Feature Extraction
Transformers
Safetensors
cage_detector
audio
watermark
watermark-detection
provenance
vocbulwark
custom_code
Instructions to use mlr2000/vocoder-large-watermark-detector with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use mlr2000/vocoder-large-watermark-detector with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="mlr2000/vocoder-large-watermark-detector", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("mlr2000/vocoder-large-watermark-detector", trust_remote_code=True, device_map="auto") - Notebooks
- Google Colab
- Kaggle
| """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 | |