---
annotations_creators:
- expert-generated
language_creators:
- found
languages:
- en
license:
- other
multilinguality:
- monolingual
pretty_name: SpgiSpeech
size_categories:
- 1M<n<10M
source_datasets:
- original
task_categories:
  - automatic-speech-recognition
task_ids: []
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---

# Dataset Card for SPGISpeech

## 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)
- [Terms of Usage](#terms-of-usage)
  

## Dataset Description

- **Homepage:** https://datasets.kensho.com/datasets/spgispeech
- **Repository:** 
- **Paper:** https://arxiv.org/abs/2104.02014
- **Leaderboard:** 
- **Point of Contact:** [data@kensho.com](mailto:data@kensho.com )

## 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](#data-splits) 
for for more information. To download the S configuration:
```python
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:
```python
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

```python
{
    '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](https://lowerquality.com/gentle/), a double-pass forced aligner, 
with the beginning and end of each slice of audio imputed by voice activity detection with 
[py-webrtc](https://github.com/wiseman/py-webrtcvad).

#### 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:

```bibtext
@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](https://github.com/sanchit-gandhi), [@patrickvonplaten](https://github.com/patrickvonplaten), 
and [@polinaeterna](https://github.com/polinaeterna) for adding this dataset.

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Section 3 - PRIVACY

3.1 Access and Collection. In order to access this Content, during the registration process, either you or your employer will be required to provide Kensho with certain information; including your name, employer or academic institution, and e-mail address (“Registration Data”). In addition, when you request or view Content, Kensho may obtain user identifiable information related to your request of, or access to, such Content (“Access Data”). For example, while you are accessing this Content, our Web servers may recognize your: (a) domain name; (b) ISP’s domain name; (c) IP address; (d) browser type; and (e) operating system. If you contact us with a technical question, we may collect certain information about your systems, including: (a) your browser type, version and settings (e.g., Java and cookie settings); (b) connectivity information (e.g., SSL/HTTPS compatibility, bandwidth capacity); and browser plug-in information (e.g., do you have Adobe, what is your media player, can you open Flash files, etc.).

3.2 Use of Your Information. Registration Data and Access Data may be used by Kensho for research and development purposes and to communicate with users and to troubleshoot any technical issues pertaining to the Content. You acknowledge that in the event that a separate agreement is required, Kensho may share Registration Data with its Affiliates (as defined below).

3.3 Disclosure of Your Information. Except as otherwise noted herein, Kensho will not disclose, rent or sell personal information collected from or about you without your permission. For the purposes specified in the preceding paragraph, we may transfer or disclose Registration Data and Access Data to S&P Global Inc. and its affiliates (“Kensho Affiliates”) and third parties who are contracted to perform services on behalf of Kensho, such as those who assist Kensho in bringing you this Content and providing you with certain features and functionality included within or accessible via this Content. We may also disclose Registration Data and Access Data to Kensho Affiliates and third parties in connection with their providing you access to this Content. Disclosures to these third parties will be subject to confidentiality agreements and, where required, governed by contract. Kensho may also be required to disclose information to governmental, regulatory or self-regulatory entities or agencies in response to regulatory inquiries or to comply with applicable laws, rules, regulations, orders, subpoenas or other legal processes.

3.4 Consent. By (a) agreeing to these Terms of Usage, or (b) by using this Content, and, in either case, providing any information that may be required, requested or otherwise collected by us as set forth above, you freely consent to Kensho processing your information in the United States and in other countries and territories for the purposes set out in these Terms of Usage, and you also consent to the transfer of your information for such purposes to any third party content provider wherever such entity may from time to time be located and to any third parties as described above and in accordance with applicable law and regulations. If you do not permit Kensho to collect any of your information or do not agree with any of the terms and conditions of these Terms of Usage, you should not use this Content and should exit this page and/or Content, as the case may be. If after registering with Kensho, you desire to withdraw the consent granted in this Section 3.4 for all future use of your information by Kensho, you must notify Kensho in writing at the address listed below in Section 3.8 and immediately cease use of this Content.

3.5 Inquiries. If you have any questions regarding these Terms of Usage or your information that is held by us, please contact Kensho in writing using the contact information provided below. If we receive a request regarding your personal information held by us, we will use reasonable means to provide you with such information that we can reasonably compile. You will be given the opportunity to rectify any inaccuracies in such information.

3.6 Encryption. Kensho may use encryption technology to protect certain transmissions of data to/from this Content, but e-mail and other communications, unless otherwise noted on this Content, are not encrypted to/from this Content. Therefore, you should not send any personal or identifying information, such as account numbers, credit card numbers, Social Security numbers, passwords, etc., to Kensho via e-mail. By utilizing e-mail or other electronic communication means you acknowledge that you have no expectation of privacy with respect to the information delivered thereby and that Kensho will not be responsible for any loss or damage that could result from interception by third parties of any information so sent.

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 privacy@kensho.com 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.