v1.10.0

## Add VibeVoice training, trained model inference, and streaming voice presets
### Training
- VibeVoice LoRA finetuning via subprocess (dataset validation, auto audio conversion, train_vibevoice.jsonl generation)
- Added Parameter UI: batch size, learning rate, epochs, save interval, DDPM batch multiplier, diffusion/CE loss weights, voice prompt drop, gradient accumulation, warmup steps, train diffusion head toggle, EMA on/off
- Separate Qwen3 and VibeVoice parameter sections.
- Auto-save/restore of all training params per engine via accordion expand
- Stop Training button — terminates subprocess mid-run with clean status
- Auto --train_connectors when training diffusion head

### Voice Presets — VibeVoice Trained
- Load and generate with trained VibeVoice LoRA models (language model LoRA + diffusion head + connectors)
- LoRA loading with PEFT task_type auto-correction (CAUSAL_LM → FEATURE_EXTRACTION)
- Advanced generation params: cfg_scale, num_steps, do_sample, temperature, top_k, top_p, repetition_penalty
- Trained model caching with smart reload on checkpoint change
- Added option to scale the Lora effect, when applied to a sample

### Voice Presets — VibeVoice Streaming (Fast Generation)
- VibeVoice Streaming 0.5B with 7 built-in preset voices (Carter, Davis, Emma, Frank, Grace, Mike, Samuel)
- Auto-downloads voice prompt .pt files from GitHub, caches locally
- Streaming generation params: cfg_scale, ddpm_steps
- Voice prompt KV-cache for fast repeated generation

### LLM — Added Qwen3.5 models to list.
- As Llama.cpp is compatible with Qwen3.5, replace Qwen3 with newer 3.5 models.

### Bug Fixes
- Added optional DeepFilterNet installation for audio denoising, as well as made python3.11 the new default to prevent issues with missing wheels with python 3.12
- Fixed visibility issues in Gradio, but also tweaked requirement to make Gradio 6.7 the default
- Improved Progress Notification with VibeVoice.