Hardware & run-time guidance¶
Plan analysis on your machine. TranscriptX detects a coarse hardware band for this process, shows a guidance-only wall-time range on Run Analysis / Batch, and suggests LLM size and STT backends that fit. Estimates never block Run analysis.
For maintainer measurement recipes and lab timings, see performance envelopes. For post-run observed timings, use View → Performance.
What we detect¶
Signal |
Meaning |
|---|---|
Memory band |
|
CPU class |
Coarse core-count band |
Acceleration |
What this process can use: |
Container |
Whether the analysis process looks like Docker/cgroup |
Install profile |
|
Honesty: Docker analysis often has no GPU even when the host can run CUDA WhisperX. Acceleration describes the analysis process, not host STT VRAM. We do not call nvidia-smi from the analysis image.
Run-time estimates¶
On Run Analysis and Batch, after you pick a preset and modules, a caption shows a range such as:
Estimated wall time: about 8–15 min (heuristic). Guidance, not a promise.
Confidence |
Meaning |
|---|---|
heuristic |
Coefficient table (preset × audio length × modules × LLM size × hardware band) |
calibrated |
Blended with similar past runs on this machine ( |
low |
Duration/workload unknown — wider range |
Caveats that often add minutes: first-run model downloads, Ollama holding another large model, Thorough + chart_descriptions, group = members + aggregation.
LLM size vs RAM¶
Memory band |
Prefer |
Avoid on this band |
|---|---|---|
low |
small (~3–4B), e.g. |
27B-class tags |
mid |
mid (~7–14B), e.g. |
Large tags unless you accept swap risk |
high / very_high |
mid or large when you want quality |
Loading several multi-GB models at once (contention) |
Settings → Models and the Run Analysis LLM expander show a one-line hardware hint for installed tags. Your selection is never rewritten automatically. Detail: llm.md.
Analysis models¶
Defaults (en_core_web_md + MiniLM) are CPU-friendly. On low/mid RAM bands, stay there unless you have headroom. Higher-accuracy env presets (lg / mpnet / transformer sentiment) need more memory — see models.md.
Thorough with install profile core may skip heavy extras (BERTopic, voice).
STT backends¶
Transcription engines stay out of the analysis image. Transcribe Audio captions a recommendation and preselects provider/device when you have not chosen yet:
Situation |
Recommendation |
|---|---|
macOS + whispermlx available |
whispermlx (MLX) |
CUDA visible to this process |
WhisperX Docker cuda (host must pass |
Analysis container without CUDA |
WhisperX Docker cpu; host STT may still use a GPU via a separate recipe |
Apple Silicon without whispermlx |
WhisperX Docker usually CPU-only — prefer installing whispermlx |
No STT wall-clock ETA in v1. Stacks: transcription.md, stt-stacks.