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
- Xet hash:
- 88b8c8cd4e94a7e8d672c3957e8907e41e90fdb0567faa28971e4d43287dd063
- Size of remote file:
- 2.61 MB
- SHA256:
- a1e9d11e749c65af36893011c5871409ffd5f74e3a42ef72c7387a3daac0bfb9
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