# CLAP

CLAP (Contrastive Language-Audio Pretraining) is a neural network model that learns acoustic concepts from natural language supervision. It achieves SoTA in “Zero-Shot” classification, Audio-Text & Text-Audio Retrieval, and in some datasets when finetuned.

<img width="832" alt="clap_diagram_v3" src="https://user-images.githubusercontent.com/26778834/199842089-39ef6a2e-8abb-4338-bdfe-680abab70f53.png">

## Citation
https://arxiv.org/pdf/2206.04769.pdf
```
@article{elizalde2022clap,
  title={Clap: Learning audio concepts from natural language supervision},
  author={Elizalde, Benjamin and Deshmukh, Soham and Ismail, Mahmoud Al and Wang, Huaming},
  journal={arXiv preprint arXiv:2206.04769},
  year={2022}
}
```

## Request CLAP weights:
```
https://forms.office.com/r/ULb4k9GL1F
```


### Usage
- Load model
```python
from CLAP_API import CLAP 

clap_model = CLAP("<PATH TO WEIGHTS>", use_cuda=False)
```

- Extract text embeddings
```python

text_embeddings = clap_model.get_text_embeddings(class_labels: List[str])
```

- Extract audio embeddings
```python

audio_embeddings = clap_model.get_audio_embeddings(file_paths: List[str])
```

- Compute similarity 
```python
# For using the below function, DO NOT normalize the text and audio embeddings
sim = clap_model.compute_similarity(audio_embeddings, text_embeddings)
```

### Zero-Shot Classification of [ESC50 dataset](https://github.com/karolpiczak/ESC-50) 

```python
from CLAP_API import CLAP
from esc50 import ESC50
import torch.nn.functional as F
import numpy as np
from tqdm import tqdm
from sklearn.metrics import accuracy_score

# Load CLAP
weights_path = # Add weight path here
clap_model = CLAP(weights_path, use_cuda=False)

# Load dataset
dataset = ESC50(root='path/ESC-50-master', download=False)
prompt = 'this is a sound of '
Y = [prompt + x for x in dataset.classes]

# Computing text embeddings
text_embeddings = clap_model.get_text_embeddings(Y)

# Computing audio embeddings
y_preds, y_labels = [], []
for i in tqdm(range(len(dataset))):
    x, _, one_hot_target = dataset.__getitem__(i)
    audio_embeddings = clap_model.get_audio_embeddings([x], resample=True)
    similarity = clap_model.compute_similarity(audio_embeddings, text_embeddings)
    y_pred = F.softmax(similarity.detach().cpu(), dim=1).numpy()
    y_preds.append(y_pred)
    y_labels.append(one_hot_target.detach().cpu().numpy())

y_labels, y_preds = np.concatenate(y_labels, axis=0), np.concatenate(y_preds, axis=0)
acc = accuracy_score(np.argmax(y_labels, axis=1), np.argmax(y_preds, axis=1))
print('ESC50 Accuracy {}'.format(acc))
```
The output:

```
ESC50 Accuracy: 82.6%
```

## Contributing

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