mirror of https://github.com/decolua/9router.git synced 2026-05-08 12:01:28 +00:00

Files

decolua 5c62e73cc6 - Cowork: ComboFormModal

- BaseUrlSelect: add cloud endpoint option, custom URL local state, always
  default to first option; new cliEndpointMatch helper; CLI tool cards refactor
- API: new /v1/audio/voices and /v1/models/info; /v1/models filters disabled
  models, drop unused timestamp
- initializeApp: guard tunnel/tailscale auto-resume to once-per-process
- geminiHelper: ensureObjectType for schemas with properties but no type
- skills: minor SKILL.md tweaks (chat/embeddings/image/stt/tts/web-*)

2026-05-07 15:45:09 +07:00

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name, description

name	description
9router-stt	Speech-to-text via 9Router /v1/audio/transcriptions using OpenAI Whisper / Groq / Gemini / Deepgram / AssemblyAI / NVIDIA / HuggingFace models. Use when the user wants to transcribe audio, convert speech to text, or get subtitles from audio files.

9Router — Speech-to-Text

Requires NINEROUTER_URL (and NINEROUTER_KEY if auth enabled). See https://raw.githubusercontent.com/decolua/9router/refs/heads/master/skills/9router/SKILL.md for setup.

Discover

curl $NINEROUTER_URL/v1/models/stt | jq '.data[].id'
# Per-model params (language, response_format, prompt, temperature support)
curl "$NINEROUTER_URL/v1/models/info?id=openai/whisper-1"

model = STT model ID (e.g. openai/whisper-1, groq/whisper-large-v3, deepgram/nova-3, gemini/gemini-2.5-flash).

Endpoint

POST $NINEROUTER_URL/v1/audio/transcriptions (OpenAI Whisper compatible, multipart/form-data)

Field	Required	Notes
`model`	yes	from `/v1/models/stt`
`file`	yes	audio file (mp3, wav, m4a, webm, ogg, flac)
`language`	no	ISO-639-1 (e.g. `en`, `vi`)
`prompt`	no	hint text to guide transcription
`response_format`	no	`json` (default) / `text` / `verbose_json` / `srt` / `vtt`
`temperature`	no	0–1

Examples

curl -X POST "$NINEROUTER_URL/v1/audio/transcriptions" \
  -H "Authorization: Bearer $NINEROUTER_KEY" \
  -F "model=openai/whisper-1" \
  -F "file=@audio.mp3" \
  -F "language=vi"

JS (Node):

import { createReadStream } from "node:fs";
const form = new FormData();
form.append("model", "groq/whisper-large-v3-turbo");
form.append("file", new Blob([await (await import("node:fs/promises")).readFile("audio.mp3")]), "audio.mp3");
const r = await fetch(`${process.env.NINEROUTER_URL}/v1/audio/transcriptions`, {
  method: "POST",
  headers: { "Authorization": `Bearer ${process.env.NINEROUTER_KEY}` },
  body: form,
});
const { text } = await r.json();
console.log(text);

Response shape

Default (response_format=json):

{ "text": "Xin chào, đây là bản ghi âm." }

verbose_json adds language, duration, segments[] with timestamps. srt / vtt return subtitle text.

Provider quirks

Provider	`model` format	Notes
`openai`	`whisper-1`, `gpt-4o-transcribe`, `gpt-4o-mini-transcribe`	Native OpenAI shape
`groq`	`whisper-large-v3`, `whisper-large-v3-turbo`, `distil-whisper-large-v3-en`	Fastest; OpenAI shape
`gemini`	`gemini-2.5-flash`, `gemini-2.5-pro`, `gemini-2.5-flash-lite`	Server converts to `generateContent` with audio inline
`deepgram`	`nova-3`, `nova-2`, `whisper-large`	Token auth; server adapts response
`assemblyai`	`universal-3-pro`, `universal-2`	Async upload+poll handled server-side
`nvidia`	`nvidia/parakeet-ctc-1.1b-asr`	NIM endpoint
`huggingface`	`openai/whisper-large-v3`, `openai/whisper-small`	HF Inference API

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