Local speech experiments with reproducible inputs and scoring. One workflow transcribes mixed Chinese–English clips with no, a correct or an unrelated glossary; on 200 frozen ASCEND clips (600 CPU runs) the correct glossary did not recover missed terms, and the small drop in error rate was within noise. The other separates two overlapping speakers, matches them, transcribes with Qwen and writes meeting notes with Ollama.

Reproducible H100 benchmark of eager PyTorch, vLLM 0.17.1 and TensorRT-LLM on the autoregressive backbone of a multi-stage TTS model. vLLM reaches about 453 tok/s against 213 for TensorRT-LLM and 21 for eager, but the LLM is only about 21% of end-to-end latency; the flow-matching stage takes about 68%, which these engines do not cover.

A keyboard-first, self-hosted workbench for translation quality assessment and speech transcription review. Blind golden sets, append-only audit trails, an LLM judge that must be calibrated before use, and content-hashed exports are part of the data model.

Open-weight TTS and ASR run as a production-style AWS service: Kokoro-82M text-to-speech on ECS Fargate (Graviton), a CPU Whisper transcription endpoint, infrastructure in AWS CDK, keyless OIDC CI/CD, and a GPU LoRA fine-tuning track.

A diagnosis of Direct Preference Optimization on a continuous-latent flow-matching TTS model. The automatic-metric reward consistently prefers the candidate the model finds less likely, so standard DPO pushes the model the wrong way and quality degrades. Shown with a held-out benchmark, a training-free mechanistic probe and a cross-architecture replication, followed by the alignment routes that do work.

Real-time Chinese–English interpretation for medical phone calls, built on Twilio Media Streams and the OpenAI Realtime API, with a live web dashboard and auto-generated SOAP notes. First-token latency is about 400–800 ms end to end.