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: 594 Bytes
66157f2 | 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 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | {
"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
]
} |