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 ## Quick Start
 
-We provide a **[beginner recipe](VALLE_V2/)** to demonstrate how to train a cutting edge TTS model. Specifically, it is Amphion's re-implementation for [Vall-E](https://arxiv.org/abs/2301.02111), which is a zero-shot TTS architecture that uses a neural codec language model with discrete codes.
+We provide a **[beginner recipe](VALLE_V2/)** to demonstrate how to train a cutting edge TTS model. Specifically, it is Amphion's re-implementation for [VALL-E](https://arxiv.org/abs/2301.02111), which is a zero-shot TTS architecture that uses a neural codec language model with discrete codes.
 
 ## Supported Model Architectures
 
 Until now, Amphion TTS supports the following models or architectures,
 - **[FastSpeech2](FastSpeech2)**: A non-autoregressive TTS architecture that utilizes feed-forward Transformer blocks.
 - **[VITS](VITS)**: An end-to-end TTS architecture that utilizes conditional variational autoencoder with adversarial learning
-- **[Vall-E](VALLE_V2)**: A zero-shot TTS architecture that uses a neural codec language model with discrete codes. This model is our updated VALL-E implementation as of June 2024 which uses Llama as its underlying architecture. The previous version of VALL-E release can be found [here](VALLE)
+- **[VALL-E](VALLE_V2)**: A zero-shot TTS architecture that uses a neural codec language model with discrete codes. This model is our updated VALL-E implementation as of June 2024 which uses Llama as its underlying architecture. The previous version of VALL-E release can be found [here](VALLE)
 - **[NaturalSpeech2](NaturalSpeech2)** (👨‍💻 developing): An architecture for TTS that utilizes a latent diffusion model to generate natural-sounding voices.
 
 ## Amphion TTS Demo