add extract acoustic feature script

1 files changed, 100 insertions, 0 deletions

diff --git a/scripts/extract_acoustic_feature.py b/scripts/extract_acoustic_feature.py
new file mode 100644
index 0000000..a9d229f
--- /dev/null
+++ b/scripts/extract_acoustic_feature.py
@@ -0,0 +1,100 @@
+"""
+extract alignments voices.
+"""
+
+import argparse
+import multiprocessing
+from pathlib import Path
+
+import numpy
+
+from become_yukarin.dataset.dataset import AcousticFeatureProcess
+from become_yukarin.dataset.dataset import Wave
+from become_yukarin.dataset.dataset import WaveFileLoadProcess
+from become_yukarin.dataset.utility import MFCCAligner
+from become_yukarin.param import AcousticFeatureParam
+from become_yukarin.param import VoiceParam
+
+base_voice_param = VoiceParam()
+base_acoustic_feature_param = AcousticFeatureParam()
+
+parser = argparse.ArgumentParser()
+parser.add_argument('--input1_directory', '-i1', type=Path)
+parser.add_argument('--input2_directory', '-i2', type=Path)
+parser.add_argument('--output1_directory', '-o1', type=Path)
+parser.add_argument('--output2_directory', '-o2', type=Path)
+parser.add_argument('--sample_rate', type=int, default=base_voice_param.sample_rate)
+parser.add_argument('--top_db', type=float, default=base_voice_param.top_db)
+parser.add_argument('--frame_period', type=int, default=base_acoustic_feature_param.frame_period)
+parser.add_argument('--order', type=int, default=base_acoustic_feature_param.order)
+parser.add_argument('--alpha', type=float, default=base_acoustic_feature_param.alpha)
+arguments = parser.parse_args()
+
+
+def make_feature(
+        path,
+        sample_rate,
+        top_db,
+        frame_period,
+        order,
+        alpha,
+):
+    wave = WaveFileLoadProcess(sample_rate=sample_rate, top_db=top_db)(path, test=True)
+    feature = AcousticFeatureProcess(frame_period=frame_period, order=order, alpha=alpha)(wave, test=True)
+    return feature
+
+
+def process(path1, path2):
+    # load wave and padding
+    wave_file_load_process = WaveFileLoadProcess(
+        sample_rate=arguments.sample_rate,
+        top_db=arguments.top_db,
+    )
+    wave1 = wave_file_load_process(path1, test=True)
+    wave2 = wave_file_load_process(path2, test=True)
+
+    m = max(len(wave1.wave), len(wave2.wave))
+    wave1 = Wave(wave=numpy.pad(wave1.wave, (0, m - len(wave1.wave)), mode='mean'), sampling_rate=wave1.sampling_rate)
+    wave2 = Wave(wave=numpy.pad(wave2.wave, (0, m - len(wave2.wave)), mode='mean'), sampling_rate=wave2.sampling_rate)
+
+    # make acoustic feature
+    acoustic_feature_process = AcousticFeatureProcess(
+        frame_period=arguments.frame_period,
+        order=arguments.order,
+        alpha=arguments.alpha,
+    )
+    f1 = acoustic_feature_process(wave1, test=True)
+    f2 = acoustic_feature_process(wave2, test=True)
+
+    # alignment
+    aligner = MFCCAligner(f1.mfcc, f2.mfcc)
+
+    f0_1, f0_2 = aligner.align(f1.f0, f2.f0)
+    spectrogram_1, spectrogram_2 = aligner.align(f1.spectrogram, f2.spectrogram)
+    aperiodicity_1, aperiodicity_2 = aligner.align(f1.aperiodicity, f2.aperiodicity)
+    mfcc_1, mfcc_2 = aligner.align(f1.mfcc, f2.mfcc)
+
+    # save
+    path = Path(arguments.output1_directory, path1.stem + '.npy')
+    numpy.save(path.absolute(), dict(f0=f0_1, spectrogram=spectrogram_1, aperiodicity=aperiodicity_1, mfcc=mfcc_1))
+    print('saved!', path)
+
+    path = Path(arguments.output2_directory, path2.stem + '.npy')
+    numpy.save(path.absolute(), dict(f0=f0_2, spectrogram=spectrogram_2, aperiodicity=aperiodicity_2, mfcc=mfcc_2))
+    print('saved!', path)
+
+
+def main():
+    paths1 = list(sorted(arguments.input1_directory.glob('*')))
+    paths2 = list(sorted(arguments.input2_directory.glob('*')))
+    assert len(paths1) == len(paths2)
+
+    arguments.output1_directory.mkdir(exist_ok=True)
+    arguments.output2_directory.mkdir(exist_ok=True)
+
+    pool = multiprocessing.Pool()
+    pool.starmap(process, zip(paths1, paths2))
+
+
+if __name__ == '__main__':
+    main()