Datasets:

kensho
/

spgispeech_demo

Name: spgispeech_demo
Creator: Kensho
License: https://choosealicense.com/licenses/other/

Tasks:

Automatic Speech Recognition

Size:

1M<n<10M

ArXiv:

License:

Dataset card Files Files and versions Community

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Dataset Card for SPGISpeech

Dataset Description

SPGISpeech (rhymes with “squeegee-speech”) is a large-scale transcription dataset, freely available for academic research. SPGISpeech is a corpus of 5,000 hours of professionally-transcribed financial audio. SPGISpeech contains a broad cross-section of L1 and L2 English accents, strongly varying audio quality, and both spontaneous and narrated speech. The transcripts have each been cross-checked by multiple professional editors for high accuracy and are fully formatted, including capitalization, punctuation, and denormalization of non-standard words.

SPGISpeech consists of 5,000 hours of recorded company earnings calls and their respective transcriptions. The original calls were split into slices ranging from 5 to 15 seconds in length to allow easy training for speech recognition systems. Calls represent a broad cross-section of international business English; SPGISpeech contains approximately 50,000 speakers, one of the largest numbers of any speech corpus, and offers a variety of L1 and L2 English accents. The format of each WAV file is single channel, 16kHz, 16 bit audio.

Example Usage

The training split has several configurations of various size: S, M, L. See the Section Data Splits for for more information. To download the S configuration:

from datasets import load_dataset

spgi = load_dataset("kensho/spgispeech", "S", use_auth_token=True)

# see structure
print(spgi)

# load audio sample on the fly
audio_input = spgi["train"][0]["audio"]  # first decoded audio sample
transcription = spgi["train"][0]["text"]  # first transcription

It is possible to download only the development or test data:

spgi_dev = load_dataset("kensho/spgispeech", "dev", use_auth_token=True)
spgi_test = load_dataset("kensho/spgispeech", "test", use_auth_token=True)

Supported Tasks and Leaderboards

automatic-speech-recognition: The dataset can be used to train a model for Automatic Speech Recognition (ASR). The model is presented with an audio file and asked to transcribe the audio file to written text. The most common evaluation metric is the word error rate (WER).

Languages

SPGISpeech contains audio and transcription data in business English and offers a variety of L1 and L2 accents.

Dataset Structure

Data Instances

{
    'wav_filename': '32bcf9c9dc707fb61a04290e296f31eb/99.wav',
    'audio': {
        'path': '/home/user/.cache/huggingface/datasets/downloads/extracted/c7082e2bd5b.../dev_part_2/32bcf9c9dc707fb61a04290e296f31eb/99.wav',
        'array': array([-0.00039673, -0.00057983, -0.00057983, ..., -0.0007019 ,
         -0.00027466,  0.00021362], dtype=float32),
        'sampling_rate': 16000
    },
    'wav_filesize': 292844,
    'transcript': 'This is proving to be true, and through focused execution we are on track to exceed our targeted savings in 2017. As a reminder,'
}

Data Fields

wav_filename (string) - audio filename (includes parent directory).
audio (Audio feature) - a dictionary containing the path to the audio, the decoded audio array, and the sampling rate. In non-streaming mode (default), the path points to the locally extracted audio. In streaming mode, the path is the relative path of an audio inside its archive (as files are not downloaded and extracted locally).
wav_filesize (int) - size of the file in bytes.
transcript (string) - transcription of the file.

Data Splits

The dataset has three splits: train, evaluation (dev) and test. The train split has three configurations of various sizes: S, M, L. Larger subsets are supersets of smaller subsets, e.g., the L subset contains all the data from the M subset.

Transcribed Subsets Size

Subset	Size
S	22Gb
M	107Gb
L	530Gb
dev	11Gb
test	11Gb

Dataset Creation

Curation Rationale

[More Information Needed]

Source Data

The dataset contains S&P Global company earnings calls.

Initial Data Collection and Normalization

[More Information Needed]

Who are the source language producers?

English speakers with a diverse selection of accents, including non-native ones (L2), producing both spontaneous and narrated speech.

Annotations

Annotation process

Data is orthographically transcribed according to a professional style guide detailing conventions for capitalization, punctuation, denormalization of non-standard words and transcription of disfluencies in spontaneous speech. The transcripts have each been cross-checked by multiple professional editors for high accuracy and are fully formatted.

Full earnings calls last 30-60 minutes in length and are typically transcribed as whole units, without internal timestamps. In order to produce short audio slices suitable for STT training, the files were segmented with Gentle, a double-pass forced aligner, with the beginning and end of each slice of audio imputed by voice activity detection with py-webrtc.

Who are the annotators?

Earning calls are manually transcribed by S&P Global, Inc.

Personal and Sensitive Information

Though earnings calls are public, we nevertheless identified full names with the spaCy en core web large model. We withheld samples containing names that appeared fewer than ten times (7% of total). Full names appearing ten times or more in the data were considered to be public figures and were retained. This necessarily incomplete approach to named entity recognition was complemented with randomized manual spot checks which uncovered no false negatives missed by the automated approach.

