Benjamin Elizalde
28 lines
989 B
Python
28 lines
989 B
Python
from CLAP_API import CLAP
|
|
from esc50 import ESC50
|
|
import time
|
|
import torch.nn.functional as F
|
|
|
|
start_time = time.time()
|
|
weights_path = 'C:\\Users\\sdeshmukh\\Desktop\\CLAP_package\\model\\new\\best.pth'
|
|
clap_model = CLAP(weights_path, use_cuda=False)
|
|
print("Finished loading CLAP. Total time: {}".format(time.time() - start_time))
|
|
|
|
esc50_dataset = ESC50(root='data', download=False)
|
|
x, target = esc50_dataset[1000]
|
|
x = [x]
|
|
y = esc50_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(x, 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"{y[index]:>16s}: {100 * value.item():.2f}%") |