---
annotations_creators:
- other
language_creators:
- crowdsourced
language:
- en
license:
- cc-by-4.0
multilinguality:
- monolingual
pretty_name: SpeechCommands
source_datasets:
- original
task_categories:
- audio-classification
task_ids:
- keyword-spotting
size_categories:
- 100K<n<1M
- 10K<n<100K
configs:
- v0.01
- v0.02
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
---

# 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.