
# Dataset Card for lj_speech

## 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:** [The LJ Speech Dataset](https://keithito.com/LJ-Speech-Dataset/)
- **Repository:** [N/A]
- **Paper:** [N/A]
- **Leaderboard:** [Paperswithcode Leaderboard](https://paperswithcode.com/sota/text-to-speech-synthesis-on-ljspeech)
- **Point of Contact:** [Keith Ito](mailto:kito@kito.us)

### Dataset Summary

This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books in English. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours.

The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded in 2016-17 by the LibriVox project and is also in the public domain.

### Supported Tasks and Leaderboards

The dataset can be used to train a model for Automatic Speech Recognition (ASR) or Text-to-Speech (TTS).
- `automatic-speech-recognition`: An ASR model is presented with an audio file and asked to transcribe the audio file to written text.
The most common ASR evaluation metric is the word error rate (WER).
- `text-to-speech`, `text-to-audio`: A TTS model is given a written text in natural language and asked to generate a speech audio file.
A reasonable evaluation metric is the mean opinion score (MOS) of audio quality.
The dataset has an active leaderboard which can be found at https://paperswithcode.com/sota/text-to-speech-synthesis-on-ljspeech

### Languages

The transcriptions and audio are in English.

## Dataset Structure

### Data Instances

A data point comprises the path to the audio file, called `file` and its transcription, called `text`.
A normalized version of the text is also provided.

```
{
    'id': 'LJ002-0026',
    'file': '/datasets/downloads/extracted/05bfe561f096e4c52667e3639af495226afe4e5d08763f2d76d069e7a453c543/LJSpeech-1.1/wavs/LJ002-0026.wav',
	'audio': {'path': '/datasets/downloads/extracted/05bfe561f096e4c52667e3639af495226afe4e5d08763f2d76d069e7a453c543/LJSpeech-1.1/wavs/LJ002-0026.wav',
	  'array': array([-0.00048828, -0.00018311, -0.00137329, ...,  0.00079346,
			  0.00091553,  0.00085449], dtype=float32),
	  'sampling_rate': 22050},
    'text': 'in the three years between 1813 and 1816,'
    'normalized_text': 'in the three years between eighteen thirteen and eighteen sixteen,',
}
```

Each audio file is a single-channel 16-bit PCM WAV with a sample rate of 22050 Hz.

### Data Fields

- id: unique id of the data sample.

- file: a path to the downloaded audio file in .wav format.

- audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: `dataset[0]["audio"]` the audio file is automatically decoded and resampled to `dataset.features["audio"].sampling_rate`. Decoding and resampling of a large number of audio files 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]`.

- text: the transcription of the audio file.

- normalized_text: the transcription with numbers, ordinals, and monetary units expanded into full words.

### Data Splits

The dataset is not pre-split. Some statistics:

- Total Clips: 13,100
- Total Words: 225,715
- Total Characters: 1,308,678
- Total Duration: 23:55:17
- Mean Clip Duration: 6.57 sec
- Min Clip Duration: 1.11 sec
- Max Clip Duration: 10.10 sec
- Mean Words per Clip: 17.23
- Distinct Words: 13,821

## Dataset Creation

### Curation Rationale

[Needs More Information]

### Source Data

#### Initial Data Collection and Normalization

This dataset consists of excerpts from the following works:

- Morris, William, et al. Arts and Crafts Essays. 1893.
- Griffiths, Arthur. The Chronicles of Newgate, Vol. 2. 1884.
- Roosevelt, Franklin D. The Fireside Chats of Franklin Delano Roosevelt. 1933-42.
- Harland, Marion. Marion Harland's Cookery for Beginners. 1893.
- Rolt-Wheeler, Francis. The Science - History of the Universe, Vol. 5: Biology. 1910.
- Banks, Edgar J. The Seven Wonders of the Ancient World. 1916.
- President's Commission on the Assassination of President Kennedy. Report of the President's Commission on the Assassination of President Kennedy. 1964.

