Update README.md

README.md

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 ![MusicLang logo](https://github.com/MusicLang/musiclang/blob/main/documentation/images/MusicLang.png?raw=true "MusicLang")
 
-At MusicLang we explore the endless possibilities of AI symbolic generative music, that is helpful to artists.
+<h2 align="center">MusicLang” is the contraction of “Music” & “language”: we aim to bring advanced controllability features and high-quality music generation by manipulating symbolic music.</h2>
 
-We have a collection of symbolic music model for various musical tasks such as music outpainting, inpainting, chord and scales prediction. Check our models below !
+<h3> Our vision:</h3>
 
-We crafted an expressive and powerful music language which is available in Python [(Github)](https://github.com/MusicLang/musiclang). 
+Music, as a universal language and a common cultural good, inherently belongs to everyone. Adopting an open approach to music generation aligns with this ethos, fostering inclusivity and collaboration within the creative community. Transparent access to code and algorithms empowers collaboration among musicians, researchers, and developers, advancing both the technical capabilities of music AI and fostering diverse artistic expression.
+
+<h3>Our product:</h3> 
+<ul>
+  <li><strong><a href="https://huggingface.co/musiclang/musiclang-v2">MusicLang foundation model:</a></strong> our fondation model for creating and generating original midi soundtracks;</li>
+  <li><strong><a href="https://github.com/musiclang/musiclang_predict?tab=readme-ov-file">MusicLang predict:</a></strong> our AI prediction api of the MusicLang package;</li>
+  <li><strong><a href="https://github.com/MusicLang/musiclang">MusicLang Language:</a></strong> a new language for tonal music. This language allows composers to load, write, transform and predict symbolic music in a simple, condensed and high level manner;</li>
+  <li><strong><a href="https://huggingface.co/spaces/musiclang/musiclang-predict">MusicLang Demo Space:</a></strong> all the above in action, to offer advanced controllability features and high-quality music generation by manipulating symbolic music. Also available in our <a href="https://colab.research.google.com/drive/1MA2mek826c05BjbWk2nRkVv2rW7kIU_S?usp=sharing">Colab</a>.</li>
+</ul>
+
+<h3>Technical details:</h3>
+
+If you want to learn more about how we are moving toward symbolic music generation, go to our [technical blog](https://musiclang.github.io/). The tokenization, the model are described in great details: 
+<ul>
+  <li><a href="https://musiclang.github.io/chord_parsing/"> Annotate chords and scales progression of MIDIs using MusicLang analysis</a></li>
+  <li><a href="https://musiclang.github.io/tokenizer/"> The MusicLang tokenizer : Toward controllable symbolic music generation</a></li>
+</ul>
+
+<h3>What for?</h3>
+<ul>
+  <li>🎶 You can, for example, enforce a chord progression on the generated music;</li>
+  <li>🎹 Generate a music from scratch that you can export to your favorite DAW in MIDI format;</li>
+  <li>🕺 Use your own music as a template to create new composition to extend an existing track;</li>
+  <li>🪩 Fast & reliable: We target fast inference on CPU aiming to distribute it in DAWs & plugins;</li>
+  <li>And more.</li>
+</ul>
+
+<h3>For whom?</h3>
+MusicLang is well suited for:
+<ul>
+  <li><strong>R&D engineers working on music generation use cases:</strong> we have come up with a SOTA model to bring advanced controllability features over music generation;</li>
+  <li><strong>For technical creators:</strong> with a bit of technical knowledge, you can add a brick to your composition workflow. MusicLang is well suited if you want to generate music that you can export to your favorite DAW in MIDI, generate a chord progression with a full control on the desired chord progression, time signature & more;</li>
+  <li><strong>For musicians: </strong>adjust your own creations endlessly until you discover the perfect variation for your project.</li>
+</ul>
+
+<h3 id="share">Contributing & spread the word 🤝</h3> 
+
+We are looking for contributors to help us improve the model, the tokenization, the performances and the documentation.
+Whether you're contributing code or just saying hello, we'd love to hear about the work you are creating with MusicLang. Here's how you can reach out to us: 
+* Join our [Discord](https://discord.gg/2g7eA5vP) to ask your questions and get support
+* Follow us on [Linkedin](https://www.linkedin.com/company/musiclang/)
+* Add your star on [GitHub](https://github.com/musiclang/musiclang_predict?tab=readme-ov-file) or [HuggingFace](https://huggingface.co/musiclang/musiclang-4k)
 
-Check our Google Colab for an AI music creation [demo](https://colab.research.google.com/drive/1MA2mek826c05BjbWk2nRkVv2rW7kIU_S?usp=sharing)
-Or our [HF space](https://huggingface.co/spaces/musiclang/musiclang-predict)
 
 If you are interested to integrate one of our model or want a custom model into your product, please reach me at [florian.gardin@musiclang.io](florian.gardin@musiclang.io). I am looking forward to hearing from your project !