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import torch
import numpy as np
import soundfile as sf
from src.audio import load_audio, get_melspec
from src.config import SR
from src.utils import get_idx, to_square
# https://www.kaggle.com/code/tarunpaparaju/birdcall-identification-spectrogram-loader
def to_imagenet(X, mean=None, std=None, norm_max=None, norm_min=None, eps=1e-6):
mean = mean or X.mean()
X = X - mean
std = std or X.std()
Xstd = X / (std + eps)
_min, _max = Xstd.min(), Xstd.max()
norm_max = norm_max or _max
norm_min = norm_min or _min
if (_max - _min) > eps:
# Normalize to [0, 255]
V = Xstd
V[V < norm_min] = norm_min
V[V > norm_max] = norm_max
V = (V - norm_min) / (norm_max - norm_min)
else:
# Just zero
V = np.zeros_like(Xstd, dtype=np.uint8)
return V #np.stack([V]*3, axis=-1)
def extract_melspec_as_imgarr(fp, n_secs=8, random_chunk=True, convert_to_int8=False):
info = sf.info(fp)
y, _ = load_audio(fp, SR) #, offset=start, duration=n_secs
while True:
start, end = get_idx(info.duration, n_secs, random_chunk=random_chunk)
y2 = y[start:end]
if len(y2):
y = y2
break
mel_dB = to_square(get_melspec(y, SR))
try:
normalised_db = to_imagenet(mel_dB) # replaced minmax_scale
except:
normalised_db = torch.zeros_like(torch.as_tensor(mel_dB))
db_array = np.asarray(normalised_db)*255
if convert_to_int8:
db_array = db_array.astype(np.uint8)
return db_array[::-1].astype(float)
def generate_test_images(fp, n=10):
arrs = []
for _ in range(n):
arrs.append(extract_melspec_as_imgarr(fp))
return torch.as_tensor(np.array(arrs)).unsqueeze(1) |