Word-by-word analysis

Ctrl+A breaks down the sentence currently shown in the current-sentence card word by word, showing a translation and a pronunciation guide for each word. It uses a locally running Ollama instance — like whisper-cli, Ollama runs entirely on your own computer and isn't bundled with TrangoPlayer, so it needs to be installed separately.

Setting up Ollama

  1. Install Ollama from ollama.com.
  2. Make sure it's running (ollama serve, or however your install starts it).
  3. Pull at least one model: ollama pull llama3.1 (or any model you prefer).

TrangoPlayer talks to Ollama's default local address, http://localhost:11434 — no configuration needed if Ollama is running with its own defaults.

Picking a model and target language

The Subtitles dialog's "Ollama model" row opens a picker listing whatever models ollama list would show. The pick is remembered across restarts, the same way the whisper model is (see Settings).

The "Target language" field next to it (defaults to "English") is what translations and pronunciations are produced in — type any language name. It saves as you type and is remembered across restarts. Changing it only affects sentences analyzed after the change; sentences already analyzed keep whatever language they were analyzed in until re-analyzed (delete the cache file described below to force re-analysis in a new language).

Using it

Ctrl+A works in both Normal and Sentence-by-sentence mode, on whichever sentence the current-sentence card is showing — in Normal mode, the card automatically follows along as the video plays, so Ctrl+A always analyzes the line currently on screen, not whatever line happened to be current when you switched into Normal mode. The first time a given sentence is analyzed, it calls Ollama (a few seconds, depending on the model and machine); every time after that — including across restarts — it's instant, since the result is cached to a <subtitle-name>.wordanalysis.json file right next to the subtitle file (e.g. movie.srt → movie.wordanalysis.json).

"Analyze all sentences" (also in the Subtitles dialog, next to the Ollama model row) runs the same analysis for every sentence in the currently linked subtitle in one background pass — useful for pre-analyzing a whole video before watching it, rather than one sentence at a time via Ctrl+A. It writes to the same cache file, skipping sentences already analyzed, so it's safe to stop partway through (close the app, or just decide you have enough) and pick up later — including after adding individual Ctrl+A analyses in between. A sentence that fails to analyze is retried a few times before the run moves on to the next one; if it still fails, it's saved with a blank analysis rather than stopping the whole run — re-run Ctrl+A on that sentence later to fill it in.

Both features need a subtitle to be linked and an Ollama model selected first; TrangoPlayer shows a clear inline message rather than a generic error if either is missing.

Hebrew pronunciation

For Hebrew sentences specifically, the pronunciation guide isn't guessed by Ollama — small local models transliterate Hebrew unreliably even when they translate it correctly. Instead TrangoPlayer runs a separate niqud (vowel-point) diacritization model (Phonikud) directly and derives the pronunciation deterministically from its output. Hebrew sentences are detected automatically from their script — nothing to configure there.

The model itself needs a one-time setup: download phonikud-1.0.int8.onnx and tokenizer.json into the same folder, then point Settings → "HEBREW NIQUD MODEL (.ONNX)" at the .onnx file. If no model is configured, or loading it fails, Ctrl+A/"Analyze all sentences" still work exactly as before, just with Ollama's own (less accurate) pronunciation guess for Hebrew lines.

Also needs ONNX Runtime itself installed — TrangoPlayer's .deb package pulls in Ubuntu/Debian's libonnxruntime1.23 automatically, so this needs no action if you installed that way. Installed some other way? Install libonnxruntime1.23 (or newer) yourself; TrangoPlayer finds it in the usual system library locations without any extra configuration — see ort for why this is a separate runtime dependency rather than bundled.

If a model returns bad or empty analyses

Run with the --debug flag to see exactly what prompt was sent to Ollama and the raw text it returned:

cargo run --release -p trango -- --debug video.mp4 subs.srt

This is the most common way to diagnose a model returning nothing: some reasoning-capable models (e.g. the qwen3 family) can spend their whole generation budget "thinking" instead of answering unless told not to. TrangoPlayer already asks models not to do this, but if a similar issue turns up with a different model, the debug log shows the raw response that failed to parse. See Keyboard shortcuts for more on --debug.