Instructions to use danilotpnta/HuBERT-Genre-Clf with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use danilotpnta/HuBERT-Genre-Clf with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("audio-classification", model="danilotpnta/HuBERT-Genre-Clf")# pip install -U transformers accelerate # Load model directly from transformers import AutoProcessor, AutoModelForAudioClassification processor = AutoProcessor.from_pretrained("danilotpnta/HuBERT-Genre-Clf") model = AutoModelForAudioClassification.from_pretrained("danilotpnta/HuBERT-Genre-Clf", device_map="auto") - Diffusers
How to use danilotpnta/HuBERT-Genre-Clf with Diffusers:
pip install -U diffusers transformers accelerate
import torch from diffusers import DiffusionPipeline # switch to "mps" for apple devices pipe = DiffusionPipeline.from_pretrained("danilotpnta/HuBERT-Genre-Clf", dtype=torch.bfloat16, device_map="cuda") prompt = "DistilHuBERT, Audio Encoder, Transfer Learning" image = pipe(prompt).images[0] - Notebooks
- Google Colab
- Kaggle
Download preprocessor_config.json from danilotpnta/HuBERT-Genre-Clf: direct link, hf CLI and curl.
- Browser
- Download file 212 Bytes
-
https://huggingface.co/danilotpnta/HuBERT-Genre-Clf/resolve/main/preprocessor_config.json
- Command line
-
hf download hf://danilotpnta/HuBERT-Genre-Clf/preprocessor_config.json
-
curl -L -o preprocessor_config.json https://huggingface.co/danilotpnta/HuBERT-Genre-Clf/resolve/main/preprocessor_config.json
212 Bytes
| { | |
| "do_normalize": true, | |
| "feature_extractor_type": "Wav2Vec2FeatureExtractor", | |
| "feature_size": 1, | |
| "padding_side": "right", | |
| "padding_value": 0, | |
| "return_attention_mask": true, | |
| "sampling_rate": 16000 | |
| } | |