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
| { | |
| "model_type": "cage_detector", | |
| "architectures": [ | |
| "CageDetector" | |
| ], | |
| "auto_map": { | |
| "AutoConfig": "configuration_detector.CageDetectorConfig", | |
| "AutoModel": "modeling_detector.CageDetector" | |
| }, | |
| "watermark_bits": 32, | |
| "in_channels": 1, | |
| "fine_kernel": 3, | |
| "mid_kernel": 5, | |
| "coarse_kernel": 7, | |
| "sample_rate": 24000, | |
| "fixed_watermark": [ | |
| 1, | |
| 0, | |
| 1, | |
| 1, | |
| 0, | |
| 0, | |
| 1, | |
| 0, | |
| 1, | |
| 1, | |
| 0, | |
| 1, | |
| 0, | |
| 0, | |
| 1, | |
| 1, | |
| 0, | |
| 1, | |
| 1, | |
| 0, | |
| 1, | |
| 0, | |
| 0, | |
| 1, | |
| 1, | |
| 1, | |
| 0, | |
| 0, | |
| 1, | |
| 0, | |
| 1, | |
| 1 | |
| ] | |
| } |