Feature Extraction
Transformers
Safetensors
English
speech_jepa
speech
audio
self-supervised-learning
jepa
speech-jepa
custom_code
Instructions to use labhamlet/speech-jepa-base with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use labhamlet/speech-jepa-base with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="labhamlet/speech-jepa-base", trust_remote_code=True)# pip install -U transformers accelerate # Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("labhamlet/speech-jepa-base", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 214 Bytes
fe17b0f | 1 2 3 4 5 6 7 8 9 10 | {
"do_normalize": true,
"feature_extractor_type": "Wav2Vec2FeatureExtractor",
"feature_size": 1,
"padding_side": "right",
"padding_value": 0.0,
"return_attention_mask": true,
"sampling_rate": 16000
}
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