Some details about normalization:
- The normalized transcription has the numbers, ordinals, and monetary units expanded into full words (UTF-8)
- 19 of the transcriptions contain non-ASCII characters (for example, LJ016-0257 contains "raison d'être").
- The following abbreviations appear in the text. They may be expanded as follows:

| Abbreviation | Expansion |
|--------------|-----------|
| Mr. | Mister |
| Mrs. | Misess (*) |
| Dr. | Doctor |
| No. | Number |
| St. | Saint |
| Co. | Company |
| Jr. | Junior |
| Maj. | Major |
| Gen. | General |
| Drs. | Doctors |
| Rev. | Reverend |
| Lt. | Lieutenant |
| Hon. | Honorable |
| Sgt. | Sergeant |
| Capt. | Captain |
| Esq. | Esquire |
| Ltd. | Limited |
| Col. | Colonel |
| Ft. | Fort |
(*) there's no standard expansion for "Mrs."

#### Who are the source language producers?

[Needs More Information]

### Annotations

#### Annotation process

- The audio clips range in length from approximately 1 second to 10 seconds. They were segmented automatically based on silences in the recording. Clip boundaries generally align with sentence or clause boundaries, but not always.
- The text was matched to the audio manually, and a QA pass was done to ensure that the text accurately matched the words spoken in the audio.

#### Who are the annotators?

Recordings by Linda Johnson from LibriVox. Alignment and annotation by Keith Ito.

### 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

[Needs More Information]

### Discussion of Biases

[Needs More Information]

### Other Known Limitations

- The original LibriVox recordings were distributed as 128 kbps MP3 files. As a result, they may contain artifacts introduced by the MP3 encoding.

## Additional Information

### Dataset Curators

The dataset was initially created by Keith Ito and Linda Johnson.

### Licensing Information

Public Domain ([LibriVox](https://librivox.org/pages/public-domain/))

### Citation Information

```
@misc{ljspeech17,
  author       = {Keith Ito and Linda Johnson},
  title        = {The LJ Speech Dataset},
  howpublished = {\url{https://keithito.com/LJ-Speech-Dataset/}},
  year         = 2017
}
```

### Contributions

Thanks to [@anton-l](https://github.com/anton-l) for adding this dataset.
task_categories:automatic-speech-recognition task_categories:text-to-speech task_categories:text-to-audio annotations_creators:expert-generated language_creators:found multilinguality:monolingual size_categories:10K<n<100K source_datasets:original language:en license:unlicense region:us
{"annotations_creators": ["expert-generated"], "language_creators": ["found"], "language": ["en"], "license": ["unlicense"], "multilinguality": ["monolingual"], "size_categories": ["10K<n<100K"], "source_datasets": ["original"], "task_categories": ["automatic-speech-recognition", "text-to-speech", "text-to-audio"], "task_ids": [], "paperswithcode_id": "ljspeech", "pretty_name": "LJ Speech", "dataset_info": {"features": [{"name": "id", "dtype": "string"}, {"name": "audio", "dtype": {"audio": {"sampling_rate": 22050}}}, {"name": "file", "dtype": "string"}, {"name": "text", "dtype": "string"}, {"name": "normalized_text", "dtype": "string"}], "config_name": "main", "splits": [{"name": "train", "num_bytes": 4667022, "num_examples": 13100}], "download_size": 2748572632, "dataset_size": 4667022}, "train-eval-index": [{"config": "main", "task": "automatic-speech-recognition", "task_id": "speech_recognition", "splits": {"train_split": "train"}, "col_mapping": {"file": "path", "text": "text"}, "metrics": [{"type": "wer", "name": "WER"}, {"type": "cer", "name": "CER"}]}]}
TAGS
#task_categories-automatic-speech-recognition #task_categories-text-to-speech #task_categories-text-to-audio #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unlicense #region-us 

