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#
# Copyright (C) 2016-2019 by Nathan Lovato, Daniel Oakey, Razvan Radulescu, and contributors
#
# This file is part of Power Sequencer.
#
# Power Sequencer is free software: you can redistribute it and/or modify it under the terms of the
# GNU General Public License as published by the Free Software Foundation, either version 3 of the
# License, or (at your option) any later version.
#
# Power Sequencer is distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY;
# without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
# GNU General Public License for more details.
#
# You should have received a copy of the GNU General Public License along with Power Sequencer. If
# not, see <https://www.gnu.org/licenses/>.
#
"""
This code is an adaptation of 'audio-offset-finder' by BBC
"""
import os
import warnings
import numpy as np
from .mfcc import mfcc
from .convert_and_trim import convert_and_trim
from .std_mfcc import std_mfcc
from .cross_correlation import cross_correlation
from .ensure_non_zero import ensure_non_zero
def find_offset(file1, file2, freq=8000, trim=60 * 15, correl_nframes=1000):
"""
Determine the offset (in seconds) between 2 audio files
Uses cross-correlation of standardised Mel-Frequency Cepstral
Coefficients
"""
from scipy.io import wavfile
file1 = os.path.abspath(file1)
file2 = os.path.abspath(file2)
wav1_path = convert_and_trim(file1, freq, trim)
wav2_path = convert_and_trim(file2, freq, trim)
rate1, data1 = wavfile.read(wav1_path, mmap=True)
data1 = data1 / (2.0 ** 15)
rate2, data2 = wavfile.read(wav2_path, mmap=True)
data2 = data2 / (2.0 ** 15)
data1 = ensure_non_zero(data1)
data2 = ensure_non_zero(data2)
mfcc1 = mfcc(data1, nwin=256, nfft=512, fs=freq, nceps=13)[0]
mfcc2 = mfcc(data2, nwin=256, nfft=512, fs=freq, nceps=13)[0]
mfcc1 = std_mfcc(mfcc1)
mfcc2 = std_mfcc(mfcc2)
frames1 = mfcc1.shape[0]
frames2 = mfcc2.shape[0]
if frames1 > frames2:
flip = 1
else:
flip = -1
mfcc1, mfcc2 = mfcc2, mfcc1
c = cross_correlation(mfcc1, mfcc2, nframes=correl_nframes)
try:
c.any()
except AttributeError:
os.remove(wav1_path)
os.remove(wav2_path)
return 0, 0
max_k_index = np.argmax(c)
offset = max_k_index * 160.0 / float(freq)
score = (c[max_k_index] - np.mean(c)) / np.std(c)
os.remove(wav1_path)
os.remove(wav2_path)
return offset * flip, score
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