Revise grammar

README.md

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 MAGNeT is a text-to-music and text-to-sound model capable of generating high-quality audio samples conditioned on text descriptions.
 It is a masked generative non-autoregressive Transformer trained over a 32kHz EnCodec tokenizer with 4 codebooks sampled at 50 Hz. 
-Unlike prior work, MAGNeT doesn't require neither semantic token conditioning nor model cascading, and it generates all 4 codebooks using a single non-autoregressive Transformer. 
+Unlike prior work, MAGNeT requires neither semantic token conditioning nor 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*.