Datasets:

s3prl
/

superb

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Our results demonstrate that the framework is promising\nas SSL representations show competitive generalizability and accessibility\nacross SUPERB tasks. We release SUPERB as a challenge with a leaderboard and a\nbenchmark toolkit to fuel the research in representation learning and general\nspeech processing.\n\nNote that in order to limit the required storage for preparing this dataset, the\naudio is stored in the .wav format and is not converted to a float32 array. To\nconvert the audio file to a float32 array, please make use of the `.map()`\nfunction as follows:\n\n\n```python\nimport soundfile as sf\n\ndef map_to_array(batch):\n speech_array, _ = sf.read(batch[\"file\"])\n batch[\"speech\"] = speech_array\n return batch\n\ndataset = dataset.map(map_to_array, remove_columns=[\"file\"])\n```\n", "citation": "@article{DBLP:journals/corr/abs-2105-01051,\n author = {Shu{-}Wen Yang and\n Po{-}Han Chi and\n Yung{-}Sung Chuang and\n Cheng{-}I Jeff Lai and\n Kushal Lakhotia and\n Yist Y. 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Liu and\n Jiatong Shi and\n Xuankai Chang and\n Guan{-}Ting Lin and\n Tzu{-}Hsien Huang and\n Wei{-}Cheng Tseng and\n Ko{-}tik Lee and\n Da{-}Rong Liu and\n Zili Huang and\n Shuyan Dong and\n Shang{-}Wen Li and\n Shinji Watanabe and\n Abdelrahman Mohamed and\n Hung{-}yi Lee},\n title = {{SUPERB:} Speech processing Universal PERformance Benchmark},\n journal = {CoRR},\n volume = {abs/2105.01051},\n year = {2021},\n url = {https://arxiv.org/abs/2105.01051},\n archivePrefix = {arXiv},\n eprint = {2105.01051},\n timestamp = {Thu, 01 Jul 2021 13:30:22 +0200},\n biburl = {https://dblp.org/rec/journals/corr/abs-2105-01051.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "http://www.openslr.org/12", "license": "", "features": {"file": {"dtype": "string", "id": null, "_type": "Value"}, "audio": {"sampling_rate": 16000, "mono": true, "decode": true, "id": null, "_type": "Audio"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "speaker_id": {"dtype": "int64", "id": null, "_type": "Value"}, "chapter_id": {"dtype": "int64", "id": null, "_type": "Value"}, "id": {"dtype": "string", "id": null, "_type": "Value"}}, "post_processed": null, "supervised_keys": {"input": "file", "output": "text"}, "task_templates": [{"task": "automatic-speech-recognition", "audio_column": "audio", "transcription_column": "text"}], "builder_name": "superb", "config_name": "asr", "version": {"version_str": "1.9.0", "description": "", "major": 1, "minor": 9, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 11852430, "num_examples": 28539, "dataset_name": "superb"}, "validation": {"name": "validation", "num_bytes": 897213, "num_examples": 2703, "dataset_name": "superb"}, "test": {"name": "test", "num_bytes": 871234, "num_examples": 2620, "dataset_name": "superb"}}, "download_checksums": {"http://www.openslr.org/resources/12/dev-clean.tar.gz": {"num_bytes": 337926286, "checksum": "76f87d090650617fca0cac8f88b9416e0ebf80350acb97b343a85fa903728ab3"}, "http://www.openslr.org/resources/12/test-clean.tar.gz": {"num_bytes": 346663984, "checksum": "39fde525e59672dc6d1551919b1478f724438a95aa55f874b576be21967e6c23"}, "http://www.openslr.org/resources/12/train-clean-100.tar.gz": {"num_bytes": 6387309499, "checksum": "d4ddd1d5a6ab303066f14971d768ee43278a5f2a0aa43dc716b0e64ecbbbf6e2"}}, "download_size": 7071899769, "post_processing_size": null, "dataset_size": 