delete due to wrong path

pages.py

@@ -1,626 +0,0 @@
-import streamlit as st
-from app.draw_diagram import *
-from app.content import *
-from app.summarization import *
-from app.show_examples import *
-
-def dataset_contents(dataset, metrics):
-    
-    custom_css = """
-                <style>
-                .my-dataset-info {
-                # background-color: #F9EBEA;
-                # padding: 10px;
-                color: #050505;
-                font-style: normal;
-                font-size: 8px;
-                height: auto;
-                }
-                </style>
-                """
-    st.markdown(custom_css, unsafe_allow_html=True)
-    st.markdown(f"""<div class="my-dataset-info">
-                    <p><b>About this dataset</b>: {dataset}</p>
-                    </div>""", unsafe_allow_html=True)
-    st.markdown(f"""<div class="my-dataset-info">
-                    <p><b>About this metric</b>: {metrics}</p>
-                    </div>""", unsafe_allow_html=True)
-
-
-def dashboard():
-
-    with st.container():
-        st.title("Leaderboard for AudioBench")
-   
-        st.markdown("""
-            [gh1]: https://github.com/AudioLLMs/AudioBench
-            [gh2]: https://github.com/AudioLLMs/AudioBench
-            **Toolkit:** [![GitHub Repo stars](https://img.shields.io/github/stars/AudioLLMs/AudioBench?style=social)][gh1] | 
-            [**Paper @ NAACL 2025**](https://arxiv.org/abs/2406.16020) | 
-            **Resource for AudioLLMs:** [![GitHub Repo stars](https://img.shields.io/github/stars/AudioLLMs/Awesome-Audio-LLM?style=social)][gh2]
-            """)
-
-
-    st.markdown("""
-            #### Recent updates
-            - **Jan. 2025**: AudioBench is officially accepted to NAACL 2025!
-            - **Jan. 2025**: Update the layout.
-            - **Dec. 2024**: Added MuChoMusic dataset for Music Understanding - MCQ Questions. From Paper: https://arxiv.org/abs/2408.01337.
-            - **Dec. 2024**: Singlish ASR task added! The datasets are available on [HF](https://huggingface.co/datasets/MERaLiON/MNSC).
-            - **Dec. 2024**: Updated layout and added support for comparison between models with similar sizes. 1) Reorganized layout for a better user experience. 2) Added performance summary for each task.
-            - **Aug. 2024**: Initial leaderboard is now online.
-            """)
-
-    st.divider()
-    
-    st.markdown("""
-                #### Evaluating Audio-based Large Language Models
-                
-                - AudioBench is a comprehensive evaluation benchmark designed for general instruction-following audio large language models.
-                - AudioBench is an evaluation benchmark that we continually improve and maintain.
-                
-                Below are the initial 26 datasets that are included in AudioBench. We are now exteneded to over 40 datasets and going to extend to more in the future.
-                """
-                )
-
-
-    with st.container():
-        
-        st.markdown('''
-                ''')
-        
-        st.markdown("###### :dart: Our Benchmark includes: ")
-        cols = st.columns(8)
-        cols[0].metric(label="Tasks", value=">8")
-        cols[1].metric(label="Datasets", value=">40")
-        cols[2].metric(label="Evaluated Models", value=">5")
-    
-    st.divider()
-    with st.container():
-        left_co, right_co = st.columns([1, 0.1])
-
-        with left_co:
-            st.markdown("""
-                        ##### Citations :round_pushpin:
-                        ```
-                        @article{wang2024audiobench,
-                            title={AudioBench: A Universal Benchmark for Audio Large Language Models},
-                            author={Wang, Bin and Zou, Xunlong and Lin, Geyu and Sun, Shuo and Liu, Zhuohan and Zhang, Wenyu and Liu, Zhengyuan and Aw, AiTi and Chen, Nancy F},
-                            journal={NAACL},
-                            year={2025}
-                            }
-                        ```
-                        ```
-                        @article{zhang2024mowe,
