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