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https://github.com/ajcwebdev/autoshow
End-to-end scripting workflow to automatically generate show notes from audio/video transcripts with Whisper.cpp, Llama.cpp, yt-dlp, and Commander.js
https://github.com/ajcwebdev/autoshow
chatgpt claude cohere llama-cpp mistral ollama whisper-cpp yt-dlp
Last synced: 8 days ago
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End-to-end scripting workflow to automatically generate show notes from audio/video transcripts with Whisper.cpp, Llama.cpp, yt-dlp, and Commander.js
- Host: GitHub
- URL: https://github.com/ajcwebdev/autoshow
- Owner: ajcwebdev
- License: mit
- Created: 2024-04-17T03:46:50.000Z (9 months ago)
- Default Branch: main
- Last Pushed: 2024-10-23T20:38:08.000Z (2 months ago)
- Last Synced: 2024-10-24T08:25:42.210Z (2 months ago)
- Topics: chatgpt, claude, cohere, llama-cpp, mistral, ollama, whisper-cpp, yt-dlp
- Language: TypeScript
- Homepage:
- Size: 1.47 MB
- Stars: 29
- Watchers: 2
- Forks: 4
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- Funding: .github/FUNDING.yml
- License: LICENSE
- Roadmap: docs/roadmap.md
Awesome Lists containing this project
README
Autoshow
## Outline
- [Project Overview](#project-overview)
- [Key Features](#key-features)
- [Setup](#setup)
- [Run Autoshow Node Scripts](#run-autoshow-node-scripts)
- [Project Structure](#project-structure)
- [Contributors](#contributors)
## Project Overview
Autoshow automates the processing of audio and video content from various sources, including YouTube videos, playlists, podcast RSS feeds, and local media files. It performs transcription, summarization, and chapter generation using different language models (LLMs) and transcription services.
The Autoshow workflow includes the following steps:
1. The user provides input (video URL, playlist, RSS feed, or local file).
2. The system downloads the audio (if necessary).
3. Transcription is performed using the selected service.
4. The transcript is processed by the chosen LLM to generate a summary and chapters.
5. Results are saved in markdown format with front matter.
### Key Features
- Support for multiple input types (YouTube links, RSS feeds, local video and audio files)
- Integration with various:
- LLMs (ChatGPT, Claude, Gemini, Cohere, Mistral, Fireworks, Together, Groq)
- Transcription services (Whisper.cpp, Deepgram, Assembly)
- Local LLM support with Ollama
- Customizable prompts for generating titles, summaries, chapter titles/descriptions, key takeaways, and questions to test comprehension
- Markdown output with metadata and formatted content
- Command-line interface for easy usage
- *WIP: Node.js server and React frontend*
See [`docs/roadmap.md`](/docs/roadmap.md) for details about current development work and future potential capabilities.
## Setup
`scripts/setup.sh` checks to ensure a `.env` file exists, Node dependencies are installed, and the `whisper.cpp` repository is cloned and built. Run the script with the `setup` script in `package.json`.
```bash
npm run setup
```
## Run Autoshow Node Scripts
Run on a single YouTube video.
```bash
npm run as -- --video "https://www.youtube.com/watch?v=MORMZXEaONk"
```
Run on a YouTube playlist.
```bash
npm run as -- --playlist "https://www.youtube.com/playlist?list=PLCVnrVv4KhXPz0SoAVu8Rc1emAdGPbSbr"
```
Run on a list of arbitrary URLs.
```bash
npm run as -- --urls "content/example-urls.md"
```
Run on a local audio or video file.
```bash
npm run as -- --file "content/audio.mp3"
```
Run on a podcast RSS feed.
```bash
npm run as -- --rss "https://ajcwebdev.substack.com/feed"
```
Use local LLM.
```bash
npm run as -- --video "https://www.youtube.com/watch?v=MORMZXEaONk" --ollama
```
Use 3rd party LLM providers.
```bash
npm run as -- --video "https://www.youtube.com/watch?v=MORMZXEaONk" --chatgpt GPT_4o_MINI
npm run as -- --video "https://www.youtube.com/watch?v=MORMZXEaONk" --claude CLAUDE_3_5_SONNET
npm run as -- --video "https://www.youtube.com/watch?v=MORMZXEaONk" --gemini GEMINI_1_5_PRO
npm run as -- --video "https://www.youtube.com/watch?v=MORMZXEaONk" --cohere COMMAND_R_PLUS
npm run as -- --video "https://www.youtube.com/watch?v=MORMZXEaONk" --mistral MISTRAL_LARGE
npm run as -- --video "https://www.youtube.com/watch?v=MORMZXEaONk" --fireworks
npm run as -- --video "https://www.youtube.com/watch?v=MORMZXEaONk" --together
npm run as -- --video "https://www.youtube.com/watch?v=MORMZXEaONk" --groq
```
Example commands for all available CLI options can be found in [`docs/examples.md`](/docs/examples.md).
## Project Structure
- Main Entry Points (`src/cli`)
- `commander.ts`: Defines the command-line interface using Commander
- `interactive.ts`: Defines the interactive terminal interface using Inquirer
- Command Processors (`src/commands`)
- `process-file.ts`: Handles local audio/video file processing
- `process-video.ts`: Handles single YouTube video processing
- `process-urls.ts`: Processes videos from a list of URLs in a file
- `process-playlist.ts`: Processes all videos in a YouTube playlist
- `process-channel.ts`: Processes all videos from a YouTube channel
- `process-rss.ts`: Processes podcast RSS feeds
- Utility Functions (`src/utils`)
- `generate-markdown.ts`: Creates initial markdown files with metadata
- `download-audio.ts`: Downloads audio from YouTube videos
- `run-transcription.ts`: Manages the transcription process
- `run-llm.ts`: Handles LLM processing for summarization and chapter generation
- `clean-up-files.ts`: Removes temporary files after processing
- `logging.ts`: Reusable Chalk functions for logging colors
- `validate-option.ts`: Functions for validating CLI options and handling errors
- Transcription Services (`src/transcription`)
- `whisper.ts`: Uses Whisper.cpp, openai-whisper, or whisper-diarization for transcription
- `deepgram.ts`: Integrates Deepgram transcription service
- `assembly.ts`: Integrates AssemblyAI transcription service
- `transcription-utils.ts`: Transcript formatting functions
- Language Models (`src/llms`)
- `ollama.ts`: Integrations Ollama's locally available models
- `chatgpt.ts`: Integrates OpenAI's GPT models
- `claude.ts`: Integrates Anthropic's Claude models
- `gemini.ts`: Integrates Google's Gemini models
- `cohere.ts`: Integrates Cohere's language models
- `mistral.ts`: Integrates Mistral AI's language models
- `fireworks.ts`: Integrates Fireworks's open source models
- `together.ts`: Integrates Together's open source models
- `groq.ts`: Integrates Groq's open source models
- `prompt.ts`: Defines the prompt structure for summarization and chapter generation
## Contributors
- ✨Hello beautiful human! ✨[Jenn Junod](https://jennjunod.dev/) host of [Teach Jenn Tech](https://teachjenntech.com/)