-                            title={MoWE-Audio: Multitask AudioLLMs with Mixture of Weak Encoders},
-                            author={Zhang, Wenyu and Sun, Shuo and Wang, Bin and Zou, Xunlong and Liu, Zhuohan and He, Yingxu and Lin, Geyu and Chen, Nancy F and Aw, Ai Ti},
-                            journal={ICASSP},
-                            year={2025}
-                            }
-                        ```
-                        ```
-                        @article{wang2025advancing,
-                            title={Advancing Singlish Understanding: Bridging the Gap with Datasets and Multimodal Models},
-                            author={Wang, Bin and Zou, Xunlong and Sun, Shuo and Zhang, Wenyu and He, Yingxu and Liu, Zhuohan and Wei, Chengwei and Chen, Nancy F and Aw, AiTi},
-                            journal={arXiv preprint arXiv:2501.01034},
-                            year={2025}
-                            }
-                        ```
-                        ```
-                        @article{he2024meralion,
-                            title={MERaLiON-AudioLLM: Technical Report},
-                            author={He, Yingxu and Liu, Zhuohan and Sun, Shuo and Wang, Bin and Zhang, Wenyu and Zou, Xunlong and Chen, Nancy F and Aw, Ai Ti},
-                            journal={arXiv preprint arXiv:2412.09818},
-                            year={2024}
-                            }
-                        ```
-
-                        """)
-
-
-
-
-
-
-
-def asr_english():
-    st.title("Task: Automatic Speech Recognition - English")
-    
-    sum = ['Overall']
-    dataset_lists = [
-                    'LibriSpeech-Clean', 
-                    'LibriSpeech-Other', 
-                    'CommonVoice-15-EN', 
-                    'Peoples-Speech', 
-                    'GigaSpeech-1', 
-                    'Earnings-21', 
-                    'Earnings-22', 
-                    'TED-LIUM-3', 
-                    'TED-LIUM-3-LongForm', 
-                    ]
-
-    filters_levelone = sum + dataset_lists
-    
-    left, center, _, middle, right = st.columns([0.4, 0.2, 0.2, 0.2 ,0.2])
-    
-    with left:
-        filter_1 = st.selectbox('Dataset', filters_levelone)
-    
-    if filter_1:
-        if filter_1 in sum:
-            sum_table_mulit_metrix('asr_english', ['wer'])
-        else:
-            dataset_contents(dataset_diaplay_information[filter_1], metrics_info['wer'])
-            draw('su', 'asr_english', filter_1, 'wer', cus_sort=True)
-
-
-
-
-
-def asr_singlish():
-    st.title("Task: Automatic Speech Recognition - Singlish")
-
-    sum = ['Overall']
-    dataset_lists = [
-                    'MNSC-PART1-ASR', 
-                    'MNSC-PART2-ASR',
-                    'MNSC-PART3-ASR',
-                    'MNSC-PART4-ASR',
-                    'MNSC-PART5-ASR',
-                    'MNSC-PART6-ASR',
-                    ]
-
-    filters_levelone = sum + dataset_lists
-    
-    left, center, _, middle, right = st.columns([0.4, 0.2, 0.2, 0.2 ,0.2])
-    
-    with left:
-        filter_1 = st.selectbox('Dataset', filters_levelone)
-    
-    if filter_1:
-        if filter_1 in sum:
-            sum_table_mulit_metrix('asr_singlish', ['wer'])
-        else:
-            dataset_contents(dataset_diaplay_information[filter_1], metrics_info['wer'])
-            draw('su', 'asr_singlish', filter_1, 'wer')
-
-
-
-
-def asr_mandarin():
-    st.title("Task: Automatic Speech Recognition - Mandarin")
-
-    sum = ['Overall']
-    dataset_lists = [
-                    'AISHELL-ASR-ZH', 
-                    ]
-
-    filters_levelone = sum + dataset_lists
-    
-    left, center, _, middle, right = st.columns([0.4, 0.2, 0.2, 0.2 ,0.2])
-    
-    with left:
-        filter_1 = st.selectbox('Dataset', filters_levelone)
-    
-    if filter_1:
-        if filter_1 in sum:
-            sum_table_mulit_metrix('asr_mandarin', ['wer'])
-        else:
-            dataset_contents(dataset_diaplay_information[filter_1], metrics_info['wer'])
-            draw('su', 'asr_mandarin', filter_1, 'wer')
-
-    
-
-
-def speech_translation():
