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
Update README.md
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README.md
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```python
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import torch
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from transformers import AutoModel
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---
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license: mit
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library_name: transformers
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tags:
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- audio grounding
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- audio-text retrieval
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- sound-event-detection
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- multimodal
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- clap
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pipeline_tag: feature-extraction
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---
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# FineLAP: Taming Heterogeneous Supervision for Fine-grained Language-Audio Pretraining
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FineLAP is a strong contrastively pre-trained audio-language model that excels in both clip- and frame-level audio understanding tasks
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```python
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import torch
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from transformers import AutoModel
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