https://github.com/nezhar/speech-condenser
A tool for summarizing dialogues from videos or audio
https://github.com/nezhar/speech-condenser
asr speach-recognition speaker-diarization speaker-identification summarization
Last synced: 3 months ago
JSON representation
A tool for summarizing dialogues from videos or audio
- Host: GitHub
- URL: https://github.com/nezhar/speech-condenser
- Owner: nezhar
- Created: 2023-01-05T05:49:41.000Z (almost 3 years ago)
- Default Branch: main
- Last Pushed: 2023-08-29T19:37:46.000Z (about 2 years ago)
- Last Synced: 2025-04-19T17:26:01.738Z (6 months ago)
- Topics: asr, speach-recognition, speaker-diarization, speaker-identification, summarization
- Language: Python
- Homepage:
- Size: 241 KB
- Stars: 82
- Watchers: 4
- Forks: 10
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
Awesome Lists containing this project
README
# Speech Condenser
Speech condenser is a tool for reducing the size of a dialogue.
## Pipeline
It combines several tools to achieve the goal of reducing the size of a dialogue. Each step of the above pipleine runs inside a container.
Steps:
1. Audio extraction - Extracts the audio from the video file.
2. Speaker diarization - Identifies the speakers in the audio file.
3. Split audio - Splits the audio file into smaller chunks based on the speaker diarization.
4. Speech to text - Transcribes the audio chunks into text.
5. Combine ASR and diarization - Combines the results of the ASR and diarization to get the text for each speaker as a dialogue.
6. Summarization - Summarizes the dialogue.
## Installation
The setup uses docker or podman to run the containers. A set of local scripts are provided to run the pipeline.
* build.sh - Builds the containers.
* pipeline.sh - Runs the pipeline.
* yt-pipeline.sh - Runs the pipeline on a youtube video.
Videos needs to be provided in the `data/input` directory. `yt-pipeline.sh` will use this directory to download to cache the video.
The output will be in the `data/output` directory.
Make sure to create a `.env` based on the `.env.example` file and privide the required values:
* SC_RUNTIME - The runtime to use for the containers. Either `docker` or `podman`.
* HF_TOKEN - The [Hugging Face token](https://huggingface.co/settings/tokens) to use for the summarization step.
Make sure to visit [hf.co/pyannote/speaker-diarization](http://hf.co/pyannote/speaker-diarization) and [hf.co/pyannote/segmentation](https://hf.co/pyannote/segmentation) and accept user conditions. This required in order to be able to run the speaker diarization.
## Usage
Run agains a local video file:
```bash
./pipeline.sh "data/input/video.mp4"
```
Run against a youtube video:
```bash
./yt-pipeline.sh "https://www.youtube.com/watch?v=video_id"
```