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	# coding=utf-8
	# Copyright 2021 The TensorFlow Datasets Authors and the HuggingFace Datasets Authors.
	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
	# You may obtain a copy of the License at
	#
	# http://www.apache.org/licenses/LICENSE-2.0
	#
	# Unless required by applicable law or agreed to in writing, software
	# distributed under the License is distributed on an "AS IS" BASIS,
	# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
	# See the License for the specific language governing permissions and
	# limitations under the License.

	# Lint as: python3
	"""SUPERB: Speech processing Universal PERformance Benchmark."""


	import base64
	import json
	import textwrap

	import datasets
	import numpy as np

	_CITATION = """\
	@article{DBLP:journals/corr/abs-2105-01051,
	author = {Shu{-}Wen Yang and
	Po{-}Han Chi and
	Yung{-}Sung Chuang and
	Cheng{-}I Jeff Lai and
	Kushal Lakhotia and
	Yist Y. Lin and
	Andy T. Liu and
	Jiatong Shi and
	Xuankai Chang and
	Guan{-}Ting Lin and
	Tzu{-}Hsien Huang and
	Wei{-}Cheng Tseng and
	Ko{-}tik Lee and
	Da{-}Rong Liu and
	Zili Huang and
	Shuyan Dong and
	Shang{-}Wen Li and
	Shinji Watanabe and
	Abdelrahman Mohamed and
	Hung{-}yi Lee},
	title = {{SUPERB:} Speech processing Universal PERformance Benchmark},
	journal = {CoRR},
	volume = {abs/2105.01051},
	year = {2021},
	url = {https://arxiv.org/abs/2105.01051},
	archivePrefix = {arXiv},
	eprint = {2105.01051},
	timestamp = {Thu, 01 Jul 2021 13:30:22 +0200},
	biburl = {https://dblp.org/rec/journals/corr/abs-2105-01051.bib},
	bibsource = {dblp computer science bibliography, https://dblp.org}
	}
	"""

	_DESCRIPTION = """\
	Self-supervised learning (SSL) has proven vital for advancing research in
	natural language processing (NLP) and computer vision (CV). The paradigm
	pretrains a shared model on large volumes of unlabeled data and achieves
	state-of-the-art (SOTA) for various tasks with minimal adaptation. However, the
	speech processing community lacks a similar setup to systematically explore the
	paradigm. To bridge this gap, we introduce Speech processing Universal
	PERformance Benchmark (SUPERB). SUPERB is a leaderboard to benchmark the
	performance of a shared model across a wide range of speech processing tasks
	with minimal architecture changes and labeled data. Among multiple usages of the
	shared model, we especially focus on extracting the representation learned from
	SSL due to its preferable re-usability. We present a simple framework to solve
	SUPERB tasks by learning task-specialized lightweight prediction heads on top of
	the frozen shared model. Our results demonstrate that the framework is promising
	as SSL representations show competitive generalizability and accessibility
	across SUPERB tasks. We release SUPERB as a challenge with a leaderboard and a
	benchmark toolkit to fuel the research in representation learning and general
	speech processing.
	"""


	class SuperbConfig(datasets.BuilderConfig):
	"""BuilderConfig for Superb."""

	def __init__(
	self,
	features,
	url,
	data_url=None,
	supervised_keys=None,
	task_templates=None,
	**kwargs,
	):
	super().__init__(version=datasets.Version("1.9.0", ""), **kwargs)
	self.features = features
	self.data_url = data_url
	self.url = url
	self.supervised_keys = supervised_keys
	self.task_templates = task_templates


	class Superb(datasets.GeneratorBasedBuilder):
	"""Superb dataset."""

