panns_inference/config.py

import os
#import numpy as np
import csv
#from pathlib import Path

sample_rate = 32000

labels_csv_path = 'panns_data/class_labels_indices.csv' # changed to current directory

# Download labels if not exist
if not os.path.isfile(labels_csv_path):
	os.makedirs(os.path.dirname(labels_csv_path), exist_ok=True)
	os.system('wget -O "{}" "http://storage.googleapis.com/us_audioset/youtube_corpus/v1/csv/class_labels_indices.csv"'.format(labels_csv_path))

# Load label
with open(labels_csv_path, 'r') as f:
    reader = csv.reader(f, delimiter=',')
    lines = list(reader)

labels = []
ids = []    # Each label has a unique id such as "/m/068hy"
for i1 in range(1, len(lines)):
    id = lines[i1][1]
    label = lines[i1][2]
    ids.append(id)
    labels.append(label)

classes_num = len(labels)

lb_to_ix = {label : i for i, label in enumerate(labels)}
ix_to_lb = {i : label for i, label in enumerate(labels)}

id_to_ix = {id : i for i, id in enumerate(ids)}
ix_to_id = {i : id for i, id in enumerate(ids)}