app/content.py

asr_english_datasets = [
    'LibriSpeech-Clean', 
    'LibriSpeech-Other', 
    'CommonVoice-15-EN', 
    'Peoples-Speech', 
    'GigaSpeech-1', 
    'Earnings-21', 
    'Earnings-22', 
    'TED-LIUM-3', 
    'TED-LIUM-3-LongForm', 
]


asr_singlish_datasets = [
    'MNSC-PART1-ASR', 
    'MNSC-PART2-ASR',
    'MNSC-PART3-ASR',
    'MNSC-PART4-ASR',
    'MNSC-PART5-ASR',
    'MNSC-PART6-ASR',
]


asr_mandarin_datasets = [
    'AISHELL-ASR-ZH', 
    'CommonVoice-ZH',
    'YouTube ASR: Chinese with English Prompt',
]


asr_malay_datasets = [
    'YouTube ASR: Malay with English Prompt'
]


asr_tamil_datasets = [
    'CommonVoice-17-Tamil',
    'Fleurs-Tamil',
    'YouTube ASR: Tamil with English Prompt'
]


asr_indonesian_datasets = [
    'CommonVoice-17-Indonesian', 
    'GigaSpeech-2-Indonesain',
]


asr_thai_datasets = [
    'GigaSpeech-2-Thai',
    'Lotus-Thai'
]


asr_vietnamese_datasets = [
    'CommonVoice-17-Vietnamese',
    'GigaSpeech-2-Vietnamese'
]


asr_private_datasets =  [
    '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',
]


speech_translation_datasets = [
    'CoVoST2-EN-ID', 
    'CoVoST2-EN-ZH',
    'CoVoST2-EN-TA', 
    'CoVoST2-ID-EN', 
    'CoVoST2-ZH-EN', 
    'CoVoST2-TA-EN'
]


speech_qa_english_datasets = [
    'CN-College-Listen-MCQ',
    'DREAM-TTS-MCQ',
    'SLUE-P2-SQA5', 
    'Public-SG-Speech-QA', 
    'Spoken-SQuAD',
    'MMAU-mini'
]


speech_qa_singlish_datasets = [
    'MNSC-PART3-SQA', 
    'MNSC-PART4-SQA',
    'MNSC-PART5-SQA',
    'MNSC-PART6-SQA',
]


sds_datasets = [
    'MNSC-PART3-SDS', 
    'MNSC-PART4-SDS',
    'MNSC-PART5-SDS',
    'MNSC-PART6-SDS',
]


si_datasets = [
    'OpenHermes-Audio', 
    'ALPACA-Audio',
]


ac_datasets = [
    'WavCaps', 
    'AudioCaps',
]


asqa_datasets = [
    'Clotho-AQA', 
    'WavCaps-QA', 
    'AudioCaps-QA'
]


er_datasets = [
    'IEMOCAP-Emotion', 
    'MELD-Sentiment', 
    'MELD-Emotion',
]


ar_datasets = [
    'VoxCeleb-Accent',
    'MNSC-AR-Sentence',
    'MNSC-AR-Dialogue',
]


gr_datasets = [
    'VoxCeleb-Gender', 
    'IEMOCAP-Gender'
]


music_datasets = ['MuChoMusic']


wer_development_datasets = [
    'YouTube ASR: Malay with Malay Prompt',
    'YouTube ASR: Chinese with Chinese Prompt',
    'SEAME-Dev-Mandarin',
    'SEAME-Dev-Singlish',
]


non_wer_development_datasets = [
    'YouTube SQA: English with Singapore Content',
    'YouTube SDS: English with Singapore Content',
    'YouTube PQA: English with Singapore Content',
]


wer_displayname2datasetname = {
    'LibriSpeech-Clean'    : 'librispeech_test_clean',
    'LibriSpeech-Other'    : 'librispeech_test_other',
    'CommonVoice-15-EN'    : 'common_voice_15_en_test',
    'Peoples-Speech'       : 'peoples_speech_test',
    'GigaSpeech-1'         : 'gigaspeech_test',
    'Earnings-21'          : 'earnings21_test',
    'Earnings-22'          : 'earnings22_test',
    'TED-LIUM-3'           : 'tedlium3_test',
    'TED-LIUM-3-LongForm'  : 'tedlium3_long_form_test',

