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The dataset preview is not available for this split.
The split features (columns) cannot be extracted.
Error code:   FeaturesError
Exception:    AttributeError
Message:      'int' object has no attribute 'keys'
Traceback:    Traceback (most recent call last):
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 132, in _generate_tables
                  pa_table = paj.read_json(
                File "pyarrow/_json.pyx", line 246, in pyarrow._json.read_json
                File "pyarrow/error.pxi", line 143, in pyarrow.lib.pyarrow_internal_check_status
                File "pyarrow/error.pxi", line 99, in pyarrow.lib.check_status
              pyarrow.lib.ArrowInvalid: JSON parse error: Column() changed from object to number in row 0
              
              During handling of the above exception, another exception occurred:
              
              Traceback (most recent call last):
                File "/src/workers/datasets_based/src/datasets_based/workers/first_rows.py", line 475, in compute_first_rows_response
                  iterable_dataset = iterable_dataset._resolve_features()
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 1526, in _resolve_features
                  features = _infer_features_from_batch(self._head())
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 766, in _head
                  return _examples_to_batch([x for key, x in islice(self._iter(), n)])
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 766, in <listcomp>
                  return _examples_to_batch([x for key, x in islice(self._iter(), n)])
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 788, in _iter
                  yield from ex_iterable
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 113, in __iter__
                  yield from self.generate_examples_fn(**self.kwargs)
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/iterable_dataset.py", line 713, in wrapper
                  for key, table in generate_tables_fn(**kwargs):
                File "/src/workers/datasets_based/.venv/lib/python3.9/site-packages/datasets/packaged_modules/json/json.py", line 171, in _generate_tables
                  f"This JSON file contain the following fields: {str(list(dataset.keys()))}. "
              AttributeError: 'int' object has no attribute 'keys'

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YAML Metadata Warning: The task_ids "speech-recognition" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-generation, dialogue-modeling, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering
YAML Metadata Warning: The task_ids "robust-speech-recognition" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-generation, dialogue-modeling, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering
YAML Metadata Warning: The task_ids "noisy-speech-recognition" is not in the official list: acceptability-classification, entity-linking-classification, fact-checking, intent-classification, multi-class-classification, multi-label-classification, multi-input-text-classification, natural-language-inference, semantic-similarity-classification, sentiment-classification, topic-classification, semantic-similarity-scoring, sentiment-scoring, sentiment-analysis, hate-speech-detection, text-scoring, named-entity-recognition, part-of-speech, parsing, lemmatization, word-sense-disambiguation, coreference-resolution, extractive-qa, open-domain-qa, closed-domain-qa, news-articles-summarization, news-articles-headline-generation, dialogue-generation, dialogue-modeling, language-modeling, text-simplification, explanation-generation, abstractive-qa, open-domain-abstractive-qa, closed-domain-qa, open-book-qa, closed-book-qa, slot-filling, masked-language-modeling, keyword-spotting, speaker-identification, audio-intent-classification, audio-emotion-recognition, audio-language-identification, multi-label-image-classification, multi-class-image-classification, face-detection, vehicle-detection, instance-segmentation, semantic-segmentation, panoptic-segmentation, image-captioning, grasping, task-planning, tabular-multi-class-classification, tabular-multi-label-classification, tabular-single-column-regression, rdf-to-text, multiple-choice-qa, multiple-choice-coreference-resolution, document-retrieval, utterance-retrieval, entity-linking-retrieval, fact-checking-retrieval, univariate-time-series-forecasting, multivariate-time-series-forecasting, visual-question-answering, document-question-answering

Dataset Card for People's Speech

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}
}
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