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, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| from transformers import PreTrainedModel, AutoConfig, AutoModel | |
| from .model import SincNet | |
| from .config import SincNetConfig | |
| class SincNetModel(PreTrainedModel): | |
| config_class = SincNetConfig | |
| base_model_prefix = "sincnet" | |
| def __init__(self, config: SincNetConfig): | |
| super().__init__(config) | |
| self.model = SincNet( | |
| sinc_filter_stride=config.stride, | |
| num_sinc_filters=config.num_sinc_filters, | |
| sinc_filter_length=config.sinc_filter_length, | |
| num_conv_filters=config.num_conv_filters, | |
| conv_filter_length=config.conv_filter_length, | |
| pool_kernel_size=config.pool_kernel_size, | |
| pool_stride=config.pool_stride, | |
| sample_rate=config.sample_rate, | |
| ) | |
| def forward(self, waveforms): | |
| return self.model(waveforms) | |
| AutoConfig.register('sincnet', SincNetConfig) | |
| AutoModel.register(SincNetConfig, SincNetModel) | |