    'MNSC-PART1-ASR'       : 'imda_part1_asr_test',
    'MNSC-PART2-ASR'       : 'imda_part2_asr_test',
    'MNSC-PART3-ASR'       : 'imda_part3_30s_asr_test',
    'MNSC-PART4-ASR'       : 'imda_part4_30s_asr_test',
    'MNSC-PART5-ASR'       : 'imda_part5_30s_asr_test',
    'MNSC-PART6-ASR'       : 'imda_part6_30s_asr_test',

    'AISHELL-ASR-ZH'       : 'aishell_asr_zh_test',
    'CommonVoice-ZH'       : 'commonvoice_zh_asr',

    'CommonVoice-17-Indonesian'    : 'commonvoice_17_id_asr',
    'CommonVoice-17-Tamil'         : 'commonvoice_17_ta_asr',
    'CommonVoice-17-Thai'          : 'commonvoice_17_th_asr',
    'CommonVoice-17-Vietnamese'    : 'commonvoice_17_vi_asr',
    'GigaSpeech-2-Indonesain'      : 'gigaspeech2_id_test',
    'GigaSpeech-2-Thai'            : 'gigaspeech2_th_test',
    'GigaSpeech-2-Vietnamese'      : 'gigaspeech2_vi_test',
    'Fleurs-Tamil'                 : 'fleurs_tamil_ta_30_asr',
    'Lotus-Thai'                   : 'lotus_thai_th_30_asr',

    'CNA'             : 'cna_test',
    'IDPC'            : 'idpc_test',
    'Parliament'      : 'parliament_test',
    'UKUS-News'       : 'ukusnews_test',
    'Mediacorp'       : 'mediacorp_test',
    'IDPC-Short'      : 'idpc_short_test',
    'Parliament-Short': 'parliament_short_test',
    'UKUS-News-Short' : 'ukusnews_short_test',
    'Mediacorp-Short' : 'mediacorp_short_test',
    
    'YouTube ASR: English Singapore Content': 'ytb_asr_batch1',
    'YouTube ASR: English with Strong Emotion': 'ytb_asr_batch2',
    'YouTube ASR: Malay with English Prompt': 'ytb_asr_batch3_malay',
    'YouTube ASR: Chinese with English Prompt': 'ytb_asr_batch3_chinese',
    'YouTube ASR: Tamil with English Prompt': 'ytb_asr_batch3_tamil',

    'YouTube ASR: Malay with Malay Prompt': 'ytb_asr_batch3_ms_ms_prompt',
    'YouTube ASR: Chinese with Chinese Prompt': 'ytb_asr_batch3_zh_zh_prompt',
    
    'SEAME-Dev-Mandarin'   : 'seame_dev_man',
    'SEAME-Dev-Singlish'   : 'seame_dev_sge',
}


non_wer_displayname2datasetname = {
    'CoVoST2-EN-ID'        : 'covost2_en_id_test',
    'CoVoST2-EN-ZH'        : 'covost2_en_zh_test',
    'CoVoST2-EN-TA'        : 'covost2_en_ta_test',
    'CoVoST2-ID-EN'        : 'covost2_id_en_test',
    'CoVoST2-ZH-EN'        : 'covost2_zh_en_test',
    'CoVoST2-TA-EN'        : 'covost2_ta_en_test',

    'CN-College-Listen-MCQ': 'cn_college_listen_mcq_test',
    'DREAM-TTS-MCQ'        : 'dream_tts_mcq_test',
    'SLUE-P2-SQA5'         : 'slue_p2_sqa5_test',
    'Public-SG-Speech-QA'  : 'public_sg_speech_qa_test',
    'Spoken-SQuAD'         : 'spoken_squad_test',
    'MMAU-mini'            : 'mmau_mini',

    'MNSC-PART3-SQA'       : 'imda_part3_30s_sqa_human_test',
    'MNSC-PART4-SQA'       : 'imda_part4_30s_sqa_human_test',
    'MNSC-PART5-SQA'       : 'imda_part5_30s_sqa_human_test',
    'MNSC-PART6-SQA'       : 'imda_part6_30s_sqa_human_test',

    'MNSC-PART3-SDS'       : 'imda_part3_30s_ds_human_test',
    'MNSC-PART4-SDS'       : 'imda_part4_30s_ds_human_test',
    'MNSC-PART5-SDS'       : 'imda_part5_30s_ds_human_test',
    'MNSC-PART6-SDS'       : 'imda_part6_30s_ds_human_test',

    'OpenHermes-Audio'     : 'openhermes_audio_test',
    'ALPACA-Audio'         : 'alpaca_audio_test',

    'WavCaps'              : 'wavcaps_test',
    'AudioCaps'            : 'audiocaps_test',

