use edge
========
scroll page automatically, every 500ms
setInterval(function() {
    window.scrollBy(0, 100);
}, 500);

save page as complete with images


use tessarc to convert images to text
=======================================
mkdir -p  output && for file in *.jpg *.jpeg *.png; do tesseract "$file" "output/${file%.*}.txt";done

#cat *.txt concatenates all .txt files in the current directory.
#sed 's/flat/pancake/g' replaces all occurrences of “flat” with “pancake”.
#> merged.txt redirects the output to a file named “merged.txt”.
#&& is a logical AND operator that only runs the next command if the previous command was successful.
#espeak-ng -f merged.txt -w output.wav uses espeak-ng to convert the text in “merged.txt” into a .wav file named “output.wav”.

cat *.txt | sed 's/flat/pancake/g' > merged.txt && espeak-ng -f merged.txt -w output.wav


can also do cat *.txt > merged.txt


convert wav to mp3
====================
lame output.wav output.mp3


using mozilla tts and https://docs.coqui.ai/en/latest/inference.html
====================================================================
pip install TTS
import torch
from TTS.api import TTS

# Get device
device =  "cpu"

# List available
print(TTS().list_models())

# Init TTS
tts = TTS(model_name="tts_models/en/ljspeech/tacotron2-DDC", progress_bar=False).to(device)
# Read the text file
with open('input.txt', 'r') as file:
    text = file.read()

# Run TTS
tts.tts_to_file(text=text, file_path="output_moz.wav")


# Run TTS
# ❗ Since this model is multi-lingual voice cloning model, we must set the target speaker_wav and language
# Text to speech list of amplitude values as output
#wav = tts.tts(text="Hello world!", speaker_wav="~/cloning/audio.wav", language="en")
# Text to speech to a file
#tts.tts_to_file(text="Hello world!", speaker_wav="~/cloning/audio.wav", language="en", file_path="output.wav")


re-number files 1 to total
=========================
count=1; for file in *.txt; do mv -- "$file" "$count.txt"; ((count++)); done