audiocraft

from audiocraft.models import MusicGen from audiocraft.data.audio import audio_write import torch # For moving to CPU if needed # Load a pretrained MusicGen model ('small' is generally recommended for quick tests) # This will download model weights (~2GB for 'small'). Ensure stable internet and disk space. # Specify device if needed: model = MusicGen.get_pretrained('small', device='cuda') # Ensure you have a compatible PyTorch/CUDA setup for GPU usage. model = MusicGen.get_pretrained('small') model.set_generation_params(duration=8) # Generate 8 seconds of audio # Define a description for the music description = "a retro synthwave track with a driving beat" print(f"Generating audio for: '{description}'...") # The generate method takes a list of descriptions. For unconditional generation, pass descriptions=None. samples = model.generate(descriptions=[description], progress=True) # Save the generated audio to a WAV file # `samples` is a torch.Tensor. It's good practice to move to CPU before saving if it's on GPU. audio_write( 'my_synthwave_track', samples[0].cpu(), # Take the first generated sample and move to CPU model.sample_rate, strategy="loudness", loudness_compressor=True # Recommended for better audio quality ) print("Audio saved as 'my_synthwave_track.wav'")