate_image(
        text, 
        seed, 
        grid_size, 
        top_k=top_k, 
        is_verbose=True
    )
    #save_image(image, image_path)
    #image = Image.open("generated.png")
    return image 

#Let us generate the images from our summary text
generated_images = []
for senten in plot[:-1]:
  #print(senten)
  image=generate_image(
    is_mega='store_true',
    text=senten,
    seed=1,
    grid_size=1,
    top_k=256,
    image_path='generated',
    models_root='pretrained',
    fp16=256,)
  #display(image)
  generated_images.append(image)

# Step 4- Creation of the subtitles
sentences =plot[:-1]
num_sentences=len(sentences)
assert len(generated_images) == len(sentences) , print('Something is wrong')
#We can generate our list of subtitles
from nltk import tokenize
c = 0
sub_names = []
for k in range(len(generated_images)): 
  subtitles=tokenize.sent_tokenize(sentences[k])
  sub_names.append(subtitles)
  #print(subtitles, len(subtitles))
  #!ls  /usr/share/fonts/truetype/liberation
# Step 5- Adding Subtitles to the Images
def draw_multiple_line_text(image, text, font, text_color, text_start_height):
    draw = ImageDraw.Draw(image)
    image_width, image_height = image.size
    y_text = text_start_height
    lines = textwrap.wrap(text, width=40)
    for line in lines:
        line_width, line_height = font.getsize(line)
        draw.text(((image_width - line_width) / 2, y_text), 
                  line, font=font, fill=text_color)
        y_text += line_height

def add_text_to_img(text1,image_input):
    '''
    Testing draw_multiple_line_text
    '''
    image =image_input
    fontsize = 13  # starting font size
    path_font="/usr/share/fonts/truetype/liberation/LiberationSans-Bold.ttf"
    font = ImageFont.truetype(path_font, fontsize)
    text_color = (255,255,0)
    text_start_height = 200
    draw_multiple_line_text(image, text1, font, text_color, text_start_height)
    return image

# Testing
#for k in range(len(generated_images)):
#    display(generated_images[k])
#    print(sentences[k])

generated_images_sub = []
for k in range(len(generated_images)): 
  imagenes = generated_images[k].copy()
  text_to_add=sub_names[k][0]
  result=add_text_to_img(text_to_add,imagenes)
  generated_images_sub.append(result)
  #display(result)
  #print(text_to_add, len(sub_names[k]))


# Step  7 - Creation of audio 
c = 0
mp3_names = []
mp3_lengths = []
for k in range(len(generated_images)):
    text_to_add=sub_names[k][0]
    print(text_to_add)
    f_name = 'audio_'+str(c)+'.mp3'
    mp3_names.append(f_name)
    # The text that you want to convert to audio
    mytext = text_to_add
    # Language in which you want to convert
    language = 'en'
    # Passing the text and language to the engine,
    # here we have marked slow=False. Which tells
    # the module that the converted audio should
    # have a high speed
    myobj = gTTS(text=mytext, lang=language, slow=False)
    # Saving the converted audio in a mp3 file named
    sound_file=f_name
    myobj.save(sound_file)
    audio = MP3(sound_file)
    duration=audio.info.length
    mp3_lengths.append(duration)
    print(audio.info.length)
    c+=1
#print(mp3_names)
#print(mp3_lengths)

# Step 8 - Merge audio files
cwd = (getcwd()).replace(chr(92), '/')
#export_path = f'{cwd}/result.mp3'
export_path ='result.mp3'
MP3_FILES = glob.glob(pathname=f'{cwd}/*.mp3', recursive=True)
silence = AudioSegment.silent(duration=500)
full_audio = AudioSegment.empty()    # this will accumulate the entire mp3 audios
for n, mp3_file in enumerate(mp3_names):
    mp3_file = mp3_file.replace(chr(92), '/')
    print(n, mp3_file)

    # Load the current mp3 into `audio_segment`
    audio_segment = AudioSegment.from_mp3(mp3_file)

    # Just accumulate the new `audio_segment` + `silence`
    full_audio += audio_segment + silence
    print('Merging ', n)

# The loop will exit once all files in the list have been used
# Then export    
full_audio.export(export_path, format='mp3')
print('\ndone!')

# Step 9 - Creation of the video with adjusted times of the sound
c = 0
file_names = []
for img in generated_images_sub:
  f_name = 'img_'+str(c)+'.jpg'
  file_names.append(f_name)
  img = img.save(f_name)
  c+=1
print(file_names)
clips=[]
d=0
for m in file_names:
  duration=mp3_lengths[d]
  print(d,duration)
  clips.append(ImageClip(m).set_duration(duration+0.5))
  d+=1
concat_clip = concatenate_videoclips(clips, method="compose")
concat_clip.write_videofile("result_new.mp4", fps=24)

# Step 10 - Merge Video + Audio
movie_name = 'result_new.mp4'
export_path='result.mp3'
movie_final= 'result_final.mp4'

def combine_audio(vidname, audname, outname, fps=60): 
    import moviepy.editor as mpe
    my_clip = mpe.VideoFileClip(vidname)
    audio_background = mpe.AudioFileClip(audname)
    final_clip = my_clip.set_audio(audio_background)
    final_clip.write_videofile(outname,fps=fps)

combine_audio(movie_name, export_path, movie_final) # i create a new file

# Show video
mp4 = open('result_final.mp4','rb').read()
data_url = "data:video/mp4;base64," + b64encode(mp4).decode()
HTML("""
<video width=400 controls>
      <source src="%s" type="video/mp4">
</video>
""" % data_url)