Instructions to use omarimc/musicgen-stereo-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use omarimc/musicgen-stereo-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="omarimc/musicgen-stereo-large")# Load model directly from transformers import AutoTokenizer, AutoModelForTextToWaveform tokenizer = AutoTokenizer.from_pretrained("omarimc/musicgen-stereo-large") model = AutoModelForTextToWaveform.from_pretrained("omarimc/musicgen-stereo-large") - Audiocraft
How to use omarimc/musicgen-stereo-large with Audiocraft:
from audiocraft.models import MusicGen model = MusicGen.get_pretrained("omarimc/musicgen-stereo-large") descriptions = ['happy rock', 'energetic EDM', 'sad jazz'] wav = model.generate(descriptions) # generates 3 samples. - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 03c0344508e84c86b34eb34acf5c91cd2c49f7ace96c7eca0523a43ee9ea8cb7
- Size of remote file:
- 1.93 GB
- SHA256:
- 48f5f7455d34730da89922969119e732e86a4eb540a3769695d37823244ff944
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