Instructions to use ntua-slp/CultureMERT-95M with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use ntua-slp/CultureMERT-95M with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="ntua-slp/CultureMERT-95M", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("ntua-slp/CultureMERT-95M", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
Download method_figure.png from ntua-slp/CultureMERT-95M: direct link, hf CLI and curl.
- Browser
- Download file 222 kB
-
https://huggingface.co/ntua-slp/CultureMERT-95M/resolve/main/method_figure.png
- Command line
-
hf download hf://ntua-slp/CultureMERT-95M/method_figure.png
-
curl -L -o method_figure.png https://huggingface.co/ntua-slp/CultureMERT-95M/resolve/main/method_figure.png
222 kB

- Xet hash:
- 286a1197a8a03a34fdca76602729e37bced708e20feb6fc98bdc692791cc6108
- Size of remote file:
- 222 kB
- SHA256:
- 91b5e6e9e4256b098d572b6f196bfbd8a1e756701f6da52b48b1f2d7a9782524
·
Xet efficiently stores Large Files inside Git, intelligently splitting files into unique chunks and accelerating uploads and downloads. More info.