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"""Create spectrograms from audio files using matplotlib"""
import logging
from dataclasses import dataclass
from pathlib import Path
from typing import Tuple, Union
import matplotlib as mpl
mpl.use('Agg')
import librosa
import librosa.display
import matplotlib.pyplot as plt
import numpy as np
import scipy.signal as signal
logging.basicConfig(format='%(asctime)s: %(message)s', level=logging.INFO)
@dataclass
class FFTConfig():
n_fft: Union[int, None] = 2**12
win_length: Union[int, None] = None
hop_length: int = 512
sr: int = 22_050
db: bool = False
mel: bool = False
fmin: int = 50
fmax: int = 10_000
y_axis: str = 'linear'
denoise: Union[str, None] = None
pcen: bool = False
cmap: str = 'magma'
n_mels: int = 128
vmin: Union[float, None] = None
vmax: Union[float, None] = None
bandpass: bool = True
ylim: Union[Tuple[float, float], None] = (0, 512)
def load_wav(fpath):
y, sr = librosa.load(fpath)
audio, _ = librosa.effects.trim(y)
return audio, sr
def calc_stft(audio, fft_config):
stft = librosa.stft(audio, n_fft=fft_config.n_fft, hop_length=fft_config.hop_length, win_length=fft_config.win_length)
return np.abs(stft)
def plot_spec(inp, output, fft_config: FFTConfig):
# Audio returns sr and audio! (opposite of librosa)
sr, audio = inp
fft_config.sr = sr
if fft_config.bandpass:
audio = fish_filter(audio, fs=sr)
stft = calc_stft(audio, fft_config)
if fft_config.pcen:
# Scale PCEN: https://librosa.org/doc/latest/generated/librosa.pcen.html?highlight=pcen#librosa.pcen
stft = librosa.pcen(stft * (2**31), sr=fft_config.sr, hop_length=fft_config.hop_length)
fft_config.db = True
if fft_config.mel:
stft = librosa.feature.melspectrogram(
y=audio,
sr=fft_config.sr,
n_mels=fft_config.n_mels,
fmin=fft_config.fmin,
fmax=fft_config.fmax
)
# Mel is in db
fft_config.db = True
if fft_config.db:
stft = librosa.amplitude_to_db(stft, ref=np.max)
fig, ax = plt.subplots(1, 1)
_ = librosa.display.specshow(
stft,
sr=fft_config.sr,
hop_length=fft_config.hop_length,
x_axis='time',
y_axis=fft_config.y_axis,
fmin=fft_config.fmin,
fmax=fft_config.fmax,
cmap=fft_config.cmap,
ax=ax,
vmin=fft_config.vmin,
vmax=fft_config.vmax
)
ax.set_axis_off()
if fft_config.ylim is not None:
ax.set_ylim(fft_config.ylim)
if output:
fig.savefig(output, bbox_inches='tight', pad_inches=0)
plt.close(fig=fig)
plt.close('all')
def fish_filter(call, low=50, high=512, order=8, fs=22_050):
sos = signal.butter(order, [low, high], 'bandpass', output='sos', fs=fs)
return signal.sosfilt(sos, call)
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