Update README.md

README.md

@@ -20,7 +20,66 @@ musicgen-songstarter-v0.1 is a [`musicgen-melody`](https://huggingface.co/facebo
 
 This is a proof of concept. Hopefully, we will be able to collect more data and train a better models in the future.
 
-## Prompt Format
+## Usage
+
+Install [audiocraft](https://github.com/facebookresearch/audiocraft):
+
+```
+pip install -U git+https://github.com/facebookresearch/audiocraft#egg=audiocraft
+```
+
+Then, you should be able to load this model just like any other musicgen checkpoint here on the Hub:
+
+```python
+from audiocraft.models import musicgen
+
+model = musicgen.MusicGen.get_pretrained('nateraw/musicgen-songstarter-v0.1', device='cuda')
+```
+
+To generate and save audio samples, you can do:
+
+```python
+from datetime import datetime
+from pathlib import Path
+
+from audiocraft.models import musicgen
+from audiocraft.data.audio import audio_write
+from audiocraft.utils.notebook import display_audio
+
+model = musicgen.MusicGen.get_pretrained('nateraw/musicgen-songstarter-v0.1', device='cuda')
+
+# path to save our samples.
+out_dir = Path("./samples")
+out_dir.mkdir(exist_ok=True, parents=True)
+
+model.set_generation_params(
+    duration=15,
+    use_sampling=True,
+    temperature=1.0,
+    top_k=250,
+    cfg_coef=3.0,
+)
+
+text = "hip hop, soul, piano, chords, jazz, neo jazz, G# minor, 140 bpm"
+N = 4
+out = model.generate(
+    [text] * N,
+    progress=True,
+)
+
+# Write to files
+dt_str = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
+for i in range(N):
+    audio_write(
+        out_dir / f"{dt_str}_{i:02d}",
+        out[i].cpu(),
+        model.sample_rate,
+        strategy="loudness",
+    )
+
+# Or, if in a notebook, display audio widgets
+# display_audio(out, model.sample_rate)
+```
 
 ## Prompt Format
 
@@ -30,8 +89,14 @@ Follow the following prompt format:
 {tag_1}, {tag_1}, ..., {tag_n}, {key}, {bpm} bpm
 ```
 
-<details>
-  <summary>The training dataset had the following tags in it (click)</summary>
+For example:
+
+```
+hip hop, soul, piano, chords, jazz, neo jazz, G# minor, 140 bpm
+```
+
+The training dataset had the following tags in it:
+
 ```
 hip hop
 trap
@@ -194,72 +259,3 @@ music box
 glitch
 clarinet
 ```
-</details>
-
-
-For example:
-
-```
-hip hop, soul, piano, chords, jazz, neo jazz, G# minor, 140 bpm
-```
-
-## Usage
-
-Install [audiocraft](https://github.com/facebookresearch/audiocraft):
-
-```
-pip install -U git+https://github.com/facebookresearch/audiocraft#egg=audiocraft
-```
-
-Then, you should be able to load this model just like any other musicgen checkpoint here on the Hub:
-
-```python
-from audiocraft.models import musicgen
-
-model = musicgen.MusicGen.get_pretrained('nateraw/musicgen-songstarter-v0.1', device='cuda')
-```
-
-To generate and save audio samples, you can do:
-
-```python
-from datetime import datetime
-from pathlib import Path
-
-from audiocraft.models import musicgen
-from audiocraft.data.audio import audio_write
-from audiocraft.utils.notebook import display_audio
-
-model = musicgen.MusicGen.get_pretrained('nateraw/musicgen-songstarter-v0.1', device='cuda')
-
-# path to save our samples.
-out_dir = Path("./samples")
-out_dir.mkdir(exist_ok=True, parents=True)
-
-model.set_generation_params(
-    duration=15,
-    use_sampling=True,
-    temperature=1.0,
-    top_k=250,
-    cfg_coef=3.0,
-)
-
-text = "hip hop, soul, piano, chords, jazz, neo jazz, G# minor, 140 bpm"
-N = 4
-out = model.generate(
-    [text] * N,
-    progress=True,
-)
-
-# Write to files
-dt_str = datetime.now().strftime("%Y-%m-%d_%H-%M-%S")
-for i in range(N):
-    audio_write(
-        out_dir / f"{dt_str}_{i:02d}",
-        out[i].cpu(),
-        model.sample_rate,
-        strategy="loudness",
-    )
-
-# Or, if in a notebook, display audio widgets
-# display_audio(out, model.sample_rate)
-```