# Audio / voice

Ollama can *understand* audio with speech-capable models (e.g. Gemma 4 `e4b`/`e2b`/
`12b`), but only via its **native `/api/chat`** with the audio base64 in the
`images` field — the OpenAI `input_audio` format is silently dropped, and there's
no native speech-to-text endpoint. This router bridges that gap.

Set the model with `ROUTER_AUDIO_MODEL` (default `gemma4:e4b`). It must be a model
whose `ollama show` capabilities include `audio`. **Note:** some fine-tunes/quants
have broken audio even if the flag is set — test before relying on one.

## Speech-to-text (Whisper-shaped)

```bash
curl http://<host>:8080/v1/audio/transcriptions \
  -F file=@recording.wav \
  -F model=whisper-1
# -> {"text": "the transcription"}
```
- `response_format=text` returns plain text instead of JSON.
- `/v1/audio/translations` does the same but translates to English.
- Works with the OpenAI SDK: set `base_url` to the router and call
  `audio.transcriptions.create(...)`. The `model` field is ignored — the router
  always uses `ROUTER_AUDIO_MODEL`.

## Audio inside a chat

Send OpenAI `input_audio` content to `/v1/chat/completions` and ask about it:
```json
{"model":"auto","messages":[{"role":"user","content":[
  {"type":"text","text":"answer the question in this audio"},
  {"type":"input_audio","input_audio":{"data":"<base64-wav>","format":"wav"}}
]}]}
```
The router detects the audio, translates it to Ollama's native call, runs it on
`ROUTER_AUDIO_MODEL`, and returns a normal OpenAI response (streaming supported).

## Limitations

- **WAV is verified.** Other formats (mp3/m4a/ogg) depend on Ollama's decoding —
  transcode to WAV if they fail.
- **Not streaming STT** — it transcribes a complete clip per request (like
  Whisper's file API), not a live mic stream. Perfect for record-then-send.
- Audio only works through the router's native bridge, so audio requests always
  target `ROUTER_AUDIO_MODEL` (they bypass the normal model-selection map).