admin
172 lines
4.7 KiB
Markdown
172 lines
4.7 KiB
Markdown
# ScrAIbe – LocalAI-Backed Transcription and Summarization
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ScrAIbe is a lightweight transcription and summarization client that:
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- Sends audio to a LocalAI server running vibevoice.cpp for transcription and speaker diarization.
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- Optionally uses a second LLM to generate a detailed, structured summary of the conversation.
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No local speech models or heavy dependencies are required. ScrAIbe is designed to be run as a thin client in front of your own AI services.
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## Features
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- Transcription with speaker diarization via LocalAI:
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- Uses the `/v1/audio/diarization` endpoint.
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- Compatible with vibevoice.cpp and other diarization-capable backends.
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- Optional AI-powered summarization:
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- Task: `transcript_and_summarize`
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- Chunks long transcripts, summarizes each chunk, then generates a final comprehensive summary.
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- Summary highlights:
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- Main topics and discussion points
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- Key decisions and outcomes
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- Action items and responsibilities
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- Open issues and risks
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- CLI and Python API:
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- Simple command-line interface.
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- Drop-in `Scraibe` class for integration into other tools.
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- Docker-ready:
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- Lightweight container, configured via environment variables.
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## Architecture
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- LocalAI (vibevoice.cpp):
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- Handles audio → transcript + speaker segments.
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- Summarizer LLM (OpenAI-compatible chat endpoint):
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- Handles transcript → structured summary.
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- ScrAIbe:
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- Orchestrates:
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- File upload to LocalAI
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- Transcript assembly
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- Chunked summarization
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- Output formatting (e.g., .md with transcript + summary)
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## Quick Start (CLI)
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Basic usage:
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- Transcribe:
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- python3 -m scraibe.cli -f "audio.wav" -o "./output" -of txt
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- Transcribe and summarize:
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- python3 -m scraibe.cli -f "audio.wav" -o "./output" --task transcript_and_summarize
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Environment variables must be set to point to your LocalAI and summarizer LLM.
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## Python API
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Example: transcribe only
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- from scraibe import Scraibe
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- client = Scraibe()
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- text = client.transcribe("audio.wav")
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- print(text)
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Example: transcribe and summarize
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- from scraibe import Scraibe
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- client = Scraibe()
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- result = client.transcript_and_summarize("audio.wav")
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- transcript = result["transcript"]
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- summary = result["summary"]
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You can override endpoints and models via environment variables or constructor parameters if needed.
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## Command-Line Options
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Run:
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- python3 -m scraibe.cli -h
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Key options:
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- -f / --audio-files:
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- One or more audio files to process.
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- --task:
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- transcribe (default)
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- transcript_and_summarize
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- -o / --output-directory:
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- Output folder for generated files.
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- -of / --output-format:
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- txt, json, md, html
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- For transcript_and_summarize, output is always saved as .md with:
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- # Transcript
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- # Summary
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Other options (e.g., --language, --num-speakers) are accepted and forwarded where applicable; many legacy Whisper/Pyannote flags are kept for compatibility but ignored.
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## Docker Usage
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ScrAIbe is designed to run in Docker as a client to your LocalAI and summarizer LLM.
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### Basic run (transcribe)
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- docker run -it \
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-e LOCALAI_API_URL=http://localai:8080 \
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-v /path/to/audio:/audio \
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scraibe:latest \
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-f /audio/meeting.wav -o /audio/output -of txt
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### Basic run (transcribe + summarize)
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- docker run -it \
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-e LOCALAI_API_URL=http://localai:8080 \
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-e SUMMARIZER_API_URL=http://llm:8080 \
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-v /path/to/audio:/audio \
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scraibe:latest \
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-f /audio/meeting.wav -o /audio/output --task transcript_and_summarize
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### Docker Environment Variables
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The following environment variables configure ScrAIbe in Docker.
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Transcription / Diarization (LocalAI):
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- LOCALAI_API_URL:
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- Required.
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- Base URL of the LocalAI server.
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- Example: http://localai:8080
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- LOCALAI_API_KEY:
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- Optional.
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- API key for LocalAI, if configured.
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- LOCALAI_MODEL:
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- Optional (default: vibevoice-diarize).
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- Model name used for transcription/diarization.
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Summarization LLM:
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- SUMMARIZER_API_URL:
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- Required when using --task transcript_and_summarize.
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- Base URL of the summarization LLM (OpenAI-compatible /v1/chat/completions).
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- Example: http://llm:8080
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- SUMMARIZER_API_KEY:
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- Optional.
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- API key for the summarization LLM, if required.
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- SUMMARIZER_MODEL:
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- Optional (default: llama-3.1-8b-instruct).
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- Model name used for summarization.
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All of these can also be overridden from the CLI when needed (e.g., --localai-api-url, --summarizer-api-url).
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## Dependencies
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Core runtime dependencies:
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- Python 3.9+
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- httpx
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- numpy
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- tqdm
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- ffmpeg (for audio preprocessing)
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No local Whisper, PyTorch, or Pyannote models are required.
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## Contributing
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Contributions are welcome. Please refer to CONTRIBUTING.md for guidelines.
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## License
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This project is licensed under GPL-3.0. See LICENSE for details.
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