Dataset Card for lj\_speech
===========================


Table of Contents
-----------------


* Dataset Description
	+ Dataset Summary
	+ Supported Tasks and Leaderboards
	+ Languages
* Dataset Structure
	+ Data Instances
	+ Data Fields
	+ Data Splits
* Dataset Creation
	+ Curation Rationale
	+ Source Data
	+ Annotations
	+ Personal and Sensitive Information
* Considerations for Using the Data
	+ Social Impact of Dataset
	+ Discussion of Biases
	+ Other Known Limitations
* Additional Information
	+ Dataset Curators
	+ Licensing Information
	+ Citation Information
	+ Contributions


Dataset Description
-------------------


* Homepage: The LJ Speech Dataset
* Repository: [N/A]
* Paper: [N/A]
* Leaderboard: Paperswithcode Leaderboard
* Point of Contact: Keith Ito


### Dataset Summary


This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books in English. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours.


The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded in 2016-17 by the LibriVox project and is also in the public domain.


### Supported Tasks and Leaderboards


The dataset can be used to train a model for Automatic Speech Recognition (ASR) or Text-to-Speech (TTS).


* 'automatic-speech-recognition': An ASR model is presented with an audio file and asked to transcribe the audio file to written text.
The most common ASR evaluation metric is the word error rate (WER).
* 'text-to-speech', 'text-to-audio': A TTS model is given a written text in natural language and asked to generate a speech audio file.
A reasonable evaluation metric is the mean opinion score (MOS) of audio quality.
The dataset has an active leaderboard which can be found at URL


### Languages


The transcriptions and audio are in English.


Dataset Structure
-----------------


### Data Instances


A data point comprises the path to the audio file, called 'file' and its transcription, called 'text'.
A normalized version of the text is also provided.


Each audio file is a single-channel 16-bit PCM WAV with a sample rate of 22050 Hz.


### Data Fields


* id: unique id of the data sample.
* file: a path to the downloaded audio file in .wav format.
* audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0]["audio"]' the audio file is automatically decoded and resampled to 'dataset.features["audio"].sampling\_rate'. Decoding and resampling of a large number of audio files 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]'.
* text: the transcription of the audio file.
* normalized\_text: the transcription with numbers, ordinals, and monetary units expanded into full words.


### Data Splits


The dataset is not pre-split. Some statistics:


* Total Clips: 13,100
* Total Words: 225,715
* Total Characters: 1,308,678
* Total Duration: 23:55:17
* Mean Clip Duration: 6.57 sec
* Min Clip Duration: 1.11 sec
* Max Clip Duration: 10.10 sec
* Mean Words per Clip: 17.23
* Distinct Words: 13,821


Dataset Creation
----------------


### Curation Rationale


### Source Data


#### Initial Data Collection and Normalization


This dataset consists of excerpts from the following works:


* Morris, William, et al. Arts and Crafts Essays. 1893.
* Griffiths, Arthur. The Chronicles of Newgate, Vol. 2. 1884.
* Roosevelt, Franklin D. The Fireside Chats of Franklin Delano Roosevelt. 1933-42.
* Harland, Marion. Marion Harland's Cookery for Beginners. 1893.
* Rolt-Wheeler, Francis. The Science - History of the Universe, Vol. 5: Biology. 1910.
* Banks, Edgar J. The Seven Wonders of the Ancient World. 1916.
* President's Commission on the Assassination of President Kennedy. Report of the President's Commission on the Assassination of President Kennedy. 1964.


Some details about normalization:


* The normalized transcription has the numbers, ordinals, and monetary units expanded into full words (UTF-8)
* 19 of the transcriptions contain non-ASCII characters (for example, LJ016-0257 contains "raison d'être").
* The following abbreviations appear in the text. They may be expanded as follows:



#### Who are the source language producers?


### Annotations


#### Annotation process


* The audio clips range in length from approximately 1 second to 10 seconds. They were segmented automatically based on silences in the recording. Clip boundaries generally align with sentence or clause boundaries, but not always.
* The text was matched to the audio manually, and a QA pass was done to ensure that the text accurately matched the words spoken in the audio.


#### Who are the annotators?


Recordings by Linda Johnson from LibriVox. Alignment and annotation by Keith Ito.


