Add probabilistic pretrain + GRPO RL pipeline with pluggable rewards and tracking (backward‑compatible)

[hcsolakoglu]

With this PR, I'm integrating the RL workflow of the F5R into the F5-TTS while maintaining the default deterministic behavior and checkpoint compliance. Goal is to enable a two‑stage pipeline (Gaussian NLL warmup + GRPO RL
fine‑tuning) with a modular reward system and opt‑in robustness improvements, without changing the default training or inference paths.

Key changes:

• Probabilistic output head (proj_out_ln_sig) with gaussian_nll objective and backward‑compatible checkpoint loading.
• GRPO trainer and RL sampling utilities, with optional steps_plus_one and prompt‑length modes.
• Pluggable reward system (RewardProvider, registry, combiner) + built‑in FunASR WER and WeSpeaker similarity providers (optional deps, lazy import, caching).
• Reward logging improvements and optional Trackio support (drop‑in for W&B).
• Optional stability knobs for GRPO (rl.kl_eps, rl.density_eps) while keeping F5R‑parity defaults.
• Dynamic batch sampler optimization to avoid materializing repeated batches in memory.
• Extensive tests covering Gaussian head, checkpoint compatibility, RL training step, reward plugins, device handling, and new opt‑ins.

Notes on compatibility:

• Defaults remain deterministic (output_dist=deterministic, objective=mse), so existing training/inference and checkpoints work unchanged.

• All deviations from F5R behavior are opt‑in and documented in README_RL.md.

• README_RL.md updated with a concise RL runbook, dataset prep, reward model fetch, and recommended opt‑ins.

[hcsolakoglu]

I have several ideas on how to initialize the probabilistic output head, so I will be implementing and testing multiple approaches. This is still a work in progress, but I have made significant headway. If anyone would like to guide the direction, feel free to run tests and share your feedback. @SWivid