Datasets:
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:
- extended|speech_commands
task_categories:
- audio-classification
task_ids:
- keyword-spotting
pretty_name: SpeechCommands
config_names:
- v0.01
- v0.02
tags:
- spotlight
- enriched
- renumics
- enhanced
- audio
- classification
- extended
dataset_info:
- config_name: enrichment_only
features:
- name: label_string
dtype: string
- name: probability
dtype: float64
- name: probability_vector
sequence: float32
- name: prediction
dtype: int64
- name: prediction_string
dtype: string
- name: embedding_reduced
sequence: float32
splits:
- name: train
num_bytes: 8763867
num_examples: 51093
- name: validation
num_bytes: 1165942
num_examples: 6799
- name: test
num_bytes: 528408
num_examples: 3081
download_size: 0
dataset_size: 10458217
- config_name: raw_and_enrichment_combined
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
- name: logits
sequence: float64
- name: embedding
sequence: float32
- name: label_string
dtype: string
- name: probability
dtype: float64
- name: probability_vector
sequence: float32
- name: prediction
dtype: int64
- name: prediction_string
dtype: string
- name: embedding_reduced
sequence: float32
splits:
- name: train
num_bytes: 1803565876.375
num_examples: 51093
- name: validation
num_bytes: 240795605.125
num_examples: 6799
- name: test
num_bytes: 109673146.875
num_examples: 3081
download_size: 0
dataset_size: 2154034628.375
configs:
- config_name: enrichment_only
data_files:
- split: train
path: enrichment_only/train-*
- split: validation
path: enrichment_only/validation-*
- split: test
path: enrichment_only/test-*
- config_name: raw_and_enrichment_combined
data_files:
- split: train
path: raw_and_enrichment_combined/train-*
- split: validation
path: raw_and_enrichment_combined/validation-*
- split: test
path: raw_and_enrichment_combined/test-*
Dataset Card for SpeechCommands
Dataset Description
- Homepage: Renumics Homepage
- GitHub Spotlight
- Dataset Homepage tensorflow.org/datasets
- Paper: Speech Commands: A Dataset for Limited-Vocabulary Speech Recognition
- Leaderboard: [More Information Needed]
Dataset Summary
📊 Data-centric AI principles have become increasingly important for real-world use cases.
At Renumics we believe that classical benchmark datasets and competitions should be extended to reflect this development.
🔍 This is why we are publishing benchmark datasets with application-specific enrichments (e.g. embeddings, baseline results, uncertainties, label error scores). We hope this helps the ML community in the following ways:
- Enable new researchers to quickly develop a profound understanding of the dataset.
- Popularize data-centric AI principles and tooling in the ML community.
- Encourage the sharing of meaningful qualitative insights in addition to traditional quantitative metrics.
📚 This dataset is an enriched version of the SpeechCommands Dataset.
Explore the Dataset
There are two configurations of the dataset: Enrichment only provides the enrichments calculated by Renumics using the MIT AST transformer, while raw_and_enrichment_combined provides a concatenated dataset of the original speech commands and the enrichment.
The enrichments allow you to quickly gain insights into the dataset. The open source data curation tool Renumics Spotlight enables that with just a few lines of code:
Install datasets and Spotlight via pip:
!pip install renumics-spotlight datasets[audio]
Notice: On Linux, non-Python dependency on libsndfile package must be installed manually. See Datasets - Installation for more information.
Load the dataset from huggingface in your notebook and start exploring with a simple view:
import datasets
from renumics import spotlight
from renumics.spotlight.layouts import debug_classification
dataset = datasets.load_dataset("renumics/speech_commands_enrichment_only", "raw_and_enrichment_combined")
joined_dataset = datasets.concatenate_datasets([dataset["train"], dataset["validation"], dataset["test"]])
layout = debug_classification(label='label_string', prediction='prediction', embedding='embedding_reduced',
features=["label", "prediction", "probability"], inspect={'audio': spotlight.Audio})
dtypes = {
"audio": spotlight.Audio,
"embedding_reduced": spotlight.Embedding
}
spotlight.show(
joined_dataset,
dtype=dtypes,
layout= layout
)
You can use the UI to interactively configure the view on the data. Depending on the concrete tasks (e.g. model comparison, debugging, outlier detection) you might want to leverage different enrichments and metadata.
As a plug and play option, you can check out the Huggingface space: Huggingface Space for speech enrichment
Alternatively, you can run the notebook exploration.ipynb locally.
SpeechCommands Dataset
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.
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):
{
"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)
{
"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:
{
"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 todataset.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 overdataset["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 toFalseif a word is a core word or_silence_,Trueif a word is an auxiliary word.speaker_id: unique id of a speaker. Equals toNoneif 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 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:
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 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
[More Information Needed]