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

Citation Information

Please cite this paper:

@ARTICLE{2021arXiv210402014O,
       author = {{O'Neill}, Patrick K. and {Lavrukhin}, Vitaly and {Majumdar}, 
       Somshubra and {Noroozi}, Vahid and {Zhang}, Yuekai and {Kuchaiev}, Oleksii and {Balam}, 
       Jagadeesh and {Dovzhenko}, Yuliya and {Freyberg}, Keenan and {Shulman}, Michael D. and {Ginsburg}, 
       Boris and {Watanabe}, Shinji and {Kucsko}, Georg},
        title = "{SPGISpeech: 5,000 hours of transcribed financial audio for fully formatted end-to-end speech recognition}",
      journal = {arXiv e-prints},
     keywords = {Computer Science - Computation and Language, Electrical Engineering and Systems Science - Audio and Speech Processing},
         year = 2021,
        month = apr,
          eid = {arXiv:2104.02014},
        pages = {arXiv:2104.02014},
archivePrefix = {arXiv},
       eprint = {2104.02014},
 primaryClass = {cs.CL},
       adsurl = {https://ui.adsabs.harvard.edu/abs/2021arXiv210402014O},
      adsnote = {Provided by the SAO/NASA Astrophysics Data System}
}

Contributions

Thanks to @sanchit-gandhi, @patrickvonplaten, and @polinaeterna for adding this dataset.

Terms of Usage

If you do not wish to be bound by these Terms of Usage, you must not use this Content. PLEASE READ THESE TERMS OF USAGE CAREFULLY BEFORE USING THIS CONTENT.

Section 1 – THE CONTENT

1.1 The Content is provided for academic research purposes and internal use only and must not be used to:

assemble or create a database;
construct or facilitate the construction of products which compete with the Content;
identify or attempt to identify or contact any individual; or link to another dataset.

The Content, which is comprised of public earnings calls in audio and corresponding text format, and all accompanying derived products is proprietary to Kensho and its third-party content providers. You shall not modify the Content; create derivative works based on the Content, rewrite or reprocess the Content except as expressly provided herein. You must not publish, display, transfer or redistribute the Content or any portions or derivative versions thereof to anyone without prior written consent from Kensho. You agree not to contact Kensho or its affiliates concerning individuals whose information may be included in the Content.

1.2 Disclaimer. Content to which you are provided access, either directly or indirectly, from or on this Content will not have been reviewed or monitored by Kensho, and Kensho cannot and does not guarantee or make any representation or warranty, either express or implied, as to the accuracy, validity, timeliness, completeness or continued availability of any such content.

The Content is provided for your convenience only and is not a republication or reconfirmation of the opinion or information contained therein. The provision of the Content is without any obligation on the part of Kensho or its third-party content providers to review such or any liability or responsibility arising out of your use thereof. Kensho does not guarantee or make any representation or warranty, either express or implied, as to the accuracy, validity, timeliness, completeness or continued availability of any Content and shall not be liable for any errors, delays, or actions taken in reliance on information. In addition, the Content speaks only as of the date issued and is based on conference calls that may contain projections of other forward-looking statements. You should not rely on the Content as expressing Kensho’s opinion or as representing current information. None of Kensho or the third-party content providers has undertaken, and do not undertake any duty to update any Content or otherwise advise you of any changes in the Content.

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Section 2 - DISCLAIMER OF WARRANTY AND LIMITATION OF LIABILITY

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Section 3 - PRIVACY

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3.7 Contact Information. In the event you have any questions regarding these Terms of Use, this Privacy Statement or to make any requests or queries regarding your information that is held by us you may contact us in writing at [email protected] or Kensho Technologies LLC, Attn: General Counsel, 55 Water Street, New York, NY 10041.

Section 4 - MISCELLANEOUS

4.1 Entire Agreement. These Terms of Usage constitute the entire agreement of the parties hereto with respect to the subject matter hereof and supersede all prior agreements and undertakings, both written and oral, between the parties with respect to the subject matter hereof.

4.2 Severability. If any term or other provision of these Terms of Usage is invalid, illegal or incapable of being enforced by any law or public policy, all other terms and provisions of these Terms of Usage shall nevertheless remain in full force and effect so long as the economic or legal substance of the transactions contemplated hereby is not affected in any manner materially adverse to any party.

4.3 Governing Law; Forum. These Terms of Usage shall be governed in all respects by the laws of the State of New York, and any litigation arising out of or connected in any way with these Terms of Usage shall take place in a State or Federal court of competent jurisdiction in New York County, State of New York.

4.4 Waiver of Jury Trial. YOU WAIVE TO THE FULLEST EXTENT PERMITTED BY APPLICABLE LAW ANY RIGHT YOU MAY HAVE TO A TRIAL BY JURY WITH RESPECT TO ANY ACTIONS OR PROCEEDINGS DIRECTLY OR INDIRECTLY ARISING OUT OF, UNDER OR IN CONNECTION WITH THESE TERMS OF USAGE.

4.5 Conflict. In the event of a conflict between these Terms of Use and any other agreement with Kensho that relates to Third-Party Content, the more restrictive terms shall prevail.

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