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SPEECH-COCO
  • Contributor Laurent Besacier
SPEECH-COCO is an augmentation of MS-COCO dataset where speech is added to image and text. Speech captions were generated using text-to-speech (TTS) synthesis resulting in 616,767 spoken captions (>600h) paired with images. Disfluencies and speed perturbation were added to the signal in order to sound more natural. Each speech signal (WAV) is paired with a JSON file containing exact timecode for each word/syllable/phoneme in the spoken caption. Such a corpus could be used for Language and Vision (LaVi) tasks including speech input or output instead of text.
Read me file
readme.rst
Read me file
Introduction
============

Our corpus is an extension of the MSCOCO image recognition and captioning dataset. MSCOCO comprises images paired with a set of five captions. Yet, it does not include any speech. Therefore, we used `Voxygen's text-to-speech system `_ to synthesised the available captions.


The addition of speech as a new modality enables MSCOCO to be used for researches in the field of language acquisition, unsupervised term discovery, keyword spotting, or semantic embedding using speech and vision.

Our corpus is licenced under a `Creative Commons Attribution 4.0 License `_.

Data Set
============

- This corpus contains **616,767** spoken captions from MSCOCO's val2014 and train2014 subsets (respectively 414,113 for train2014 and 202,654 for val2014).
- We used 8 different voices. 4 of them have a British accent (Paul, Bronwen, Judith and Elizabeth) and the 4 others have an American accent (Phil, Bruce, Amanda, Jenny).
- In order to make the captions sound more natural, we used SOX *tempo* command, enabling us to change the speed without changing the pitch. 1/3 of the captions are 10% slower than the original pace, 1/3 are 10% faster. The last third of the captions was kept untouched.
- We also modified approximately 30% of the original captions and added **disfluencies** such as "um", "uh", "er" so that the captions would sound more natural.
- Each WAV file is paired with a JSON file containing various information: timecode of each word in the caption, name of the speaker, name of the WAV file, etc. The JSON files have the following data structure:

.. code:: json

{
"duration": float,
"speaker": string,
"synthesisedCaption": string,
"timecode": list,
"speed": float,
"wavFilename": string,
"captionID": int,
"imgID": int,
"disfluency": list
}


- On average, each caption comprises 10.79 tokens, disfluencies included. The WAV files are on average 3.52 second long.


Inter- and intra-speaker variability
====================================

To be sure our corpus was realistic enough, we computed inter- and intra-speaker variability.
We had some sample sentences synthesised by each speaker. We then extracted words that were repeated across a few sentences. DTW was then computed for all the occurrences of the same word by the same speaker (e.g. Amanda vs Amanda) and for all the occurrences of the same word pronounced by different speakers (e.g Amanda vs Bronwen, Amanda vs Bruce, etc.).

*MFCC features were extracted using* `Librosa `_ *. DTW was computed using the following* `DTW Python module `_ *. We used* euclidean *distance as cost measure.*
*The voice* William *is a real human voice*. gTTS *refers to Google's TTS system.*

========== =========== =========== =========== =========== =========== =========== =========== =========== =========== ===========
**Voices** Amanda Bronwen Bruce Elizabeth Jenny Judith Paul Phil William gTTS
---------- ----------- ----------- ----------- ----------- ----------- ----------- ----------- ----------- ----------- -----------
Amanda 38,28 60,80 66,10 62,00 53,30 61,67 69,43 68,93 70,41 69,67
Bronwen 60,80 37,39 61,75 52,89 52,84 57,34 57,06 62,23 72,19 63,77
Bruce 66,10 61,75 38,35 59,34 54,68 64,65 57,23 54,83 77,02 69,71
Elizabeth 62,00 52,89 59,34 34,04 53,17 56,13 58,87 62,44 71,83 65,27
Jenny 53,30 52,84 54,68 53,17 38,30 56,00 61,02 60,05 69,21 62,28
Judith 61,67 57,34 64,65 56,13 56,00 47,49 64,94 67,59 72,08 64,16
Paul 69,43 57,06 57,23 58,87 61,02 64,94 40,54 60,37 73,73 68,41
Phil 68,93 62,23 54,83 62,44 60,05 67,59 60,37 45,57 79,60 75,38
William 70,41 72,19 77,02 71,83 69,21 72,08 73,73 79,60 46,47 76,74
gTTS 69,67 63,77 69,71 65,27 62,28 64,16 68,41 75,38 76,74 45,77
========== =========== =========== =========== =========== =========== =========== =========== =========== =========== ===========

As expected intra-speaker variability is lower than inter-speaker variability. Inter-variability is greater between William and the other voices, which is to be expected as William is a real human voice. However, **inter-variability between the synthetic voices is still high** meaning the corpus we synthesised is realistic.

