Feature Extraction
Transformers
Safetensors
finelap
audio grounding
audio-text retrieval
sound-event-detection
multimodal
clap
custom_code
Instructions to use AndreasXi/FineLAP with libraries, inference providers, notebooks, and local apps. Follow these links to get started.
- Libraries
- Transformers
How to use AndreasXi/FineLAP with Transformers:
# Use a pipeline as a high-level helper from transformers import pipeline pipe = pipeline("feature-extraction", model="AndreasXi/FineLAP", trust_remote_code=True)# Load model directly from transformers import AutoModel model = AutoModel.from_pretrained("AndreasXi/FineLAP", trust_remote_code=True, dtype="auto") - Notebooks
- Google Colab
- Kaggle
Upload folder using huggingface_hub
Browse files- .gitattributes +1 -0
- modeling_finelap.py +1 -0
- resources/1.wav +3 -0
.gitattributes
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resources/1.wav filter=lfs diff=lfs merge=lfs -text
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modeling_finelap.py
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plt.tight_layout()
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plt.savefig(output_path, dpi=150, bbox_inches='tight')
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plt.close()
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def forward(self, audio_path=None, text_labels=None):
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plt.tight_layout()
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plt.savefig(output_path, dpi=150, bbox_inches='tight')
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print(f"Figure saved to {output_path}")
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plt.close()
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def forward(self, audio_path=None, text_labels=None):
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resources/1.wav
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version https://git-lfs.github.com/spec/v1
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oid sha256:198ea39cc1e772f80f1f508c593b1a5fd3182b0b6c02f8aea4717258f0cc5280
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size 1764044
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