Update README.md

README.md

@@ -169,3 +169,24 @@ More information can be found in the paper [Masked Audio Generation using a Sing
 **Risks and harms:** Biases and limitations of the model may lead to generation of samples that may be considered as biased, inappropriate or offensive. We believe that providing the code to reproduce the research and train new models will allow to broaden the application to new and more representative data.
 
 **Use cases:** Users must be aware of the biases, limitations and risks of the model. MAGNeT is a model developed for artificial intelligence research on music generation. As such, it should not be used for downstream applications without further investigation and mitigation of risks.
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+
+## Audio-MAGNeT - Sound-effect generation models
+
+### Training datasets
+
+The audio-magnet models were trained on the following data sources: a subset of AudioSet (Gemmeke et al., 2017), [BBC sound effects](https://sound-effects.bbcrewind.co.uk/), AudioCaps (Kim et al., 2019), Clotho v2 (Drossos et al., 2020), VGG-Sound (Chen et al., 2020), FSD50K (Fonseca et al., 2021), [Free To Use Sounds](https://www.freetousesounds.com/all-in-one-bundle/), [Sonniss Game Effects](https://sonniss.com/gameaudiogdc), [WeSoundEffects](https://wesoundeffects.com/we-sound-effects-bundle-2020/), [Paramount Motion - Odeon Cinematic Sound Effects](https://www.paramountmotion.com/odeon-sound-effects).
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+
+### Evaluation datasets
+
+The audio-magnet models (sound effect generation) were evaluated on the [AudioCaps benchmark](https://audiocaps.github.io/).
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+### Evaluation results
+
+Below are the objective metrics obtained with the released audio-magnet models on AudioCaps (consisting of 10-second long samples). 
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+| Model | Frechet Audio Distance | KLD |
+|---|---|---|
+| facebook/audio-magnet-small | 3.21 | 1.42 |
+| facebook/audio-magnet-medium | 2.32 | 1.64 |