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import os
import torch
import numpy as np
def load_lpcnet_features(feature_file, version=2):
if version == 2:
layout = {
'cepstrum': [0,18],
'periods': [18, 19],
'pitch_corr': [19, 20],
'lpc': [20, 36]
}
frame_length = 36
elif version == 1:
layout = {
'cepstrum': [0,18],
'periods': [36, 37],
'pitch_corr': [37, 38],
'lpc': [39, 55],
}
frame_length = 55
else:
raise ValueError(f'unknown feature version: {version}')
raw_features = torch.from_numpy(np.fromfile(feature_file, dtype='float32'))
raw_features = raw_features.reshape((-1, frame_length))
features = torch.cat(
[
raw_features[:, layout['cepstrum'][0] : layout['cepstrum'][1]],
raw_features[:, layout['pitch_corr'][0] : layout['pitch_corr'][1]]
],
dim=1
)
lpcs = raw_features[:, layout['lpc'][0] : layout['lpc'][1]]
periods = (0.1 + 50 * raw_features[:, layout['periods'][0] : layout['periods'][1]] + 100).long()
return {'features' : features, 'periods' : periods, 'lpcs' : lpcs}
def create_new_data(signal_path, reference_data_path, new_data_path, offset=320, preemph_factor=0.85):
ref_data = np.memmap(reference_data_path, dtype=np.int16)
signal = np.memmap(signal_path, dtype=np.int16)
signal_preemph_path = os.path.splitext(signal_path)[0] + '_preemph.raw'
signal_preemph = np.memmap(signal_preemph_path, dtype=np.int16, mode='write', shape=signal.shape)
assert len(signal) % 160 == 0
num_frames = len(signal) // 160
mem = np.zeros(1)
for fr in range(len(signal)//160):
signal_preemph[fr * 160 : (fr + 1) * 160] = np.convolve(np.concatenate((mem, signal[fr * 160 : (fr + 1) * 160])), [1, -preemph_factor], mode='valid')
mem = signal[(fr + 1) * 160 - 1 : (fr + 1) * 160]
new_data = np.memmap(new_data_path, dtype=np.int16, mode='write', shape=ref_data.shape)
new_data[:] = 0
N = len(signal) - offset
new_data[1 : 2*N + 1: 2] = signal_preemph[offset:]
new_data[2 : 2*N + 2: 2] = signal_preemph[offset:]
def parse_warpq_scores(output_file):
""" extracts warpq scores from output file """
with open(output_file, "r") as f:
lines = f.readlines()
scores = [float(line.split("WARP-Q score:")[-1]) for line in lines if line.startswith("WARP-Q score:")]
return scores
def parse_stats_file(file):
with open(file, "r") as f:
lines = f.readlines()
mean = float(lines[0].split(":")[-1])
bt_mean = float(lines[1].split(":")[-1])
top_mean = float(lines[2].split(":")[-1])
return mean, bt_mean, top_mean
def collect_test_stats(test_folder):
""" collects statistics for all discovered metrics from test folder """
metrics = {'pesq', 'warpq', 'pitch_error', 'voicing_error'}
results = dict()
content = os.listdir(test_folder)
stats_files = [file for file in content if file.startswith('stats_')]
for file in stats_files:
metric = file[len("stats_") : -len(".txt")]
if metric not in metrics:
print(f"warning: unknown metric {metric}")
mean, bt_mean, top_mean = parse_stats_file(os.path.join(test_folder, file))
results[metric] = [mean, bt_mean, top_mean]
return results
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