### 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


### Discussion of Biases


### Other Known Limitations


* The original LibriVox recordings were distributed as 128 kbps MP3 files. As a result, they may contain artifacts introduced by the MP3 encoding.


Additional Information
----------------------


### Dataset Curators


The dataset was initially created by Keith Ito and Linda Johnson.


### Licensing Information


Public Domain (LibriVox)


### Contributions


Thanks to @anton-l for adding this dataset.



### Dataset Summary


This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books in English. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours.


The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded in 2016-17 by the LibriVox project and is also in the public domain. ### Supported Tasks and Leaderboards


The dataset can be used to train a model for Automatic Speech Recognition (ASR) or Text-to-Speech (TTS).


* 'automatic-speech-recognition': An ASR model is presented with an audio file and asked to transcribe the audio file to written text.
The most common ASR evaluation metric is the word error rate (WER).
* 'text-to-speech', 'text-to-audio': A TTS model is given a written text in natural language and asked to generate a speech audio file.
A reasonable evaluation metric is the mean opinion score (MOS) of audio quality.
The dataset has an active leaderboard which can be found at URL ### Languages


The transcriptions and audio are in English.


Dataset Structure
----------------- ### Data Instances


A data point comprises the path to the audio file, called 'file' and its transcription, called 'text'.
A normalized version of the text is also provided.


Each audio file is a single-channel 16-bit PCM WAV with a sample rate of 22050 Hz. ### Data Fields


* id: unique id of the data sample.
* file: a path to the downloaded audio file in .wav format.
* audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0]["audio"]' the audio file is automatically decoded and resampled to 'dataset.features["audio"].sampling\_rate'. Decoding and resampling of a large number of audio files 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]'.
* text: the transcription of the audio file.
* normalized\_text: the transcription with numbers, ordinals, and monetary units expanded into full words. ### Data Splits


The dataset is not pre-split. Some statistics:


* Total Clips: 13,100
* Total Words: 225,715
* Total Characters: 1,308,678
* Total Duration: 23:55:17
* Mean Clip Duration: 6.57 sec
* Min Clip Duration: 1.11 sec
* Max Clip Duration: 10.10 sec
* Mean Words per Clip: 17.23
* Distinct Words: 13,821


Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization


This dataset consists of excerpts from the following works:


* Morris, William, et al. Arts and Crafts Essays. 1893.
* Griffiths, Arthur. The Chronicles of Newgate, Vol. 2. 1884.
* Roosevelt, Franklin D. The Fireside Chats of Franklin Delano Roosevelt. 1933-42.
* Harland, Marion. Marion Harland's Cookery for Beginners. 1893.
* Rolt-Wheeler, Francis. The Science - History of the Universe, Vol. 5: Biology. 1910.
* Banks, Edgar J. The Seven Wonders of the Ancient World. 1916.
* President's Commission on the Assassination of President Kennedy. Report of the President's Commission on the Assassination of President Kennedy. 1964.


Some details about normalization:


* The normalized transcription has the numbers, ordinals, and monetary units expanded into full words (UTF-8)
* 19 of the transcriptions contain non-ASCII characters (for example, LJ016-0257 contains "raison d'être").
* The following abbreviations appear in the text. They may be expanded as follows: #### Who are the source language producers? ### Annotations #### Annotation process


* The audio clips range in length from approximately 1 second to 10 seconds. They were segmented automatically based on silences in the recording. Clip boundaries generally align with sentence or clause boundaries, but not always.
* The text was matched to the audio manually, and a QA pass was done to ensure that the text accurately matched the words spoken in the audio. #### Who are the annotators?


Recordings by Linda Johnson from LibriVox. Alignment and annotation by Keith Ito. ### 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 ### Discussion of Biases ### Other Known Limitations


* The original LibriVox recordings were distributed as 128 kbps MP3 files. As a result, they may contain artifacts introduced by the MP3 encoding.


