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Implementation of "Look, Listen and Recognise:character-aware audio-visual subtitling"

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# Character-aware audio-visual subtitling

[[arXiv]](https://arxiv.org/abs/2401.12039) [[project_page]](https://www.robots.ox.ac.uk/~vgg/research/look-listen-recognise/)

Character-aware audio-visual subtitling is an audiovisual diarisation pipeline for scripted TV content. It combines WhisperX for transcription, a custom audiovisual attention model, RetinaFace-based face clustering, and SpeechBrain speaker embeddings to produce per-character subtitles aligned with the original video.

![Character-Aware Subtitle Pipeline](https://raw.githubusercontent.com/JaesungHuh/ca-subtitle/main/assets/ca-subtitle.jpg)

![Output Example](https://raw.githubusercontent.com/JaesungHuh/ca-subtitle/main/assets/output.gif)

## Environment Setup
- Use python = 3.10
```
git clone https://github.com/JaesungHuh/ca-subtitle.git
cd ca-subtitle
pip install -r requirements.txt
```

- If you use uv
```
uv sync
source .venv/bin/activate
```

## Download models and sample data
- Download checkpoints and sample data to run this pipeline.

```
bash download_data.sh
```
## Configuration

All runtime options live in YAML under `config/`. Two templates are provided:

- `config/pipeline_sample.yaml` – minimal example pointing at `sample_data/`.
- `config/pipeline_config.yaml` – richer template for full-season batch processing.

Important sections:

- `paths`: input/output roots, weight locations, temp directories.
- `processing`: per-stage parameters (Whisper model size, AV batch size, face thresholds, speaker clustering).
- `runtime`: GPU assignment, number of parallel episodes, and caching/cleanup flags.

Copy one of the templates, adjust paths, and pass it to `run_pipeline.py --config /path/to/config.yaml`.

### Data directory layout

The paths under `paths.*_root` expect a consistent naming scheme so the orchestrator can discover assets automatically:

- `video_root/<ShowName>/<ShowName>_<SS>x<EE>.mp4` – source episode video per show, where `SS` and `EE` are zero-padded season/episode numbers (e.g. `Seinfeld/Seinfeld_03x05.mp4`).
- `audio_root/<ShowName>/<ShowName>_<SS>x<EE>.wav` – pre-extracted mono 16kHz WAV audio aligned with the corresponding video file.
- `castlist_root/<ShowName>/<ShowName>_<SS>x<EE>.txt` – CSV (no header) with `CharacterName,RealName` rows used to map diarisation outputs to on-screen names.
- `person_images/<ShowName>_<SS>x<EE>/<CharacterFolder>/*.jpg` – reference face crops for each character. If per-episode folders are unavailable, place character folders directly under `person_images/CharacterFolder/*.jpg` to share exemplars across episodes.

Adjust the extensions if your media differs, but keep the episode ID format identical to what is configured under `shows` so the pipeline can resolve files.

## Running the Pipeline

```bash
# Process every episode defined in the config sequentially
python run_pipeline.py --config config/pipeline_sample.yaml --sequential

# Process specific episodes (parallel by default)
python run_pipeline.py --episodes Seinfeld_03x01 Seinfeld_03x02

# Clean intermediate artifacts after a successful run
python run_pipeline.py --cleanup
```

Stages run in order: `audio_processing → av_analysis → face_processing → speaker_identification`. Results are saved under `exp/<run_name>/output/<show>/<episode>/` as JSON plus RTTM/SRT subtitle files.

If a run is interrupted, the pipeline writes `pipeline_checkpoint.json` under the intermediate directory; re-running will resume from the last unfinished stage.

## Outputs

Each episode directory contains:

- `<episode>.json` – aggregated stage results.
- `<episode>.srt` – speaker-labelled subtitles with Whisper transcripts.
- `<episode>.rttm` – diarisation file for evaluation tools.
- `logs/`, `temp/`, `intermediate/` – per-stage diagnostics and cached artifacts (optional, based on runtime flags).

## Development Tips

- Keep `config/pipeline_sample.yaml` aligned with `sample_data/` to ensure smoke tests stay functional.
- Large intermediate files live under `exp/`; clean the directory regularly or enable `runtime.cleanup_temp`.
- GPU assignment is controlled via `runtime.gpu_ids`. Leave the list empty to force CPU-only execution (slower); invalid or unavailable IDs are ignored automatically and the pipeline falls back to CPU.
- AV analysis uses CuPy and Torch; ensure your CUDA toolkit matches the versions specified in `requirements.txt`.

## Comments

- In our original [paper](https://arxiv.org/abs/2401.12039), we used [CLIPPAD](https://www.robots.ox.ac.uk/~vgg/publications/2022/Korbar22/korbar22.pdf) to recognise characters. This repository uses [RetinaFace](https://github.com/serengil/retinaface) plus a SENet‑based face‑embedding model, which produces much better results.
- In the original paper, we applied the same nearest-neighbour–based audio filtering across all episodes within each show, whereas this repository applies it within each episode individually.
- We use only off‑the‑shelf models in this pipeline. You can plug in new models to obtain better transcription, diarisation, or character‑recognition results.

## License

This project is licensed under the Apache License, Version 2.0. See the [LICENSE](LICENSE) file for details.

## Acknowledgements

- [WhisperX](https://github.com/m-bain/whisperX)
- [avobjects_repo](https://github.com/afourast/avobjects)
- [Speechbrain](https://huggingface.co/speechbrain/spkrec-ecapa-voxceleb)
- [RetinaFace](https://github.com/serengil/retinaface)
- Special thanks to [Codex](https://openai.com/codex/) who helped me to refactor the original indecipherable code.
- This is a joint work with [Bruno](https://scholar.google.com/citations?user=T0yIjk4AAAAJ&hl=en) and [AZ](https://scholar.google.com/citations?user=UZ5wscMAAAAJ&hl=en).

## Citation
Please cite this if you use this for academic purposes.
```
@inproceedings{korbar24,
  author       = "Bruno Korbar and Jaesung Huh and Andrew Zisserman",
  title        = "Look, Listen and Recognise: character-aware audio-visual subtitling",
  booktitle    = "International Conference on Acoustics, Speech, and Signal Processing ",
  year         = "2024",
}
```

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