solab-stt

What is this model?

A self-hosted speech-to-text (STT) model based on faster-whisper, running on our own GPU infrastructure. It transcribes audio to text. Two usage modes: one-shot transcription by uploading a file, or a WebSocket for real-time (live) streaming.

Capability & type

Capability	Speech to Text (STT)
Model type	Speech-to-text (STT) (type=8)
Base model	faster-whisper
Provider	arpanet (local / self-hosted)
Streaming	✓ supported

Endpoint

POST /v1/audio/transcriptions

Secondary endpoints:

• GET /v1/audio/stream (WebSocket, real-time)

Base URL: https://app.qevron.ai/v1

Authentication

Every request needs the Authorization: Bearer <KEY> header.

Authorization: Bearer <KEY>

Request schema & parameters

Parameter	Type	Required	Description
model	string	✓	solab-stt
file	file	✓	Audio file (multipart)
language	string	—	Language code (e.g. tr, en)

Response schema

{ "text": "transcribed text ..." }

Streaming

Add "stream": true; the server replies incrementally over Server-Sent Events.

Code examples

curl

curl https://app.qevron.ai/v1/audio/transcriptions \
  -H "Authorization: Bearer $QEVRON_KEY" \
  -F model="solab-stt" \
  -F file=@audio.wav

Python

from openai import OpenAI
client = OpenAI(api_key="$QEVRON_KEY", base_url="https://app.qevron.ai/v1")
with open("audio.wav", "rb") as f:
    r = client.audio.transcriptions.create(model="solab-stt", file=f)
print(r.text)

Pricing

Input	Output	Unit
$0.003	—	1K tokens

Rate limits

RPM/TPM are governed by your group quota. Check your current quota with: GET /v1/dashboard/billing/quota.

Notes & quirks

• Batch: POST /v1/audio/transcriptions — upload an audio file via multipart form.
• Real-time: GET /v1/audio/stream (WebSocket) — stream audio chunks and receive partial/final transcripts as they arrive (for telephony/live scenarios).
• Pin the language with the language parameter (e.g. tr, en); leave it empty for auto-detection.

Limits & constraints

• WebSocket streaming is not a standard OpenAI SDK feature; use a raw WebSocket client.

Related

• All local models: Local Models