| `base` | 142 MB | Fast | Fair | Short clips, clear audio |
| `small` | 466 MB | Moderate | Good | **Default** — good for most content |
| `medium` | 1.5 GB | Slow | Very good | Important content, noisy audio, music |
| `large-v3` | 3.1 GB | Slowest | Best | Production quality |
**Only add `.en` suffix when the user explicitly says the audio is English.**`.en` models are slightly more accurate for English but will TRANSLATE non-English audio instead of transcribing it.
**Critical: `.en` models translate non-English audio into English** — they don't transcribe it. If the audio might not be English, always use a model without the `.en` suffix and pass `--language` to specify the source language. If you're unsure of the language, use `small` (not `small.en`) without `--language` — whisper will auto-detect.
**Music and vocals over instrumentation**: `small.en` will misidentify lyrics — use `medium.en` as the minimum, or import lyrics manually. Even `medium.en` struggles with heavily produced tracks; for music videos, providing known lyrics as an SRT/VTT and importing with `hyperframes transcribe lyrics.srt` will always beat automated transcription.
## Transcript Quality Check (Mandatory)
After every transcription, **read the transcript and check for quality issues before proceeding.** Bad transcripts produce nonsensical captions. Never skip this step.
| 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 |
### Automatic retry rules
**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:
1.**Retry with `medium.en`** if the original used `small.en` or smaller:
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:
- Providing lyrics manually as an SRT/VTT file
- Using an external API (OpenAI or Groq Whisper — see below)
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.
### Cleaning a transcript
After transcription (even with a good model), strip non-word entries:
```js
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;
});
```
### When to use which model (decision tree)
1. **Is this speech over silence/light background?** → `small.en` is fine
2. **Is this speech over music, or music with vocals?** → Start with `medium.en`
3. **Is this a produced music track (vocals + full instrumentation)?** → Start with `medium.en`, expect to need manual lyrics or an external API
4. **Is this multilingual?** → Use `medium` or `large-v3` (no `.en` suffix)
## Using External Transcription APIs
For the best accuracy, use an external API and import the result:
