README related osce fixes

dnn/torch/osce/scripts/concatenator.py

@@ -0,0 +1,85 @@
+import os
+import argparse
+
+import numpy as np
+from scipy import signal
+from scipy.io import wavfile
+import resampy
+
+
+
+
+parser = argparse.ArgumentParser()
+
+parser.add_argument("filelist", type=str, help="file with filenames for concatenation in WAVE format")
+parser.add_argument("target_fs", type=int, help="target sampling rate of concatenated file")
+parser.add_argument("output", type=str, help="binary output file (integer16)")
+parser.add_argument("--basedir", type=str, help="basedir for filenames in filelist, defaults to ./", default="./")
+parser.add_argument("--normalize", action="store_true", help="apply normalization")
+parser.add_argument("--db_max", type=float, help="max DB for random normalization", default=0)
+parser.add_argument("--db_min", type=float, help="min DB for random normalization", default=0)
+parser.add_argument("--verbose", action="store_true")
+
+def read_filelist(basedir, filelist):
+    with open(filelist, "r") as f:
+        files = f.readlines()
+
+    fullfiles = [os.path.join(basedir, f.rstrip('\n')) for f in files if len(f.rstrip('\n')) > 0]
+
+    return fullfiles
+
+def read_wave(file, target_fs):
+    fs, x = wavfile.read(file)
+
+    if fs < target_fs:
+        return None
+        print(f"[read_wave] warning: file {file} will be up-sampled from {fs} to {target_fs} Hz")
+
+    if fs != target_fs:
+        x = resampy.resample(x, fs, target_fs)
+
+    return x.astype(np.float32)
+
+def random_normalize(x, db_min, db_max, max_val=2**15 - 1):
+    db = np.random.uniform(db_min, db_max, 1)
+    m = np.abs(x).max()
+    c = 10**(db/20) * max_val / m
+
+    return c * x
+
+
+def concatenate(filelist : str, output : str, target_fs: int, normalize=True, db_min=0, db_max=0, verbose=False):
+
+    overlap_size = int(40 * target_fs / 8000)
+    overlap_mem = np.zeros(overlap_size, dtype=np.float32)
+    overlap_win1 = (0.5 + 0.5 * np.cos(np.arange(0, overlap_size) * np.pi / overlap_size)).astype(np.float32)
+    overlap_win2 = np.flipud(overlap_win1)
+
+    with open(output, 'wb') as f:
+        for file in filelist:
+            x = read_wave(file, target_fs)
+            if x is None: continue
+
+            if len(x) < 10 * overlap_size:
+                if verbose: print(f"skipping {file}...")
+                continue
+            elif verbose:
+                print(f"processing {file}...")
+
+            if normalize:
+                x = random_normalize(x, db_min, db_max)
+
+            x1 = x[:-overlap_size]
+            x1[:overlap_size] = overlap_win1 * overlap_mem + overlap_win2 * x1[:overlap_size]
+
+            f.write(x1.astype(np.int16).tobytes())
+
+            overlap_mem = x1[-overlap_size]
+
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+
+    filelist = read_filelist(args.basedir, args.filelist)
+
+    concatenate(filelist, args.output, args.target_fs, normalize=args.normalize, db_min=args.db_min, db_max=args.db_max, verbose=args.verbose)