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Overlay a Word-Counter on Video Stream
Introduction
With an endless stream of new video content on our feeds, engaging the audience with dynamic visual elements can make educational and promotional videos much more impactful. VideoDB's suite of features allows you to enhance videos with programmatic editing.
In this tutorial, we'll explore how to create a video that visually counts and displays instances of a specified word as it's spoken. We'll use VideoDB’s to index spoken words, and then apply audio and to show a counter updating in real-time with synchronized audio cues.
Setup
📦 Installing packages
%pip install videodb
🔑 API Keys
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
Steps
🌐 Step 1: Connect to VideoDB
Establish a session for uploading videos. Import the necessary modules from VideoDB library to access functionalities.
from videodb import connect
conn = connect()
coll = conn.get_collection()
🗳️ Step 2: Upload Video
Upload and play the video to ensure it's correctly loaded. We’ll be using for the purpose of this tutorial.
video = coll.upload(url="https://www.youtube.com/watch?v=Js4rTM2Z1Eg")
video.play()
📝 Step 3: Indexing Spoken Words
Index the video to identify and timestamp all spoken words.
video.index_spoken_words()
🔍 Step 4: Keyword Search
Search within the video for the keyword ("education" in this example), and note each occurrence.
from videodb import SearchType
result = video.search(query="education", search_type=SearchType.keyword)
🎼 Step 5: Setup Timeline and Audio
Initialize the timeline and prepare an audio asset to use for each word occurrence.
from videodb.editor import Timeline, Track, Clip, AudioAsset, VideoAsset, TextAsset
from videodb.editor import Font, Background, Alignment, HorizontalAlignment, VerticalAlignment, Position, Offset
from videodb import MediaType
timeline = Timeline(conn)
# Upload the twink sound effect
audio = conn.upload(url="https://github.com/video-db/videodb-cookbook-assets/raw/main/audios/twink.mp3", media_type=MediaType.audio)
💬 Step 6: Overlay Text and Audio
Add text and audio overlays at each instance where the word is spoken using the `Track` and `Clip` pattern.
Note: Adding the 'padding' is an optional step. It helps in adding a little more context to the exact instance identified, thus resulting in a better compiled output.
video_duration = min(300, int(video.length)) # First 5 minutes only
audio_offset = 1 # Delay audio/text update by 1 second for better sync
# Create timeline and tracks
timeline = Timeline(conn)
video_track = Track()
text_track = Track()
audio_track = Track()
# Add video clip (first 5 minutes)
video_clip = Clip(
asset=VideoAsset(id=video.id, start=0),
duration=video_duration
)
video_track.add_clip(0, video_clip)
# Filter shots within our duration
shots_in_range = [s for s in result.shots if int(s.start) + audio_offset < video_duration]
# Add text overlays that update at each word occurrence
for i, shot in enumerate(shots_in_range):
trigger_time = int(shot.start) + audio_offset
# Initial "Count-0" from start until first word
if i == 0 and trigger_time > 0:
text_asset = TextAsset(
text="Count-0",
font=Font(family="Do Hyeon", size=72, color="#000100"),
background=Background(color="#F702A4", opacity=1.0),
alignment=Alignment(horizontal=HorizontalAlignment.right, vertical=VerticalAlignment.top),
)
text_clip = Clip(asset=text_asset, duration=trigger_time,
position=Position.top_right, offset=Offset(x=-0.05, y=0.05))
text_track.add_clip(0, text_clip)
# Duration until next word or end of video
if i + 1 < len(shots_in_range):
next_trigger = int(shots_in_range[i + 1].start) + audio_offset
else:
next_trigger = video_duration
text_dur = next_trigger - trigger_time
# Text overlay with updated count
text_asset = TextAsset(
text=f"Count-{i + 1}",
font=Font(family="Do Hyeon", size=72, color="#000100"),
background=Background(color="#F702A4", opacity=1.0),
alignment=Alignment(horizontal=HorizontalAlignment.right, vertical=VerticalAlignment.top),
)
text_clip = Clip(asset=text_asset, duration=text_dur, position=Position.top_right, offset=Offset(x=-0.05, y=0.05))
text_track.add_clip(trigger_time, text_clip)
# Audio cue at same trigger time
if trigger_time < video_duration - 2:
audio_clip = Clip(asset=AudioAsset(id=audio.id), duration=2)
audio_track.add_clip(trigger_time, audio_clip)
# Add all tracks to timeline
timeline.add_track(video_track)
timeline.add_track(text_track)
timeline.add_track(audio_track)
⚡️ Step 7: Generate and Play the Stream
Finally, generate a streaming URL for your edited video and play it.
from videodb import play_stream
stream_url = timeline.generate_stream()
play_stream(stream_url)
Here’s a preview of showing occurrence of the word Education
This embedded link can't be shown.
Conclusion
This tutorial showcases VideoDB’s capabilities to create a video that programmatically counts and displays the frequency of a specific keyword spoken throughout the video. This method can be adapted for various applications where dynamic text overlays add significant value to video content.
Tips and Tricks
For more such explorations, refer to the and join the VideoDB community on or for support and collaboration.
Feel free to share your creations via social media or other platforms to inspire others in the community. Join us on , or
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