13620877, "size_in_bytes": 7085520646}, "sd": {"description": "Self-supervised learning (SSL) has proven vital for advancing research in\nnatural language processing (NLP) and computer vision (CV). The paradigm\npretrains a shared model on large volumes of unlabeled data and achieves\nstate-of-the-art (SOTA) for various tasks with minimal adaptation. However, the\nspeech processing community lacks a similar setup to systematically explore the\nparadigm. To bridge this gap, we introduce Speech processing Universal\nPERformance Benchmark (SUPERB). SUPERB is a leaderboard to benchmark the\nperformance of a shared model across a wide range of speech processing tasks\nwith minimal architecture changes and labeled data. Among multiple usages of the\nshared model, we especially focus on extracting the representation learned from\nSSL due to its preferable re-usability. We present a simple framework to solve\nSUPERB tasks by learning task-specialized lightweight prediction heads on top of\nthe frozen shared model. Our results demonstrate that the framework is promising\nas SSL representations show competitive generalizability and accessibility\nacross SUPERB tasks. We release SUPERB as a challenge with a leaderboard and a\nbenchmark toolkit to fuel the research in representation learning and general\nspeech processing.\n\nNote that in order to limit the required storage for preparing this dataset, the\naudio is stored in the .flac format and is not converted to a float32 array. To\nconvert, the audio file to a float32 array, please make use of the `.map()`\nfunction as follows:\n\n\n```python\nimport soundfile as sf\n\ndef map_to_array(batch):\n speech_array, _ = sf.read(batch[\"file\"])\n batch[\"speech\"] = speech_array\n return batch\n\ndataset = dataset.map(map_to_array, remove_columns=[\"file\"])\n```\n", "citation": "@article{DBLP:journals/corr/abs-2105-01051,\n author = {Shu{-}Wen Yang and\n Po{-}Han Chi and\n Yung{-}Sung Chuang and\n Cheng{-}I Jeff Lai and\n Kushal Lakhotia and\n Yist Y. Lin and\n Andy T. Liu and\n Jiatong Shi and\n Xuankai Chang and\n Guan{-}Ting Lin and\n Tzu{-}Hsien Huang and\n Wei{-}Cheng Tseng and\n Ko{-}tik Lee and\n Da{-}Rong Liu and\n Zili Huang and\n Shuyan Dong and\n Shang{-}Wen Li and\n Shinji Watanabe and\n Abdelrahman Mohamed and\n Hung{-}yi Lee},\n title = {{SUPERB:} Speech processing Universal PERformance Benchmark},\n journal = {CoRR},\n volume = {abs/2105.01051},\n year = {2021},\n url = {https://arxiv.org/abs/2105.01051},\n archivePrefix = {arXiv},\n eprint = {2105.01051},\n timestamp = {Thu, 01 Jul 2021 13:30:22 +0200},\n biburl = {https://dblp.org/rec/journals/corr/abs-2105-01051.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "https://github.com/ftshijt/LibriMix", "license": "", "features": {"record_id": {"dtype": "string", "id": null, "_type": "Value"}, "file": {"dtype": "string", "id": null, "_type": "Value"}, "start": {"dtype": "int64", "id": null, "_type": "Value"}, "end": {"dtype": "int64", "id": null, "_type": "Value"}, "speakers": [{"speaker_id": {"dtype": "string", "id": null, "_type": "Value"}, "start": {"dtype": "int64", "id": null, "_type": "Value"}, "end": {"dtype": "int64", "id": null, "_type": "Value"}}]}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "superb", "config_name": "sd", "version": {"version_str": "1.9.0", "description": "", "major": 1, "minor": 9, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 4622013, "num_examples": 13901, "dataset_name": "superb"}, "dev": {"name": "dev", "num_bytes": 