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
| { | |
| "architectures": [ | |
| "SincNetModel" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "config.SincNetConfig", | |
| "AutoModel": "model_hf.SincNetModel" | |
| }, | |
| "conv_filter_length": 5, | |
| "min_band_hz": 50, | |
| "min_low_hz": 50, | |
| "model_type": "sincnet", | |
| "num_conv_filters": 60, | |
| "num_sinc_filters": 80, | |
| "num_wavform_channels": 1, | |
| "pool_kernel_size": 3, | |
| "pool_stride": 3, | |
| "sample_rate": 16000, | |
| "sinc_filter_dilation": 1, | |
| "sinc_filter_in_channels": 1, | |
| "sinc_filter_length": 251, | |
| "sinc_filter_padding": 0, | |
| "sinc_filter_stride": 10, | |
| "stride": 10, | |
| "torch_dtype": "float32", | |
| "transformers_version": "4.37.2" | |
| } | |