Datasets:
speech_commands

	---
	annotations_creators:
	- other
	language_creators:
	- crowdsourced
	language:
	- en
	license:
	- cc-by-4.0
	multilinguality:
	- monolingual
	size_categories:
	- 100K<n<1M
	- 10K<n<100K
	source_datasets:
	- original
	task_categories:
	- audio-classification
	task_ids:
	- keyword-spotting
	pretty_name: SpeechCommands
	dataset_info:
	- config_name: v0.01
	features:
	- name: file
	dtype: string
	- name: audio
	dtype:
	audio:
	sampling_rate: 16000
	- name: label
	dtype:
	class_label:
	names:
	'0': 'yes'
	'1': 'no'
	'2': up
	'3': down
	'4': left
	'5': right
	'6': 'on'
	'7': 'off'
	'8': stop
	'9': go
	'10': zero
	'11': one
	'12': two
	'13': three
	'14': four
	'15': five
	'16': six
	'17': seven
	'18': eight
	'19': nine
	'20': bed
	'21': bird
	'22': cat
	'23': dog
	'24': happy
	'25': house
	'26': marvin
	'27': sheila
	'28': tree
	'29': wow
	'30': _silence_
	- name: is_unknown
	dtype: bool
	- name: speaker_id
	dtype: string
	- name: utterance_id
	dtype: int8
	splits:
	- name: train
	num_bytes: 1626283624
	num_examples: 51093
	- name: validation
	num_bytes: 217204539
	num_examples: 6799
	- name: test
	num_bytes: 98979965
	num_examples: 3081
	download_size: 1454702755
	dataset_size: 1942468128
	- config_name: v0.02
	features:
	- name: file
	dtype: string
	- name: audio
	dtype:
	audio:
	sampling_rate: 16000
	- name: label
	dtype:
	class_label:
	names:
	'0': 'yes'
	'1': 'no'
	'2': up
	'3': down
	'4': left
	'5': right
	'6': 'on'
	'7': 'off'
	'8': stop
	'9': go
	'10': zero
	'11': one
	'12': two
	'13': three
	'14': four
	'15': five
	'16': six
	'17': seven
	'18': eight
	'19': nine
	'20': bed
	'21': bird
	'22': cat
	'23': dog
	'24': happy
	'25': house
	'26': marvin
	'27': sheila
	'28': tree
	'29': wow
	'30': backward
	'31': forward
	'32': follow
	'33': learn
	'34': visual
	'35': _silence_
	- name: is_unknown
	dtype: bool
	- name: speaker_id
	dtype: string
	- name: utterance_id
	dtype: int8
	splits:
	- name: train
	num_bytes: 2684381672
	num_examples: 84848
	- name: validation
	num_bytes: 316435178
	num_examples: 9982
	- name: test
	num_bytes: 157096106
	num_examples: 4890
	download_size: 2285975869
	dataset_size: 3157912956
	config_names:
	- v0.01
	- v0.02
	---

	# Dataset Card for SpeechCommands

	## Table of Contents
	- [Table of Contents](#table-of-contents)
	- [Dataset Description](#dataset-description)
	- [Dataset Summary](#dataset-summary)
	- [Supported Tasks and Leaderboards](#supported-tasks-and-leaderboards)
	- [Languages](#languages)
	- [Dataset Structure](#dataset-structure)
	- [Data Instances](#data-instances)
	- [Data Fields](#data-fields)
	- [Data Splits](#data-splits)
	- [Dataset Creation](#dataset-creation)
	- [Curation Rationale](#curation-rationale)
	- [Source Data](#source-data)
	- [Annotations](#annotations)
	- [Personal and Sensitive Information](#personal-and-sensitive-information)
	- [Considerations for Using the Data](#considerations-for-using-the-data)
	- [Social Impact of Dataset](#social-impact-of-dataset)
	- [Discussion of Biases](#discussion-of-biases)
	- [Other Known Limitations](#other-known-limitations)
	- [Additional Information](#additional-information)
	- [Dataset Curators](#dataset-curators)
	- [Licensing Information](#licensing-information)
	- [Citation Information](#citation-information)
	- [Contributions](#contributions)

	## Dataset Description

	- Homepage: https://www.tensorflow.org/datasets/catalog/speech_commands
	- Repository: [More Information Needed]
	- Paper: [Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition](https://arxiv.org/pdf/1804.03209.pdf)
	- Leaderboard: [More Information Needed]
	- Point of Contact: Pete Warden, petewarden@google.com

	### Dataset Summary

	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. It 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.

	Version 0.02 of the data set (configuration `"v0.02"`) was released on April 11th 2018 and
	contains 105,829 audio files.


	### Supported Tasks and Leaderboards

	* `keyword-spotting`: the dataset can be used to train and evaluate keyword
	spotting systems. The task is to detect 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.

	### Languages

	The language data in SpeechCommands is in English (BCP-47 `en`).

