| --- |
| annotations_creators: |
| - expert-generated |
| language_creators: |
| - found |
| language: |
| - en |
| license: |
| - unlicense |
| multilinguality: |
| - monolingual |
| paperswithcode_id: ljspeech |
| pretty_name: LJ Speech |
| size_categories: |
| - 10K<n<100K |
| source_datasets: |
| - original |
| task_categories: |
| - automatic-speech-recognition |
| - text-to-speech |
| - text-to-audio |
| task_ids: [] |
| 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 |
| 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 |
| --- |
| |
| # 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. |
