diff options
Diffstat (limited to 'dnn/torch/osce/utils/silk_features.py')
| -rw-r--r-- | dnn/torch/osce/utils/silk_features.py | 16 |
1 files changed, 5 insertions, 11 deletions
diff --git a/dnn/torch/osce/utils/silk_features.py b/dnn/torch/osce/utils/silk_features.py index 2997ef5f..8c5dbf05 100644 --- a/dnn/torch/osce/utils/silk_features.py +++ b/dnn/torch/osce/utils/silk_features.py @@ -33,6 +33,7 @@ import numpy as np import torch import scipy +import scipy.signal from utils.pitch import hangover, calculate_acorr_window from utils.spec import create_filter_bank, cepstrum, log_spectrum, log_spectrum_from_lpc @@ -59,7 +60,6 @@ def silk_feature_factory(no_pitch_value=256, num_bands_noisy_spec=18, noisy_spec_scale='opus', noisy_apply_dct=True, - add_offset=False, add_double_lag_acorr=False ): @@ -67,7 +67,7 @@ def silk_feature_factory(no_pitch_value=256, fb_clean_spec = create_filter_bank(num_bands_clean_spec, 320, scale='erb', round_center_bins=True, normalize=True) fb_noisy_spec = create_filter_bank(num_bands_noisy_spec, 320, scale=noisy_spec_scale, round_center_bins=True, normalize=True) - def create_features(noisy, noisy_history, lpcs, gains, ltps, periods, offsets): + def create_features(noisy, noisy_history, lpcs, gains, ltps, periods): periods = periods.copy() @@ -89,10 +89,7 @@ def silk_feature_factory(no_pitch_value=256, acorr, _ = calculate_acorr_window(noisy, 80, periods, noisy_history, radius=acorr_radius, add_double_lag_acorr=add_double_lag_acorr) - if add_offset: - features = np.concatenate((clean_spectrum, noisy_cepstrum, acorr, ltps, log_gains, offsets.reshape(-1, 1)), axis=-1, dtype=np.float32) - else: - features = np.concatenate((clean_spectrum, noisy_cepstrum, acorr, ltps, log_gains), axis=-1, dtype=np.float32) + features = np.concatenate((clean_spectrum, noisy_cepstrum, acorr, ltps, log_gains), axis=-1, dtype=np.float32) return features, periods.astype(np.int64) @@ -110,7 +107,6 @@ def load_inference_data(path, num_bands_noisy_spec=18, noisy_spec_scale='opus', noisy_apply_dct=True, - add_offset=False, add_double_lag_acorr=False, **kwargs): @@ -122,13 +118,12 @@ def load_inference_data(path, periods = np.fromfile(os.path.join(path, 'features_period.s16'), dtype=np.int16) num_bits = np.fromfile(os.path.join(path, 'features_num_bits.s32'), dtype=np.int32).astype(np.float32).reshape(-1, 1) num_bits_smooth = np.fromfile(os.path.join(path, 'features_num_bits_smooth.f32'), dtype=np.float32).reshape(-1, 1) - offsets = np.fromfile(os.path.join(path, 'features_offset.f32'), dtype=np.float32) # load signal, add back delay and pre-emphasize signal = np.fromfile(os.path.join(path, 'noisy.s16'), dtype=np.int16).astype(np.float32) / (2 ** 15) signal = np.concatenate((np.zeros(skip, dtype=np.float32), signal), dtype=np.float32) - create_features = silk_feature_factory(no_pitch_value, acorr_radius, pitch_hangover, num_bands_clean_spec, num_bands_noisy_spec, noisy_spec_scale, noisy_apply_dct, add_offset, add_double_lag_acorr) + create_features = silk_feature_factory(no_pitch_value, acorr_radius, pitch_hangover, num_bands_clean_spec, num_bands_noisy_spec, noisy_spec_scale, noisy_apply_dct, add_double_lag_acorr) num_frames = min((len(signal) // 320) * 4, len(lpcs)) signal = signal[: num_frames * 80] @@ -138,11 +133,10 @@ def load_inference_data(path, periods = periods[: num_frames] num_bits = num_bits[: num_frames // 4] num_bits_smooth = num_bits[: num_frames // 4] - offsets = offsets[: num_frames] numbits = np.repeat(np.concatenate((num_bits, num_bits_smooth), axis=-1, dtype=np.float32), 4, axis=0) - features, periods = create_features(signal, np.zeros(350, dtype=signal.dtype), lpcs, gains, ltps, periods, offsets) + features, periods = create_features(signal, np.zeros(350, dtype=signal.dtype), lpcs, gains, ltps, periods) if preemph > 0: signal[1:] -= preemph * signal[:-1] |