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"""SUPERB: Speech processing Universal PERformance Benchmark.""" |
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import glob |
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import os |
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import textwrap |
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import datasets |
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from datasets.tasks import AutomaticSpeechRecognition |
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_CITATION = """\ |
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@article{DBLP:journals/corr/abs-2105-01051, |
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author = {Shu{-}Wen Yang and |
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Po{-}Han Chi and |
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Yung{-}Sung Chuang and |
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Cheng{-}I Jeff Lai and |
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Kushal Lakhotia and |
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Yist Y. Lin and |
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Andy T. Liu and |
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Jiatong Shi and |
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Xuankai Chang and |
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Guan{-}Ting Lin and |
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Tzu{-}Hsien Huang and |
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Wei{-}Cheng Tseng and |
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Ko{-}tik Lee and |
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Da{-}Rong Liu and |
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Zili Huang and |
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Shuyan Dong and |
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Shang{-}Wen Li and |
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Shinji Watanabe and |
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Abdelrahman Mohamed and |
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Hung{-}yi Lee}, |
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title = {{SUPERB:} Speech processing Universal PERformance Benchmark}, |
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journal = {CoRR}, |
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volume = {abs/2105.01051}, |
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year = {2021}, |
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url = {https://arxiv.org/abs/2105.01051}, |
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archivePrefix = {arXiv}, |
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eprint = {2105.01051}, |
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timestamp = {Thu, 01 Jul 2021 13:30:22 +0200}, |
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biburl = {https://dblp.org/rec/journals/corr/abs-2105-01051.bib}, |
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bibsource = {dblp computer science bibliography, https://dblp.org} |
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} |
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""" |
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_DESCRIPTION = """\ |
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Self-supervised learning (SSL) has proven vital for advancing research in |
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natural language processing (NLP) and computer vision (CV). The paradigm |
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pretrains a shared model on large volumes of unlabeled data and achieves |
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state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the |
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speech processing community lacks a similar setup to systematically explore the |
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paradigm. To bridge this gap, we introduce Speech processing Universal |
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PERformance Benchmark (SUPERB). SUPERB is a leaderboard to benchmark the |
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performance of a shared model across a wide range of speech processing tasks |
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with minimal architecture changes and labeled data. Among multiple usages of the |
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shared model, we especially focus on extracting the representation learned from |
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SSL due to its preferable re-usability. We present a simple framework to solve |
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SUPERB tasks by learning task-specialized lightweight prediction heads on top of |
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the frozen shared model. Our results demonstrate that the framework is promising |
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as SSL representations show competitive generalizability and accessibility |
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across SUPERB tasks. We release SUPERB as a challenge with a leaderboard and a |
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benchmark toolkit to fuel the research in representation learning and general |
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speech processing. |
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Note that in order to limit the required storage for preparing this dataset, the |
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audio is stored in the .flac format and is not converted to a float32 array. To |
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convert, the audio file to a float32 array, please make use of the `.map()` |
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function as follows: |
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```python |
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import soundfile as sf |
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def map_to_array(batch): |
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speech_array, _ = sf.read(batch["file"]) |
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batch["speech"] = speech_array |
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return batch |
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dataset = dataset.map(map_to_array, remove_columns=["file"]) |
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``` |
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""" |
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class AsrDummybConfig(datasets.BuilderConfig): |
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"""BuilderConfig for Superb.""" |
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def __init__( |
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self, |
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data_url, |
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url, |
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task_templates=None, |
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**kwargs, |
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): |
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super(AsrDummybConfig, self).__init__( |
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version=datasets.Version("1.9.0", ""), **kwargs |
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) |
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self.data_url = data_url |
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self.url = url |
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self.task_templates = task_templates |
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class AsrDummy(datasets.GeneratorBasedBuilder): |
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"""Superb dataset.""" |
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BUILDER_CONFIGS = [ |
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AsrDummybConfig( |
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name="asr", |
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description=textwrap.dedent( |
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"""\ |
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ASR transcribes utterances into words. While PR analyzes the |
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improvement in modeling phonetics, ASR reflects the significance of |
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the improvement in a real-world scenario. LibriSpeech |
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train-clean-100/dev-clean/test-clean subsets are used for |
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training/validation/testing. The evaluation metric is word error |
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rate (WER).""" |
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), |
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url="http://www.openslr.org/12", |
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data_url="http://www.openslr.org/resources/12/", |
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task_templates=[ |
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AutomaticSpeechRecognition( |
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audio_file_path_column="file", transcription_column="text" |
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) |
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], |
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) |
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] |
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DEFAULT_CONFIG_NAME = "asr" |
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def _info(self): |
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return datasets.DatasetInfo( |
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description=_DESCRIPTION, |
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features=datasets.Features( |
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{ |
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"id": datasets.Value("string"), |
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"file": datasets.Value("string"), |
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} |
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), |
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supervised_keys=("file",), |
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homepage=self.config.url, |
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citation=_CITATION, |
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task_templates=self.config.task_templates, |
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) |
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def _split_generators(self, dl_manager): |
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DL_URLS = [ |
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f"https://huggingface.co/datasets/Narsil/asr_dummy/raw/main/{i}.flac" |
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for i in range(1, 5) |
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] |
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archive_path = dl_manager.download_and_extract(DL_URLS) |
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return [ |
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datasets.SplitGenerator( |
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name=datasets.Split.TEST, |
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gen_kwargs={"archive_path": archive_path}, |
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), |
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] |
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def _generate_examples(self, archive_path): |
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"""Generate examples.""" |
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for i, filename in enumerate(archive_path): |
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key = str(i) |
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example = { |
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"id": key, |
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"file": filename, |
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} |
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yield key, example |
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