added LPCNet torch implementation

Signed-off-by: Jan Buethe <jbuethe@amazon.de>

dnn/torch/lpcnet/utils/data.py

@@ -0,0 +1,112 @@
+import os
+
+import torch
+import numpy as np
+
+def load_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