Feature Extraction
Transformers
Safetensors
audio_embeddings
audio
custom_code
self-supervised-learning
audio-embeddings
best-rq-2
audioset
Instructions to use ltuncay/BEST-RQ-2 with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ltuncay/BEST-RQ-2 with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="ltuncay/BEST-RQ-2", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ltuncay/BEST-RQ-2", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download audio-embeddings/src/data/__init__.py from ltuncay/BEST-RQ-2: direct link, hf CLI and curl.
- Browser
- Download file 231 Bytes
-
https://huggingface.co/ltuncay/BEST-RQ-2/resolve/main/audio-embeddings/src/data/__init__.py
- Command line
-
hf download hf://ltuncay/BEST-RQ-2/audio-embeddings/src/data/__init__.py
-
curl -L -o __init__.py https://huggingface.co/ltuncay/BEST-RQ-2/resolve/main/audio-embeddings/src/data/__init__.py
231 Bytes
| from .audioset_datamodule import AudioSetDataModule, AudioSetDataset | |
| from .yt1b_datamodule import YT1BDataModule, YT1BDataset | |
| __all__ = [ | |
| "AudioSetDataModule", | |
| "AudioSetDataset", | |
| "YT1BDataModule", | |
| "YT1BDataset", | |
| ] | |