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.

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

from CLAP_API import CLAP 

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

audio_files = ["audio_file1.wav", "audio_file2.wav"]
class_labels = ["label1", "label2", "label3", "label4"]

# get audio embeddings for downstream applications 
audio_embeddings = clap_model.get_audio_embeddings(audio_files)

# get text embeddings for downstream applications 
text_embeddings = clap_model.get_text_embeddings(class_labels)

Examples

Zero-Shot Prediction

The code below performs zero-shot prediction using CLAP. This example takes an audio from the ESC50 dataset, and predicts the most likely labels among the 50 textual labels from the dataset.

from CLAP_API import CLAP
from esc50 import ESC50
import time
import torch.nn.functional as F

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

# Load dataset
dataset = ESC50(root='data', download=False)
x, target = dataset[1000]
y = dataset.classes

# Add prompt
prompt = 'this is a sound of '
y = dataset.classes
y_queries = [prompt + x for x in y]

# Compute embeddings and similarity matrix
text_embeddings = model.get_text_embeddings(y)
audio_embeddings = model.get_audio_embeddings(x, resample=True)
similarity = 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"{y[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%

Note that this example uses the get_text_embeddings() and get_audio_embeddings() methods that return the encoded features of given inputs.

Zero-Shot Evaluation

The code below performs zero-shot evaluation using CLAP to compute performance on ESC50 dataset dataset.

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
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_embedding = clap_model.get_audio_embeddings([x], resample=True)
    similarity = clap_model.compute_similarity(audio_embedding, 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%

Extract embeddings

• Extract text embeddings

text_embeddings = clap_model.get_text_embeddings(class_labels: List[str])
text_embeddings = text_embeddings/torch.norm(text_embeddings, dim=-1, keepdim=True)

• Extract audio embeddings

audio_embeddings = clap_model.get_audio_embeddings(file_paths: List[str])
audio_embeddings = audio_embeddings/torch.norm(audio_embeddings, dim=-1, keepdim=True)

• Compute similarity

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

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. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.

Trademarks

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