`preprocess.py` could silently drop training data

[Idiotabtcodes]

During the parameter rename (idx0 → output_key), the final norm_write call was dedented out of the slicer loop:

Retrieval-based-Voice-Conversion-WebUI/train/preprocess.py

Lines 96 to 109 in 81eed5e

	for audio in self.slicer.slice(audio):
	i = 0
	while 1:
	start = int(self.sr * (self.per - self.overlap) * i)
	i += 1
	if len(audio[start:]) > self.tail * self.sr:
	tmp_audio = audio[start : start + int(self.per * self.sr)]
	self.norm_write(tmp_audio, output_key, idx1)
	idx1 += 1
	else:
	tmp_audio = audio[start:]
	idx1 += 1
	break
	self.norm_write(tmp_audio, output_key, idx1)

Retrieval-based-Voice-Conversion-WebUI/train/preprocess.py

Line 52 in 81eed5e

self.tail = self.per + self.overlap

The tail chunk is a full-length training sample. And since the slicer's min_length is 1500 ms, any sliced utterance shorter than 4 s consists entirely of that tail chunk, so it's dropped whole.