update model card

README.md

@@ -1,3 +1,56 @@
 ---
 license: mit
+tags:
+- audiocraft
+- Musicgen
 ---
+
+# micro-musicgen-jungle
+
+Curated and trained by Aaron Abebe. 
+
+![image/webp](https://cdn-uploads.huggingface.co/production/uploads/65829a887cec0a2080d4bb3f/mbtvUtbIUbZJ8DbxtIPUo.webp)
+
+> [!WARNING]  
+> WARNING: **These models WILL sound bad to a lot of people.** The goal is not create pleasant sounding music,
+> but to spark creativity by using the weird sounds of Neural Codecs for music production and sampling!
+ 
+Micro-Musicgen is a new family of super small music generation models focussing on experimental music and latent space exploration capabilities. 
+These models have unique abilities and drawbacks which should enhance creativity when working with them while creating music. 
+
+- **only unconditional generation**: Trained without text-conditioning to reduce model size.
+- **very fast generation times**: ~8secs for 10x 10sec samples.
+- **permissive licensing**: The models are trained from scratch using royalty-free samples and handmade chops,
+which allows them to be released via the MIT License.
+
+
+The first entry in the series is a model trained on chopped jungle drum breaks called `micro-musicgen-jungle`.
+
+If you find this model interesting, please consider:
+
+- following me on [GitHub](https://github.com/aaronabebe)
+- following me on [Twitter](https://twitter.com/mcaaroni)
+
+
+## Usage
+
+Install my [audiocraft](https://github.com/facebookresearch/audiocraft) fork:
+
+```
+pip install -U git+https://github.com/aaronabebe/audiocraft#egg=audiocraft
+```
+
+Then, you should be able to load this model just like any other musicgen checkpoint here on the Hub:
+
+```python
+import torchaudio
+from audiocraft.models import MusicGen
+from audiocraft.data.audio import audio_write
+model = MusicGen.get_pretrained('pharoAIsanders420/micro-musicgen-jungle')
+model.set_generation_params(duration=10)
+wav = model.generate_unconditional(10)
+
+for idx, one_wav in enumerate(wav):
+    # Will save under {idx}.wav, with loudness normalization at -14 db LUFS.
+    audio_write(f'{idx}', one_wav.cpu(), model.sample_rate, strategy="loudness", loudness_compressor=True)
+```