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Beep curse words in real-time
Overview
VideoDB’s makes it easy to personalize content to meet users’ requirements. If users prefer not to include curse words in their content, VideoDB allows for these words to be either removed or replaced with a sound overlay such as beep sound.
This task, typically complex for video editors, can be accomplished with just a few lines of code using VideoDB.
This technique can also serve as a valuable Content Moderation component for any social content platform, ensuring that content meets the preferences and standards of its audience.
Let’s dive in!
🔗 Prerequisites
Ensure you have latest VideoDB installed in your environment. If not, simply run !pip install -U videodb in your terminal.
🌐 Connect to VideoDB
Connect to VideoDB using your API key. This establishes a session for uploading and manipulating video and audio files:
# create a new connection with your API key
import videodb
import os
from getpass import getpass
# Prompt user for API key securely
api_key = getpass("Please enter your VideoDB API Key: ")
os.environ["VIDEO_DB_API_KEY"] = api_key
from videodb import connect, play_stream
conn = connect()
📺 Source Content
For this tutorial, let's take the Joe Rogan clip, where he is trying to trick siri into using curse words 🤣
# Joe rogan video clip
video = conn.upload(url='https://www.youtube.com/watch?v=7MV6tUCUd-c')
# watch the original video
o_stream = video.generate_stream()
play_stream(o_stream)
📢 Index the video
Find out the curse words with the spoken Index.
# index spoken content in the video
video.index_spoken_words()
🎛️ Create beep Asset
We have a sample beep sound in this folder, beep.wav. For those looking to add a more playful or unique touch, replacing the beep with alternative sound effects, such as a quack or any other sound, can make the content more engaging and fun.
# Import Editor SDK components
from videodb.editor import VideoAsset, AudioAsset, Timeline, Track, Clip
# upload beep sound - This is just a sample, you can replace it with quack or any other sound effect.
beep = conn.upload(file_path="beep.wav")
# Create audio asset from beep sound
beep_asset = AudioAsset(id=beep.id)
🥷🏻 Moderation
To ensure appropriate content management, it's necessary to have a method for identifying profanity and applying a predefined overlay to censor it. In this tutorial, we've included a list of curse words. Feel free to customize this list according to your requirements.
curse_words_list = ['shit', 'ass', 'shity' 'fuck', 'motherfucker','damn', 'fucking', 'motherfuker']
🔍 Finding the Curse Words
We'll use few NLP techniques to identify all variations of any offensive words, eliminating the need to manually find and include each form. Additionally, by analyzing the transcript, you can gain insights into how these sounds are transcribed, acknowledging the possibility of errors.
#install spacy
!pip -q install spacy
#install dataset english core
!python -m spacy download en_core_web_sm
# load the english corpus
import spacy
import re
nlp = spacy.load("en_core_web_sm")
def get_root_word(word):
"""
This function convert each word into its root word
"""
try:
#clean punctuations
cleaned_word = re.sub(r'[^\w\s]', '', word)
# Process the sentence
doc = nlp(cleaned_word)
# Lemmatize the word
lemmatized_word = [token.lemma_ for token in doc][0] # Assuming single word input
return lemmatized_word
except Exception as e:
print(f"some issue with lemma for the word {word}")
return word
⏭️ Create Fresh Timeline
Let's create a timeline using the Track and Clip pattern. Add the video clip to the main track, then loop through the transcript to add beep overlays wherever curse words are detected.
# Create a new Timeline
timeline = Timeline(conn)
# Create main track with video
main_track = Track()
video_asset = VideoAsset(id=video.id)
video_clip = Clip(asset=video_asset, duration=float(video.length))
main_track.add_clip(0, video_clip)
timeline.add_track(main_track)
# Create overlay track for beep sounds
beep_track = Track()
for word in transcript:
text = word.get('text')
if text not in ['-']:
root_word = get_root_word(text)
if root_word in curse_words_list:
beep_start_time = float(word.get('start'))
beep_end_time = float(word.get('end'))
beep_duration = beep_end_time - beep_start_time
# Add beep clip at this timestamp with increased volume
print(f"beep the word: {text}, {beep_start_time}:{beep_end_time}")
beep_clip = Clip(
asset=AudioAsset(id=beep.id, start=0, volume=2.0),
duration=beep_duration
)
beep_track.add_clip(beep_start_time-0.25, beep_clip)
timeline.add_track(beep_track)
stream_url = timeline.generate_stream()
🌐 Review and Share Your Moderated Video
Finally, watch and share your new stream:
from videodb import play_stream
play_stream(stream_url)
⚡️ The Real Power of Programmable Streams
If you have videos pre-uploaded and indexed, running this beep pipeline is real-time. So, based on your users' choices or your platform's policy, you can use information from spoken content to automatically moderate.
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