Feature Extraction
Transformers
Safetensors
cage_detector
audio
watermark
watermark-detection
provenance
vocbulwark
custom_code
Instructions to use mlr2000/vocoder-small-watermark-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlr2000/vocoder-small-watermark-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="mlr2000/vocoder-small-watermark-detector", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mlr2000/vocoder-small-watermark-detector", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
File size: 398 Bytes
66157f2 | 1 2 3 4 5 6 7 8 9 10 11 12 | """Config for the standalone Cage watermark detector."""
from .cage_config import CageExtractorConfig
class CageDetectorConfig(CageExtractorConfig):
model_type = "cage_detector"
def __init__(self, fixed_watermark=None, **kwargs):
super().__init__(**kwargs)
# The fixed signature this detector checks extracted bits against.
self.fixed_watermark = fixed_watermark
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