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@mberman84
Created June 13, 2023 14:14
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from IPython import display as ipd
from audiocraft.models import musicgen
from audiocraft.utils.notebook import display_audio
import torch
import torchaudio
import os
# Define the output directory
output_dir = r'C:\Users\USER\OneDrive\Desktop\musicgen2'
model = musicgen.MusicGen.get_pretrained('medium', device='cuda')
model.set_generation_params(duration=30)
res = model.generate([
'crazy EDM, heavy bang',
'classic reggae track with an electronic guitar solo',
'lofi slow bpm electro chill with organic samples',
'rock with saturated guitars, a heavy bass line and crazy drum break and fills.',
'earthy tones, environmentally conscious, ukulele-infused, harmonic, breezy, easygoing, organic instrumentation, gentle grooves',
], progress=True)
# Save the audio files
for i, audio in enumerate(res):
audio_cpu = audio.cpu()
file_path = os.path.join(output_dir, f'audio_{i}.wav')
torchaudio.save(file_path, audio_cpu, sample_rate=32000)
# Display the saved audio files
for i in range(len(res)):
file_path = os.path.join(output_dir, f'audio_{i}.wav')
audio, sample_rate = torchaudio.load(file_path)
display_audio(audio, sample_rate=sample_rate)
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