ned(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"maestro_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))  AudioPipelineOutput  class diffusers.AudioPipelineOutput  < source > ( audios: ndarray  )   Par