Instructions to use adepusricharan/echoforge-musicgen with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use adepusricharan/echoforge-musicgen with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("text-to-audio", model="adepusricharan/echoforge-musicgen")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForTextToWaveform processor = AutoProcessor.from_pretrained("adepusricharan/echoforge-musicgen") model = AutoModelForTextToWaveform.from_pretrained("adepusricharan/echoforge-musicgen", device_map="auto") - Audiocraft
How to use adepusricharan/echoforge-musicgen with Audiocraft:
from audiocraft.models import MusicGen model = MusicGen.get_pretrained("adepusricharan/echoforge-musicgen") descriptions = ['happy rock', 'energetic EDM', 'sad jazz'] wav = model.generate(descriptions) # generates 3 samples. - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from adepusricharan/echoforge-musicgen: direct link, hf CLI and curl.
- Browser
- Download file 275 Bytes
-
https://huggingface.co/adepusricharan/echoforge-musicgen/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://adepusricharan/echoforge-musicgen/preprocessor_config.json
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curl -L -o preprocessor_config.json https://huggingface.co/adepusricharan/echoforge-musicgen/resolve/main/preprocessor_config.json
275 Bytes
| { | |
| "chunk_length_s": null, | |
| "feature_extractor_type": "EncodecFeatureExtractor", | |
| "feature_size": 2, | |
| "overlap": null, | |
| "padding_side": "left", | |
| "padding_value": 0.0, | |
| "processor_class": "MusicgenProcessor", | |
| "return_attention_mask": true, | |
| "sampling_rate": 32000 | |
| } | |