Update README.md

README.md

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 ## Model description
 
-**Matcha-TTS** is an encoder-decoder architecture designed for fast acoustic modelling in TTS. The encoder predicts phoneme durations and its average acoustic features. 
-And the decoder is essentially a U-Net inspired by [Grad-TTS](https://arxiv.org/pdf/2105.06337.pdf), that is based on Transformers architecture but combined 
-with 1D instead of 2D CNNs, making a high reduction on memory consumption and speedy synthesis.
+**Matcha-TTS** is an encoder-decoder architecture designed for fast acoustic modelling in TTS. The encoder predicts phoneme durations and their averaged acoustic features. 
+The decoder backbone is essentially a U-Net inspired by [Grad-TTS](https://arxiv.org/pdf/2105.06337.pdf) based on Transformers architecture. By replacing 2D CNNs by 1D CNNs, 
+a large reduction in memory consumption and fast synthesis is achieved.
 
-**Matcha-TTS** is non-autorregressive model and is trained using optimal-transport conditional flow matching (OT-CFM). 
-This yields an ODE-based decoder capable of high output quality in fewer synthesis steps than models trained using score matching.
+**Matcha-TTS** is a non-autorregressive model trained with optimal-transport conditional flow matching (OT-CFM). 
+This yields an ODE-based decoder capable of generating high output quality in fewer synthesis steps than models trained using score matching.
 
 ## Intended uses and limitations
 
 This model is intended to serve as an acoustic feature generator for multispeaker text-to-speech systems for the Catalan language. 
-It has been finetuned using a Catalan phonemizer, therefore if the model is used in other languages it may will not produce intelligible samples after converting its output 
-into a speech waveform. 
+It has been finetuned using a Catalan phonemizer, therefore if the model is used for other languages it may will not produce intelligible samples after mapping 
+its output into a speech waveform. 
 
 The quality of the samples can vary depending on the speaker. 
-This may be due to the sensitivity of the model in learning specific frequencies and also due to the samples used for each speaker.
+This may be due to the sensitivity of the model in learning specific frequencies and also due to the quality of samples for each speaker.
 
 ## How to use