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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",
),
]
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.VALIDATION,
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
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