diff options
| -rw-r--r-- | become_yukarin/param.py | 2 | ||||
| -rw-r--r-- | scripts/extract_acoustic_feature.py | 54 |
2 files changed, 50 insertions, 6 deletions
diff --git a/become_yukarin/param.py b/become_yukarin/param.py index a1b8843..db49c0a 100644 --- a/become_yukarin/param.py +++ b/become_yukarin/param.py @@ -8,7 +8,7 @@ class VoiceParam(NamedTuple): class AcousticFeatureParam(NamedTuple): frame_period: int = 5 - order: int = 25 + order: int = 59 alpha: float = 0.466 diff --git a/scripts/extract_acoustic_feature.py b/scripts/extract_acoustic_feature.py index a9d229f..edc85c3 100644 --- a/scripts/extract_acoustic_feature.py +++ b/scripts/extract_acoustic_feature.py @@ -44,7 +44,7 @@ def make_feature( return feature -def process(path1, path2): +def generate_feature(path1, path2): # load wave and padding wave_file_load_process = WaveFileLoadProcess( sample_rate=arguments.sample_rate, @@ -53,9 +53,9 @@ def process(path1, path2): 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) + # 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( @@ -84,6 +84,47 @@ def process(path1, path2): print('saved!', path) +def generate_mean_var(path_directory: Path): + path_mean = Path(path_directory, 'mean.npy') + var_mean = Path(path_directory, 'var.npy') + if path_mean.exists(): + path_mean.unlink() + if var_mean.exists(): + var_mean.unlink() + + f0_list = [] + spectrogram_list = [] + aperiodicity_list = [] + mfcc_list = [] + for path in path_directory.glob('*'): + d = numpy.load(path).item() # type: dict + f0_list.append(d['f0'].ravel()) + spectrogram_list.append(d['spectrogram'].ravel()) + aperiodicity_list.append(d['aperiodicity'].ravel()) + mfcc_list.append(d['mfcc'].ravel()) + + f0_list = numpy.concatenate(f0_list) + spectrogram_list = numpy.concatenate(spectrogram_list) + aperiodicity_list = numpy.concatenate(aperiodicity_list) + mfcc_list = numpy.concatenate(mfcc_list) + + mean = dict( + f0=numpy.mean(f0_list), + spectrogram=numpy.mean(spectrogram_list), + aperiodicity=numpy.mean(aperiodicity_list), + mfcc=numpy.mean(mfcc_list), + ) + var = dict( + f0=numpy.var(f0_list), + spectrogram=numpy.var(spectrogram_list), + aperiodicity=numpy.var(aperiodicity_list), + mfcc=numpy.var(mfcc_list), + ) + + numpy.save(path_mean.absolute(), mean) + numpy.save(var_mean.absolute(), var) + + def main(): paths1 = list(sorted(arguments.input1_directory.glob('*'))) paths2 = list(sorted(arguments.input2_directory.glob('*'))) @@ -93,7 +134,10 @@ def main(): arguments.output2_directory.mkdir(exist_ok=True) pool = multiprocessing.Pool() - pool.starmap(process, zip(paths1, paths2)) + pool.starmap(generate_feature, zip(paths1, paths2)) + + generate_mean_var(arguments.output1_directory) + generate_mean_var(arguments.output2_directory) if __name__ == '__main__': |