  This paper details the experimental results of adapting the OpenAI's Whisper
model for Code-Switch Mandarin-English Speech Recognition (ASR) on the SEAME
and ASRU2019 corpora. We conducted 2 experiments: a) using adaptation data from
1 to 100/200 hours to demonstrate effectiveness of adaptation, b) examining
different language ID setup on Whisper prompt.
  The Mixed Error Rate results show that the amount of adaptation data may be
as low as $1\sim10$ hours to achieve saturation in performance gain (SEAME)
while the ASRU task continued to show performance with more adaptation data
($>$100 hours). For the language prompt, the results show that although various
prompting strategies initially produce different outcomes, adapting the Whisper
model with code-switch data uniformly improves its performance.
  These results may be relevant also to the community when applying Whisper for
related tasks of adapting to new target domains.
