---
base_model:
- nvidia/audio-flamingo-2
pipeline_tag: feature-extraction
---
# AFClap - Audio Flamingo CLAP

Audio-Flamingo-2のCLAPモデル（epoch_16.pt）。

## Model Info

- **Base**: HTSAT + T5
- **Audio Embed Dim**: 2048
- **Sample Rate**: 16000 Hz
- **Original**: [nvidia/audio-flamingo-2](https://huggingface.co/nvidia/audio-flamingo-2)

## Installation

```bash
pip install laion-clap librosa soundfile
```

## Usage

```python
import torch
from laion_clap import CLAP_Module

def load_afclap(ckpt_path):
    model = CLAP_Module(
        enable_fusion=True,
        amodel='HTSAT-afclap',
        tmodel='t5'
    ).cuda()
    model.load_afclap_ckpt(ckpt=ckpt_path, verbose=True)
    return model

# Load model
model = load_afclap("epoch_16.pt")

# Get audio embedding
audio_embed = model.get_audio_embedding_from_filelist(
    ["audio1.wav", "audio2.wav"],
    sr=16000,
    use_tensor=True
)

# Get text embedding
text_embed = model.get_text_embedding(
    ["This is a classical song.", "This is a rock song."],
    use_tensor=True
)

# Compute similarity
similarities = torch.tensor(audio_embed) @ torch.tensor(text_embed).t()
print(similarities)
```