Datasets:
speech_commands

	# coding=utf-8
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	#
	# Licensed under the Apache License, Version 2.0 (the "License");
	# you may not use this file except in compliance with the License.
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	# http://www.apache.org/licenses/LICENSE-2.0
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	# distributed under the License is distributed on an "AS IS" BASIS,
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	"""Speech Commands, an audio dataset of spoken words designed to help train and evaluate keyword spotting systems. """


	import textwrap

	import datasets


	_CITATION = """
	@article{speechcommandsv2,
	author = { {Warden}, P.},
	title = "{Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition}",
	journal = {ArXiv e-prints},
	archivePrefix = "arXiv",
	eprint = {1804.03209},
	primaryClass = "cs.CL",
	keywords = {Computer Science - Computation and Language, Computer Science - Human-Computer Interaction},
	year = 2018,
	month = apr,
	url = {https://arxiv.org/abs/1804.03209},
	}
	"""

	_DESCRIPTION = """
	This is a set of one-second .wav audio files, each containing a single spoken
	English word or background noise. These words are from a small set of commands, and are spoken by a
	variety of different speakers. This data set is designed to help train simple
	machine learning models. This dataset is covered in more detail at
	[https://arxiv.org/abs/1804.03209](https://arxiv.org/abs/1804.03209).

	Version 0.01 of the data set (configuration `"v0.01"`) was released on August 3rd 2017 and contains
	64,727 audio files.

	In version 0.01 thirty different words were recoded: "Yes", "No", "Up", "Down", "Left",
	"Right", "On", "Off", "Stop", "Go", "Zero", "One", "Two", "Three", "Four", "Five", "Six", "Seven", "Eight", "Nine",
	"Bed", "Bird", "Cat", "Dog", "Happy", "House", "Marvin", "Sheila", "Tree", "Wow".


	In version 0.02 more words were added: "Backward", "Forward", "Follow", "Learn", "Visual".

	In both versions, ten of them are used as commands by convention: "Yes", "No", "Up", "Down", "Left",
	"Right", "On", "Off", "Stop", "Go". Other words are considered to be auxiliary (in current implementation
	it is marked by `True` value of `"is_unknown"` feature). Their function is to teach a model to distinguish core words
	from unrecognized ones.

	The `_silence_` class contains a set of longer audio clips that are either recordings or
	a mathematical simulation of noise.

	"""

	_LICENSE = "Creative Commons BY 4.0 License"

	_URL = "https://www.tensorflow.org/datasets/catalog/speech_commands"

	_DL_URL = "https://s3.amazonaws.com/datasets.huggingface.co/SpeechCommands/{name}/{name}_{split}.tar.gz"

	WORDS = [
	"yes",
	"no",
	"up",
	"down",
	"left",
	"right",
	"on",
	"off",
	"stop",
	"go",
	]

	UNKNOWN_WORDS_V1 = [
	"zero",
	"one",
	"two",
	"three",
	"four",
	"five",
	"six",
	"seven",
	"eight",
	"nine",
	"bed",
	"bird",
	"cat",
	"dog",
	"happy",
	"house",
	"marvin",
	"sheila",
	"tree",
	"wow",
	]

	UNKNOWN_WORDS_V2 = UNKNOWN_WORDS_V1 + [
	"backward",
	"forward",
	"follow",
	"learn",
	"visual",
	]

	SILENCE = "_silence_" # background noise
	LABELS_V1 = WORDS + UNKNOWN_WORDS_V1 + [SILENCE]
	LABELS_V2 = WORDS + UNKNOWN_WORDS_V2 + [SILENCE]


	class SpeechCommandsConfig(datasets.BuilderConfig):
	"""BuilderConfig for SpeechCommands."""

	def __init__(self, labels, **kwargs):
	super(SpeechCommandsConfig, self).__init__(**kwargs)
	self.labels = labels


	class SpeechCommands(datasets.GeneratorBasedBuilder):
	BUILDER_CONFIGS = [
	SpeechCommandsConfig(
	name="v0.01",
	description=textwrap.dedent(
	"""\
	Version 0.01 of the SpeechCommands dataset. Contains 30 words
	(20 of them are auxiliary) and background noise.
	"""
	),
	labels=LABELS_V1,
	version=datasets.Version("0.1.0"),
	),
	SpeechCommandsConfig(
	name="v0.02",
	description=textwrap.dedent(
	"""\
	Version 0.02 of the SpeechCommands dataset.
	Contains 35 words (25 of them are auxiliary) and background noise.
	"""
	),
	labels=LABELS_V2,
	version=datasets.Version("0.2.0"),
	),
	]

	def _info(self):
	return datasets.DatasetInfo(
	description=_DESCRIPTION,
	features=datasets.Features(
	{
	"file": datasets.Value("string"),
	"audio": datasets.features.Audio(sampling_rate=16_000),
	"label": datasets.ClassLabel(names=self.config.labels),
	"is_unknown": datasets.Value("bool"),
	"speaker_id": datasets.Value("string"),
	"utterance_id": datasets.Value("int8"),
	}
	),
	homepage=_URL,
	citation=_CITATION,
	license=_LICENSE,
	version=self.config.version,
	)

	def _split_generators(self, dl_manager):

	archive_paths = dl_manager.download(
	{
	"train": _DL_URL.format(name=self.config.name, split="train"),
	"validation": _DL_URL.format(name=self.config.name, split="validation"),
	"test": _DL_URL.format(name=self.config.name, split="test"),
	}
	)

	return [
	datasets.SplitGenerator(
	name=datasets.Split.TRAIN,
	gen_kwargs={
	"archive": dl_manager.iter_archive(archive_paths["train"]),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.VALIDATION,
	gen_kwargs={
	"archive": dl_manager.iter_archive(archive_paths["validation"]),
	},
	),
	datasets.SplitGenerator(
	name=datasets.Split.TEST,
	gen_kwargs={
	"archive": dl_manager.iter_archive(archive_paths["test"]),
	},
	),
	]

	def _generate_examples(self, archive):
	for path, file in archive:
	if not path.endswith(".wav"):
	continue

	word, audio_filename = path.split("/")
	is_unknown = False

	if word == SILENCE:
	speaker_id, utterance_id = None, 0

	else: # word is either in WORDS or unknown
	if word not in WORDS:
	is_unknown = True
	# an audio filename looks like `0bac8a71_nohash_0.wav`
	speaker_id, _, utterance_id = audio_filename.split(".wav")[0].split("_")

	yield path, {
	"file": path,
	"audio": {"path": path, "bytes": file.read()},
	"label": word,
	"is_unknown": is_unknown,
	"speaker_id": speaker_id,
	"utterance_id": utterance_id,
	}