File size: 2,029 Bytes
fb0011a | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 | import os
import numpy as np
from scipy.io import wavfile
def is_extreme(wav_path, threshold=95):
"""
Return True if any sample in wav_path exceeds ±threshold% of full-scale.
"""
try:
_, data = wavfile.read(wav_path)
except Exception as e:
print(f"[SKIP] {wav_path!r}: {e}")
# Treat unreadable files as "extreme" so they're dropped
return True
samples = data.flatten().astype(np.float32)
dt = data.dtype
if dt == np.int16:
max_val = 32767.0
elif dt == np.int32:
max_val = 2147483647.0
elif dt == np.uint8:
samples -= 128.0
max_val = 127.0
else:
# assume float32/64 normalized to [-1,1]
max_val = 1.0
thresh_val = (threshold / 100.0) * max_val
return np.any(samples > thresh_val) or np.any(samples < -thresh_val)
def filter_to_new(input_list,
output_list,
prefix="../",
threshold=95):
"""
Read input_list, drop lines whose wavs exceed ±threshold%, and
write the remaining lines to output_list.
"""
with open(input_list, "r", encoding="utf-8") as f:
lines = [L for L in f if L.strip()]
kept = []
for L in lines:
rel_path = L.split("|", 1)[0].strip()
wav_path = os.path.join(prefix, rel_path)
if not os.path.isfile(wav_path):
print(f"[MISSING] {wav_path}")
continue
if is_extreme(wav_path, threshold):
print(f"[DROP] {wav_path}")
else:
kept.append(L)
with open(output_list, "w", encoding="utf-8") as f:
f.writelines(kept)
print(f"\nKept {len(kept)} of {len(lines)} entries.")
print(f"Written filtered list to: {output_list}")
if __name__ == "__main__":
filter_to_new(
input_list="train_list_filtered.txt",
output_list="train_list_no_clipped_90.txt",
prefix="../",
threshold=90
) |