Take a URL, single video, list of URLs, or list of local videos + URLs and feed it into the script and have each video transcribed (and downloaded if not local) using faster-whisper. Transcriptions can then be shuffled off to an LLM API endpoint of your choice, whether that be local or remote. Any site supported by yt-dl is supported, so you can use this with sites besides just youtube.

I personally recommend Sonnet, for the price it's very nice.

Original: YouTube contains an incredible amount of knowledge, much of which is locked inside multi-hour videos. Let's extract and summarize it with AI!

tl/dr: Download Videos -> Transcribe -> Summarize. Scripted.

Use the script to transcribe a local file or remote url. Any url youtube-dl supports should work. If you pass an API name (openai/anthropic/cohere) as a second argument, and add your API key to the config file, you can have your resulting transcriptions summarized as well.
- The current approach to summarization is currently 'dumb'/naive, and will likely be replaced or additional functionality added to reflect actual practices and not just 'dump txt in and get an answer' approach.

Save time and use the config.txt file, it allows you to set these settings and have them used when ran.

usage: diarize.py [-h] [--api_url API_URL] [--num_speakers NUM_SPEAKERS] [--whisper_model WHISPER_MODEL]
                  [--offset OFFSET] [--vad_filter]
                  [input_path]

positional arguments:
  input_path            Path or URL of the video

options:
  -h, --help            show this help message and exit
  --api_name API_NAME   API name for summarization (optional)
  --api_key API_KEY     API key for summarization (optional)
  --num_speakers NUM_SPEAKERS
                        Number of speakers (default: 2)
  --whisper_model WHISPER_MODEL
                        Whisper model (default: small.en)
  --offset OFFSET       Offset in seconds (default: 0)
  --vad_filter          Enable VAD filter
  --log_level {DEBUG,INFO,WARNING,ERROR,CRITICAL}
                        Log level (default: INFO)

>python diarize.py ./local/file_on_your/system --api_name anthropic
>python diarize.py https://www.youtube.com/watch?v=4nd1CDZP21s --api_name anthropic
>python diarize.py https://www.youtube.com/watch?v=4nd1CDZP21s --api_name openai 
>python diarize.py https://www.youtube.com/watch?v=4nd1CDZP21s --api_name anthropic --api_key lolyearight
>python diarize.py https://www.youtube.com/watch?v=4nd1CDZP21s --api_name openai --api_key lolyearight

Pieces

Workflow
1. Setup python + packages
2. Setup ffmpeg
3. Run python diarize.py <video_url> or python diarize.py <List_of_videos.txt>
4. If you want summarization, add your API keys (if needed[is needed for now]) to the config.txt file, and then re-run the script, passing in the name of the API [or URL endpoint - to be added] to the script.
- python diarize.py https://www.youtube.com/watch?v=4nd1CDZP21s --api_name anthropic - This will attempt to download the video, then upload the resulting json file to the anthropic API endpoint, referring to values set in the config file (API key and model) to request summarization.
- OpenAI:
- Anthropic:
  - Opus: claude-3-opus-20240229
  - Sonnet: claude-3-sonnet-20240229
  - Haiku: claude-3-haiku-20240307
- Cohere:

What's in the repo?

diarize.py - download, transcribe and diarize audio
1. First uses yt-dlp to download audio(optionally video) from supplied URL
2. Next, it uses ffmpeg to convert the resulting .m4a file to .wav
3. Then it uses faster_whisper to transcribe the .wav file to .txt
4. After that, it uses pyannote to perform 'diarorization'
5. Finally, it'll send the resulting txt to an LLM endpoint of your choice for summarization of the text.
- Goal is to support OpenAI/Claude/Cohere/Groq/local OpenAI endpoint (oobabooga/llama.cpp/exllama2) so you can either do a batch query to X endpoint, or just feed them one at a time. Your choice.
chunker.py - break text into parts and prepare each part for LLM summarization
roller-*.py - rolling summarization
- can-ai-code - interview executors to run LLM inference
compare.py - prepare LLM outputs for webapp
compare-app.py - summary viewer webapp

Setup

Linux
1. Download necessary packages (Python3, ffmpeg[sudo apt install ffmpeg / dnf install ffmpeg], ?)
2. Create a virtual env: python -m venv ./
3. Launch/activate your virtual env: . .\scripts\activate.sh
4. See Linux && Windows
Windows
1. Download necessary packages (Python3, ffmpeg, ?)
2. Create a virtual env: python -m venv .\
3. Launch/activate your virtual env: . .\scripts\activate.ps1
4. See Linux && Windows
Linux && Windows
1. pip install -r requirements.txt - may take a bit of time...
2. Run python ./diarize.py <video_url> - The video URL does not have to be a youtube URL. It can be any site that ytdl supports.
3. You'll then be asked if you'd like to run the transcription through GPU(1) or CPU(2).
4. Next, the video will be downloaded to the local directory by ytdl.
5. Then the video will be transcribed by faster_whisper. (You can see this in the console output) * The resulting transcription output will be stored as both a json file with timestamps, as well as a txt file with no timestamps.
6. Finally, you can have the transcription summarized through feeding it into an LLM of your choice.
7. For running it locally, here's the commands to do so: * FIXME
8. For feeding the transcriptions to the API of your choice, simply use the corresponding script for your API provider. * FIXME: add scripts for OpenAI api (generic) and others

Usage

Single file (remote URL) transcription
- Single URL: python diarize.py https://example.com/video.mp4
Single file (local) transcription)
- Transcribe a local file: python diarize.py /path/to/your/localfile.mp4
Multiple files (local & remote)
- List of Files(can be URLs and local files mixed): python diarize.py ./path/to/your/text_file.txt"

README.md

TL/DW: Too Long, Didnt Watch

tl/dr: Download Videos -> Transcribe -> Summarize. Scripted.

Pieces

What's in the repo?

Setup

Usage

Credits