	## Dataset Structure

	### Data Instances

	Example of a core word (`"label"` is a word, `"is_unknown"` is `False`):
	```python
	{
	"file": "no/7846fd85_nohash_0.wav",
	"audio": {
	"path": "no/7846fd85_nohash_0.wav",
	"array": array([ -0.00021362, -0.00027466, -0.00036621, ..., 0.00079346,
	0.00091553, 0.00079346]),
	"sampling_rate": 16000
	},
	"label": 1, # "no"
	"is_unknown": False,
	"speaker_id": "7846fd85",
	"utterance_id": 0
	}
	```

	Example of an auxiliary word (`"label"` is a word, `"is_unknown"` is `True`)
	```python
	{
	"file": "tree/8b775397_nohash_0.wav",
	"audio": {
	"path": "tree/8b775397_nohash_0.wav",
	"array": array([ -0.00854492, -0.01339722, -0.02026367, ..., 0.00274658,
	0.00335693, 0.0005188]),
	"sampling_rate": 16000
	},
	"label": 28, # "tree"
	"is_unknown": True,
	"speaker_id": "1b88bf70",
	"utterance_id": 0
	}
	```

	Example of background noise (`_silence_`) class:

	```python
	{
	"file": "_silence_/doing_the_dishes.wav",
	"audio": {
	"path": "_silence_/doing_the_dishes.wav",
	"array": array([ 0. , 0. , 0. , ..., -0.00592041,
	-0.00405884, -0.00253296]),
	"sampling_rate": 16000
	},
	"label": 30, # "_silence_"
	"is_unknown": False,
	"speaker_id": "None",
	"utterance_id": 0 # doesn't make sense here
	}
	```

	### Data Fields

	* `file`: relative audio filename inside the original archive.
	* `audio`: dictionary containing a relative audio filename,
	a decoded audio array, and the sampling rate. Note that when accessing
	the audio column: `dataset[0]["audio"]` the audio is automatically decoded
	and resampled to `dataset.features["audio"].sampling_rate`.
	Decoding and resampling of a large number of audios might take a significant
	amount of time. Thus, it is important to first query the sample index before
	the `"audio"` column, i.e. `dataset[0]["audio"]` should always be preferred
	over `dataset["audio"][0]`.
	* `label`: either word pronounced in an audio sample or background noise (`_silence_`) class.
	Note that it's an integer value corresponding to the class name.
	* `is_unknown`: if a word is auxiliary. Equals to `False` if a word is a core word or `_silence_`,
	`True` if a word is an auxiliary word.
	* `speaker_id`: unique id of a speaker. Equals to `None` if label is `_silence_`.
	* `utterance_id`: incremental id of a word utterance within the same speaker.

	### Data Splits

	The dataset has two versions (= configurations): `"v0.01"` and `"v0.02"`. `"v0.02"`
	contains more words (see section [Source Data](#source-data) for more details).

	\| \| train \| validation \| test \|
	\|----- \|------:\|-----------:\|-----:\|
	\| v0.01 \| 51093 \| 6799 \| 3081 \|
	\| v0.02 \| 84848 \| 9982 \| 4890 \|

	Note that in train and validation sets examples of `_silence_` class are longer than 1 second.
	You can use the following code to sample 1-second examples from the longer ones:

	```python
	def sample_noise(example):
	# Use this function to extract random 1 sec slices of each _silence_ utterance,
	# e.g. inside `torch.utils.data.Dataset.__getitem__()`
	from random import randint

	if example["label"] == "_silence_":
	random_offset = randint(0, len(example["speech"]) - example["sample_rate"] - 1)
	example["speech"] = example["speech"][random_offset : random_offset + example["sample_rate"]]

	return example
	```

	## Dataset Creation

	### Curation Rationale

	The primary goal of the dataset is to provide a way to build and test small
	models that can detect a single word from a set of target words and differentiate it
	from background noise or unrelated speech with as few false positives as possible.

	### Source Data

	#### Initial Data Collection and Normalization

	The audio files were collected using crowdsourcing, see
	[aiyprojects.withgoogle.com/open_speech_recording](https://github.com/petewarden/extract_loudest_section)
	for some of the open source audio collection code that was used. The goal was to gather examples of
	people speaking single-word commands, rather than conversational sentences, so
	they were prompted for individual words over the course of a five minute
	session.

	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_` label contains a set of longer audio clips that are either recordings or
	a mathematical simulation of noise.

	#### Who are the source language producers?

	The audio files were collected using crowdsourcing.

	### Annotations

	#### Annotation process

	Labels are the list of words prepared in advances.
	Speakers were prompted for individual words over the course of a five minute
	session.

	#### Who are the annotators?

	[More Information Needed]

	### Personal and Sensitive Information

	The dataset consists of people who have donated their voice online. You agree to not attempt to determine the identity of speakers in this dataset.

	## Considerations for Using the Data

	### Social Impact of Dataset

	[More Information Needed]

	### Discussion of Biases

	[More Information Needed]

	### Other Known Limitations

	[More Information Needed]

	## Additional Information

	### Dataset Curators

	[More Information Needed]

	### Licensing Information

	Creative Commons BY 4.0 License ((CC-BY-4.0)[https://creativecommons.org/licenses/by/4.0/legalcode]).

	### Citation Information

	```
	@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},
	}
	```
	### Contributions

	Thanks to [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.