File size: 11,375 Bytes
5bd2aed |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 |
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',
'AISHELL-ASR-ZH' : 'aishell_asr_zh_test',
'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',
'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',
'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',
'IEMOCAP-Emotion' : 'iemocap_emotion_test',
'MELD-Sentiment' : 'meld_sentiment_test',
'MELD-Emotion' : 'meld_emotion_test',
'MuChoMusic' : 'muchomusic_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',
'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',
'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 with Singapore Content': 'ytb_asr_batch1',
'YouTube ASR: English with Strong Emotion': 'ytb_asr_batch2',
'YouTube ASR: Malay with English Prompt': 'ytb_asr_batch3_ms',
'YouTube ASR: Malay with Malay Prompt': 'ytb_asr_batch3_ms_ms_prompt',
'SEAME-Dev-Mandarin' : 'seame_dev_man',
'SEAME-Dev-Singlish' : 'seame_dev_sge',
'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',
}
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',
'YouTube ASR: English Singapore Content' : '''\nYouTube Evaluation Dataset for ASR Task: This dataset include English and Singlish with Singapore Content.''',
'YouTube ASR: English with Strong Emotion' : '\nYouTube Evaluation Dataset for ASR Task. English with strong emotions',
'YouTube ASR: Malay English Prompt': 'YouTube ASR Dataset, Malay and Malay-English CondeSwitch',
'YouTube ASR: Malay with Malay Prompt': 'YouTube ASR Dataset, Malay and Malay-English CondeSwitch. Use Malay prompts',
'SEAME-Dev-Mandarin' : 'Under Development',
'SEAME-Dev-Singlish' : 'Under Development',
'YouTube SQA: English with Singapore Content': 'Under Development',
'YouTube SDS: English with Singapore Content': 'Under Development',
'YouTube PQA: English with Singapore Content': 'Under Development',
}
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.',
}
|