superb_demo.py

# coding=utf-8
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# Lint as: python3
"""SUPERB: Speech processing Universal PERformance Benchmark."""

import csv
import glob
import os
import textwrap

import datasets
from datasets.tasks import AutomaticSpeechRecognition

_CITATION = """\
@article{DBLP:journals/corr/abs-2105-01051,
  author    = {Zhi{-}Jun Lee and
               Jia{-}Jie Sehn},
  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.
Note that in order to limit the required storage for preparing this dataset, the
audio is stored in the .flac format and is not converted to a float32 array. To
convert, the audio file to a float32 array, please make use of the `.map()`
function as follows:
```python
import soundfile as sf
def map_to_array(batch):
    speech_array, _ = sf.read(batch["file"])
    batch["speech"] = speech_array
    return batch
dataset = dataset.map(map_to_array, remove_columns=["file"])
```
"""


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"),
                    "audio": datasets.features.Audio(sampling_rate=16_000),
                    "label": datasets.ClassLabel(
                        names=[
                            "neunit",
                            "wake",
                            "_unknown_",
                        ]
                    ),
                }
            ),
            supervised_keys=("file", "label"),
            url="https://www.tensorflow.org/datasets/catalog/speech_commands",
            data_url="data/speech_commands_test_set_v0.01.zip",
        ),
    ]

    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):
        if self.config.name == "ks":
            archive_path = dl_manager.download_and_extract(self.config.data_url)
            return [
                datasets.SplitGenerator(
                    name=datasets.Split.TEST, gen_kwargs={"archive_path": archive_path, "split": "test"}
                ),
            ]

    def _generate_examples(self, archive_path, split=None):
        """Generate examples."""
        if self.config.name == "ks":
            words = ["neunit", "wake"]
            splits = _split_ks_files(archive_path, split)
            for key, audio_file in enumerate(sorted(splits[split])):
                base_dir, file_name = os.path.split(audio_file)
                _, word = os.path.split(base_dir)
                if word in words:
                    label = word
                else:
                    label = "_unknown_"
                yield key, {"file": audio_file, "audio": audio_file, "label": label}

def _split_ks_files(archive_path, split):
    audio_path = os.path.join(archive_path, "**/*.wav")
    audio_paths = glob.glob(audio_path)
    if split == "test":
        # use all available files for the test archive
        return {"test": audio_paths}

    val_list_file = os.path.join(archive_path, "validation_list.txt")
    test_list_file = os.path.join(archive_path, "testing_list.txt")
    with open(val_list_file, encoding="utf-8") as f:
        val_paths = f.read().strip().splitlines()
        val_paths = [os.path.join(archive_path, p) for p in val_paths]
    with open(test_list_file, encoding="utf-8") as f:
        test_paths = f.read().strip().splitlines()
        test_paths = [os.path.join(archive_path, p) for p in test_paths]

    # the paths for the train set is just whichever paths that do not exist in
    # either the test or validation splits
    train_paths = list(set(audio_paths) - set(val_paths) - set(test_paths))

    return {"train": train_paths, "val": val_paths}