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# Pre-process LRS3
We provide a pre-processing pipeline in this repository for detecting and cropping full-face regions of interest (ROIs) as well as corresponding audio waveforms for LRS3.
## Introduction
Before feeding the raw stream into our model, each video sequence has to undergo a specific pre-processing procedure. This involves three critical steps. The first step is to perform face detection. Following that, each individual frame is aligned to a referenced frame, commonly known as the mean face, in order to normalize rotation and size differences across frames. The final step in the pre-processing module is to crop the face region from the aligned face image.
<div align="center">
<table style="display: inline-table;">
<tr><td><img src="https://download.pytorch.org/torchaudio/doc-assets/avsr/original.gif", width="144"></td><td><img src="https://download.pytorch.org/torchaudio/doc-assets/avsr/detected.gif" width="144"></td><td><img src="https://download.pytorch.org/torchaudio/doc-assets/avsr/transformed.gif" width="144"></td><td><img src="https://download.pytorch.org/torchaudio/doc-assets/avsr/cropped.gif" width="144"></td></tr>
<tr><td>0. Original</td> <td>1. Detection</td> <td>2. Transformation</td> <td>3. Face ROIs</td> </tr>
</table>
</div>
## Preparation
1. Install all dependency-packages.
```Shell
pip install -r requirements.txt
```
2. Install [retinaface](./tools) or [mediapipe](https://pypi.org/project/mediapipe/) tracker. If you have installed the tracker, please skip it.
## Preprocessing LRS3
To pre-process the LRS3 dataset, plrase follow these steps:
1. Download the LRS3 dataset from the official website.
2. Run the following command to preprocess the dataset:
```Shell
python preprocess_lrs3.py \
--data-dir=[data_dir] \
--detector=[detector] \
--dataset=[dataset] \
--root-dir=[root] \
--subset=[subset] \
--seg-duration=[seg_duration] \
--groups=[n] \
--job-index=[j]
```
- `data-dir`: Path to the directory containing video files.
- `detector`: Type of face detector. Valid values are: `mediapipe` and `retinaface`. Default: `retinaface`.
- `dataset`: Name of the dataset. Valid value is: `lrs3`.
- `root-dir`: Path to the root directory where all preprocessed files will be stored.
- `subset`: Name of the subset. Valid values are: `train` and `test`.
- `seg-duration`: Length of the maximal segment in seconds. Default: `16`.
- `groups`: Number of groups to split the dataset into.
- `job-index`: Job index for the current group. Valid values are an integer within the range of `[0, n)`.
3. Run the following command to merge all labels:
```Shell
python merge.py \
--root-dir=[root_dir] \
--dataset=[dataset] \
--subset=[subset] \
--seg-duration=[seg_duration] \
--groups=[n]
```
- `root-dir`: Path to the root directory where all preprocessed files will be stored.
- `dataset`: Name of the dataset. Valid values are: `lrs2` and `lrs3`.
- `subset`: The subset name of the dataset. For LRS2, valid values are `train`, `val`, and `test`. For LRS3, valid values are `train` and `test`.
- `seg-duration`: Length of the maximal segment in seconds. Default: `16`.
- `groups`: Number of groups to split the dataset into.
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