860472, "num_examples": 3014, "dataset_name": "superb"}, "test": {"name": "test", "num_bytes": 847803, "num_examples": 3002, "dataset_name": "superb"}}, "download_checksums": {"https://huggingface.co/datasets/superb/superb-data/resolve/main/sd/train/reco2dur": {"num_bytes": 540906, "checksum": "879dca4b1108c93bd86df879463fca15a4de42a0f95a7e6987138dc6029b5554"}, "https://huggingface.co/datasets/superb/superb-data/resolve/main/sd/train/segments": {"num_bytes": 5723993, "checksum": "f19cb0ecc342f8d2cd855118879a111822d7cf55fcd078ef156f5147233a8e11"}, "https://huggingface.co/datasets/superb/superb-data/resolve/main/sd/train/utt2spk": {"num_bytes": 3165995, "checksum": "a4295726caf05d72f5ad24706180b9dbccffe6c0c2fc0128ca4b02b7b828a28a"}, "https://huggingface.co/datasets/superb/superb-data/resolve/main/sd/train/wav.zip": {"num_bytes": 5706733518, "checksum": "4231070427ffbc9b3bddae874dba32f3985a0db0b0feb4dfa29ed4d1d11bf41b"}, "https://huggingface.co/datasets/superb/superb-data/resolve/main/sd/dev/reco2dur": {"num_bytes": 115918, "checksum": "a30fd59ad01db0315a82cad7a64baea009e6c2bcdfb6b2501bc8873ede72de06"}, "https://huggingface.co/datasets/superb/superb-data/resolve/main/sd/dev/segments": {"num_bytes": 673006, "checksum": "2b977917e7ab9feec03afb4fd6a4662df90e48dbcc42977a4b9c89c8d40432ee"}, "https://huggingface.co/datasets/superb/superb-data/resolve/main/sd/dev/utt2spk": {"num_bytes": 374794, "checksum": "9f47a7bed76e7a03e57d66ba9cc5f57d85d91f748d0b1eb20301d09e6c24cd20"}, "https://huggingface.co/datasets/superb/superb-data/resolve/main/sd/dev/wav.zip": {"num_bytes": 765594100, "checksum": "e28b3422ce59e2a5273be924e6ed6b8f115c0983db1997e56441973c27ee1cd8"}, "https://huggingface.co/datasets/superb/superb-data/resolve/main/sd/test/reco2dur": {"num_bytes": 113357, "checksum": "6e013d917015031e2f1383871b52dfc1122e7b16cdee53bd8e5e0a7fbc57e406"}, "https://huggingface.co/datasets/superb/superb-data/resolve/main/sd/test/segments": {"num_bytes": 650742, "checksum": "92f8de0f56c55a34e9111542c24ea13f2d2efaf9ebe64af31250cadab020f987"}, "https://huggingface.co/datasets/superb/superb-data/resolve/main/sd/test/utt2spk": {"num_bytes": 361548, "checksum": "19dcb558aa886f0d553d8d9b8735ea1998b83e96d5245e5511cb732c84625ffd"}, "https://huggingface.co/datasets/superb/superb-data/resolve/main/sd/test/wav.zip": {"num_bytes": 706322334, "checksum": "9c8ee97d3068759c0101bf88684abab77183374dbb3bb40f7c0b25d385992ea6"}}, "download_size": 7190370211, "post_processing_size": null, "dataset_size": 6330288, "size_in_bytes": 7196700499}, "ks": {"description": "Self-supervised learning (SSL) has proven vital for advancing research in\nnatural language processing (NLP) and computer vision (CV). The paradigm\npretrains a shared model on large volumes of unlabeled data and achieves\nstate-of-the-art (SOTA) for various tasks with minimal adaptation. However, the\nspeech processing community lacks a similar setup to systematically explore the\nparadigm. To bridge this gap, we introduce Speech processing Universal\nPERformance Benchmark (SUPERB). SUPERB is a leaderboard to benchmark the\nperformance of a shared model across a wide range of speech processing tasks\nwith minimal architecture changes and labeled data. Among multiple usages of the\nshared model, we especially focus on extracting the representation learned from\nSSL due to its preferable re-usability. We present a simple framework to solve\nSUPERB tasks by learning task-specialized lightweight prediction heads on top of\nthe frozen shared model. Our results demonstrate that the framework is promising\nas SSL representations show competitive generalizability and accessibility\nacross SUPERB tasks. We release SUPERB as a challenge with a leaderboard and a\nbenchmark toolkit to fuel the research in representation learning and general\nspeech processing.