[codex] add long-form generation mode

[Lee-take]

Summary

• add VoxCPM.generate_long_form() for long scripts by splitting text into shorter segments
• reuse the first generated segment as a fixed continuation prompt when no external reference is provided
• keep an explicit external reference audio as the stable reference voice when the caller supplies one
• ensure the seed prompt_text matches the spoken transcript, excluding voice-design control text that is not spoken
• expose the mode through CLI flags: --long-form, --long-form-max-chars, and --long-form-silence-ms
• document Python and CLI usage in the English and Chinese READMEs

Why

Very long single-pass generation can accumulate autoregressive drift. The long-form path keeps each generation pass short and re-anchors later segments with stable prompt context, which is an API/CLI-level mitigation that does not change model weights or decoder internals.

The generated seed segment is no longer duplicated as both reference audio and prompt audio. If no external reference is supplied, the long-form path uses prompt continuation only; if the caller supplies an external reference, it is preserved as reference conditioning. This avoids over-conditioning on the same generated seed audio while still keeping a stable continuation anchor.

The seed prompt transcript must match the seed prompt audio exactly. Voice-design control text guides the first generation pass, but it is not part of the spoken audio; including it in later continuation prompt text can make the continuation conditioning inconsistent and degrade long-form output.

Tests

• python -m pytest tests/test_core_long_form.py tests/test_cli.py -q
• python -m compileall src tests -q
• local installed VoxCPM2 checks: python tests/test_voxcpm_long_form.py and python tests/test_voxcpm2_voice_anchor.py
• local long-form smoke run with VoxCPM2 CUDA runtime produced a 108.14s WAV at 48kHz mono using fixed seed-prompt continuation and shorter default segments

Not run: full model-dependent upstream test suite, because this local PR environment only installed lightweight unit-test dependencies and no full torch/model runtime.