Update index.html

index.html

@@ -37,7 +37,26 @@
         </ul>
 
         <!-- Task Description -->
-        <h2>Task Description</h2>
+
+        <h2>🔊 Task Description</h2>
+        <p>
+            The Iqra'Eval task focuses on <strong>automatic pronunciation assessment</strong> in Qur’anic context. 
+            Given a spoken audio clip of a verse and its fully vowelized reference text, your system should predict 
+            the <strong>correct phoneme sequence</strong> actually spoken by the reciter.
+        </p>
+        <p>
+            By comparing this predicted sequence to the reference text and the gold phoneme sequence annotation, we can automatically detect pronunciation issues, such as:
+        </p>
+        <ul>
+            <li><strong>Substitutions</strong>: e.g., saying /k/ instead of /q/</li>
+            <li><strong>Insertions</strong>: adding a sound not present in the reference</li>
+            <li><strong>Deletions</strong>: skipping a required phoneme</li>
+        </ul>
+        <p>
+            This task helps diagnose and localize pronunciation errors, enabling educational feedback in applications like Qur’anic tutoring or speech evaluation tools.
+        </p>
+
+        <!-- <h2>Task Description</h2>
         <p>
             The Iqra’Eval shared task focuses on automatic mispronunciation detection and diagnosis in Qur’anic recitation. Given:
         </p>
@@ -59,10 +78,10 @@
             <li>Detect substitutions (e.g., pronouncing /q/ as /k/), deletions (e.g., dropping a hamza), or insertions (e.g., adding an extra vowel) of phonemes.</li>
             <li>Localize the error to a specific phoneme index in the utterance.</li>
             <li>Classify what type of mistake occurred based on Tajweed (e.g., madd errors, ikhfa, idgham, etc.).</li>
-        </ul>
+        </ul> -->
 
         <!-- Example & Illustration -->
-        <h2>Example</h2>
+        <!-- <h2>Example</h2>
         <p>
             Suppose the reference verse (fully vowelized) is:
         </p>
@@ -95,62 +114,9 @@ inna l l aa h a  ʕ a l a  k u l l i  ʃ a y ’ i n  q a d i r u n
             <p style="font-size: 0.9em; color: #555;">
                 <em>Figure: Example of a phoneme-level comparison between reference vs. predicted for an Arabic Qur’anic recitation.</em>
             </p>
-        </div>
+        </div> -->
 
         <!-- Evaluation Criteria -->
-        <h2>Evaluation Criteria</h2>
-        <p>
-            Systems will be scored on their ability to detect and correctly classify phoneme-level errors:
-        </p>
-        <ul>
-            <li><strong>Detection accuracy:</strong> Did the system spot that a phoneme-level error occurred in the segment?</li>
-            <li><strong>Localization precision:</strong> Did the system mark the correct positions (indices) in the phoneme sequence where the error(s) occurred?</li>
-            <li><strong>Classification F1-score:</strong> Given that an error is detected at a particular position, did the system assign the correct error type (e.g., substitution vs. insertion vs. deletion, plus the specific Tajweed subcategory)?</li>
-        </ul>
-        <p>
-            A final <strong>Composite Error Score (CES)</strong> will be computed by combining:
-        </p>
-        <ol>
-            <li>Boundary-aware detection accuracy (punish off-by-one index errors lightly),</li>
-            <li>Per-error-type classification F1-score (substitution, deletion, insertion), and</li>
-            <li>Overall phoneme-sequence alignment score (Levenshtein-based alignment to reward correct sequences).  
-            <!-- Note: Detailed weightings will be released along with the test data. -->
-        </li>
-        </ol>
-        <p>
-            <em>(Detailed evaluation weights and scripts will be made available on June 5, 2025.)</em>
-        </p>
-
-        <!-- Submission Details -->
-        <h2>Submission Details (Draft)</h2>
-        <p>
-            Participants are required to submit a CSV file named <code>submission.csv</code> containing the predicted phoneme sequences for each audio sample. The file must have exactly two columns:
-        </p>
-        <ul>
-            <li><strong>ID:</strong> Unique identifier of the audio sample.</li>
-            <li><strong>Labels:</strong> The predicted phoneme sequence, with each phoneme separated by a single space.</li>
-        </ul>
-        <p>
-            Below is a minimal example illustrating the required format:
-        </p>
-        <pre>
-ID,Labels
-0000_0001, i n n a m a a y a k h a l l a h a m i n ʕ i b a a d i h u l ʕ u l a m
-0000_0002, m a a n a n s a k h u m i n i ʕ a a y a t i n
-0000_0003, y u k h i k u m u n n u ʔ a u ʔ a m a n a t a n m m i n h u
-…  
-        </pre>
-        <p>
-            The first column (ID) should match exactly the audio filenames (without extension). The second column (Labels) is the predicted phoneme string. 
-        </p>
-        <p>
-            <strong>Important:</strong>  
-            <ul>
-                <li>Use UTF-8 encoding.</li>
-                <li>Do not include extra spaces at the start or end of each line.</li>
-                <li>Submit a single CSV file (no archives). Filename must be <code>submission.csv</code>.</li>
-            </ul>
-        </p>
 
