https://github.com/enhuiz/phoenix-datasets
PyTorch dataset wrappers for PHOENIX 2014 & PHOENIX-2014-T sign language datasets.
https://github.com/enhuiz/phoenix-datasets
rwth-phoenix-dataset sign-language sign-language-datasets
Last synced: 4 months ago
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PyTorch dataset wrappers for PHOENIX 2014 & PHOENIX-2014-T sign language datasets.
- Host: GitHub
- URL: https://github.com/enhuiz/phoenix-datasets
- Owner: enhuiz
- License: mit
- Created: 2020-10-26T12:07:27.000Z (over 4 years ago)
- Default Branch: main
- Last Pushed: 2021-01-21T01:43:53.000Z (over 4 years ago)
- Last Synced: 2025-01-07T18:29:30.653Z (5 months ago)
- Topics: rwth-phoenix-dataset, sign-language, sign-language-datasets
- Language: Lex
- Homepage:
- Size: 64.5 KB
- Stars: 20
- Watchers: 3
- Forks: 3
- Open Issues: 2
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# PHOENIX Datasets 🐦
## Introduction
[PHOENIX-2014](https://www-i6.informatik.rwth-aachen.de/~koller/RWTH-PHOENIX/) and [PHOENIX-2014-T](https://www-i6.informatik.rwth-aachen.de/~koller/RWTH-PHOENIX-2014-T/) are popular large scale German sign language datasets developed by Human Language Technology & Pattern Recognition Group from RWTH Aachen University, Germany. This package provides a PyTorch dataset wrapper for those two datasets to make the building of PyTorch model on these two datasets easier.
## Installation
```bash
pip install git+https://github.com/enhuiz/phoenix-datasets
```
## Example Usage
### Dataset
```python
from phoenix_datasets import PhoenixVideoTextDataset
from torch.utils.data import DataLoader
dtrain = PhoenixVideoTextDataset(
# your path to this folder, download it from official website first.
root="data/phoenix-2014-multisigner",
split="train",
p_drop=0.5,
random_drop=True,
random_crop=True,
base_size=[256, 256]
crop_size=[224, 224],
)
vocab = dtrain.vocab
print("Vocab", vocab)
dl = DataLoader(dtrain, collate_fn=dtrain.collate_fn)
for batch in dl:
video = batch["video"]
label = batch["label"]
signer = batch["signer"]
assert len(video) == len(label)
print(len(video))
print(video[0].shape)
print(label[0].shape)
print(signer)
break
```
### Evaluation
Go to `phoenix-2014-multisigner/evaluation/NIST-sclite_sctk-2.4.0-20091110-0958.tar.bz2` to install `sclite` (the official tool for WER calculation) first and then put it in your PATH.
```python
from phoenix_datasets.evaluators import PhoenixEvaluator
evaluator = PhoenixEvaluator("data/phoenix-2014-multisigner")
hyp = evaluator.corpus.load_data_frame("dev")["annotation"].apply(" ".join).tolist()
hyp[0] = "THIS SENTENCE IS WRONG"
results = evaluator.evaluate("dev", hyp)
print(results["parsed_dtl"])
print(results["sum"])
```
## Supported Features
- [x] Load the automatic alignments for PHOENIX-2014
- [x] Randomly/evenly frame dropping augmentation
- [x] Evaluation for Phoenix-2014
- [x] Language Model
## TODOs
- [ ] Implement Corpus and evaluation for PHOENIX-2014-T