Feature Extraction
Transformers
Safetensors
sincnet
audio
voice-activity-detection
speaker-recognition
speaker-segmentation
arxiv:1808.00158
custom_code
Instructions to use D4ve-R/sincnet with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use D4ve-R/sincnet with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="D4ve-R/sincnet", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("D4ve-R/sincnet", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
- Xet hash:
- 02bcd908853b796b6cba7ae25eae553e06ce57f033cee830bfd26891b0a7aec4
- Size of remote file:
- 173 kB
- SHA256:
- 0ee420350cd91021fb66f46c6a932835d37ab57840eff4f6db812f44dd94627a
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