import numpy as np
np.random.seed(1337)

import xml.etree.ElementTree as ET
import glob
import csv
import os
import shutil
import tqdm
from bird import preprocessing as pp


# Settings
segment_size_seconds = 3

# Paths
xml_paths = glob.glob("datasets/birdClef2016/xml/*.xml")
source_dir = "./datasets/birdClef2016/wav"
preprocessed_dir = "./datasets/birdClef2016Whole"
noise_dir = os.path.join(preprocessed_dir, "noise")

print("Loading xml roots... ")
progress = tqdm.tqdm(range(len(xml_paths)))
xml_roots = [ET.parse(f) for (p, f) in zip(progress, xml_paths)]

if not os.path.exists(preprocessed_dir):
    print("Create diractory: ", preprocessed_dir)
    os.makedirs(preprocessed_dir)

if not os.path.exists(noise_dir):
    print("Create diractory: ", noise_dir)
    os.makedirs(noise_dir)

print("Preprocessing random species subset...")
progress = tqdm.tqdm(range(len(xml_paths)))
for (p, r) in zip(progress, xml_paths):
    species = r.find("ClassId").text
    filename = r.find("FileName").text
    filepath = os.path.join(source_dir, filename)

    class_dir = os.path.join(preprocessed_dir, "train", species)

    if not os.path.exists(class_dir):
        os.makedirs(class_dir)

    # preprocess the sound file, and save signal to class_dir, noise to
    # noise_dir with specified segment size
    pp.preprocess_sound_file(filepath, class_dir, noise_dir,
                             segment_size_seconds)

Add preprocessing script for birdclef data
Add preprocessing script for birdclef data
