Setting the file. One moment.
Subchapter 27.41
audio/references/captions/transcript-handling.mdMarkdown5 KBView on GitHub
For the transcribe CLI invocation, the .en-translates-non-English rule, and whisper model selection, see ../transcribe.md. This file covers what to do with the resulting transcript when authoring captions: input formats, mandatory quality checks, cleaning code, external-API fallbacks.
scripts/11 filesThe CLI auto-detects and normalizes these formats:
| Format | Extension | Source | Word-level? |
|---|---|---|---|
| whisper.cpp JSON | .json | hyperframes init --video, hyperframes transcribe | Yes |
| OpenAI Whisper API | .json | openai.audio.transcriptions.create({ timestamp_granularities: ["word"] }) | Yes |
| SRT subtitles | .srt | Video editors, subtitle tools, YouTube | No (phrase-level) |
| VTT subtitles | .vtt | Web players, YouTube, transcription services | No (phrase-level) |
| Normalized word array | .json | Pre-processed by any tool | Yes |
Word-level timestamps produce better captions. SRT/VTT give phrase-level timing, which works but can’t do per-word animation effects.
After every transcription, read the transcript and check for quality issues before proceeding. Bad transcripts produce nonsensical captions. Never skip this step.
| Signal | Example | Cause |
|---|---|---|
Music note tokens (♪, �) | { "text": "♪" } or { "text": "�" } | Whisper detected music, not speech |
| Garbled / nonsense words | “Do a chin”, “Get so gay”, “huh” | Model misheard lyrics or background noise |
| Long gaps with no words | 20+ seconds of only ♪ tokens | Instrumental section — expected, but high ratio means speech is being missed |
| Repeated filler | Many “huh”, “uh”, “oh” entries | Model is hallucinating on music |
| Very short word spans | Words with end - start < 0.05 | Unreliable timestamp alignment |
If more than 20% of entries are ♪/� tokens, or the transcript contains obvious nonsense words, the transcription failed. Do not proceed with the bad transcript. Instead:
medium.en if the original used small.en or smaller:
npx hyperframes transcribe audio.mp3 --model medium.enmedium.en also fails (still >20% music tokens or garbled), tell the user the audio is too noisy for local transcription and suggest:
♪/� tokens and entries where text is a single non-word character. Only real words should reach the caption composition.After transcription (even with a good model), strip non-word entries:
var raw = JSON.parse(transcriptJson);
var words = raw.filter(function (w) {
if (!w.text || w.text.trim().length === 0) return false;
if (/^[♪�\u266a\u266b\u266c\u266d\u266e\u266f]+$/.test(w.text)) return false;
if (/^(huh|uh|um|ah|oh)$/i.test(w.text) && w.end - w.start < 0.1) return false;
return true;
});For model-selection guidance by content type, see ../transcribe.md → “Picking a model by content type”.
For the best accuracy, use an external API and import the result:
OpenAI Whisper API (recommended for quality):
# Generate with word timestamps, then import
curl https://api.openai.com/v1/audio/transcriptions \
-H "Authorization: Bearer $OPENAI_API_KEY" \
-F file=@audio.mp3 -F model=whisper-1 \
-F response_format=verbose_json \
-F "timestamp_granularities[]=word" \
-o transcript-openai.json
npx hyperframes transcribe transcript-openai.jsonGroq Whisper API (fast, free tier available):
curl https://api.groq.com/openai/v1/audio/transcriptions \
-H "Authorization: Bearer $GROQ_API_KEY" \
-F file=@audio.mp3 -F model=whisper-large-v3 \
-F response_format=verbose_json \
-F "timestamp_granularities[]=word" \
-o transcript-groq.json
npx hyperframes transcribe transcript-groq.jsontranscript.json, .srt, or .vtt files.../transcribe.md — pick the starting model from “Picking a model by content type” there.