Instructions to use onurio/musicgen-large with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use onurio/musicgen-large with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="onurio/musicgen-large")# Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("onurio/musicgen-large") model = AutoModelForTextToWaveform.from_pretrained("onurio/musicgen-large", device_map="auto") - Audiocraft
How to use onurio/musicgen-large with Audiocraft:
from audiocraft.models import MusicGen model = MusicGen.get_pretrained("onurio/musicgen-large") descriptions = ['happy rock', 'energetic EDM', 'sad jazz'] wav = model.generate(descriptions) # generates 3 samples. - Notebooks
- Google Colab
- Kaggle
| from handler import EndpointHandler | |
| import soundfile as sf | |
| import numpy as np | |
| # init handler | |
| my_handler = EndpointHandler(path=".") | |
| # prepare sample payload | |
| payload = {"inputs": "Lowfi hiphop with deep bass"} | |
| # test the handler | |
| pred=my_handler(payload) | |
| audio_data = np.array(pred["audio_data"], dtype=np.float32) | |
| sampling_rate = pred["sampling_rate"] | |
| # Write the audio data to a WAV file | |
| output_file_path = "output.wav" # Specify the file path | |
| sf.write(output_file_path, audio_data, sampling_rate) | |
| print("Audio file saved successfully.") | |