         <!-- Dataset Description -->
         <h2>Dataset Description</h2>
@@ -161,24 +127,21 @@ ID,Labels
             <li>
                 <strong>Training set:</strong> 79 hours of Modern Standard Arabic (MSA) speech, augmented with multiple Qur’anic recitations.  
                 <br />
-                <code>df = load_dataset("mostafaashahin/IqraEval_Training_Data", split="train")</code>
+                <code>df = load_dataset("IqraEval/Iqra_train", split="train")</code>
             </li>
             <li>
-                <strong>Development set (QuranMB):</strong> 3.4 hours reserved for tuning and validation.  
+                <strong>Development set:</strong> 3.4 hours reserved for tuning and validation.  
                 <br />
-                <code>df = load_dataset("mostafaashahin/IqraEval_Training_Data", split="dev")</code>
+                <code>df = load_dataset("IqraEval/Iqra_train", split="dev")</code>
             </li>
         </ul>
-        <p>
-            A sample submission file (<code>sample_submission.csv</code>) is also provided in the repository.  
-        </p>
         <p>
             <strong>Column Definitions:</strong>
         </p>
         <ul>
+            <li><code>audio</code>: Speech Array.</li>
             <li><code>sentence</code>: Original sentence text (may be partially diacritized or non-diacritized).</li>
-            <li><code>q_index</code>: If from the Quran, the verse index (0–6265, including Basmalah); otherwise <code>-1</code>.</li>
-            <li><code>start_word_index</code>, <code>end_word_index</code>: Word positions within the verse (or <code>-1</code> if non-Quranic).</li>
+            <li><code>index</code>: If from the Quran, the verse index (0–6265, including Basmalah); otherwise <code>-1</code>.</li>
             <li><code>tashkeel_sentence</code>: Fully diacritized sentence (auto-generated via a diacritization tool).</li>
             <li><code>phoneme</code>: Phoneme sequence corresponding to the diacritized sentence (Nawar Halabi phonetizer).</li>
         </ul>
@@ -195,23 +158,15 @@ ID,Labels
             We also provide a high-quality TTS corpus for auxiliary experiments (e.g., data augmentation, synthetic pronunciation error simulation). This TTS set can be loaded via:
         </p>
         <ul>
-            <li><code>df_tts = load_dataset("IqraEval/Iqra_TTS", split="train")</code></li>
+            <li><code>df_tts = load_dataset("IqraEval/Iqra_TTS")</code></li>
         </ul>
-        <p>
-            Researchers who wish to experiment with “synthetic mispronunciations” can use the TTS waveform + forced-alignment pipeline to generate various kinds of pronunciation errors in a controlled manner.  
-        </p>
 
         <!-- Resources & Links -->
         <h2>Resources</h2>
         <ul>
-            <li>
-                <a href="https://huggingface.co/datasets/mostafaashahin/IqraEval_Training_Data" target="_blank">
-                    Training &amp; Development Data on Hugging Face
-                </a>
-            </li>
             <li>
                 <a href="https://huggingface.co/datasets/IqraEval/Iqra_train" target="_blank">
-                    IqraEval_Training_Data (alias)
+                    Training &amp; Development Data on Hugging Face
                 </a>
             </li>
             <li>
@@ -231,6 +186,49 @@ ID,Labels
             </em>
         </p>
 
+              <h2>Evaluation Criteria</h2>
+        <p>
+            Systems will be scored on their ability to detect and correctly classify phoneme-level errors:
+        </p>
+        <ul>
+            <li><strong>Detection accuracy:</strong> Did the system spot that a phoneme-level error occurred in the segment?</li>
+            <li><strong>Classification F1-score:</strong> Mispronunciation Detection F1-score</li>
+        </ul>
+        <p>
+            <em>(Detailed evaluation weights and scripts will be made available on June 5, 2025.)</em>
+        </p>
+
+        <!-- Submission Details -->
+        <h2>Submission Details (Draft)</h2>
+        <p>
+            Participants are required to submit a CSV file named <code>submission.csv</code> containing the predicted phoneme sequences for each audio sample. The file must have exactly two columns:
+        </p>
+        <ul>
+            <li><strong>ID:</strong> Unique identifier of the audio sample.</li>
+            <li><strong>Labels:</strong> The predicted phoneme sequence, with each phoneme separated by a single space.</li>
+        </ul>
+        <p>
+            Below is a minimal example illustrating the required format:
+        </p>
+        <pre>
+ID,Labels
+0000_0001, i n n a m a a y a k h a l l a h a m i n ʕ i b a a d i h u l ʕ u l a m
+0000_0002, m a a n a n s a k h u m i n i ʕ a a y a t i n
+0000_0003, y u k h i k u m u n n u ʔ a u ʔ a m a n a t a n m m i n h u
+…  
+        </pre>
+        <p>
+            The first column (ID) should match exactly the audio filenames (without extension). The second column (Labels) is the predicted phoneme string. 
+        </p>
+        <p>
+            <strong>Important:</strong>  
+            <ul>
+                <li>Use UTF-8 encoding.</li>
+                <li>Do not include extra spaces at the start or end of each line.</li>
+                <li>Submit a single CSV file (no archives). Filename must be <code>submission.csv</code>.</li>
+            </ul>
+        </p>
+      
         <!-- Placeholder for Future Details -->
         <h2>Future Updates</h2>
         <p>