Update README.md

README.md

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 MAGNeT is a text-to-music model capable of generating high-quality music samples conditioned on text descriptions.
 It is a single stage, non-autoregressive Transformer model trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. 
-Unlike existing methods, like MusicLM, MusicGen doesn't require a self-supervised semantic representation, and it generates all 4 codebooks in one pass. 
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+Unlike prior work, MAGNeT doesn't require neither self-supervised semantic representation nor generative model cascading, and it generates all 4 codebooks using a single non-autoregressive transformer. 
 
 MAGNeT was published in [Masked Audio Generation using a Single Non-Autoregressive Transformer](https://arxiv.org/abs/2401.04577) by *Alon Ziv, Itai Gat, Gael Le Lan, Tal Remez, Felix Kreuk, Alexandre Défossez, Jade Copet, Gabriel Synnaeve, Yossi Adi*.
 
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 **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. MusicGen is a model developed for artificial intelligence research on controllable music generation. As such, it should not be used for downstream applications without further investigation and mitigation of risks.
+**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.