| 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}")
|
|
|
| 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:
|
|
|
| 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
|
| ) |