\n\nNote that in order to limit the required storage for preparing this dataset, the\naudio is stored in the .wav format and is not converted to a float32 array. To\nconvert the audio file to a float32 array, please make use of the `.map()`\nfunction as follows:\n\n\n```python\nimport soundfile as sf\n\ndef map_to_array(batch):\n speech_array, _ = sf.read(batch[\"file\"])\n batch[\"speech\"] = speech_array\n return batch\n\ndataset = dataset.map(map_to_array, remove_columns=[\"file\"])\n```\n", "citation": "@article{DBLP:journals/corr/abs-2105-01051,\n author = {Shu{-}Wen Yang and\n Po{-}Han Chi and\n Yung{-}Sung Chuang and\n Cheng{-}I Jeff Lai and\n Kushal Lakhotia and\n Yist Y. Lin and\n Andy T. Liu and\n Jiatong Shi and\n Xuankai Chang and\n Guan{-}Ting Lin and\n Tzu{-}Hsien Huang and\n Wei{-}Cheng Tseng and\n Ko{-}tik Lee and\n Da{-}Rong Liu and\n Zili Huang and\n Shuyan Dong and\n Shang{-}Wen Li and\n Shinji Watanabe and\n Abdelrahman Mohamed and\n Hung{-}yi Lee},\n title = {{SUPERB:} Speech processing Universal PERformance Benchmark},\n journal = {CoRR},\n volume = {abs/2105.01051},\n year = {2021},\n url = {https://arxiv.org/abs/2105.01051},\n archivePrefix = {arXiv},\n eprint = {2105.01051},\n timestamp = {Thu, 01 Jul 2021 13:30:22 +0200},\n biburl = {https://dblp.org/rec/journals/corr/abs-2105-01051.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "https://www.tensorflow.org/datasets/catalog/speech_commands", "license": "", "features": {"file": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 12, "names": ["yes", "no", "up", "down", "left", "right", "on", "off", "stop", "go", "_silence_", "_unknown_"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": {"input": "file", "output": "label"}, "task_templates": null, "builder_name": "superb", "config_name": "ks", "version": {"version_str": "1.9.0", "description": "", "major": 1, "minor": 9, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 8467781, "num_examples": 51094, "dataset_name": "superb"}, "validation": {"name": "validation", "num_bytes": 1126476, "num_examples": 6798, "dataset_name": "superb"}, "test": {"name": "test", "num_bytes": 510619, "num_examples": 3081, "dataset_name": "superb"}}, "download_checksums": {"http://download.tensorflow.org/data/speech_commands_v0.01.tar.gz": {"num_bytes": 1489096277, "checksum": "743935421bb51cccdb6bdd152e04c5c70274e935c82119ad7faeec31780d811d"}, "http://download.tensorflow.org/data/speech_commands_test_set_v0.01.tar.gz": {"num_bytes": 71271436, "checksum": "baa084f6b62c91de660ff0588ae4dfc4e4d534aa99ac0e5f406cba75836cbd00"}}, "download_size": 1560367713, "post_processing_size": null, "dataset_size": 10104876, "size_in_bytes": 1570472589}, "ic": {"description": "Self-supervised learning (SSL) has proven vital for advancing research in\nnatural language processing (NLP) and computer vision (CV). The paradigm\npretrains a shared model on large volumes of unlabeled data and achieves\nstate-of-the-art (SOTA) for various tasks with minimal adaptation. However, the\nspeech processing community lacks a similar setup to systematically explore the\nparadigm. To bridge this