  Conventional ASR systems use frame-level phoneme posterior to conduct
force-alignment~(FA) and provide timestamps, while end-to-end ASR systems
especially AED based ones are short of such ability. This paper proposes to
perform timestamp prediction~(TP) while recognizing by utilizing continuous
integrate-and-fire~(CIF) mechanism in non-autoregressive ASR model -
Paraformer. Foucing on the fire place bias issue of CIF, we conduct
post-processing strategies including fire-delay and silence insertion. Besides,
we propose to use scaled-CIF to smooth the weights of CIF output, which is
proved beneficial for both ASR and TP task. Accumulated averaging shift~(AAS)
and diarization error rate~(DER) are adopted to measure the quality of
timestamps and we compare these metrics of proposed system and conventional
hybrid force-alignment system. The experiment results over manually-marked
timestamps testset show that the proposed optimization methods significantly
improve the accuracy of CIF timestamps, reducing 66.7\% and 82.1\% of AAS and
DER respectively. Comparing to Kaldi force-alignment trained with the same
data, optimized CIF timestamps achieved 12.3\% relative AAS reduction.
