superb_dummy.py

# coding=utf-8
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# 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,
        **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


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,
        )

    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