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app/content.py

@@ -108,29 +108,28 @@ dataset_diaplay_information = {
     'VoxCeleb-Accent'      : 'Test dataset for accent recognition, based on VoxCeleb, a large speaker identification dataset.',
     'MNSC-AR-Sentence'     : 'Accent recognition based on the IMDA NSC dataset, focusing on sentence-level accents.',
     'MNSC-AR-Dialogue'     : 'Accent recognition based on the IMDA NSC dataset, focusing on dialogue-level accents.',
-
-    'VoxCeleb-Gender': 'Test dataset for gender classification, also derived from VoxCeleb.',
-    'IEMOCAP-Gender' : 'Gender classification based on the IEMOCAP dataset.',
-    'IEMOCAP-Emotion': 'Emotion recognition test data from the IEMOCAP dataset, focusing on identifying emotions in speech.',
-    'MELD-Sentiment' : 'Sentiment recognition from speech using the MELD dataset, classifying positive, negative, or neutral sentiments.',
-    'MELD-Emotion'   : 'Emotion classification in speech using MELD, detecting specific emotions like happiness, anger, etc.',
-    'MuChoMusic'     : 'Test dataset for music understanding, from paper: MuChoMusic: Evaluating Music Understanding in Multimodal Audio-Language Models.',
-    'MNSC-PART1-ASR' : 'Speech recognition test data from the IMDA NSC project, Part 1.',
-    'MNSC-PART2-ASR' : 'Speech recognition test data from the IMDA NSC project, Part 2.',
-    'MNSC-PART3-ASR' : 'Speech recognition test data from the IMDA NSC project, Part 3.',
-    'MNSC-PART4-ASR' : 'Speech recognition test data from the IMDA NSC project, Part 4.',
-    'MNSC-PART5-ASR' : 'Speech recognition test data from the IMDA NSC project, Part 5.',
-    'MNSC-PART6-ASR' : 'Speech recognition test data from the IMDA NSC project, Part 6.',
-    'MNSC-PART3-SQA' : 'Multitak National Speech Corpus (MNSC) dataset, Question answering task, Part 3.',
-    'MNSC-PART4-SQA' : 'Multitak National Speech Corpus (MNSC) dataset, Question answering task, Part 4.',
-    'MNSC-PART5-SQA' : 'Multitak National Speech Corpus (MNSC) dataset, Question answering task, Part 5.',
-    'MNSC-PART6-SQA' : 'Multitak National Speech Corpus (MNSC) dataset, Question answering task, Part 6.',
-    'MNSC-PART3-SDS' : 'Multitak National Speech Corpus (MNSC) dataset, dialogue summarization task, Part 3.',
-    'MNSC-PART4-SDS' : 'Multitak National Speech Corpus (MNSC) dataset, dialogue summarization task, Part 4.',
-    'MNSC-PART5-SDS' : 'Multitak National Speech Corpus (MNSC) dataset, dialogue summarization task, Part 5.',
-    'MNSC-PART6-SDS' : 'Multitak National Speech Corpus (MNSC) dataset, dialogue summarization task, Part 6.',
-    'SEAME-Dev-Man'   : 'SEAME dataset, English-Mandarin Code-swithcing',
-    'SEAME-Dev-Sge'   : 'SEAME dataset, English-Mandarin Code-swithcing',
+    'VoxCeleb-Gender'      : 'Test dataset for gender classification, also derived from VoxCeleb.',
+    'IEMOCAP-Gender'       : 'Gender classification based on the IEMOCAP dataset.',
+    'IEMOCAP-Emotion'      : 'Emotion recognition test data from the IEMOCAP dataset, focusing on identifying emotions in speech.',
+    'MELD-Sentiment'       : 'Sentiment recognition from speech using the MELD dataset, classifying positive, negative, or neutral sentiments.',
+    'MELD-Emotion'         : 'Emotion classification in speech using MELD, detecting specific emotions like happiness, anger, etc.',
+    'MuChoMusic'           : 'Test dataset for music understanding, from paper: MuChoMusic: Evaluating Music Understanding in Multimodal Audio-Language Models.',
+    'MNSC-PART1-ASR'       : 'Speech recognition test data from the IMDA NSC project, Part 1.',
+    'MNSC-PART2-ASR'       : 'Speech recognition test data from the IMDA NSC project, Part 2.',
+    'MNSC-PART3-ASR'       : 'Speech recognition test data from the IMDA NSC project, Part 3.',
+    'MNSC-PART4-ASR'       : 'Speech recognition test data from the IMDA NSC project, Part 4.',
+    'MNSC-PART5-ASR'       : 'Speech recognition test data from the IMDA NSC project, Part 5.',
+    'MNSC-PART6-ASR'       : 'Speech recognition test data from the IMDA NSC project, Part 6.',
+    'MNSC-PART3-SQA'       : 'Multitak National Speech Corpus (MNSC) dataset, Question answering task, Part 3.',
+    'MNSC-PART4-SQA'       : 'Multitak National Speech Corpus (MNSC) dataset, Question answering task, Part 4.',
+    'MNSC-PART5-SQA'       : 'Multitak National Speech Corpus (MNSC) dataset, Question answering task, Part 5.',
+    'MNSC-PART6-SQA'       : 'Multitak National Speech Corpus (MNSC) dataset, Question answering task, Part 6.',
+    'MNSC-PART3-SDS'       : 'Multitak National Speech Corpus (MNSC) dataset, dialogue summarization task, Part 3.',
+    'MNSC-PART4-SDS'       : 'Multitak National Speech Corpus (MNSC) dataset, dialogue summarization task, Part 4.',
+    'MNSC-PART5-SDS'       : 'Multitak National Speech Corpus (MNSC) dataset, dialogue summarization task, Part 5.',
+    'MNSC-PART6-SDS'       : 'Multitak National Speech Corpus (MNSC) dataset, dialogue summarization task, Part 6.',
+    'SEAME-Dev-Man'        : 'SEAME dataset, English-Mandarin Code-swithcing',
+    'SEAME-Dev-Sge'        : 'SEAME dataset, English-Mandarin Code-swithcing',
 
     'CNA'             : 'Under Development',
     'IDPC'            : 'Under Development',

app/pages.py

@@ -167,6 +167,8 @@ def asr_singlish():
                     'MNSC-PART4-ASR',
                     'MNSC-PART5-ASR',
                     'MNSC-PART6-ASR',
+                    'SEAME-Dev-Man',
+                    'SEAME-Dev-Sge',
                     ]
 
     filters_levelone = sum + dataset_lists