-    st.title("Task: Speech Translation")
-    
-    sum = ['Overall']
-    dataset_lists = [
-                        'CoVoST2-EN-ID', 
-                        'CoVoST2-EN-ZH',
-                        'CoVoST2-EN-TA', 
-                        'CoVoST2-ID-EN', 
-                        'CoVoST2-ZH-EN', 
-                        'CoVoST2-TA-EN']
-
-    filters_levelone = sum + dataset_lists
-    
-    left, center, _, middle, right = st.columns([0.4, 0.2, 0.2, 0.2 ,0.2])
-    
-    with left:
-        filter_1 = st.selectbox('Dataset', filters_levelone)
-    
-    if filter_1:
-        if filter_1 in sum:
-            sum_table_mulit_metrix('st', ['bleu'])
-        else:
-            dataset_contents(dataset_diaplay_information[filter_1], metrics_info['bleu'])
-            draw('su', 'ST', filter_1, 'bleu')
-
-
-
-
-def speech_question_answering_english():
-    st.title("Task: Spoken Question Answering - English")
-    
-    sum = ['Overall']
-
-    dataset_lists = [
-                    'CN-College-Listen-MCQ',
-                    'DREAM-TTS-MCQ',
-                    'SLUE-P2-SQA5', 
-                    'Public-SG-Speech-QA', 
-                    'Spoken-SQuAD',
-                     ]
-
-    filters_levelone = sum + dataset_lists
-    
-    left, center, _, middle, right = st.columns([0.4, 0.2, 0.2, 0.2 ,0.2])
-    
-    with left:
-        filter_1 = st.selectbox('Dataset', filters_levelone)
-
-    if filter_1:
-        if filter_1 in sum:
-            sum_table_mulit_metrix('sqa_english', ['llama3_70b_judge'])
-
-        #elif filter_1 in dataset_lists:
-        #    dataset_contents(sqa_datasets[filter_1], metrics['llama3_70b_judge'])
-        #    draw('su', 'SQA', filter_1, 'llama3_70b_judge')
-        
-        else:
-            dataset_contents(dataset_diaplay_information[filter_1], metrics_info['llama3_70b_judge'])
-            draw('su', 'sqa_english', filter_1, 'llama3_70b_judge')
-
-
-
-
-def speech_question_answering_singlish():
-    st.title("Task: Spoken Question Answering - Singlish")
-    
-    sum = ['Overall']
-
-    dataset_lists = [
-              'MNSC-PART3-SQA', 
-              'MNSC-PART4-SQA',
-              'MNSC-PART5-SQA',
-              'MNSC-PART6-SQA',
-              ]
-
-
-    filters_levelone = sum + dataset_lists
-    
-    left, center, _, middle, right = st.columns([0.4, 0.2, 0.2, 0.2 ,0.2])
-    
-    with left: 
-        filter_1 = st.selectbox('Dataset', filters_levelone)
-
-    if filter_1:
-        if filter_1 in sum:
-            sum_table_mulit_metrix('sqa_singlish', ['llama3_70b_judge'])
-        
-        else:
-            dataset_contents(dataset_diaplay_information[filter_1], metrics_info['llama3_70b_judge'])
-            draw('su', 'sqa_singlish', filter_1, 'llama3_70b_judge')
-
-
-def spoken_dialogue_summarization_singlish():
-    st.title("Task: Spoken Dialogue Summarization - Singlish")
-    
-    sum = ['Overall']
-
-    dataset_lists = [
-              'MNSC-PART3-SDS', 
-              'MNSC-PART4-SDS',
-              'MNSC-PART5-SDS',
-              'MNSC-PART6-SDS',
-              ]
-
-
-    filters_levelone = sum + dataset_lists
-    
-    left, center, _, middle, right = st.columns([0.4, 0.2, 0.2, 0.2 ,0.2])
-    
-    with left: 
-        filter_1 = st.selectbox('Dataset', filters_levelone)
-
-    if filter_1:
-        if filter_1 in sum:
-            sum_table_mulit_metrix('sds_singlish', ['llama3_70b_judge'])
-        
-        else:
-            dataset_contents(dataset_diaplay_information[filter_1], metrics_info['llama3_70b_judge'])
-            draw('su', 'sds_singlish', filter_1, 'llama3_70b_judge')
-
-
-
-
-def speech_instruction():
-    st.title("Task: Speech Instruction")
-    
-    sum = ['Overall']
-
-    dataset_lists = ['OpenHermes-Audio', 
-                     'ALPACA-Audio',
-                     ]
-    
-    filters_levelone = sum + dataset_lists
-    
-    left, center, _, middle, right = st.columns([0.4, 0.2, 0.2, 0.2 ,0.2])
-    
-    with left:
-        filter_1 = st.selectbox('Dataset', filters_levelone)
-
-    if filter_1:
-        if filter_1 in sum:
-            sum_table_mulit_metrix('speech_instruction', ['llama3_70b_judge'])
