Fix Moonshine training-loss double-shift (train against labels, not labels[..., 1:]) (#46784)
* Fix Moonshine training-loss double-shift
Moonshine right-shifts labels into decoder_input_ids, then computes the loss via
ForCausalLMLoss, which shifts again, so it trains against labels[..., 1:]. Use a
plain CrossEntropyLoss instead, matching Whisper/Bart and the VisionEncoderDecoder
fix in #40863. Adds a regression test (with and without -100 padding).
* Regenerate Moonshine streaming model
* Mirror the training-loss regression test in moonshine_streaming
make fix-repo propagated the loss fix to the streaming model, so the same no-double-shift test should guard it there too.