diff options
Diffstat (limited to 'scripts/extract_acoustic_feature.py')
| -rw-r--r-- | scripts/extract_acoustic_feature.py | 47 |
1 files changed, 27 insertions, 20 deletions
diff --git a/scripts/extract_acoustic_feature.py b/scripts/extract_acoustic_feature.py index e1794cf..297c10b 100644 --- a/scripts/extract_acoustic_feature.py +++ b/scripts/extract_acoustic_feature.py @@ -116,11 +116,11 @@ def generate_feature(path1, path2): def generate_mean_var(path_directory: Path): path_mean = Path(path_directory, 'mean.npy') - var_mean = Path(path_directory, 'var.npy') + path_var = Path(path_directory, 'var.npy') if path_mean.exists(): path_mean.unlink() - if var_mean.exists(): - var_mean.unlink() + if path_var.exists(): + path_var.unlink() acoustic_feature_load_process = AcousticFeatureLoadProcess(validate=False) acoustic_feature_save_process = AcousticFeatureSaveProcess(validate=False) @@ -131,33 +131,40 @@ def generate_mean_var(path_directory: Path): mfcc_list = [] for path in path_directory.glob('*'): feature = acoustic_feature_load_process(path) - f0_list.append(numpy.ravel(feature.f0[feature.voiced])) # remove unvoiced - spectrogram_list.append(numpy.ravel(feature.spectrogram)) - aperiodicity_list.append(numpy.ravel(feature.aperiodicity)) - mfcc_list.append(numpy.ravel(feature.mfcc)) + f0_list.append(feature.f0[feature.voiced]) # remove unvoiced + spectrogram_list.append(feature.spectrogram) + aperiodicity_list.append(feature.aperiodicity) + mfcc_list.append(feature.mfcc) - f0_list = numpy.concatenate(f0_list) - spectrogram_list = numpy.concatenate(spectrogram_list) - aperiodicity_list = numpy.concatenate(aperiodicity_list) - mfcc_list = numpy.concatenate(mfcc_list) + def concatenate(arr_list): + try: + arr_list = numpy.concatenate(arr_list) + except: + pass + return arr_list + + f0_list = concatenate(f0_list) + spectrogram_list = concatenate(spectrogram_list) + aperiodicity_list = concatenate(aperiodicity_list) + mfcc_list = concatenate(mfcc_list) mean = AcousticFeature( - f0=numpy.mean(f0_list), - spectrogram=numpy.mean(spectrogram_list), - aperiodicity=numpy.mean(aperiodicity_list), - mfcc=numpy.mean(mfcc_list), + f0=numpy.mean(f0_list, axis=0, keepdims=True), + spectrogram=numpy.mean(spectrogram_list, axis=0, keepdims=True), + aperiodicity=numpy.mean(aperiodicity_list, axis=0, keepdims=True), + mfcc=numpy.mean(mfcc_list, axis=0, keepdims=True), voiced=numpy.nan, ) var = AcousticFeature( - f0=numpy.var(f0_list), - spectrogram=numpy.var(spectrogram_list), - aperiodicity=numpy.var(aperiodicity_list), - mfcc=numpy.var(mfcc_list), + f0=numpy.var(f0_list, axis=0, keepdims=True), + spectrogram=numpy.var(spectrogram_list, axis=0, keepdims=True), + aperiodicity=numpy.var(aperiodicity_list, axis=0, keepdims=True), + mfcc=numpy.var(mfcc_list, axis=0, keepdims=True), voiced=numpy.nan, ) acoustic_feature_save_process({'path': path_mean, 'feature': mean}) - acoustic_feature_save_process({'path': var_mean, 'feature': var}) + acoustic_feature_save_process({'path': path_var, 'feature': var}) def main(): |