-        else:
-            dataset_contents(dataset_diaplay_information[filter_1], metrics_info['llama3_70b_judge'])
-            draw('su', 'speech_instruction', filter_1, 'llama3_70b_judge')
-
-
-
-
-def audio_captioning():
-    st.title("Task: Audio Captioning")
-
-    filters_levelone = ['WavCaps', 
-                        'AudioCaps',
-                        ]
-    filters_leveltwo = ['Llama3-70b-judge', 'Meteor']
-    
-    left, center, _, middle, right = st.columns([0.4, 0.2, 0.2, 0.2 ,0.2])
-    
-    with left:
-        filter_1 = st.selectbox('Dataset', filters_levelone)
-    with middle:
-        metric = st.selectbox('Metric', filters_leveltwo)
-
-    if filter_1 or metric:
-        dataset_contents(dataset_diaplay_information[filter_1], metrics_info[metric.lower().replace('-', '_')])
-        draw('asu', 'audio_captioning', filter_1, metric.lower().replace('-', '_'))
-
-
-
-
-def audio_scene_question_answering():
-    st.title("Task: Audio Scene Question Answering")
-
-    sum = ['Overall']
-
-    dataset_lists = ['Clotho-AQA', 
-                    'WavCaps-QA', 
-                    'AudioCaps-QA']
-    
-    filters_levelone = sum + dataset_lists
-    
-    left, center, _, middle, right = st.columns([0.4, 0.2, 0.2, 0.2 ,0.2])
-    
-    with left:
-        filter_1 = st.selectbox('Dataset', filters_levelone)
-    
-    if filter_1:
-        if filter_1 in sum:
-            sum_table_mulit_metrix('audio_scene_question_answering', ['llama3_70b_judge'])
-        else:
-            dataset_contents(dataset_diaplay_information[filter_1], metrics_info['llama3_70b_judge'])
-            draw('asu', 'audio_scene_question_answering', filter_1, 'llama3_70b_judge')
-
-
-
-
-def emotion_recognition():
-    st.title("Task: Emotion Recognition")
-
-    sum = ['Overall']
-
-    dataset_lists = [
-                    'IEMOCAP-Emotion', 
-                    'MELD-Sentiment', 
-                    'MELD-Emotion',
-                    ]
-
-    filters_levelone = sum + dataset_lists
-    
-    left, center, _, middle, right = st.columns([0.4, 0.2, 0.2, 0.2 ,0.2])
-    
-    with left:
-        filter_1 = st.selectbox('Dataset', filters_levelone)
-
-    if filter_1:
-        if filter_1 in sum:
-            sum_table_mulit_metrix('emotion_recognition', ['llama3_70b_judge'])
-        else:
-            dataset_contents(dataset_diaplay_information[filter_1], metrics_info['llama3_70b_judge'])
-            draw('vu', 'emotion_recognition', filter_1, 'llama3_70b_judge')
-
-
-
-
-def accent_recognition():
-    st.title("Task: Accent Recognition")
-
-    sum = ['Overall']
-    dataset_lists = [
-        'VoxCeleb-Accent',
-        'MNSC-AR-Sentence',
-        'MNSC-AR-Dialogue',
-        ]
-
-
-    filters_levelone = sum + dataset_lists
-    
-    left, center, _, middle, right = st.columns([0.4, 0.2, 0.2, 0.2 ,0.2])
-    
-    with left:
-        filter_1 = st.selectbox('Dataset', filters_levelone)
-
-
-    if filter_1:
-        if filter_1 in sum:
-            sum_table_mulit_metrix('accent_recognition', ['llama3_70b_judge'])
-        else:
-            dataset_contents(dataset_diaplay_information[filter_1], metrics_info['llama3_70b_judge'])
-            draw('vu', 'accent_recognition', filter_1, 'llama3_70b_judge')
-
-
-
-
-def gender_recognition():
-    st.title("Task: Gender Recognition")
-    
-    sum = ['Overall']
-
-    dataset_lists =  [
-                        'VoxCeleb-Gender', 
-                        'IEMOCAP-Gender'
-                        ]
-
-    filters_levelone = sum + dataset_lists
-    
-    left, center, _, middle, right = st.columns([0.4, 0.2, 0.2, 0.2 ,0.2])
-    
-    with left:
-        filter_1 = st.selectbox('Dataset', filters_levelone)
-    
-    if filter_1:
-        if filter_1 in sum:
-            sum_table_mulit_metrix('gender_recognition', ['llama3_70b_judge'])
-        else:
-            dataset_contents(dataset_diaplay_information[filter_1], metrics_info['llama3_70b_judge'])
-            draw('vu', 'gender_recognition', filter_1, 'llama3_70b_judge')
-
-
-
-
-def music_understanding():
-    st.title("Task: Music Understanding - MCQ Questions")
