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
File size: 935 Bytes
258e1da | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 | 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)
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