Additional Information
---------------------- ### Dataset Curators


The dataset was initially created by Keith Ito and Linda Johnson. ### Licensing Information


Public Domain (LibriVox) ### Contributions


Thanks to @anton-l for adding this dataset.
TAGS
#task_categories-automatic-speech-recognition #task_categories-text-to-speech #task_categories-text-to-audio #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unlicense #region-us 
 ### Dataset Summary


This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books in English. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours.


The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded in 2016-17 by the LibriVox project and is also in the public domain. ### Supported Tasks and Leaderboards


The dataset can be used to train a model for Automatic Speech Recognition (ASR) or Text-to-Speech (TTS).


* 'automatic-speech-recognition': An ASR model is presented with an audio file and asked to transcribe the audio file to written text.
The most common ASR evaluation metric is the word error rate (WER).
* 'text-to-speech', 'text-to-audio': A TTS model is given a written text in natural language and asked to generate a speech audio file.
A reasonable evaluation metric is the mean opinion score (MOS) of audio quality.
The dataset has an active leaderboard which can be found at URL ### Languages


The transcriptions and audio are in English.


Dataset Structure
----------------- ### Data Instances


A data point comprises the path to the audio file, called 'file' and its transcription, called 'text'.
A normalized version of the text is also provided.


Each audio file is a single-channel 16-bit PCM WAV with a sample rate of 22050 Hz. ### Data Fields


* id: unique id of the data sample.
* file: a path to the downloaded audio file in .wav format.
* audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0]["audio"]' the audio file is automatically decoded and resampled to 'dataset.features["audio"].sampling\_rate'. Decoding and resampling of a large number of audio files 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]'.
* text: the transcription of the audio file.
* normalized\_text: the transcription with numbers, ordinals, and monetary units expanded into full words. ### Data Splits


The dataset is not pre-split. Some statistics:


* Total Clips: 13,100
* Total Words: 225,715
* Total Characters: 1,308,678
* Total Duration: 23:55:17
* Mean Clip Duration: 6.57 sec
* Min Clip Duration: 1.11 sec
* Max Clip Duration: 10.10 sec
* Mean Words per Clip: 17.23
* Distinct Words: 13,821


Dataset Creation
---------------- ### Curation Rationale ### Source Data #### Initial Data Collection and Normalization


This dataset consists of excerpts from the following works:


* Morris, William, et al. Arts and Crafts Essays. 1893.
* Griffiths, Arthur. The Chronicles of Newgate, Vol. 2. 1884.
* Roosevelt, Franklin D. The Fireside Chats of Franklin Delano Roosevelt. 1933-42.
* Harland, Marion. Marion Harland's Cookery for Beginners. 1893.
* Rolt-Wheeler, Francis. The Science - History of the Universe, Vol. 5: Biology. 1910.
* Banks, Edgar J. The Seven Wonders of the Ancient World. 1916.
* President's Commission on the Assassination of President Kennedy. Report of the President's Commission on the Assassination of President Kennedy. 1964.


Some details about normalization:


* The normalized transcription has the numbers, ordinals, and monetary units expanded into full words (UTF-8)
* 19 of the transcriptions contain non-ASCII characters (for example, LJ016-0257 contains "raison d'être").
* The following abbreviations appear in the text. They may be expanded as follows: #### Who are the source language producers? ### Annotations #### Annotation process


* The audio clips range in length from approximately 1 second to 10 seconds. They were segmented automatically based on silences in the recording. Clip boundaries generally align with sentence or clause boundaries, but not always.
* The text was matched to the audio manually, and a QA pass was done to ensure that the text accurately matched the words spoken in the audio. #### Who are the annotators?


Recordings by Linda Johnson from LibriVox. Alignment and annotation by Keith Ito. ### 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 ### Discussion of Biases ### Other Known Limitations


* The original LibriVox recordings were distributed as 128 kbps MP3 files. As a result, they may contain artifacts introduced by the MP3 encoding.


