init

.gitignore

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+GRID_alignments
+lip_coordinates
+lip_images

GRID_alignments.tar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:40abb10300984775e6465b6ab6665231c8d81f3a9af559622d6f48a48d902f11
+size 67338240

README.md

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 ---
 license: cc-by-4.0
+language:
+  - en
+size_categories:
+  - 1K<n<10K
 ---
+
+# Enhanced GRID Corpus with Lip Landmark Coordinates
+
+## Introduction
+
+This enhanced version of the GRID audiovisual sentence corpus, originally available at [Zenodo](https://zenodo.org/records/3625687), incorporates significant new features for auditory-visual speech recognition research. Building upon the preprocessed data from [LipNet-PyTorch](https://github.com/VIPL-Audio-Visual-Speech-Understanding/LipNet-PyTorch), we have added lip landmark coordinates to the dataset, providing detailed positional information of key points around the lips. This addition greatly enhances its utility in visual speech recognition and related fields. Furthermore, to facilitate ease of access and integration into existing machine learning workflows, we have published this enriched dataset on the Hugging Face platform, making it readily available to the research community.
+
+## Dataset Structure
+
+This dataset is split into 3 directories:
+
+- `lip_images`: contains the images of the lips
+  - `speaker_id`: contains the videos of a particular speaker
+    - `video_id`: contains the video frames of a particular video
+      - `frame_no.jpg`: jpg image of the lips of a particular frame
+- `lip_coordinates`: contains the landmark coordinates of the lips
+  - `speaker_id`: contains the lip landmark of a particular speaker
+    - `video_id.json`: a json file containing the lip landmark coordinates of a particular video, where the keys are the frame numbers and the values are the x, y lip landmark coordinates
+- `GRID_alignments`: contains the alignments of all the videos in the dataset
+  - `speaker_id`: contains the alignments of a particular speaker
+    - `video_id`: contains the alignments of a particular video
+      - `video_id.align`: contains the alignments of a particular video, where each line is a word and the start and end time of the word in the video
+
+## Details
+
+The lip landmark coordinates are extracted using the original videos in the GRID corpus and using the dlib library, using the [shape_predictor_68_face_landmarks_GTX.dat](https://github.com/davisking/dlib-models) pretrained model. The lip landmark coordinates are then saved in a json file, where the keys are the frame numbers and the values are the x, y lip landmark coordinates. The lip landmark coordinates are saved in the same order as the frames in the video.
+
+## Usage
+
+The dataset can be downloaded by cloning this repository.
+
+### Cloning the repository
+
+```bash
+git clone https://huggingface.co/datasets/SilentSpeak/EGCLLC
+```
+
+### Loading the dataset
+
+After cloning the repository, you can load the dataset by unpacking the tar file and using dataset_tar.py script.
+
+Alternatively, a probably faster method is that, you can un-tar the tar files using the following command:
+
+```bash
+tar -xvf lip_images.tar
+tar -xvf lip_coordinates.tar
+tar -xvf GRID_alignments.tar
+```
+
+## References
+
+Alvarez Casado, C., Bordallo Lopez, M.
+Real-time face alignment: evaluation methods, training strategies and implementation optimization.
+Springer Journal of Real-time image processing, 2021
+
+Assael, Y., Shillingford, B., Whiteson, S., & Freitas, N. (2017). LipNet: End-to-End Sentence-level Lipreading. GPU Technology Conference.
+
+Cooke, M., Barker, J., Cunningham, S., & Shao, X. (2006). The Grid Audio-Visual Speech Corpus (1.0) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.3625687
+
+```
+
+```

dataset_tar.py

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+import tarfile
+
+ALIGNMENTS_PATH = "GRID_alignments"
+LIP_COORDINATES_PATH = "lip_coordinates"
+LIP_IMAGES_PATH = "lip_images"
+
+# # Create tar files
+# with tarfile.open(f"{LIP_COORDINATES_PATH}.tar", "w") as tar:
+#     tar.add(f"{LIP_COORDINATES_PATH}/")
+
+# with tarfile.open(f"{LIP_IMAGES_PATH}.tar", "w") as tar:
+#     tar.add(f"{LIP_IMAGES_PATH}/")
+
+# with tarfile.open(f"{ALIGNMENTS_PATH}.tar", "w") as tar:
+#     tar.add(f"{ALIGNMENTS_PATH}/")
+
+# Extract tar files
+# note: may take a while to untar the files, ~2-4 hours
+with tarfile.open(f"{LIP_COORDINATES_PATH}.tar", "r") as tar:
+    tar.extractall()
+
+with tarfile.open(f"{LIP_IMAGES_PATH}.tar", "r") as tar:
+    tar.extractall()
+
+with tarfile.open(f"{ALIGNMENTS_PATH}.tar", "r") as tar:
+    tar.extractall()

lip_coordinates.tar

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+oid sha256:7917e94adcdb1e0baa7c9c18a9877d7b6a09f8dfb2afa870b020e16c7c36a9ba
+size 588185600

lip_images.tar

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+version https://git-lfs.github.com/spec/v1
+oid sha256:49111af2cac2b0754296161237c79b36daf0100bedea426bfcfacd7d9b221a58
+size 14338662400