Update README.md

README.md

@@ -1,17 +1,32 @@
+---
+license: mit
+tags:
+- audio-generation
+---
+
+[Dance Diffusion](https://github.com/Harmonai-org/sample-generator) is now available in 🧨 Diffusers.
+
 ## FP32
 
 ```python
-# !pip install git+https://github.com/huggingface/diffusers.git
+# !pip install diffusers[torch] accelerate scipy
 from diffusers import DiffusionPipeline
-import scipy
+from scipy.io.wavfile import write
 
 model_id = "harmonai/jmann-large-580k"
-pipeline = DiffusionPipeline.from_pretrained(model_id)
-pipeline = pipeline.to("cuda")
-
-audio = pipeline(audio_length_in_s=4.0).audios
-
-scipy.io.wavfile.write("maestro_test.wav", pipe.unet.sample_rate, audios)
+pipe = DiffusionPipeline.from_pretrained(model_id)
+pipe = pipe.to("cuda")
+
+audios = pipe(audio_length_in_s=4.0).audios
+
+# To save locally
+for i, audio in enumerate(audios):
+    write(f"test_{i}.wav", pipe.unet.sample_rate, audio.transpose())
+    
+# To dislay in google colab
+import IPython.display as ipd
+for audio in audios:
+    display(ipd.Audio(audio, rate=pipe.unet.sample_rate))
 ```
 
 ## FP16
@@ -19,16 +34,23 @@ scipy.io.wavfile.write("maestro_test.wav", pipe.unet.sample_rate, audios)
 Faster at a small loss of quality
 
 ```python
-# !pip install git+https://github.com/huggingface/diffusers.git
+# !pip install diffusers[torch] accelerate scipy
 from diffusers import DiffusionPipeline
-import scipy
+from scipy.io.wavfile import write
 import torch
 
 model_id = "harmonai/jmann-large-580k"
-pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
-pipeline = pipeline.to("cuda")
-
-audio = pipeline(audio_length_in_s=4.0).audios
-
-scipy.io.wavfile.write("maestro_test.wav", pipe.unet.sample_rate, audios)
+pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
+pipe = pipe.to("cuda")
+
+audios = pipeline(audio_length_in_s=4.0).audios
+
+# To save locally
+for i, audio in enumerate(audios):
+    write(f"{i}.wav", pipe.unet.sample_rate, audio.transpose())
+    
+# To dislay in google colab
+import IPython.display as ipd
+for audio in audios:
+    display(ipd.Audio(audio, rate=pipe.unet.sample_rate))
 ```