55 lines
1.8 KiB
Markdown
55 lines
1.8 KiB
Markdown
# audio-summarize
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An audio summarizer that glues together [faster-whisper](https://github.com/SYSTRAN/faster-whisper) and [BART](https://huggingface.co/facebook/bart-large-cnn).
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## Dependencies
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- Python 3 (tested: 3.12)
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## Setup
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Create a virtual environment for python, activate it and install the required python packages:
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```bash
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python3 -m venv .venv
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source .venv/bin/activate
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pip3 install -r requirements.txt
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```
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## Run
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1. In your terminal, make shure you have your python venv activated
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2. Run audio-summarize.py
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### Usage
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```
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./audio-summarize.py -i filepath -o filepath
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[--summin n] [--summax n] [--segmax n]
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[--lang lang] [-m name]
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options:
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-h, --help show this help message and exit
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--summin n The minimum lenght of a segment summary [10, min: 5]
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--summax n The maximum lenght of a segment summary [90, min: 5]
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--segmax n The maximum number of tokens per segment [375, 5 - 500]
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--lang lang The language of the audio source ['en']
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-m name The name of the whisper model to be used ['small.en']
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-i filepath The path to the media file
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-o filepath Where to save the output text to
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```
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Example:
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```bash
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./audio-summarize.py -i ./tmp/test.webm -o ./tmp/output.txt
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```
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## How does it work?
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To summarize a media file, the program executes the following steps:
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1. Convert and transcribe the media file using [faster-whisper](https://github.com/SYSTRAN/faster-whisper), using [ffmpeg](https://www.ffmpeg.org/) and [ctranslate2](https://github.com/OpenNMT/CTranslate2/) under the hood
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2. Semantically split up the transcript into segments using [semantic-text-splitter](https://github.com/benbrandt/text-splitter) and the tokenizer for BART
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3. Summarize each segment using BART ([`facebook/bart-large-cnn`](https://huggingface.co/facebook/bart-large-cnn))
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4. Write the results to a text file
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