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Datasets:
MLCommons
/
peoples_speech

Tasks:
Automatic Speech Recognition
Modalities:
Audio
Text
Formats:
parquet
Languages:
English
Size:
1M - 10M
ArXiv:
Tags:
robust-speech-recognition
noisy-speech-recognition
speech-recognition
Libraries:
Datasets
Dask
License:
Dataset card Viewer Files Community
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peoples_speech / README.md
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metadata
annotations_creators:
  - crowdsourced
  - machine-generated
language_creators:
  - crowdsourced
  - machine-generated
language:
  - en
license:
  - cc-by-2.0
  - cc-by-2.5
  - cc-by-3.0
  - cc-by-4.0
  - cc-by-sa-3.0
  - cc-by-sa-4.0
multilinguality:
  - monolingual
size_categories:
  - 1T<n
source_datasets:
  - original
task_categories:
  - automatic-speech-recognition
task_ids: []
pretty_name: People's Speech
tags:
  - robust-speech-recognition
  - noisy-speech-recognition
  - speech-recognition
dataset_info:
  - config_name: clean_sa
    features:
      - name: id
        dtype: string
      - name: audio
        dtype:
          audio:
            sampling_rate: 16000
      - name: duration_ms
        dtype: int32
      - name: text
        dtype: string
    splits:
      - name: train
        num_bytes: 75267509124.558
        num_examples: 257093
      - name: validation
        num_bytes: 2075929254.254
        num_examples: 18622
      - name: test
        num_bytes: 3894954757.41
        num_examples: 34898
    download_size: 72518549222
    dataset_size: 81238393136.222
  - config_name: default
    features:
      - name: id
        dtype: string
      - name: audio
        dtype: audio
      - name: duration_ms
        dtype: int32
      - name: text
        dtype: string
    splits:
      - name: train
        num_bytes: 401733771193.124
        num_examples: 1501271
      - name: validation
        num_bytes: 2459781412.24
        num_examples: 18622
      - name: test
        num_bytes: 4324307722.96
        num_examples: 34898
    download_size: 398570070831
    dataset_size: 408517860328.32404
  - config_name: test
    features:
      - name: id
        dtype: string
      - name: audio
        dtype:
          audio:
            sampling_rate: 16000
      - name: duration_ms
        dtype: int32
      - name: text
        dtype: string
    splits:
      - name: test
        num_bytes: 3894954757.41
        num_examples: 34898
    download_size: 4087772459
    dataset_size: 3894954757.41
  - config_name: validation
    features:
      - name: id
        dtype: string
      - name: audio
        dtype:
          audio:
            sampling_rate: 16000
      - name: duration_ms
        dtype: int32
      - name: text
        dtype: string
    splits:
      - name: validation
        num_bytes: 2075929254.254
        num_examples: 18622
    download_size: 2335244149
    dataset_size: 2075929254.254
configs:
  - config_name: clean_sa
    data_files:
      - split: train
        path: clean_sa/train-*
      - split: validation
        path: clean_sa/validation-*
      - split: test
        path: clean_sa/test-*
  - config_name: default
    data_files:
      - split: train
        path: data/train-*
      - split: validation
        path: data/validation-*
      - split: test
        path: data/test-*
  - config_name: test
    data_files:
      - split: test
        path: test/test-*
  - config_name: validation
    data_files:
      - split: validation
        path: validation/validation-*

Dataset Card for People's Speech

Table of Contents

Dataset Description

Dataset Summary

The People's Speech Dataset is among the world's largest English speech recognition corpus today that is licensed for academic and commercial usage under CC-BY-SA and CC-BY 4.0. It includes 30,000+ hours of transcribed speech in English languages with a diverse set of speakers. This open dataset is large enough to train speech-to-text systems and crucially is available with a permissive license.

Supported Tasks and Leaderboards

[Needs More Information]

Languages

English

Dataset Structure

Data Instances

{ "id": "gov_DOT_uscourts_DOT_scotus_DOT_19-161/gov_DOT_uscourts_DOT_scotus_DOT_19-161_DOT_2020-03-02_DOT_mp3_00002.flac", "audio": { "path": "gov_DOT_uscourts_DOT_scotus_DOT_19-161/gov_DOT_uscourts_DOT_scotus_DOT_19-161_DOT_2020-03-02_DOT_mp3_00002.flac" "array": array([-6.10351562e-05, ...]), "sampling_rate": 16000 } "duration_ms": 14490, "text": "contends that the suspension clause requires a [...]" }

Data Fields

{ "id": datasets.Value("string"), "audio": datasets.Audio(sampling_rate=16_000), "duration_ms": datasets.Value("int32"), "text": datasets.Value("string"), }

Data Splits

We provide the following configurations for the dataset: cc-by-clean, cc-by-dirty, cc-by-sa-clean, cc-by-sa-dirty, and microset. We don't provide splits for any of the configurations.

Dataset Creation

Curation Rationale

See our paper.

Source Data

Initial Data Collection and Normalization

Data was downloaded via the archive.org API. No data inference was done.

Who are the source language producers?

[Needs More Information]

Annotations

Annotation process

No manual annotation is done. We download only source audio with already existing transcripts.

Who are the annotators?

For the test and dev sets, we paid native American English speakers to do transcriptions. We do not know the identities of the transcriptionists for data in the training set. For the training set, we have noticed that some transcriptions are likely to be the output of automatic speech recognition systems.

Personal and Sensitive Information

Several of our sources are legal and government proceedings, spoken histories, speeches, and so on. Given that these were intended as public documents and licensed as such, it is natural that the involved individuals are aware of this.

Considerations for Using the Data

Social Impact of Dataset

The dataset could be used for speech synthesis. However, this requires careful cleaning of the dataset, as background noise is not tolerable for speech synthesis.

The dataset could be used for keyword spotting tasks as well. In particular, this is good use case for the non-English audio in the dataset.

Our sincere hope is that the large breadth of sources our dataset incorporates reduces existing quality of service issues today, like speech recognition system’s poor understanding of non-native English accents. We cannot think of any unfair treatment that come from using this dataset at this time.

Discussion of Biases

Our data is downloaded from archive.org. As such, the data is biased towards whatever users decide to upload there.

Almost all of our data is American accented English.

Other Known Limitations

As of version 1.0, a portion of data in the training, test, and dev sets is poorly aligned. Specifically, some words appear in the transcript, but not the audio, or some words appear in the audio, but not the transcript. We are working on it.

Additional Information

Dataset Curators

[Needs More Information]

Licensing Information

We provide CC-BY and CC-BY-SA subsets of the dataset.

Citation Information

Please cite:

@article{DBLP:journals/corr/abs-2111-09344,
  author    = {Daniel Galvez and
               Greg Diamos and
               Juan Ciro and
               Juan Felipe Cer{\'{o}}n and
               Keith Achorn and
               Anjali Gopi and
               David Kanter and
               Maximilian Lam and
               Mark Mazumder and
               Vijay Janapa Reddi},
  title     = {The People's Speech: {A} Large-Scale Diverse English Speech Recognition
               Dataset for Commercial Usage},
  journal   = {CoRR},
  volume    = {abs/2111.09344},
  year      = {2021},
  url       = {https://arxiv.org/abs/2111.09344},
  eprinttype = {arXiv},
  eprint    = {2111.09344},
  timestamp = {Mon, 22 Nov 2021 16:44:07 +0100},
  biburl    = {https://dblp.org/rec/journals/corr/abs-2111-09344.bib},
  bibsource = {dblp computer science bibliography, https://dblp.org}
}