    'Clotho-AQA'           : 'clotho_aqa_test',
    'WavCaps-QA'           : 'wavcaps_qa_test',
    'AudioCaps-QA'         : 'audiocaps_qa_test',

    'IEMOCAP-Emotion'      : 'iemocap_emotion_test',
    'MELD-Sentiment'       : 'meld_sentiment_test',
    'MELD-Emotion'         : 'meld_emotion_test',

    'VoxCeleb-Accent'      : 'voxceleb_accent_test',
    'MNSC-AR-Sentence'     : 'imda_ar_sentence',
    'MNSC-AR-Dialogue'     : 'imda_ar_dialogue',

    'VoxCeleb-Gender'      : 'voxceleb_gender_test',
    'IEMOCAP-Gender'       : 'iemocap_gender_test',

    'MuChoMusic'           : 'muchomusic_test',

    'YouTube SQA: English with Singapore Content': 'ytb_sqa_batch1',
    'YouTube SDS: English with Singapore Content': 'ytb_sds_batch1',
    'YouTube PQA: English with Singapore Content': 'ytb_pqa_batch1',
    
    'YouTube SQA: Malay': 'ytb_sqa_batch3_malay',
    'YouTube SQA: Chinese': 'ytb_sqa_batch3_chinese',
    'YouTube SQA: Tamil': 'ytb_sqa_batch3_tamil',
    
    'YouTube SDS: Malay': 'ytb_sds_batch3_malay',
    'YouTube SDS: Chinese': 'ytb_sds_batch3_chinese',
    'YouTube SDS: Tamil': 'ytb_sds_batch3_tamil',
    
    'YouTube-TA-En':'ytb_asr_batch3_ta_en',
    'YouTube-ZH-En':'ytb_asr_batch3_zh_en',
    'YouTube-MA-En':'ytb_asr_batch3_ma_en',
    
}


displayname2datasetname = {}
displayname2datasetname.update(wer_displayname2datasetname)
displayname2datasetname.update(non_wer_displayname2datasetname)


datasetname2diaplayname = {datasetname: displayname for displayname, datasetname in displayname2datasetname.items()}


dataset_diaplay_information = {
    'LibriSpeech-Clean'    : 'A clean, high-quality testset of the LibriSpeech dataset, used for ASR testing.',
    'LibriSpeech-Other'    : 'A more challenging, noisier testset of the LibriSpeech dataset for ASR testing.',
    'CommonVoice-15-EN'    : 'Test set from the Common Voice project, which is a crowd-sourced, multilingual speech dataset.',
    'Peoples-Speech'       : 'A large-scale, open-source speech recognition dataset, with diverse accents and domains.',
    'GigaSpeech-1'         : 'A large-scale ASR dataset with diverse audio sources like podcasts, interviews, etc.',
    'Earnings-21'          : 'ASR test dataset focused on earnings calls from 2021, with professional speech and financial jargon.',
    'Earnings-22'          : 'Similar to Earnings21, but covering earnings calls from 2022.',
    'TED-LIUM-3'           : 'A test set derived from TED talks, covering diverse speakers and topics.',
    'TED-LIUM-3-LongForm'  : 'A longer version of the TED-LIUM dataset, containing extended audio samples. This poses challenges to existing fusion methods in handling long audios. However, it provides benchmark for future development.',
    'AISHELL-ASR-ZH'       : 'ASR test dataset for Mandarin Chinese, based on the Aishell dataset.',
    'CoVoST2-EN-ID'        : 'CoVoST 2 dataset for speech translation from English to Indonesian.',
    'CoVoST2-EN-ZH'        : 'CoVoST 2 dataset for speech translation from English to Chinese.',
    'CoVoST2-EN-TA'        : 'CoVoST 2 dataset for speech translation from English to Tamil.',
    'CoVoST2-ID-EN'        : 'CoVoST 2 dataset for speech translation from Indonesian to English.',
    'CoVoST2-ZH-EN'        : 'CoVoST 2 dataset for speech translation from Chinese to English.',
    'CoVoST2-TA-EN'        : 'CoVoST 2 dataset for speech translation from Tamil to English.',
    'CN-College-Listen-MCQ': 'Chinese College English Listening Test, with multiple-choice questions.',
    'DREAM-TTS-MCQ'        : 'DREAM dataset for spoken question-answering, derived from textual data and synthesized speech.',
    'SLUE-P2-SQA5'         : 'Spoken Language Understanding Evaluation (SLUE) dataset, part 2, focused on QA tasks.',
    'Public-SG-Speech-QA'  : 'Public dataset for speech-based question answering, gathered from Singapore.',
    'Spoken-SQuAD'         : 'Spoken SQuAD dataset, based on the textual SQuAD dataset, converted into audio.',
    'OpenHermes-Audio'     : 'Test set for spoken instructions. Synthesized from the OpenHermes dataset.',
    'ALPACA-Audio'         : 'Spoken version of the ALPACA dataset, used for evaluating instruction following in audio.',
    'WavCaps'              : 'WavCaps is a dataset for testing audio captioning, where models generate textual descriptions of audio clips.',
    'AudioCaps'            : 'AudioCaps dataset, used for generating captions from general audio events.',
    'Clotho-AQA'           : 'Clotho dataset adapted for audio-based question answering, containing audio clips and questions.',
    'WavCaps-QA'           : 'Question-answering test dataset derived from WavCaps, focusing on audio content.',
    'AudioCaps-QA'         : 'AudioCaps adapted for question-answering tasks, using audio events as input for Q&A.',
    '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.',

