Improve dataset card: Add metadata, links, description, and sample usage

README.md

@@ -1,4 +1,19 @@
 ---
+---
+task_categories:
+- automatic-speech-recognition
+license: mit
+language:
+- ar
+size_categories:
+- 100h<n<1kh
+tags:
+- quran
+- tajweed
+- arabic
+- speech
+- pronunciation-error-detection
+- speech-segmentation
 configs:
 - config_name: moshaf_metadata
   data_files:
@@ -1948,3 +1963,114 @@ configs:
   - dtype: float32
     name: match_ratio
 ---
+
+# Holy Quran Recitations Dataset for Pronunciation Error Detection and Correction
+
+This dataset provides `850+ hours` of audio (approximately `300K annotated utterances`) specifically designed for Automatic Pronunciation Error Detection and Correction of Holy Quran learners. The dataset was introduced in the paper [Automatic Pronunciation Error Detection and Correction of the Holy Quran's Learners Using Deep Learning](https://huggingface.co/papers/2509.00094).
+
+The data generation process includes:
+1.  Collection of recitations from expert reciters.
+2.  Segmentation at pause points (waqf) using a fine-tuned wav2vec2-BERT model.
+3.  Transcription of segments.
+4.  Transcript verification via a novel Tasmeea algorithm.
+
+A custom Quran Phonetic Script (QPS) is used to encode Tajweed rules, differentiating it from standard IPA for Modern Standard Arabic. This QPS uses a two-level script: Phoneme level (encodes Arabic letters with short/long vowels) and Sifa level (encodes articulation characteristics of every phoneme).
+
+-   **Paper:** [Automatic Pronunciation Error Detection and Correction of the Holy Quran's Learners Using Deep Learning](https://huggingface.co/papers/2509.00094)
+-   **Project Page:** [https://obadx.github.io/prepare-quran-dataset/](https://obadx.github.io/prepare-quran-dataset/)
+-   **Code (Recitations Segmenter):** [https://github.com/obadx/recitations-segmenter](https://github.com/obadx/recitations-segmenter)
+
+## Sample Usage
+
+You can use the `recitations-segmenter` library to process audio files and extract speech intervals.
+
+First, ensure you have the necessary system-level dependencies (`ffmpeg`, `libsndfile`) installed. For Linux:
+```bash
+sudo apt-get update
+sudo apt-get install -y ffmpeg libsndfile1 portaudio19-dev
+```
+For Windows & Mac using Anaconda:
+```bash
+conda create -n segment python=3.12
+conda activate segment
+conda install -c conda-forge ffmpeg libsndfile
+```
+Then install the Python package:
+```bash
+pip install recitations-segmenter
+```
+
+You can then segment recitations programmatically:
+
+```python
+from pathlib import Path
+
+from recitations_segmenter import segment_recitations, read_audio, clean_speech_intervals
+from transformers import AutoFeatureExtractor, AutoModelForAudioFrameClassification
+import torch
+
+if __name__ == '__main__':
+    device = torch.device('cuda')
+    dtype = torch.bfloat16
+
+    processor = AutoFeatureExtractor.from_pretrained(
+        "obadx/recitation-segmenter-v2")
+    model = AutoModelForAudioFrameClassification.from_pretrained(
+        "obadx/recitation-segmenter-v2",
+    )
+
+    model.to(device, dtype=dtype)
+
+    # Change this to the file pathes of Holy Quran recitations
+    # File pathes with the Holy Quran Recitations
+    file_pathes = [
+        './assets/dussary_002282.mp3',
+        './assets/hussary_053001.mp3',
+    ]
+    waves = [read_audio(p) for p in file_pathes]
+
+    # Extracting speech inervals in samples according to 16000 Sample rate
+    sampled_outputs = segment_recitations(
+        waves,
+        model,
+        processor,
+        device=device,
+        dtype=dtype,
+        batch_size=8,
+    )
+
+    for out, path in zip(sampled_outputs, file_pathes):
+        # Clean The speech intervals by:
+        # * merging small silence durations
+        # * remove small speech durations
+        # * add padding to each speech duration
+        # Raises:
+        # * NoSpeechIntervals: if the wav is complete silence
+        # * TooHighMinSpeechDruation: if `min_speech_duration` is too high which
+        # resuls for deleting all speech intervals
+        clean_out = clean_speech_intervals(
+            out.speech_intervals,
+            out.is_complete,
+            min_silence_duration_ms=30,
+            min_speech_duration_ms=30,
+            pad_duration_ms=30,
+            return_seconds=True,
+        )
+
+        print(f'Speech Intervals of: {Path(path).name}: ')
+        print(clean_out.clean_speech_intervals)
+        print(f'Is Recitation Complete: {clean_out.is_complete}')
+        print('-' * 40)
+```
+
+## Citation
+
+```bibtex
+@article{yassine2024automatic,
+  title={Automatic Pronunciation Error Detection and Correction of the Holy Quran's Learners Using Deep Learning},
+  author={Yassine, Obada},
+  journal={arXiv preprint arXiv:2509.00094},
+  year={2024},
+  url={https://huggingface.co/papers/2509.00094}
+}
+```