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98 lines
5.3 KiB
Markdown
98 lines
5.3 KiB
Markdown
# Transcript Guide
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For the `transcribe` CLI invocation, the `.en`-translates-non-English rule, and whisper model selection, see [`../transcribe.md`](../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.
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## Supported Input Formats
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The CLI auto-detects and normalizes these formats:
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| Format | Extension | Source | Word-level? |
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| --------------------- | --------- | --------------------------------------------------------------------------- | ----------------- |
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| whisper.cpp JSON | `.json` | `hyperframes init --video`, `hyperframes transcribe` | Yes |
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| OpenAI Whisper API | `.json` | `openai.audio.transcriptions.create({ timestamp_granularities: ["word"] })` | Yes |
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| SRT subtitles | `.srt` | Video editors, subtitle tools, YouTube | No (phrase-level) |
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| VTT subtitles | `.vtt` | Web players, YouTube, transcription services | No (phrase-level) |
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| Normalized word array | `.json` | Pre-processed by any tool | Yes |
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**Word-level timestamps produce better captions.** SRT/VTT give phrase-level timing, which works but can't do per-word animation effects.
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## Transcript Quality Check (Mandatory)
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After every transcription, **read the transcript and check for quality issues before proceeding.** Bad transcripts produce nonsensical captions. Never skip this step.
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### What to look for
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| Signal | Example | Cause |
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| ---------------------------- | -------------------------------------- | ---------------------------------------------------------------------------- |
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| Music note tokens (`♪`, `�`) | `{ "text": "♪" }` or `{ "text": "�" }` | Whisper detected music, not speech |
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| Garbled / nonsense words | "Do a chin", "Get so gay", "huh" | Model misheard lyrics or background noise |
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| Long gaps with no words | 20+ seconds of only `♪` tokens | Instrumental section — expected, but high ratio means speech is being missed |
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| Repeated filler | Many "huh", "uh", "oh" entries | Model is hallucinating on music |
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| Very short word spans | Words with `end - start < 0.05` | Unreliable timestamp alignment |
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### Automatic retry rules
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**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:
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1. **Retry with `medium.en`** if the original used `small.en` or smaller:
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```bash
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npx hyperframes transcribe audio.mp3 --model medium.en
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```
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2. **If `medium.en` also fails** (still >20% music tokens or garbled), tell the user the audio is too noisy for local transcription and suggest:
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- Providing lyrics manually as an SRT/VTT file
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- Using an external API (OpenAI or Groq Whisper — see below)
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3. **Always clean the transcript** before building captions — filter out `♪`/`�` tokens and entries where `text` is a single non-word character. Only real words should reach the caption composition.
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### Cleaning a transcript
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After transcription (even with a good model), strip non-word entries:
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```js
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var raw = JSON.parse(transcriptJson);
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var words = raw.filter(function (w) {
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if (!w.text || w.text.trim().length === 0) return false;
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if (/^[♪�\u266a\u266b\u266c\u266d\u266e\u266f]+$/.test(w.text)) return false;
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if (/^(huh|uh|um|ah|oh)$/i.test(w.text) && w.end - w.start < 0.1) return false;
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return true;
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});
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```
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For model-selection guidance by content type, see [`../transcribe.md`](../transcribe.md) → "Picking a model by content type".
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## Using External Transcription APIs
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For the best accuracy, use an external API and import the result:
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**OpenAI Whisper API** (recommended for quality):
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```bash
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# Generate with word timestamps, then import
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curl https://api.openai.com/v1/audio/transcriptions \
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-H "Authorization: Bearer $OPENAI_API_KEY" \
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-F file=@audio.mp3 -F model=whisper-1 \
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-F response_format=verbose_json \
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-F "timestamp_granularities[]=word" \
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-o transcript-openai.json
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npx hyperframes transcribe transcript-openai.json
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```
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**Groq Whisper API** (fast, free tier available):
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```bash
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curl https://api.groq.com/openai/v1/audio/transcriptions \
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-H "Authorization: Bearer $GROQ_API_KEY" \
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-F file=@audio.mp3 -F model=whisper-large-v3 \
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-F response_format=verbose_json \
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-F "timestamp_granularities[]=word" \
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-o transcript-groq.json
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npx hyperframes transcribe transcript-groq.json
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```
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## If No Transcript Exists
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1. Check the project root for `transcript.json`, `.srt`, or `.vtt` files.
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2. If none found, run [`../transcribe.md`](../transcribe.md) — pick the starting model from "Picking a model by content type" there.
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3. Run the quality check above. If it fails, retry with a larger model or fall back to manual lyrics / external API.
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