    'CNA'             : 'Under Development',
    'IDPC'            : 'Under Development',
    'Parliament'      : 'Under Development',
    'UKUS-News'       : 'Under Development',
    'Mediacorp'       : 'Under Development',
    'IDPC-Short'      : 'Under Development',
    'Parliament-Short': 'Under Development',
    'UKUS-News-Short' : 'Under Development',
    'Mediacorp-Short' : 'Under Development',

    'CommonVoice-ZH'               : 'Under Development',
    'CommonVoice-17-Indonesian'    : 'Under Development',
    'CommonVoice-17-Tamil'         : 'Under Development',
    'CommonVoice-17-Thai'          : 'Under Development',
    'CommonVoice-17-Vietnamese'    : 'Under Development',
    'GigaSpeech-2-Indonesain'      : 'Under Development',
    'GigaSpeech-2-Thai'            : 'Under Development',
    'GigaSpeech-2-Vietnamese'      : 'Under Development',
    'Fleurs-Tamil'                 : 'Under Development',
    'Lotus-Thai'                   : 'Under Development',
    'MMAU-mini'                    : 'Under Development',

    
    'YouTube ASR: English Singapore Content'  : 'YouTube Evaluation Dataset for ASR Task: <br> This dataset contains English and Singlish audio clips, featuring Singapore-related content. <br> It includes approximately 2.5 hours of audio, with individual clips ranging from 2 seconds to 30 seconds in length.',
    
    'YouTube ASR: English with Strong Emotion'  : 'YouTube Evaluation Dataset for ASR Task: <br> This dataset contains English, Singlish and some unknown languages audio clips, featuring speech with strong emotional expression. <br> It includes approximately 3.9 hours of audio, with each clip lasting 30 seconds.',
    
    'YouTube ASR: Malay with English Prompt': 'YouTube Evaluation Dataset for ASR Task: <br> This dataset mainly contains Malay and some Malay-English codeswitch audio clips, featuring with English prompts. <br> It includes approximately 2.55 hours of audio, with indicidual clips ranging form 30 seconds to 95 seconds in length.',
    
    'YouTube ASR: Malay with Malay Prompt': 'YouTube Evaluation Dataset for ASR Task: <br> This dataset use the same audio from <i>YouTube ASR: Malay English Prompt</i>, except featuring with Malay prompts. <br> It includes approximately 2.55 hours of audio, with indicidual clips ranging form 30 seconds to 95 seconds in length.',
    
    'YouTube ASR: Chinese with English Prompt': 'YouTube Evaluation Dataset for ASR Task: <br> This dataset contains Chinese and some Chinese-English codeswitch audio clips, featuring with English prompts. <br> It includes approximately 3.32 hours of audio, with individual clips ranging from 17 seconds to 1966 seconds in length.',
    
    'YouTube ASR: Chinese with Chinese Prompt': 'YouTube Evaluation Dataset for ASR Task: <br> This dataset contains Chinese and some Chinese-English codeswitch audio clips, featuring with Chinese prompts. <br> It includes approximately 3.32 hours of audio, with individual clips ranging from 17 seconds to 1966 seconds in length.',
    
    'YouTube ASR: Tamil with Tamil Prompt': 'YouTube Evaluation Dataset for ASR Task: <br> This dataset contains Tamil and some Tamil-English codeswitch audio clips, featuring with Tamil prompts. <br> It includes approximately 2.44 hours of audio, with individual clips ranging from 30 seconds to 324 seconds in length.',
    