gap, we introduce Speech processing Universal\nPERformance Benchmark (SUPERB). SUPERB is a leaderboard to benchmark the\nperformance of a shared model across a wide range of speech processing tasks\nwith minimal architecture changes and labeled data. Among multiple usages of the\nshared model, we especially focus on extracting the representation learned from\nSSL due to its preferable re-usability. We present a simple framework to solve\nSUPERB tasks by learning task-specialized lightweight prediction heads on top of\nthe frozen shared model. Our results demonstrate that the framework is promising\nas SSL representations show competitive generalizability and accessibility\nacross SUPERB tasks. We release SUPERB as a challenge with a leaderboard and a\nbenchmark toolkit to fuel the research in representation learning and general\nspeech processing.\n\nNote that in order to limit the required storage for preparing this dataset, the\naudio is stored in the .flac format and is not converted to a float32 array. To\nconvert, the audio file to a float32 array, please make use of the `.map()`\nfunction as follows:\n\n\n```python\nimport soundfile as sf\n\ndef map_to_array(batch):\n speech_array, _ = sf.read(batch[\"file\"])\n batch[\"speech\"] = speech_array\n return batch\n\ndataset = dataset.map(map_to_array, remove_columns=[\"file\"])\n```\n", "citation": "@article{DBLP:journals/corr/abs-2105-01051,\n author = {Shu{-}Wen Yang and\n Po{-}Han Chi and\n Yung{-}Sung Chuang and\n Cheng{-}I Jeff Lai and\n Kushal Lakhotia and\n Yist Y. Lin and\n Andy T. Liu and\n Jiatong Shi and\n Xuankai Chang and\n Guan{-}Ting Lin and\n Tzu{-}Hsien Huang and\n Wei{-}Cheng Tseng and\n Ko{-}tik Lee and\n Da{-}Rong Liu and\n Zili Huang and\n Shuyan Dong and\n Shang{-}Wen Li and\n Shinji Watanabe and\n Abdelrahman Mohamed and\n Hung{-}yi Lee},\n title = {{SUPERB:} Speech processing Universal PERformance Benchmark},\n journal = {CoRR},\n volume = {abs/2105.01051},\n year = {2021},\n url = {https://arxiv.org/abs/2105.01051},\n archivePrefix = {arXiv},\n eprint = {2105.01051},\n timestamp = {Thu, 01 Jul 2021 13:30:22 +0200},\n biburl = {https://dblp.org/rec/journals/corr/abs-2105-01051.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "https://fluent.ai/fluent-speech-commands-a-dataset-for-spoken-language-understanding-research/", "license": "", "features": {"file": {"dtype": "string", "id": null, "_type": "Value"}, "speaker_id": {"dtype": "string", "id": null, "_type": "Value"}, "text": {"dtype": "string", "id": null, "_type": "Value"}, "action": {"num_classes": 6, "names": ["activate", "bring", "change language", "deactivate", "decrease", "increase"], "names_file": null, "id": null, "_type": "ClassLabel"}, "object": {"num_classes": 14, "names": ["Chinese", "English", "German", "Korean", "heat", "juice", "lamp", "lights", "music", "newspaper", "none", "shoes", "socks", "volume"], "names_file": null, "id": null, "_type": "ClassLabel"}, "location": {"num_classes": 4, "names": ["bedroom", "kitchen", "none", "washroom"], "names_file": null, "id": null, "_type": "ClassLabel"}}, "post_processed": null, "supervised_keys": null, "task_templates": null, "builder_name": "superb", "config_name": "ic", "version": {"version_str": "1.9.0", "description": "", "major": 1, "minor": 9, "patch": 0}, "splits": {"train": {"name": "train", "num_bytes": 7071466, "num_examples": 23132, "dataset_name": "superb"}, "validation": {"name": "validation", "num_bytes": 953622, "num_examples": 