Timing showed s3gen.inference (HiFiGAN vocoder) taking 22s and ref audio
processing ~18s - both dominated by Conv1d ops hitting MIOpen fallback.
With benchmark=False (default), PyTorch passes ptr=0 size=0 workspace to
MIOpen causing GemmFwdRest to fail and fall back to a slow path every call.
With benchmark=True, PyTorch evaluates convolution algorithms with proper
workspace allocation and caches the best result via MIOPEN_USER_DB_PATH.
First inference will be slower while benchmarking; subsequent calls use cache.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Wraps s3tokenizer, voice_encoder, and s3gen.inference with timing logs
so we can see exactly which step is consuming the missing ~33 seconds.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
Wyoming-only server built around the official chatterbox TTS model.
Includes ROCm/AMD GPU support, sentence-level streaming, config.yaml
management, and Gitea CI for container builds.
Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>
