Speech dataset resources: Difference between revisions

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!Metadata
!Metadata
!Name/s of people who are entering the data
!Name/s of people who are entering the data
!Who are you entering the data?
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|[https://www.openslr.org/12 LibriSpeech]
|[https://www.openslr.org/12 LibriSpeech]
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* Dataset is made by Vassil Panayotov.  
* Dataset is made by Vassil Panayotov.  
* Guoguo Chen , Daniel Povey , Sanjeev Khudanpur are also contributed to the dataset and article below.  
* Guoguo Chen , Daniel Povey , Sanjeev Khudanpur are also contributed to the dataset and article below.  
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|'''[https://huggingface.co/datasets/google/xtreme_s/viewer/fleurs.en_us/train?row=3 Hugging Face]'''
|'''[https://huggingface.co/datasets/google/xtreme_s/viewer/fleurs.en_us/train?row=3 Hugging Face]'''
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* >100 hours of audio material and >50k parallel sentences.
* >100 hours of audio material and >50k parallel sentences.
* Quality of audio and text has been evaluated manually.
* Quality of audio and text has been evaluated manually.
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* William Havard, Laurent Besacier, Olivier Rosec
* William Havard, Laurent Besacier, Olivier Rosec
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|[https://paperswithcode.com/dataset/saf SAF (Short Answer Feedback Dataset)]
|[https://paperswithcode.com/dataset/saf SAF (Short Answer Feedback Dataset)]
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* CC BY-SA
* CC BY-SA
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* Recording Equipment: Telephony
* Recording Equipment: Telephony
* Recording Environment: Indoor Environment
* Recording Environment: Indoor Environment
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|• Gonçal V. Garcés Díaz-Munío
|• Gonçal V. Garcés Díaz-Munío
• Joan Albert Silvestre-Cerdà
• Joan Albert Silvestre-Cerdà
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|[https://paperswithcode.com/dataset/ted-lium-3 TED-LIUM 3]
|[https://paperswithcode.com/dataset/ted-lium-3 TED-LIUM 3]
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* Alignments: cover around 83.0% of audio; 3.2M words
* Alignments: cover around 83.0% of audio; 3.2M words
* Access: freely available for the research community
* Access: freely available for the research community
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Revision as of 16:39, 27 September 2023

This table summarizes dataset resources available

Note: You have editing rights to the table, so you can edit/adjust it to your needs*. Your instructors will clean it up afterwards.

Dataset name Type of data Annotation remarks License Metadata Name/s of people who are entering the data Who are you entering the data?
LibriSpeech Audio books (clean, flac format)
  • Text is converted into upper-case, removed punctuation, expanding common abbreviations and non-standard words.
  • The transcriptions are aligned and segmented automatically.
 Creative Commons Attribution 4.0 International License.
  • Language: English
  • The dataset is split into 3 sections with 100.6, 363.6, 496.7 hours of speech.
  • The gender ratio of speakers is about half and half.
  • Dataset is made by Vassil Panayotov.
  • Guoguo Chen , Daniel Povey , Sanjeev Khudanpur are also contributed to the dataset and article below.
Hugging Face
LibriVoxDeEn Audio based on audio books (.wav file format)

German text and English translation (.tsv file format)

Creative Commons Attribution 4.0 Non-Commercial ShareAlike Internation License
  • German audio and transcription, with English translation.
  • >100 hours of audio material and >50k parallel sentences.
  • Quality of audio and text has been evaluated manually.
https://github.com/jim-schwoebel/voice_datasets
SPEECH-COCO Creative Commons Attribution 4.0 International Liscence
  • Language: English
  • This corpus contains 616,767 spoken captions from MSCOCO's val2014 and train2014 subsets
  • 8 different voices. 4 of them have a British accent and the 4 others with American accent.
  • William Havard, Laurent Besacier, Olivier Rosec
SAF (Short Answer Feedback Dataset) based on audio books (.wav
  • CC BY-SA
ASR-ETELECSC WAV (PCM)

TXT (UTF-8)

Speakers' gender

Noise/laughter marked

Languages mentioned

Starting and ending time of speech

Speakers sequenced as numbers [1]/[2]

Sound unrecognized as [UNKNOWN]

Pause marked as [+]

Incomplete words marked as [~]

Ambiguity marked as [*]

MAGIC DATA

OPEN-SOURCE LICENSE

  • Total Duration: 5.04h
  • Language: EN
  • Speech Style: spontaneous conversation
  • Audio parameters: 16 kHz, 16 bits, mono
  • Recording Equipment: Telephony
  • Recording Environment: Indoor Environment
Europarl-ASR Creative Commons Attribution 4.0 License. • Language: English • Gonçal V. Garcés Díaz-Munío

• Joan Albert Silvestre-Cerdà

TED-LIUM 3
  • Language: English
  • Transcription: Yes ( format: stm)
  • Duration: 452 hrs
  • Number of talkers: 1938 (Male: 1303; Female: 635)
  • Alignments: cover around 83.0% of audio; 3.2M words
  • Access: freely available for the research community

*) this means: you may also add additional pages and link to them in this table

Notes on LibriSpeech

  • We only used the development set to test our ASR code in Python;
  • The names of the speakers who recorded all the audiobooks contained in this corpus are also available in a separate text file;
  • Exhaustive information about this dataset can be found in this article;
  • This dataset was included in the Kaldi speech recognition toolkit.
  • Another version of research on LibriSpeech by Yuxing Ouyang, Xiaoling Lin, Xueying Liu, Jingxuan Yue, can be found in Librispeech.
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