References
============

As for now, we didn't publish any article describing in details our corpus and methodology.
You might want to read the following papers if you wish to learn more about MSCOCO.

Chen, X., Fang, H., Lin, T.-Y., Vedantam, R., Gupta, S., Dollar, P., & Zitnick, C. Lawrence. (2015). Microsoft COCO Captions: Data Collection and Evaluation Server.

Lin, T.-Y., Maire, M., Belongie, S., Bourdev, L., Girshick, R., Hays, J., Dollár, P. (2014). Microsoft COCO: Common Objects in Context.


Contents
============

The Dropbox repository is organized as follows:

- CORPUS-MSCOCO (~75GB)
- **train2014/** : folder contains 413,915 captions
- json/
- wav/
- train_2014.db
- **val2014/** : folder contains 202,520 captions
- json/
- val/
- val_2014.db

- cocoWav_API.py

Filenames
============

**.wav** files contain the spoken version of a caption

**.json** files contain all the metadata of a given WAV file

**.db** files are SQLite databases containing all the information contained in the JSON files

We adopted the following naming convention for both the WAV and JSON files:

*imageID_captionID_Speaker_DisfluencyPosition_Speed[.wav/.json]*

Script
============

We created a script called **cocoWav_API.py** in order to handle the metadata and allow the user to easily find captions according to specific filters. The script uses the \*.db files.

Features:

- Aggregate all the information in the JSON files into a single SQLite database
- Find captions according to specific filters (name, gender and nationality of the speaker, disfluency position, speed, duration, and words in the caption). *The script automatically builds the SQLite query. The user can also provide his own SQLite query.*
- Find all the captions belonging to a specific image
- Parse the timecodes and have them structured

**input**:

.. code:: json

...
[1926.3068, "SYL", ""],
[1926.3068, "SEPR", " "],
[1926.3068, "WORD", "white"],
[1926.3068, "PHO", "w"],
[2050.7955, "PHO", "ai"],
[2144.6591, "PHO", "t"],
[2179.3182, "SYL", ""],
[2179.3182, "SEPR", " "]
...

**output**:

.. code:: json

...
{
'begin': 1926.3068,
'end': 2179.3182,
'syllable': [{'begin': 1926.3068,
'end': 2179.3182,
'phoneme': [{'begin': 1926.3068,
'end': 2050.7955,
'value': 'w'},
{'begin': 2050.7955,
'end': 2144.6591,
'value': 'ai'},
{'begin': 2144.6591,
'end': 2179.3182,
'value': 't'}],
'value': 'wait'}],
'value': 'white'
},
...

- Convert the timecodes to Praat TextGrid files
- Get the words, syllables and phonemes between *n* seconds/milliseconds

We provide an example on how to use the script at the end.
2017 06 07
The size of this dataset is more than 4000 Mb
Archive files
SpeechCoco_API_README.zip
2017 07 10
18.36 KB
  • setup.py 0 230 ko
  • speechcoco /
  • speechcoco / __init__.py 0 031 ko
  • speechcoco / speechcoco.py 30 514 ko
train2014.zip
2017 07 06
34.36 GB
  • json /
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val2014.zip
2017 07 06
16.77 GB
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Other metadata
  • External Identifiers:

  • Subjects:

    Computer Science, Linguistics
  • Keywords:

    language and vision, nlp, speech, unsupervised term discovery, utd
  • Corresponding tasks:

    pattern extraction, clustering
  • Encoding data format:

    json - wav - sqlite3

Havard W. N., Besacier L., Rosec O. (2017). SPEECH-COCO: 600k Visually Grounded Spoken Captions Aligned to MSCOCO Data Set. Grounding Language Understanding GLU2017, Stockholm. Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG. SPEECH-COCO [data set] published 2017, doi:10.18709/PERSCIDO.2017.06.DS80. Published 2017 via Perscido-Grenoble-Alpes;

Havard W. N., Besacier L., Rosec O. (2017). SPEECH-COCO: 600k Visually Grounded Spoken Captions Aligned to MSCOCO Data Set. Grounding Language Understanding GLU2017, Stockholm. Univ. Grenoble Alpes, CNRS, Grenoble INP, LIG. SPEECH-COCO [data set] published 2017, doi:10.18709/PERSCIDO.2017.06.DS80. Published 2017 via Perscido-Grenoble-Alpes