Soham Deshmukh
32 lines
1.2 KiB
Python
32 lines
1.2 KiB
Python
from CLAP_API import CLAP
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from esc50_dataset import ESC50
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import torch.nn.functional as F
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import numpy as np
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from tqdm import tqdm
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from sklearn.metrics import accuracy_score
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# Load CLAP
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weights_path = 'C:\\Users\\sdeshmukh\\Desktop\\CLAP_package\\model\\new\\best.pth' # Add weight path here
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clap_model = CLAP(weights_path, use_cuda=False)
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# Load dataset
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dataset = ESC50(root='data', download=True)
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prompt = 'this is a sound of '
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Y = [prompt + x for x in dataset.classes]
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# Computing text embeddings
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text_embeddings = clap_model.get_text_embeddings(Y)
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# Computing audio embeddings
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y_preds, y_labels = [], []
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for i in tqdm(range(len(dataset))):
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x, _, one_hot_target = dataset.__getitem__(i)
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audio_embeddings = clap_model.get_audio_embeddings([x], resample=True)
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similarity = clap_model.compute_similarity(audio_embeddings, text_embeddings)
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y_pred = F.softmax(similarity.detach().cpu(), dim=1).numpy()
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y_preds.append(y_pred)
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y_labels.append(one_hot_target.detach().cpu().numpy())
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y_labels, y_preds = np.concatenate(y_labels, axis=0), np.concatenate(y_preds, axis=0)
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acc = accuracy_score(np.argmax(y_labels, axis=1), np.argmax(y_preds, axis=1))
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print('ESC50 Accuracy {}'.format(acc)) |