|
|
@@ -0,0 +1,70 @@ |
|
|
#!/usr/bin/env python3 |
|
|
|
|
|
import librosa |
|
|
import numpy as np |
|
|
from pathlib import Path |
|
|
import pandas as pd |
|
|
|
|
|
|
|
|
class WaveFile(object): |
|
|
filename = None |
|
|
duration = None |
|
|
|
|
|
def __init__(self, filename): |
|
|
|
|
|
y, sr = librosa.load(filename) |
|
|
self.duration = librosa.get_duration(y, sr) |
|
|
self.filename = str(filename) |
|
|
|
|
|
self.df = pd.DataFrame(np.abs(librosa.stft(y, n_fft=256))) |
|
|
self.df['bins'] = librosa.fft_frequencies(sr=sr, n_fft=256) |
|
|
self.df['avgamp'] = self.df.mean(axis=1) |
|
|
self.df = self.df[['bins', 'avgamp']] |
|
|
|
|
|
|
|
|
def almost_eq(self, other, threshold): |
|
|
if abs(self.duration - other.duration) > 0.1: |
|
|
return False |
|
|
|
|
|
if max(abs(self.df['avgamp'] - other.df['avgamp'])) > threshold: |
|
|
return False |
|
|
|
|
|
return True |
|
|
|
|
|
|
|
|
def main(): |
|
|
print('loading ps1 waves') |
|
|
ps1waves = [WaveFile(path) |
|
|
for path in Path('stream1').rglob('*.wav')] |
|
|
|
|
|
|
|
|
print('loading PC waves') |
|
|
pcwaves = [WaveFile(path) |
|
|
for path in Path('extracted').rglob('*.wav')] |
|
|
|
|
|
|
|
|
print('comparing waves') |
|
|
for pc in pcwaves: |
|
|
candidates = [] |
|
|
for threshold in [5,4,3,2,1,0.5,0.1]: |
|
|
candidates = [ps1.filename for ps1 in ps1waves if pc.almost_eq(ps1, threshold)] |
|
|
if len(candidates) == 1: |
|
|
print('%s;%s' % (pc.filename, candidates[0])) |
|
|
break |
|
|
|
|
|
else: |
|
|
print('%s;%s' % (pc.filename, candidates)) |
|
|
|
|
|
|
|
|
def test(): |
|
|
Wario95d = WaveFile('samples/Wario95d.wav') |
|
|
Wario96c = WaveFile('samples/Wario96c.wav') |
|
|
wario96c = WaveFile('samples/wario96c.wav') |
|
|
|
|
|
assert Wario96c.almost_eq(wario96c) |
|
|
assert not Wario96c.almost_eq(Wario95d) |
|
|
|
|
|
|
|
|
|
|
|
if __name__ == '__main__': |
|
|
main() |
john-tornblom created this gist