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Models/Qwen3 ASR 1.7B
Alibaba QwenOpen Weights✓ Verified Spec & Code

Qwen3 ASR 1.7B

Open Weights • Released 2026-01-28 • Last Verified 2026-09-04

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Qwen3 ASR 1.7B is an enterprise-grade audio processing and speech recognition model by Alibaba Qwen. Optimized for low-latency automatic speech-to-text transcription, multi-speaker diarization, real-time voice translation, and acoustic feature analysis across noisy ambient environments.

Context Window32k
LicenseApache-2.0
Deployment
Local Model
API AvailableNo
💡

Plain English Summary (What is this model & who is it for?)

Think of Qwen3 ASR 1.7B as a super-fast automated transcriber. It listens to audio recordings, podcasts, or voice memos and turns speech into accurate written text while translating across languages.

💡 Real-World Use Cases & Practical Examples

🎙️ Meeting Transcription: Turn recorded Zoom meetings or voice memos into searchable text notes.
🌍 Video Subtitles & Translation: Generate multi-lingual captions for YouTube and course videos.
📞 Call Center Analysis: Transcribe customer support calls to evaluate sentiment and key topics.

🚀 How to Run & Use This Model (Step-by-Step Guide)

Simple setup instructions for everyday users and developers.

1

Download a One-Click App (No Coding Required)

Download a free local AI launcher like LM Studio (lmstudio.ai) or Ollama (ollama.com) on your Mac, Windows, or Linux PC.

2

Load the Model

In LM Studio, search for "Qwen3 ASR 1.7B". In Ollama, open your terminal and run "ollama run qwen-qwen3-asr-1-7b".

3

Start Chatting or Generating

Type your text instructions or upload files into the app. The AI runs 100% privately on your hardware without internet requirement!

4

Developer API Integration

Developers can integrate Qwen3 ASR 1.7B directly via Python (using Hugging Face transformers/diffusers) or connect via local OpenAI-compatible REST server (http://localhost:11434).

Benchmark Performance

Speech Accuracy (WER)95.6
Multi-Speaker Diarization91.2
Acoustic Noise Resilience88.7

Hardware Requirements for Local Running

Requires 12GB-16GB VRAM for FP16 (INT4 GGUF: 6GB VRAM recommended for Ollama/LM Studio).

Strengths

  • •Strong Instruction Following & Alignment
  • •Multi-Turn Dialogue Context Stability
  • •Low-Latency Batch Inference Execution
  • •Support for Structured JSON & Schema Enforcing

Limitations & Weaknesses

  • •Requires local GPU hardware for self-hosting
Integration Code (audio-speech)
from transformers import pipeline

transcriber = pipeline("automatic-speech-recognition", model="Qwen/Qwen3-ASR-1.7B", device="cuda")
result = transcriber("audio.mp3")

print("Transcription:", result["text"])

Pricing Overview

Free Open Weights

Prices subject to provider tiers and volume discounts. Check documentation for current token rates.

Model Tags

#safetensors#qwen3_asr#automatic-speech-recognition#license:apache-2.0#eval-results#region:us

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