-    
-    sum = ['Overall']
-
-    dataset_lists =  ['MuChoMusic',
-                      ]
-
-    filters_levelone = sum + dataset_lists
-    
-    left, center, _, middle, right = st.columns([0.4, 0.2, 0.2, 0.2 ,0.2])
-    
-    with left:
-        filter_1 = st.selectbox('Dataset', filters_levelone)
-    
-    if filter_1:
-        if filter_1 in sum:
-            sum_table_mulit_metrix('music_understanding', ['llama3_70b_judge'])
-        else:
-            dataset_contents(dataset_diaplay_information[filter_1], metrics_info['llama3_70b_judge'])
-            draw('vu', 'music_understanding', filter_1, 'llama3_70b_judge')
-
-
-
-
-
-
-
-
-
-
-def under_development():
-    st.title("Task: Under Development")
-    
-
-    dataset_lists =  [
-                      'CNA',
-                      'IDPC',
-                      'Parliament',
-                      'UKUS-News',
-                      'Mediacorp',
-                      'IDPC-Short',
-                      'Parliament-Short',
-                      'UKUS-News-Short',
-                      'Mediacorp-Short',
-
-                      'YouTube ASR: English Singapore Content',
-                      'YouTube ASR: English with Strong Emotion',
-                      'YouTube ASR: Malay English Prompt',
-                      'YouTube ASR: Malay with Malay Prompt',
-                      
-                      'SEAME-Dev-Mandarin',
-                      'SEAME-Dev-Singlish',
-
-                      'YouTube SQA: English with Singapore Content',
-                      'YouTube SDS: English with Singapore Content',
-                      'YouTube PQA: English with Singapore Content',
-
-                      ]
-
-    filters_levelone = dataset_lists
-    
-    left, center, _, middle, right = st.columns([0.4, 0.2, 0.2, 0.2 ,0.2])
-    
-    with left:
-        filter_1 = st.selectbox('Dataset', filters_levelone)
-    
-    dataset_contents(dataset_diaplay_information[filter_1], 'under_development')
-    
-    # = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =
-
-    '''
-    Show Dataset Examples
-    '''
-
-    # Initialize a session state variable for toggling the chart visibility
-    if "show_dataset_examples" not in st.session_state:
-        st.session_state.show_dataset_examples = False
-
-    # Create a button to toggle visibility
-    if st.button("Show Dataset Examples"):
-        st.session_state.show_dataset_examples = not st.session_state.show_dataset_examples
-
-    if st.session_state.show_dataset_examples:
-        
-        # st.markdown('To be implemented')
-
-        # # if dataset_name in ['Earnings21-Test', 'Earnings22-Test', 'Tedlium3-Test', 'Tedlium3-Long-form-Test']:
-        if filter_1 in []:
-            pass
-        else:
-            try:
-                show_dataset_examples(filter_1)
-            except:
-                st.markdown('To be implemented')
-    # = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = = =    
-    
-    if filter_1 in [
-                      'CNA',
-                      'IDPC',
-                      'Parliament',
-                      'UKUS-News',
-                      'Mediacorp',
-                      'IDPC-Short',
-                      'Parliament-Short',
-                      'UKUS-News-Short',
-                      'Mediacorp-Short',
-                      
-                      'YouTube ASR: English Singapore Content',
-                      'YouTube ASR: English with Strong Emotion',
-                      'YouTube ASR: Malay English Prompt',
-                      'YouTube ASR: Malay with Malay Prompt',
-                      
-                      'SEAME-Dev-Mandarin',
-                      'SEAME-Dev-Singlish',
-                      ]:
-        
-        draw('vu', 'under_development_wer', filter_1, 'wer')
-
-    elif filter_1 in [
-        'YouTube SQA: English with Singapore Content',
-        'YouTube SDS: English with Singapore Content',
-        'YouTube PQA: English with Singapore Content',
-        ]:
-        draw('vu', 'under_development_llama3_70b_judge', filter_1, 'llama3_70b_judge')
-
-
-
-
-