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Update README.md

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  ## FP32
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  ```python
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- # !pip install git+https://github.com/huggingface/diffusers.git
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  from diffusers import DiffusionPipeline
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- import scipy
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  model_id = "harmonai/jmann-small-190k"
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- pipeline = DiffusionPipeline.from_pretrained(model_id)
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- pipeline = pipeline.to("cuda")
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-
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- audio = pipeline(audio_length_in_s=4.0).audios
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-
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- scipy.io.wavfile.write("maestro_test.wav", pipe.unet.sample_rate, audios)
 
 
 
 
 
 
 
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  ```
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  ## FP16
@@ -19,16 +34,23 @@ scipy.io.wavfile.write("maestro_test.wav", pipe.unet.sample_rate, audios)
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  Faster at a small loss of quality
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  ```python
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- # !pip install git+https://github.com/huggingface/diffusers.git
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  from diffusers import DiffusionPipeline
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- import scipy
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  import torch
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  model_id = "harmonai/jmann-small-190k"
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- pipeline = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
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- pipeline = pipeline.to("cuda")
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-
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- audio = pipeline(audio_length_in_s=4.0).audios
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-
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- scipy.io.wavfile.write("maestro_test.wav", pipe.unet.sample_rate, audios)
 
 
 
 
 
 
 
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  ```
 
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+ ---
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+ license: mit
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+ tags:
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+ - audio-generation
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+ ---
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+
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+ [Dance Diffusion](https://github.com/Harmonai-org/sample-generator) is now available in 🧨 Diffusers.
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+
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  ## FP32
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  ```python
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+ # !pip install diffusers[torch] accelerate scipy
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  from diffusers import DiffusionPipeline
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+ from scipy.io.wavfile import write
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  model_id = "harmonai/jmann-small-190k"
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+ pipe = DiffusionPipeline.from_pretrained(model_id)
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+ pipe = pipe.to("cuda")
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+
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+ audios = pipe(audio_length_in_s=4.0).audios
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+
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+ # To save locally
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+ for i, audio in enumerate(audios):
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+ write(f"test_{i}.wav", pipe.unet.sample_rate, audio.transpose())
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+
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+ # To dislay in google colab
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+ import IPython.display as ipd
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+ for audio in audios:
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+ display(ipd.Audio(audio, rate=pipe.unet.sample_rate))
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  ```
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  ## FP16
 
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  Faster at a small loss of quality
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  ```python
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+ # !pip install diffusers[torch] accelerate scipy
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  from diffusers import DiffusionPipeline
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+ from scipy.io.wavfile import write
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  import torch
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  model_id = "harmonai/jmann-small-190k"
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+ pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
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+ pipe = pipe.to("cuda")
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+
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+ audios = pipeline(audio_length_in_s=4.0).audios
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+
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+ # To save locally
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+ for i, audio in enumerate(audios):
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+ write(f"{i}.wav", pipe.unet.sample_rate, audio.transpose())
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+
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+ # To dislay in google colab
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+ import IPython.display as ipd
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+ for audio in audios:
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+ display(ipd.Audio(audio, rate=pipe.unet.sample_rate))
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  ```