Whisper Model Has Inaccessible Logits/Log Probs

Description:
Hello, I've been tinkering with CTranslate2 with whisper converted to ct2. Using model.generate with return_logits_vocab=True and return_scores=True I'm unable to access or print out the logits entirely. For example:

result = model.generate(..., return_logits_vocab=True, return_scores=True)
print(result[0].logits[0][0])
# Output: [cpu:0 float32 storage viewed as ]
print(result[0].logits[0][0].__array_interface__)
# Output: {'shape': (), 'typestr': '<f4', 'data': (0, False), 'version': 3}

This issue is reproducible with any input, even though the sequences are transcribed correctly.

Expected Behavior:
I would expect result[0].logits to contain accessible tensors with the logit values or log probs for each token generated.

Actual Behavior:
The logits appear to be empty or not fully accessible, as indicated by the output above.

This is super strange to me because result[0].scores[0] is completely accessible and prints out exactly as expected and I'd have to imagine that the scores attribute is just some mean or derivative of all of the log probs/logits for each token generated by CTranslate2. Which leads me to believe that this is some sort of bug rather than intentional behavior to hide the log probs.

Additional Details:

• CTranslate2 Version: 4.5.0

• I also tried the latest commit (6a3dc63db5608567bf554ca5dbbfdac1f7a2321f) which seemed related to the logits issues. While the commit added the logits property on print WhisperGenerationResult with print(result[0]), even there the issue persists with the same empty logits and everything else working:

  logits=[[
  [cpu:0 float32 storage viewed as ]], [
  [cpu:0 float32 storage viewed as ]], [
  [cpu:0 float32 storage viewed as ]], [
  [cpu:0 float32 storage viewed as ]], [
  [cpu:0 float32 storage viewed as ]]
  

Environment:
Python 3.11.11
MacOS Sequoia 15.2

Running cpu float32 inference on an M1 Pro Macbook.

Please let me know if you need any more information. If this is indeed a bug (and not intended behavior for the Whisper model), I'm happy to investigate further or contribute a fix.

Thanks!

Reproduce the Whisper path through the Python model.generate call with return_logits_vocab=True and return_scores=True, then inspect the WhisperGenerationResult logits exposed by that API. Done means result[0].logits contains accessible values for each generated token, while the existing scores behavior remains intact.