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Fix ONNX link in README

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@@ -15,9 +15,20 @@ pipeline_tag: audio-classification
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  # Model for Dimensional Speech Emotion Recognition based on Wav2vec 2.0
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- Please note that this model is for research purpose only. A commercial license for a model that has been trained on much more data can be acquired with [audEERING](https://www.audeering.com/products/devaice/).
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- The model expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0...1. In addition, it also provides the pooled states of the last transformer layer. The model was created by fine-tuning [
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- Wav2Vec2-Large-Robust](https://huggingface.co/facebook/wav2vec2-large-robust) on [MSP-Podcast](https://ecs.utdallas.edu/research/researchlabs/msp-lab/MSP-Podcast.html) (v1.7). The model was pruned from 24 to 12 transformer layers before fine-tuning. An [ONNX](https://onnx.ai/") export of the model is available from [doi:10.5281/zenodo.6221127](https://zenodo.org/record/6221127). Further details are given in the associated [paper](https://arxiv.org/abs/2203.07378) and [tutorial](https://github.com/audeering/w2v2-how-to).
 
 
 
 
 
 
 
 
 
 
 
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  # Usage
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  # Model for Dimensional Speech Emotion Recognition based on Wav2vec 2.0
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+ Please note that this model is for research purpose only.
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+ A commercial license for a model
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+ that has been trained on much more data
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+ can be acquired with [audEERING](https://www.audeering.com/products/devaice/).
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+ The model expects a raw audio signal as input,
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+ and outputs predictions for arousal, dominance and valence in a range of approximately 0...1.
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+ In addition,
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+ it provides the pooled states of the last transformer layer.
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+ The model was created by fine-tuning
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+ [Wav2Vec2-Large-Robust](https://huggingface.co/facebook/wav2vec2-large-robust)
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+ on [MSP-Podcast](https://ecs.utdallas.edu/research/researchlabs/msp-lab/MSP-Podcast.html) (v1.7).
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+ The model was pruned from 24 to 12 transformer layers before fine-tuning.
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+ An [ONNX](https://onnx.ai/) export of the model is available from [doi:10.5281/zenodo.6221127](https://zenodo.org/record/6221127).
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+ Further details are given in the associated [paper](https://arxiv.org/abs/2203.07378) and [tutorial](https://github.com/audeering/w2v2-how-to).
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  # Usage
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