Leveraging AI to Gain Video Insights and Summary

Table of Contents

• Transcribing Videos with OpenAI Whisper
• Summarizing Transcripts with Anthropic Cloud
• Comparing Cloud and GPT Capabilities
• Optimizing Setup for Faster Performance
• Applying to Existing Video Files
• Conclusion and Next Steps

Transcribing Videos with OpenAI Whisper for Easier Keyword Searching

As researchers, we often find more conference talks and videos online than we have time to watch. Transcribing these videos into text makes them searchable so we can quickly find information relevant to our interests. In this post, I'll demonstrate using OpenAI Whisper to automatically transcribe a 1 hour video from the Stanford Human-Centered AI Research Center. I'll then summarize the transcript using Anthropic's Claude to extract key insights.

Installing Required Python Libraries

The code is simple Python running in Google Colab, a free online coding environment. We'll use OpenAI Whisper for transcription, yt-dlp to download the YouTube video, ffmpeg to handle the video file, and Claude from Anthropic to summarize the text.

Running the Transcription

With the libraries installed, we call Whisper on the downloaded video file and select the English language and Large model for highest accuracy. On free Colab hardware the 1 hour video took 30 minutes. Using GPU acceleration is faster than CPU. The output is a text file transcript with timestamps.

Downloading Output Text Files

Whisper outputs text in multiple formats like SRT, VTT, and JSON. The SRT format includes timestamps with each line of text. The JSON is unbroken text good for summarization. We could also ask Claude questions related to timestamped sections.

Summarizing Transcripts with Anthropic Cloud for Key Insights

With the full text transcript, we can leverage large language models like Anthropic Claude for summarization and analysis. Claude accepts up to 100K tokens of context - around a few hours of video, while GPT-3 is limited to 4K tokens.

Pasting Full Transcript into Cloud Doc

Anthropic Cloud automatically saves pasted text into a doc, handling the long input easily in one pass. The UI response time is also very fast compared to running queries locally.

Reading the Summary Response

In seconds, Claude provides an accurate top-level summary picking out the key points from the hour-long discussion without needing any document structure formatting on our end.

Comparing Cloud and GPT Capabilities

Cloud's Large Context Advantage

While GPT-3 tops out at 4K tokens, Claude can ingest the full transcript text in one go for superior summarization, no need to break up the document. This saves time and allows Cloud to extract concepts spanning the entire discussion.

Optimizing Setup for Faster Transcription

Using GPU Instead of CPU

The free Colab GPU performed transcription 4 times faster than CPU would. Upgrading to a paid Colab account allows access to more powerful GPUs for even faster turnaround times.

Breaking Up Long Transcripts

For recordings longer than Claude's 100K token capacity, break the SRT transcript file into chunks under the limit for summarization. GPT-3 may require chunking any video over 20 minutes.

Applying to Existing Video Files

The code can work directly with a local video file, skipping the YouTube download. This opens up easy automated transcription and analysis of video libraries without published links.

Conclusion and Next Steps

In summary, OpenAI Whisper provides fast, accurate automated speech recognition to unlock insights from conference videos and internal recordings. Anthropic Claude summarizes transcripts with greater context than GPT-3. Next we could build a system to regularly update researchers on new talks in their field using these AI techniques.

FAQ

Q: Can I use this for audio files or audio only sources?
A: Yes, the OpenAI Whisper transcription component can handle audio input files instead of just video files. The rest of the workflow would remain the same.

Q: Do I need coding skills to implement this workflow?
A: Basic coding skills would be helpful to run the sample code as-is. However, the concepts can be implemented through OpenAI and Anthropic's graphical user interfaces as well.

Q: What kind of insight can I expect from the AI summary?
A: You can expect a concise summary highlighting the key points and topics covered in the video or transcript text.

Q: Can I get timed transcriptions to see where key points occur?
A: Yes, some of the output formats like SRT and VTT files contain timestamp information along with the transcriptions.

Q: Does the large context window remove the need to split up long transcripts?
A: In most cases yes, but very long transcripts may still benefit from some strategic splitting to aid comprehension and summary quality.

Q: How accurate are AI generated transcripts?
A: Accuracy ranges from 80-95% on average and improves with higher quality audio input. Some human review may be needed to correct errors.

Q: Can I retrain the models on my specific use case data?
A: Whisper and Cloud are currently not trainable, but users can provide feedback on translations to continue improving them.

Q: What are some other potential uses for this workflow?
A: Other uses could include market research, content creation, competitive analysis, lead generation, and more based on video commentary.

Q: Are there limits on using the free versions of Whisper and Cloud?
A: There are reasonable limits to prevent abuse, but small-scale legitimate usage is unlikely to encounter issues.

Q: Can I get Whisper and Cloud outputs processed by other models like GPT-3?
A: Yes, the transcripts and summaries can be input into other models through their APIs or user interfaces for further analysis as well.