    'YouTube ASR: Tamil with English Prompt': 'YouTube Evaluation Dataset for ASR Task: <br> This dataset contains Tamil and some Tamil-English codeswitch audio clips, featuring with English prompts. <br> It includes approximately 2.44 hours of audio, with individual clips ranging from 30 seconds to 324 seconds in length.',
    
    'YouTube-TA-En':'YouTube Evaluation Dataset for ASR Task: <br> The audio of dataset is same as <i>YouTube ASR: Tamil<i>',
    'YouTube-ZH-En':'YouTube Evaluation Dataset for ASR Task: <br> The audio of dataset is same as <i>YouTube ASR: Chinese<i>',
    'YouTube-MA-En':'YouTube Evaluation Dataset for ASR Task: <br> The audio of dataset is same as <i>YouTube ASR: Malay<i>',
    
    # 'YouTube ASR Translation: Chinese2English': 'YouTube Evaluation Dataset for ASR Task: <br> The audio of dataset is same as <i>YouTube ASR: Chinese<i>',
    
    # 'YouTube ASR Translation: Tamil2English': 'YouTube Evaluation Dataset for ASR Task: <br> The audio of dataset is same as <i>YouTube ASR: Tamil<i>',
    
    
    
    'SEAME-Dev-Mandarin'   : 'Under Development',
    'SEAME-Dev-Singlish'   : 'Under Development',

    'YouTube SQA: English with Singapore Content': 'YouTube Evaluation Dataset for Speech-QA Task: <br> This dataset contains English and Singlish audio clips, featuring Singapore-related content. <br> It includes approximately 7.6 hours of audio, with individual clips ranging from 8 seconds to 32 seconds in length.',
    
    'YouTube SQA: Malay': 'YouTube Evaluation Dataset for Speech-QA Task: <br> The auido of this dataset is same as <i>YouTube ASR: Malay<i>, it contains Malay and some Malay-English codeswitch audio clips, featuring with English prompts. <br> It includes approximately 2.55 hours of audio, with indicidual clips ranging form 30 seconds to 95 seconds in length.',
    
    'YouTube SQA: Chinese': 'YouTube Evaluation Dataset for Speech-QA Task: <br> The auido of this dataset is same as <i>YouTube ASR: Chinese<i>',
    
    'YouTube SQA: Tamil': 'YouTube Evaluation Dataset for Speech-QA Task: <br> The auido of this dataset is same as <i>YouTube ASR: Tamil<i>',
    
    'YouTube SDS: English with Singapore Content': 'YouTube Evaluation Dataset for Summary Task: <br> This dataset contains English and Singlish audio clips, featuring Singapore-related content. <br> It includes approximately 5.4 hours of audio, with individual clips ranging from 8 seconds to 32 seconds in length.',
    
    'YouTube SDS: Malay': 'YouTube Evaluation Dataset for Speech-QA Task: <br> The auido of this dataset is same as <i>YouTube ASR: Malay<i>, it contains Malay and some Malay-English codeswitch audio clips, featuring with English prompts. <br> It includes approximately 2.55 hours of audio, with indicidual clips ranging form 30 seconds to 95 seconds in length.',
    
    'YouTube SDS: Chinese': 'YouTube Evaluation Dataset for Speech-QA Task: <br> The auido of this dataset is same as <i>YouTube ASR: Chinese<i>',
    
    'YouTube SDS: Tamil': 'YouTube Evaluation Dataset for Speech-QA Task: <br> The auido of this dataset is same as <i>YouTube ASR: Tamil<i>',
    
    
    'YouTube PQA: English with Singapore Content': 'YouTube Evaluation Dataset for Paralinguistics QA Task: <br> This dataset contains English and Singlish audio clips, featuring Singapore-related content. <br> It includes approximately 41.4 hours of audio, with individual clips ranging from 41 seconds to 83 seconds in length.',
    
    
                }


metrics_info = {
    'wer'                    : 'Word Error Rate (WER) - The Lower, the better.',
    'llama3_70b_judge_binary': 'Model-as-a-Judge Peformance. Using LLAMA-3-70B. Scale from 0-100. The higher, the better.',
    'llama3_70b_judge'       : 'Model-as-a-Judge Peformance. Using LLAMA-3-70B. Scale from 0-100. The higher, the better.',
    'meteor'                 : 'METEOR Score. The higher, the better.',
    'bleu'                   : 'BLEU Score. The higher, the better.',
}