1 files changed, 87 insertions, 0 deletions
diff --git a/power_sequencer/operators/audiosync/find_offset.py b/power_sequencer/operators/audiosync/find_offset.py
new file mode 100644
index 00000000..a25bffde
--- /dev/null
+++ b/power_sequencer/operators/audiosync/find_offset.py
@@ -0,0 +1,87 @@
+#
+# 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 scipy.io import wavfile
+
+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
+    """
+    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