update captilization

README.md

@@ -5,34 +5,32 @@ tags: []
 
 # MERaLiON
 
-MERaLiON-AudioLLM is a Speech-Text Large Language Model tailored for Singapore’s multilingual and multicultural landscape. Integrating a localised Whisper-large-v2 speech encoder and SEA-LIONv3 text decoder, MERaLiON-AudioLLM is finetuned on **260,000 hours of speech and audio data**, **8 various tasks**, to address the diverse linguistic nuances
-of Singapore's local accents and dialects.
+MERaLiON-AudioLLM is a Speech-Text Large Language Model tailored for Singapore’s multilingual and multicultural landscape. Integrating a localised [Whisper-large-v2](https://huggingface.co/openai/whisper-large-v2) speech encoder and [SEA-LION V3](https://huggingface.co/aisingapore/gemma2-9b-cpt-sea-lionv3-instruct) text decoder, MERaLiON-AudioLLM is finetuned on **260,000 hours of speech and audio data**, **8 various tasks**, to address the diverse linguistic nuances of Singapore's local accents and dialects.
 
-MERaLiON stands for <i>Multimodal Empathetic Reasoning and Learning in One Network</i>.
+MERaLiON stands for <i>**M**ultimodal **E**mpathetic **R**easoning **a**nd **L**earning **i**n **O**ne **N**etwork</i>.
 
 - **Developed by:** I<sup>2</sup>R, A\*STAR
 - **Funded by:** Singapore NRF
 - **Model type:** MultiModal LLM
 - **Language(s) (Speech):** English (Global & Singapore)
 - **Language(s) (NLP):** English, Chinese, Vietnamese, Indonesian, Thai, Filipino, Tamil, Malay, Khmer, Lao, Burmese, Javanese, Sundanese
-- **License:** [TBC]
+- **License:** MIT
 
 For more details, please refer to our [report]().
 
 ## Model Description
 
-MERaLiON-AudioLLM is designed to take in an audio-text pair as
-input and generates a text output.
+MERaLiON-AudioLLM is designed to take in an **audio-text pair** as input and generates a text output.
 
-The architecture comprises three key components: an audio encoder that transforms speech or audio inputs into sequences of vector representations, a text decoder that interprets and responds to natural language instructions, and an adaptor module that compresses the encoder representations while aligning the encoder’s hidden dimension with the text decoder’s embedding size.
+The architecture comprises three key components: an **audio encoder** that transforms speech or audio inputs into sequences of vector representations, a **text decoder** that interprets and responds to natural language instructions, and an **adaptor module** that compresses the encoder representations while aligning the encoder’s hidden dimension with the text decoder’s embedding size.
 
-Specifically, we fine-tuned the MERaLiON-Whisper encoder from Whisper-large-v2 for the audio encoder and used SEA-LION V3, a localised LLM developed by our partner AI Singapore as the text decoder.
+Specifically, we fine-tuned the **MERaLiON-Whisper** encoder from Whisper-large-v2 for the audio encoder and used SEA-LION V3, a localised LLM developed by our partner AI Singapore as the text decoder.
 
 <img src="model_architecture.png" alt="model_architecture" width="400" style="margin-left:'auto' margin-right:'auto' display:'block'"/>
 
 ## Capabilities
 
-MERaLiON-AudioLLM is trained to address 8 tasks, including Automatic Speech Recognition (ASR), Speech Translation (ST), Spoken Question Answering (SQA), Spoken Dialogue Summarization (SDS), Speech Instruction (SI), Paralinguistics (PARA), Audio Captioning (AC), and Audio Scene Question Answering (ASQA). 
+MERaLiON-AudioLLM is trained to address 8 tasks, including `Automatic Speech Recognition` (ASR), `Speech Translation` (ST), `Spoken Question Answering` (SQA), `Spoken Dialogue Summarization` (SDS), `Speech Instruction` (SI), `Paralinguistics` (PARA), `Audio Captioning` (AC), and `Audio Scene Question Answering` (ASQA). 
 
 [More information about the 8 tasks and evaluation results]
 
@@ -138,7 +136,7 @@ MERaLiON-AudioLLM is trained on a diverse collection of publicly available datas
 
 ### Compute and Infrastructure
 
-MERaLiON-AudioLLM is trained on the ASPIRE 2A+ Supercomputer Cluster, provided by the National Supercomputing Centre (NSCC). ASPIRE 2A+ cluster provides multiple H100 nodes, with each compute node equipped with 8 Nvidia H100 GPUs, 2 TB of RAM, and 30 TB of locally attached NVMe storage. These nodes are interconnected via a rail-optimised, full fat-tree topology, utilising 400 Gb/s NDR InfiniBand cables. Additionally, the cluster incorporates a 2.5 PB SSD-based Lustre file system, linked to the H100 nodes through high-speed InfiniBand connections. 
+MERaLiON-AudioLLM is trained on the **ASPIRE 2A+** Supercomputer Cluster, provided by the **National Supercomputing Centre (NSCC)**. ASPIRE 2A+ cluster provides multiple H100 nodes, with each compute node equipped with 8 Nvidia H100 GPUs, 2 TB of RAM, and 30 TB of locally attached NVMe storage. These nodes are interconnected via a rail-optimised, full fat-tree topology, utilising 400 Gb/s NDR InfiniBand cables. Additionally, the cluster incorporates a 2.5 PB SSD-based Lustre file system, linked to the H100 nodes through high-speed InfiniBand connections. 
 
 With a global batch size of 640, we train the current release of MERaLiON-AudioLLM for around 200k steps, which took 2 days to complete using 16 nodes, 128 H100 GPUs.