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1# !pip install diffusers[torch] accelerate scipy
2from diffusers import DiffusionPipeline
3from scipy.io.wavfile import write
4
5model_id = "harmonai/honk-140k"
6pipe = DiffusionPipeline.from_pretrained(model_id)
7pipe = pipe.to("cuda")
8
9audios = pipe(audio_length_in_s=4.0).audios
10
11# To save locally
12for i, audio in enumerate(audios):
13 write(f"test_{i}.wav", pipe.unet.sample_rate, audio.transpose())
14
15# To dislay in google colab
16import IPython.display as ipd
17for audio in audios:
18 display(ipd.Audio(audio, rate=pipe.unet.sample_rate))1# !pip install diffusers[torch] accelerate scipy
2from diffusers import DiffusionPipeline
3from scipy.io.wavfile import write
4import torch
5
6model_id = "harmonai/honk-140k"
7pipe = DiffusionPipeline.from_pretrained(model_id, torch_dtype=torch.float16)
8pipe = pipe.to("cuda")
9
10audios = pipeline(audio_length_in_s=4.0).audios
11
12# To save locally
13for i, audio in enumerate(audios):
14 write(f"{i}.wav", pipe.unet.sample_rate, audio.transpose())
15
16# To dislay in google colab
17import IPython.display as ipd
18for audio in audios:
19 display(ipd.Audio(audio, rate=pipe.unet.sample_rate))