retain audiocraft usage

README.md

@@ -21,17 +21,27 @@ Four checkpoints are released:
 
 ## Example
 
-Try out MusicGen yourself! 
+Try out MusicGen yourself!
+
+* Audiocraft Colab:
+
+<a target="_blank" href="https://colab.research.google.com/drive/1fxGqfg96RBUvGxZ1XXN07s3DthrKUl4-?usp=sharing">
+  <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/>
+</a>
+
+* Hugging Face Colab:
 
 <a target="_blank" href="https://colab.research.google.com/github/sanchit-gandhi/notebooks/blob/main/MusicGen.ipynb"> 
   <img src="https://colab.research.google.com/assets/colab-badge.svg" alt="Open In Colab"/> 
 </a>
 
+* Hugging Face Demo:
+
 <a target="_blank" href="https://huggingface.co/spaces/facebook/MusicGen">
   <img src="https://huggingface.co/datasets/huggingface/badges/raw/main/open-in-hf-spaces-sm.svg" alt="Open in HuggingFace"/>
 </a>
 
-### Usage
+## 🤗 Transformers Usage
 
 You can run MusicGen locally with the 🤗 Transformers library from version 4.31.0 onwards.
 
@@ -79,6 +89,38 @@ scipy.io.wavfile.write("musicgen_out.wav", rate=sampling_rate, data=audio_values
 
 For more details on using the MusicGen model for inference using the 🤗 Transformers library, refer to the [MusicGen docs](https://huggingface.co/docs/transformers/model_doc/musicgen).
 
+## Audiocraft Usage
+
+You can also run MusicGen locally through the original [Audiocraft library]((https://github.com/facebookresearch/audiocraft):
+
+1. First install the [`audiocraft` library](https://github.com/facebookresearch/audiocraft)
+```
+pip install git+https://github.com/facebookresearch/audiocraft.git
+```
+
+2. Make sure to have [`ffmpeg`](https://ffmpeg.org/download.html) installed:
+```
+apt get install ffmpeg
+```
+
+3. Run the following Python code:
+
+```py
+from audiocraft.models import MusicGen
+from audiocraft.data.audio import audio_write
+
+model = MusicGen.get_pretrained("small")
+model.set_generation_params(duration=8)  # generate 8 seconds.
+
+descriptions = ["happy rock", "energetic EDM"]
+
+wav = model.generate(descriptions)  # generates 2 samples.
+
+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")
+```
+
 ## Model details
 
 **Organization developing the model:** The FAIR team of Meta AI.