# Load zip file with zipfile.ZipFile('2000_songs.zip', 'r') as zip_ref: for file in zip_ref.namelist(): if file.endswith('.mp3') or file.endswith('.wav'): # Load audio file audio, sr = librosa.load(zip_ref.open(file)) # Extract mel spectrogram mel_spectrogram = librosa.feature.melspectrogram(y=audio, sr=sr) # Do something with the mel spectrogram (e.g., save it, analyze it)
# Load zip file with zipfile.ZipFile('2000_songs.zip', 'r') as zip_ref: for file in zip_ref.namelist(): if file.endswith('.mp3') or file.endswith('.wav'): # Load audio file audio, sr = librosa.load(zip_ref.open(file)) # Preprocess audio audio = transforms.ToTensor()(audio) # Extract audio embedding embedding = model(audio.unsqueeze(0)) # batch size 1 # Do something with the embedding (e.g., save it, analyze it) These are just a few examples of how you can extract deep features from a 2000 songs zip file. The specific approach you choose will depend on your goals and requirements. 2000 songs zip file
# Load pre-trained PANNs model model = PANNs(weights='pann_small') # Load zip file with zipfile
import torch import torch.nn as nn import torchvision.transforms as transforms from pann.sota.panns import PANNs # Load zip file with zipfile.ZipFile('2000_songs.zip'
import librosa import numpy as np import zipfile