Update README.md

README.md

@@ -1,3 +1,81 @@
 ---
 license: mit
+task_categories:
+- audio-classification
+language:
+- en
+tags:
+- SER
+- Speech Emotion Recognition
+- Speech Emotion Classification
+- Audio Classification
+- Audio
+- Emotion
+- Emo
+- Speech
+- Mosei
+pretty_name: messiah
+size_categories:
+- 10K<n<100K
 ---
+
+
+DATASET DESCRIPTION
+
+The MESSAIH dataset is a fork of [CMU MOSEI](http://multicomp.cs.cmu.edu/resources/cmu-mosei-dataset/).
+
+Unlike its parent, MESSAIH is indended for unimodal model development and focusses exclusively on audio classification, more specifically, Speech Emotion Recognition (SER).
+
+Of course, it can be used for bimodal classification by transcribing each audio track.
+
+MESSAIH currently contains 13,234 speech samples annotated according to the [CMU MOSEI](https://aclanthology.org/P18-1208/) scheme:
+
+> Each sentence is annotated for sentiment on a [-3,3] Likert scale of:
+> [−3: highly negative, −2 negative, −1 weakly negative, 0 neutral, +1 weakly positive, +2 positive, +3 highly positive].
+> Ekman emotions of {happiness, sadness, anger, fear, disgust, surprise}
+> are annotated on a [0,3] Likert scale for presence of emotion
+> x: [0: no evidence of x, 1: weakly x, 2: x, 3: highly x].
+
+The dataset is provided as a parquet file. 
+
+To facilitate inspection, a csv file is also provided, but it does not contain the audio arrays.
+
+If you train a model on this dataset, you would make us very happy by letting us know.
+
+
+UNPACKING THE DATASET
+
+A sample Python script (check the top of the script for the requirements) is also provided for illustrative purposes.
+
+The script reads the parquet file and produces the following:
+
+1. A CSV file with file names and MOSEI values (columns names are self-explanatory). This file is identical to the one already provided.
+   
+2. A folder named "wavs" containing the audio samples.
+
+
+LEGAL CONSIDERATIONS
+
+Note that producing the wav files might (or might not) constitute copyright infringement as well as a violation of Google's Terms of Service.
+
+Instead, researchers are encouraged to use the numpy arrays contained in the last column of the dataset ("wav2numpy") directly, without actually extracting any playable audio.
+
+That, I believe, may keep us in the greyzone.
+
+
+CAVEATS
+
+As one can appreciate from the charts contained in the "chart" folder, the dataset is biased towards "positive" emotions, namely happiness.
+
+Certain emotions such as fear may be underrepresented, not only in terms of number of occurences, but, more problematically, in terms of "intensity".
+
+MOSEI is considered a natural or spontaneous emotion dataset (as opposed to an actored or scripted one) showcasing "genuine" emotions.
+
+However, keep in mind that MOSEI was curated from a popular social network and social networks are notoriously abundant in fake emotions.
+
+Moreover, certain emotions may be intrinsically more difficult to detect than others, even from a human perspective.
+
+Yet, MOSEI is possibly one of the best datasets in the public domain.
+
+Also note that the original [MOSEI](http://immortal.multicomp.cs.cmu.edu/CMU-MOSEI/labels/) contains nearly twice as many entries as MESSAIH does.
+