Inquiry on "is_prompt" usage in training process

Hi VoxCPM Team,

I am exploring the fine-tuning code of VoxCPM and noticed the is_prompt parameter in the dataset processing/collation stage packers.py By default, is_prompt is set to False during fine-tuning. However, looking into the mechanics, flipping this parameter to True changes the structural arrangement of the sequence and the behavior of the loss_mask:

• When is_prompt=True: The sequence layout is restructured. The reference audio/text segment is explicitly separated and treated as a "prefix" or contextual prompt at the beginning of the sequence, paving the way for the subsequent continuation sequence.
• When is_prompt=True: The loss_mask for the prompt prefix section is forcefully set to 0. The loss computation and gradient backpropagation are tightly constrained only to the continuation part (the text/audio following the prompt prefix).

Questions & Core Concerns

1. Is this parameter explicitly designed for Continuous and Ultimate Clone training?
2. Fine-tuning mix without is_prompt=True: Will it fail to optimize continuation ability?
   Could you confirm if keeping is_prompt=False on data with references will indeed dilute or fail to optimize the targeted continuation/generation capability? What is your recommended data configuration and is_prompt toggling strategy when mixing vanilla TTS data and prefix-conditioned audio data?

Thank you for your time and for open-sourcing this amazing framework! Looking forward to your insights.

Start with src/voxcpm/training/packers.py and trace how is_prompt changes sequence layout and loss_mask during collation. Compare the false and true paths against the fine-tuning scenarios described in the issue; done means documenting whether each mode supports continuation training and how mixed data should configure is_prompt.