	BUILDER_CONFIGS = [
	SuperbConfig(
	name="ks",
	description=textwrap.dedent(
	"""\
	Keyword Spotting (KS) detects preregistered keywords by classifying utterances into a predefined set of
	words. The task is usually performed on-device for the fast response time. Thus, accuracy, model size, and
	inference time are all crucial. SUPERB uses the widely used [Speech Commands dataset v1.0] for the task.
	The dataset consists of ten classes of keywords, a class for silence, and an unknown class to include the
	false positive. The evaluation metric is accuracy (ACC)"""
	),
	features=datasets.Features(
	{
	"file": datasets.Value("string"),
	"label": datasets.ClassLabel(
	names=[
	"yes",
	"no",
	"up",
	"down",
	"left",
	"right",
	"on",
	"off",
	"stop",
	"go",
	"_silence_",
	"_unknown_",
	]
	),
	"speech": datasets.Sequence(datasets.Value("float32")),
	}
	),
	url="https://www.tensorflow.org/datasets/catalog/speech_commands",
	data_url="ks.json",
	),
	SuperbConfig(
	name="ic",
	description=textwrap.dedent(
	"""\
	Intent Classification (IC) classifies utterances into predefined classes to determine the intent of
	speakers. SUPERB uses the Fluent Speech Commands dataset, where each utterance is tagged with three intent
	labels: action, object, and location. The evaluation metric is accuracy (ACC)."""
	),
	features=datasets.Features(
	{
	"file": datasets.Value("string"),
	"speaker_id": datasets.Value("string"),
	"text": datasets.Value("string"),
	"action": datasets.ClassLabel(
	names=["activate", "bring", "change language", "deactivate", "decrease", "increase"]
	),
	"object": datasets.ClassLabel(
	names=[
	"Chinese",
	"English",
	"German",
	"Korean",
	"heat",
	"juice",
	"lamp",
	"lights",
	"music",
	"newspaper",
	"none",
	"shoes",
	"socks",
	"volume",
	]
	),
	"location": datasets.ClassLabel(names=["bedroom", "kitchen", "none", "washroom"]),
	"speech": datasets.Sequence(datasets.Value("float32")),
	}
	),
	url="https://fluent.ai/fluent-speech-commands-a-dataset-for-spoken-language-understanding-research/",
	data_url="ic.json",
	),
	SuperbConfig(
	name="si",
	description=textwrap.dedent(
	"""\
	Speaker Identification (SI) classifies each utterance for its speaker identity as a multi-class
	classification, where speakers are in the same predefined set for both training and testing. The widely
	used VoxCeleb1 dataset is adopted, and the evaluation metric is accuracy (ACC)."""
	),
	features=datasets.Features(
	{
	"file": datasets.Value("string"),
	"label": datasets.ClassLabel(names=[f"id{i+10001}" for i in range(1251)]),
	"speech": datasets.Sequence(datasets.Value("float32")),
	}
	),
	url="https://www.robots.ox.ac.uk/~vgg/data/voxceleb/vox1.html",
	data_url="si.json",
	),
	SuperbConfig(
	name="er",
	description=textwrap.dedent(
	"""\
	Emotion Recognition (ER) predicts an emotion class for each utterance. The most widely used ER dataset
	IEMOCAP is adopted, and we follow the conventional evaluation protocol: we drop the unbalance emotion
	classes to leave the final four classes with a similar amount of data points and cross-validates on five
	folds of the standard splits. The evaluation metric is accuracy (ACC)."""
	),
	features=datasets.Features(
	{
	"file": datasets.Value("string"),
	"label": datasets.ClassLabel(names=["neu", "hap", "ang", "sad"]),
	"speech": datasets.Sequence(datasets.Value("float32")),
	}
	),
	url="https://sail.usc.edu/iemocap/",
	data_url="er.json",
	),
	SuperbConfig(
	name="sd",
	description=textwrap.dedent(
	"""\
	Speaker Diarization (SD) predicts `who is speaking when` for each timestamp, and multiple speakers can
	speak simultaneously. The model has to encode rich speaker characteristics for each frame and should be
	able to represent mixtures of signals. [LibriMix] is adopted where LibriSpeech
	train-clean-100/dev-clean/test-clean are used to generate mixtures for training/validation/testing.
	We focus on the two-speaker scenario as the first step. The time-coded speaker labels were generated using
	alignments from Kaldi LibriSpeech ASR model. The evaluation metric is diarization error rate (DER)."""
	),
	features=datasets.Features(
	{
	"file": datasets.Value("string"),
	"speech": datasets.Sequence(datasets.Value("float32")),
	"speakers": [
	{
	"speaker_id": datasets.Value("string"),
	"start": datasets.Value("int64"),
	"end": datasets.Value("int64"),
	}
	],
	}
	),
	url="https://github.com/ftshijt/LibriMix",
	data_url="sd.json",
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=self.config.features,
	supervised_keys=self.config.supervised_keys,
	homepage=self.config.url,
	citation=_CITATION,
	task_templates=self.config.task_templates,
	)

	def _split_generators(self, dl_manager):
	data_path = dl_manager.download_and_extract(self.config.data_url)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={"data_path": data_path},
	)
	]

	def _generate_examples(self, data_path):
	"""Generate examples."""
	with open(data_path, "r", encoding="utf-8") as f:
	for key, line in enumerate(f):
	example = json.loads(line)
	example["speech"] = np.frombuffer(base64.b64decode(example["speech"]), dtype=np.float32)

	yield key, example