# 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}
}
```

## CLAP weights:
Request CLAP weights by filling this form: [link](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
sim = clap_model.compute_similarity(audio_embeddings, text_embeddings)
```

### Zero-Shot inference on an audio file from [ESC50 dataset](https://github.com/karolpiczak/ESC-50)

```python
from CLAP_API import CLAP
from esc50_dataset import ESC50
import time
import torch.nn.functional as F

# Load CLAP
weights_path = 'best.pth' # Add weight path here
clap_model = CLAP(weights_path, use_cuda=False)

# Load dataset
dataset = ESC50(root='data', download=True) # set download=True when dataset is not downloaded
audio_file, target, one_hot_target = dataset[1000]
audio_file = [audio_file]
prompt = 'this is a sound of '
y = [prompt + x for x in dataset.classes]

print('Computing text embeddings')
text_embeddings = clap_model.get_text_embeddings(y)
print('Computing audio embeddings')
audio_embeddings = clap_model.get_audio_embeddings(audio_file, resample=True)
similarity = clap_model.compute_similarity(audio_embeddings, text_embeddings)

similarity = F.softmax(similarity, dim=1)
values, indices = similarity[0].topk(5)
# Print the result
print("Ground Truth: {}".format(target))
print("Top predictions:\n")
for value, index in zip(values, indices):
    print(f"{dataset.classes[index]:>16s}: {100 * value.item():.2f}%")
```

The output (the exact numbers may vary):

```
Ground Truth: coughing
Top predictions:

        coughing: 86.34%
        sneezing: 9.30%
drinking sipping: 1.31%
        laughing: 1.20%
  glass breaking: 0.81%
```

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

```python
from CLAP_API import CLAP
from esc50_dataset 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='data', 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

This project welcomes contributions and suggestions.  Most contributions require you to agree to a
Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us
the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

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This project has adopted the [Microsoft Open Source Code of Conduct](https://opensource.microsoft.com/codeofconduct/).
For more information see the [Code of Conduct FAQ](https://opensource.microsoft.com/codeofconduct/faq/) or
contact [opencode@microsoft.com](mailto:opencode@microsoft.com) with any additional questions or comments.

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Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship.
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