Update README.md

README.md

@@ -8,4 +8,56 @@ tags:
 - speech
 - autoencoder
 - diffusion
----
+---
+
+# Music2Latent
+Encode and decode audio samples to compressed representations! Useful for efficient generative modelling applications and for other downstream tasks.
+
+![music2latent](music2latent.png)
+
+Read the ISMIR 2024 paper [here](https://arxiv.org/).
+
+Under the hood, __Music2Latent__ uses a __Consistency Autoencoder__ model to efficiently encode and decode audio samples.
+44.1 kHz audio is encoded into a sequence of __~10 Hz__, and each of the latents has 64 channels.
+You can then train a generative model on these embeddings, or use them for other downstream tasks.
+
+Music2Latent was trained on __music__ and on __speech__. Refer to the [paper](https://arxiv.org/) for more details.
+
+
+## Installation
+
+   ```bash
+   pip install music2latent
+   ```
+The model weights will be downloaded automatically the first time you run the code.
+
+
+## How to use
+To encode and decode audio samples to/from latent embeddings:
+   ```bash
+   audio_path = librosa.example('trumpet')
+   wv, sr = librosa.load(audio_path, sr=44100)
+
+   from music2latent2 import EncoderDecoder
+   encdec = EncoderDecoder()
+
+   latent = encdec.encode(wv)
+
+   wv_rec = encdec.decode(latent)
+   ```
+If you need to extract encoder features to use in downstream tasks, and you don't need to reconstruct the audio:
+   ```bash
+   features = encoder.encode(wv, extract_features=True)
+   ```
+These features are extracted before the encoder bottleneck, and thus have more channels (contain more information) than the latents used for reconstruction.
+
+music2latent2 supports more advanced usage, inclusing GPU memory management controls. Please refer to __tutorial.ipynb__.
+
+
+## License
+This library is released under the CC BY-NC 4.0 license. Please refer to the LICENSE file for more details.
+
+
+
+This work was conducted by [Marco Pasini](https://twitter.com/marco_ppasini) during his PhD at Queen Mary University of London, in partnership with Sony Computer Science Laboratories Paris.
+This work was supervised by Stefan Lattner and George Fazekas.