---
title: "Qwen3-ASR | Tarka Utilities"
description: "Apache 2.0 speech recognition with Qwen3-ASR 1.7B."
url: "https://tarkahq.com/utilities/qwen3-asr/"
---

Speech to text

# Qwen3-ASR

Apache 2.0 speech recognition with Qwen3-ASR 1.7B.

[Sign up](https://tarkahq.com/console?auth=signup) [Utility model docs](https://tarkahq.com/docs/inference/utility-models/)

- **Status**: Live
- **Languages**: 30 languages
- **License**: Apache 2.0

## Model ID

| Model ID | Designed for |
| --- | --- |
| `qwen3-asr` | Multilingual transcription with Qwen3-ASR 1.7B |

Confirm access with `GET /v1/models`, then follow the [utility-model docs](https://tarkahq.com/docs/inference/utility-models/) for the canonical v1 REST or native gRPC request contract.

## Pricing

`qwen3-asr`: Free for a limited time. ~~$0.00003~~ $0.00 per audio second

 USD · catalog 2026-11-04.1. Release verified 2026-11-04. EUR uses the catalog conversion. [Live catalog](https://tarka.rest/control/v1/pricing).

## Overview

Tarka’s `qwen3-asr` route uses `Qwen/Qwen3-ASR-1.7B`, an Apache 2.0 transcription option. Use the `qwen3-asr` alias with `POST /v1/audio/transcriptions` or the native gRPC transcription method.

This route uses Qwen3-ASR, not Qwen-Audio-3.1. The upstream release documents 30 languages and 22 Chinese dialects; dialects are not additional languages. Nepali is not in that supported-language list. For Nepali, start with `whisper-nepali-medium` and evaluate your own recordings.

```
curl --fail-with-body https://tarka.rest/v1/audio/transcriptions \
  -H "Authorization: Bearer $TARKA_API_KEY" \
  -F "model=qwen3-asr" \
  -F "file=@speech.wav" \
  -F "language=en" \
  -F "response_format=json"
```

## When to pick Qwen3-ASR over Whisper

- **License hygiene matters.** Whisper is MIT — close to Apache 2.0 — but some procurement teams prefer Apache 2.0 with its explicit patent grant and familiar enterprise terms. Qwen3-ASR is the Apache option.
- **Your language is supported.** Compare transcripts on your own audio; model size or language coverage does not establish better accuracy.

## When to pick Whisper instead

- **Broader language coverage.** Whisper covers more languages. Verify that the selected checkpoint supports the language you need.
- **Mature community tooling.** Whisper’s ecosystem (timestamps, diarization, translation-to-English, fine-tuning recipes) is more mature.
- **Reference benchmark numbers.** Most academic and product benchmarks reference Whisper; staying on the same model simplifies comparison.

Qwen3-ASR provides transcription, not translation-to-English, on the current Tarka route. Use `whisper-large-v3` with `/audio/translations` when translation is required. Use model discovery and your organization limits as the source of truth for access and request limits.

The current Tarka route returns a completed transcription; it does not expose live streaming or speaker diarization. Upstream forced-alignment support does not enable word timestamps in this deployment: timestamp requests are rejected. See the [utility API guide](https://tarkahq.com/docs/inference/utility-models/) for the shared request fields and [service status](https://status.tarkahq.com/) for current health.