Additional Information
---------------------- ### Dataset Curators


The dataset was initially created by Keith Ito and Linda Johnson. ### Licensing Information


Public Domain (LibriVox) ### Contributions


Thanks to @anton-l for adding this dataset.
passage: TAGS
#task_categories-automatic-speech-recognition #task_categories-text-to-speech #task_categories-text-to-audio #annotations_creators-expert-generated #language_creators-found #multilinguality-monolingual #size_categories-10K<n<100K #source_datasets-original #language-English #license-unlicense #region-us 
### Dataset Summary


This is a public domain speech dataset consisting of 13,100 short audio clips of a single speaker reading passages from 7 non-fiction books in English. A transcription is provided for each clip. Clips vary in length from 1 to 10 seconds and have a total length of approximately 24 hours.


The texts were published between 1884 and 1964, and are in the public domain. The audio was recorded in 2016-17 by the LibriVox project and is also in the public domain.### Supported Tasks and Leaderboards


The dataset can be used to train a model for Automatic Speech Recognition (ASR) or Text-to-Speech (TTS).


* 'automatic-speech-recognition': An ASR model is presented with an audio file and asked to transcribe the audio file to written text.
The most common ASR evaluation metric is the word error rate (WER).
* 'text-to-speech', 'text-to-audio': A TTS model is given a written text in natural language and asked to generate a speech audio file.
A reasonable evaluation metric is the mean opinion score (MOS) of audio quality.
The dataset has an active leaderboard which can be found at URL### Languages


The transcriptions and audio are in English.


Dataset Structure
-----------------### Data Instances


A data point comprises the path to the audio file, called 'file' and its transcription, called 'text'.
A normalized version of the text is also provided.


Each audio file is a single-channel 16-bit PCM WAV with a sample rate of 22050 Hz. passage: ### Data Fields


* id: unique id of the data sample.
* file: a path to the downloaded audio file in .wav format.
* audio: A dictionary containing the path to the downloaded audio file, the decoded audio array, and the sampling rate. Note that when accessing the audio column: 'dataset[0]["audio"]' the audio file is automatically decoded and resampled to 'dataset.features["audio"].sampling\_rate'. Decoding and resampling of a large number of audio files 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]'.
* text: the transcription of the audio file.
* normalized\_text: the transcription with numbers, ordinals, and monetary units expanded into full words.### Data Splits


The dataset is not pre-split. Some statistics:


* Total Clips: 13,100
* Total Words: 225,715
* Total Characters: 1,308,678
* Total Duration: 23:55:17
* Mean Clip Duration: 6.57 sec
* Min Clip Duration: 1.11 sec
* Max Clip Duration: 10.10 sec
* Mean Words per Clip: 17.23
* Distinct Words: 13,821


Dataset Creation
----------------### Curation Rationale### Source Data#### Initial Data Collection and Normalization


This dataset consists of excerpts from the following works:


* Morris, William, et al. Arts and Crafts Essays. 1893.
* Griffiths, Arthur. The Chronicles of Newgate, Vol. 2. 1884.
* Roosevelt, Franklin D. The Fireside Chats of Franklin Delano Roosevelt. 1933-42.
* Harland, Marion. Marion Harland's Cookery for Beginners. 1893.
* Rolt-Wheeler, Francis. The Science - History of the Universe, Vol. 5: Biology. 1910.
* Banks, Edgar J. The Seven Wonders of the Ancient World. 1916.
* President's Commission on the Assassination of President Kennedy. Report of the President's Commission on the Assassination of President Kennedy. 1964.


Some details about normalization:


* The normalized transcription has the numbers, ordinals, and monetary units expanded into full words (UTF-8)
* 19 of the transcriptions contain non-ASCII characters (for example, LJ016-0257 contains "raison d'être").
* The following abbreviations appear in the text. They may be expanded as follows:#### Who are the source language producers?### Annotations#### Annotation process


* The audio clips range in length from approximately 1 second to 10 seconds. They were segmented automatically based on silences in the recording. Clip boundaries generally align with sentence or clause boundaries, but not always.
* The text was matched to the audio manually, and a QA pass was done to ensure that the text accurately matched the words spoken in the audio.