# coding=utf-8 # Copyright 2021 The HuggingFace Datasets Authors and the current dataset script contributor. # # 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. """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, }