Improve dataset card: Add metadata, paper/project links, results, and sample usage

README.md

@@ -1,38 +1,37 @@
 ---
 configs:
-  - config_name: default
-    data_files:
-      - split: train
-        path:
-          - data/recitation_0/train/*.parquet
-          - data/recitation_1/train/*.parquet
-          - data/recitation_2/train/*.parquet
-          - data/recitation_3/train/*.parquet
-          - data/recitation_5/train/*.parquet
-          - data/recitation_6/train/*.parquet
-          - data/recitation_7/train/*.parquet
-      - split: validation
-        path:
-          - data/recitation_0/validation/*.parquet
-          - data/recitation_1/validation/*.parquet
-          - data/recitation_2/validation/*.parquet
-          - data/recitation_3/validation/*.parquet
-          - data/recitation_5/validation/*.parquet
-          - data/recitation_6/validation/*.parquet
-          - data/recitation_7/validation/*.parquet
-      - split: test
-        path:
-          - data/recitation_8/train/*.parquet
-          - data/recitation_8/validation/*.parquet
+- config_name: default
+  data_files:
+  - split: train
+    path:
+    - data/recitation_0/train/*.parquet
+    - data/recitation_1/train/*.parquet
+    - data/recitation_2/train/*.parquet
+    - data/recitation_3/train/*.parquet
+    - data/recitation_5/train/*.parquet
+    - data/recitation_6/train/*.parquet
+    - data/recitation_7/train/*.parquet
+  - split: validation
+    path:
+    - data/recitation_0/validation/*.parquet
+    - data/recitation_1/validation/*.parquet
+    - data/recitation_2/validation/*.parquet
+    - data/recitation_3/validation/*.parquet
+    - data/recitation_5/validation/*.parquet
+    - data/recitation_6/validation/*.parquet
+    - data/recitation_7/validation/*.parquet
+  - split: test
+    path:
+    - data/recitation_8/train/*.parquet
+    - data/recitation_8/validation/*.parquet
 dataset_info:
   splits:
-    - name: train
-      num_examples: 54823
-    - name: test
-      num_examples: 8787
-    - name: validation
-      num_examples: 7175
-
+  - name: train
+    num_examples: 54823
+  - name: test
+    num_examples: 8787
+  - name: validation
+    num_examples: 7175
   featrues:
   - dtype: string
     name: aya_name
@@ -85,11 +84,136 @@ dataset_info:
         - null
         - 1
     name: labels
+language:
+- ar
+license: cc-by-nc-4.0
+task_categories:
+- automatic-speech-recognition
+tags:
+- quran
+- arabic
+- speech-segmentation
+- audio-segmentation
+- audio
 ---
-# Recitation Segmentations Dataset
 
-This an modfied version of [this dataset](https://huggingface.co/datasets/obadx/recitation-segmentation) with these modifications:
+# Automatic Pronunciation Error Detection and Correction of the Holy Quran's Learners Using Deep Learning
+
+[Paper](https://huggingface.co/papers/2509.00094) | [Project Page](https://obadx.github.io/prepare-quran-dataset/) | [Code](https://github.com/obadx/recitations-segmenter)
+
+## Introduction
+This dataset is developed as part of the research presented in the paper "Automatic Pronunciation Error Detection and Correction of the Holy Quran's Learners Using Deep Learning". The work introduces a 98% automated pipeline to produce high-quality Quranic datasets, comprising over 850 hours of audio (~300K annotated utterances). This dataset supports a novel ASR-based approach for pronunciation error detection, utilizing a custom Quran Phonetic Script (QPS) designed to encode Tajweed rules.
+
+## Recitation Segmentations Dataset
+
+This is a modified version of [this dataset](https://huggingface.co/datasets/obadx/recitation-segmentation) with these modifications:
 * adding augmentation to the speed of the recitations utterance with column `speed` reflects the speed from 0.8 to 1.5 on 40% of the dataset using [audumentations](https://iver56.github.io/audiomentations/).
 * adding data augmentation with [audiomentations](https://iver56.github.io/audiomentations/) on 40% of the dataset to prepare it for training the recitations spliter.
 
 The codes for building this dataset is available at [github](https://github.com/obadx/recitations-segmenter)
+
+## Results
+The model trained with this dataset achieved the following results on an unseen test set:
+
+| Metric    | Value  |
+|-----------|--------|
+| Accuracy  | 0.9958 |
+| F1        | 0.9964 |
+| Loss      | 0.0132 |
+| Precision | 0.9976 |
+| Recall    | 0.9951 |
+
+## Sample Usage
+
+Below is a Python example demonstrating how to use the `recitations-segmenter` library (developed alongside this dataset) to segment Holy Quran recitations.
+
+First, ensure you have the necessary Python packages and `ffmpeg`/`libsndfile` installed:
+
+#### Linux
+
+```bash
+sudo apt-get update
+sudo apt-get install -y ffmpeg libsndfile1 portaudio19-dev
+```
+
+#### Winodws & Mac
+
+You can create an `anaconda` environment and then download these two libraries:
+
+```bash
+conda create -n segment python=3.12
+conda activate segment
+conda install -c conda-forge ffmpeg libsndfile
+```
+
+Install the library using pip:
+```bash
+pip install recitations-segmenter
+```
+
+Then, you can run the following Python script:
+
+```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)
+```
+
+## License
+
+This dataset is licensed under the [Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License](http://creativecommons.org/licenses/by-nc-sa/4.0/).