Update README.md

README.md

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 ZeroSwot is a state-of-the-art zero-shot end-to-end Speech Translation system.
 
-The model is created by adapting a wav2vec2.0-based encoder to the embedding space of NLLB, using a novel subword compression module and Optimal Transport, while using only ASR data. It thus enables **Speech Translation to all the 200 languages supported by NLLB**. The compression module is a light-weight transformer that takes as input the hidden state of wav2vec2.0 and the corresponding CTC predictions, and compresses them to subword-like embeddings similar to those expected from NLLB and aligns them using Optimal Transport. For inference we simply pass the output of the speech encoder to NLLB encoder.
+The model is created by adapting a wav2vec2.0-based encoder to the embedding space of NLLB, using a novel subword compression module and Optimal Transport, while only utilizing ASR data. It thus enables **Zero-shot E2E Speech Translation to all the 200 languages supported by NLLB**.
 
 For more details please refer to our [paper](https://arxiv.org/abs/2402.10422) and the [original repo](https://github.com/mt-upc/ZeroSwot) build on fairseq.
 
-This version of ZeroSwot is trained with ASR data from CommonVoice, and adapting [wav2vec2.0-large](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) to the [nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) model.
+## Architecture
+
+The compression module is a light-weight transformer that takes as input the hidden state of wav2vec2.0 and the corresponding CTC predictions, and compresses them to subword-like embeddings similar to those expected from NLLB and aligns them using Optimal Transport. For inference we simply pass the output of the speech encoder to NLLB encoder.
 
 <div align=center><img src="resources/methodology.png" height="100%" width="100%"/></div>
 
+## Version
+
+This version of ZeroSwot is trained with ASR data from CommonVoice, and adapted [wav2vec2.0-large](https://huggingface.co/facebook/wav2vec2-large-960h-lv60-self) to the [nllb-200-distilled-600M](https://huggingface.co/facebook/nllb-200-distilled-600M) model.
+
 ## Usage
 
-The usage is tested with python 3.9.16 and Transformer v4.41.2. Install also torchaudio and sentencepiece for processing.
+The model is tested with python 3.9.16 and Transformer v4.41.2. Install also torchaudio and sentencepiece for processing.
 
 ```bash
 pip install transformers torchaudio sentencepiece