3118, "dataset_name": "superb"}, "test": {"name": "test", "num_bytes": 1158347, "num_examples": 3793, "dataset_name": "superb"}}, "download_checksums": {"http://fluent.ai:2052/jf8398hf30f0381738rucj3828chfdnchs.tar.gz": {"num_bytes": 1544093324, "checksum": "4376699f7daf134a9fa57a1d880ffcaaf94a3e2551ba0b40ad894d7abb71aacb"}}, "download_size": 1544093324, "post_processing_size": null, "dataset_size": 9183435, "size_in_bytes": 1553276759}, "si": {"description": "Self-supervised learning (SSL) has proven vital for advancing research in\nnatural language processing (NLP) and computer vision (CV). The paradigm\npretrains a shared model on large volumes of unlabeled data and achieves\nstate-of-the-art (SOTA) for various tasks with minimal adaptation. However, the\nspeech processing community lacks a similar setup to systematically explore the\nparadigm. To bridge this gap, we introduce Speech processing Universal\nPERformance Benchmark (SUPERB). SUPERB is a leaderboard to benchmark the\nperformance of a shared model across a wide range of speech processing tasks\nwith minimal architecture changes and labeled data. Among multiple usages of the\nshared model, we especially focus on extracting the representation learned from\nSSL due to its preferable re-usability. We present a simple framework to solve\nSUPERB tasks by learning task-specialized lightweight prediction heads on top of\nthe frozen shared model. Our results demonstrate that the framework is promising\nas SSL representations show competitive generalizability and accessibility\nacross SUPERB tasks. We release SUPERB as a challenge with a leaderboard and a\nbenchmark toolkit to fuel the research in representation learning and general\nspeech processing.\n\nNote that in order to limit the required storage for preparing this dataset, the\naudio is stored in the .flac format and is not converted to a float32 array. To\nconvert, the audio file to a float32 array, please make use of the `.map()`\nfunction as follows:\n\n\n```python\nimport soundfile as sf\n\ndef map_to_array(batch):\n speech_array, _ = sf.read(batch[\"file\"])\n batch[\"speech\"] = speech_array\n return batch\n\ndataset = dataset.map(map_to_array, remove_columns=[\"file\"])\n```\n", "citation": "@article{DBLP:journals/corr/abs-2105-01051,\n author = {Shu{-}Wen Yang and\n Po{-}Han Chi and\n Yung{-}Sung Chuang and\n Cheng{-}I Jeff Lai and\n Kushal Lakhotia and\n Yist Y. Lin and\n Andy T. Liu and\n Jiatong Shi and\n Xuankai Chang and\n Guan{-}Ting Lin and\n Tzu{-}Hsien Huang and\n Wei{-}Cheng Tseng and\n Ko{-}tik Lee and\n Da{-}Rong Liu and\n Zili Huang and\n Shuyan Dong and\n Shang{-}Wen Li and\n Shinji Watanabe and\n Abdelrahman Mohamed and\n Hung{-}yi Lee},\n title = {{SUPERB:} Speech processing Universal PERformance Benchmark},\n journal = {CoRR},\n volume = {abs/2105.01051},\n year = {2021},\n url = {https://arxiv.org/abs/2105.01051},\n archivePrefix = {arXiv},\n eprint = {2105.01051},\n timestamp = {Thu, 01 Jul 2021 13:30:22 +0200},\n biburl = {https://dblp.org/rec/journals/corr/abs-2105-01051.bib},\n bibsource = {dblp computer science bibliography, https://dblp.org}\n}\n", "homepage": "https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html", "license": "", "features": {"file": {"dtype": "string", "id": null, "_type": "Value"}, "label": {"num_classes": 1251, "names": ["id10001", "id10002", "id10003", "id10004", "id10005", "id10006", "id10007", "id10008", 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