ref: b9912f7eb3455926987434db5e3dc97e1a24c1e9
dir: /dnn/torch/osce/utils/bwe_features.py/
"""
/* Copyright (c) 2024 Amazon
Written by Jan Buethe */
/*
Redistribution and use in source and binary forms, with or without
modification, are permitted provided that the following conditions
are met:
- Redistributions of source code must retain the above copyright
notice, this list of conditions and the following disclaimer.
- Redistributions in binary form must reproduce the above copyright
notice, this list of conditions and the following disclaimer in the
documentation and/or other materials provided with the distribution.
THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS
``AS IS'' AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT
LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR
A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER
OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL,
EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO,
PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR
PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF
LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING
NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
*/
"""
import os
import numpy as np
import torch
import scipy
import scipy.signal
from scipy.io import wavfile
from utils.spec import log_spectrum, instafreq, create_filter_bank
def bwe_feature_factory(
spec_num_bands=32,
max_instafreq_bin=40
):
""" features for bwe; we work with a fixed window size of 320 and a hop size of 160 """
w = scipy.signal.windows.cosine(320)
fb = create_filter_bank(spec_num_bands, 320, scale='erb', round_center_bins=True, normalize=True)
def create_features(x, history=None):
if history is None:
history = np.zeros(320, dtype=np.float32)
lmspec = log_spectrum(np.concatenate((history[-160:], x), dtype=x.dtype), frame_size=320, window=w, fb=fb)
freqs = instafreq(np.concatenate((history[-320:], x), dtype=x.dtype), frame_size=320, max_bin=max_instafreq_bin, window=w)
features = np.concatenate((lmspec, freqs), axis=-1, dtype=np.float32)
return features
return create_features
def load_inference_data(path,
spec_num_bands=32,
max_instafreq_bin=40,
**kwargs):
print(f"[load_inference_data]: ignoring keyword arguments {kwargs.keys()}...")
if path.endswith(".wav"):
signal = wavfile.read(path)[1].astype(np.float32) / (2 ** 15)
else:
signal = np.fromfile(path, dtype=np.int16).astype(np.float32) / (2 ** 15)
num_frames = len(signal) // 160
signal = signal[:num_frames*160]
history = np.zeros(320, dtype=np.float32)
create_features = bwe_feature_factory(spec_num_bands=spec_num_bands, max_instafreq_bin=max_instafreq_bin)
features = create_features(signal, history)
return torch.from_numpy(signal), torch.from_numpy(features)