Fix: Peak audio normalization (-1 dB), robust speaker search, and tuned CFG for crystal-clear benchmark generation

Fix: Peak audio normalization (-1 dB), robust speaker search, and tuned CFG for crystal-clear benchmark generation

Fix: Eliminate 30MB raw audio waveform IPC serialization to permanently prevent Docker /dev/shm shared memory crashes

Turbo Speed: Vectorized FFT F0 pitch, cuDNN autotune, Tensor Core precision, speaker RAM cache & prefetch DataLoader

Turbo Speed: Vectorized FFT F0 pitch, cuDNN autotune, Tensor Core precision, speaker RAM cache & prefetch DataLoader

Master Fix: Real F0 pitch, acoustic Mel-80 to 100 filterbank vocoder projection, soundfile multi-format loader, safe weights_only

Fix: CUDA OOM prevention via expandable_segments, audio duration clamping, empty_cache, and safe 3090 batching

Fix: Docker container /dev/shm exhaustion by setting mp sharing strategy to file_system and num_workers to 2

Fix: check hasattr for sampler.set_epoch on WeightedRandomSampler

Performance: 32 parallel download workers and hf_transfer for 10x faster dataset downloading

Performance: 32 parallel download workers and hf_transfer for 10x faster dataset downloading

Cloud-Sync & Auto-Resume: Rental GPU data protection with --push_to_hub and automatic cloud recovery

Master SOTA Upgrade: in-context voice conditioning, decoupled studio vocoder, EMA tracking, warmup scheduler

Clean train.py: save local step checkpoint only, no auto-upload

Live auto-sync every 10k step milestone directly to HF

Add --save_every_steps and max_steps instant checkpoint saving

Update: auto-download HF dataset fallback in train.py

Update: training fixes, LICENSE + requirements + scripts

Initial commit: ViuAI_TTS_200M architecture, prosody engine, and weights

Initial commit: ViuAI_TTS_200M architecture, prosody engine, and weights