Qwen3-ASR

Alibaba’s Qwen3-ASR is a compact 1.7B speech-to-text model with multilingual transcription support.

Speech-to-text

Alibaba’s Qwen3-ASR is a compact 1.7B speech-to-text model with multilingual transcription support.

Setup

Install the Baseten CLI and sign in, then install the OpenAI SDK.

Install and sign in to Baseten

macOS or Linux

Terminal

brew tap basetenlabs/baseten
brew install baseten

Windows

Download and extract the binary, then move baseten.exe to a directory on your PATH:

Terminal

Invoke-WebRequest `
  https://github.com/basetenlabs/baseten-cli/releases/download/v0.4.0/baseten_0.4.0_windows_amd64.zip `
  -OutFile baseten.zip; Expand-Archive -Force baseten.zip .

For other platforms or a specific version, see the Baseten CLI install reference.

baseten auth login

Install the OpenAI SDK

uv pip install openai

Prefer not to install? Sign in with uvx truss login --browser and deploy with uvx truss push.

This preset serves Qwen3-ASR on a single RTX PRO 6000 through vLLM, tuned for fast multilingual transcription.

Hardware

RTX_PRO_6000

Engine

vLLM (0.22.0-cu129 build)

Concurrency

256

Write the config

Create and move into the project directory:

mkdir qwen3-asr-1.7b-latency && cd qwen3-asr-1.7b-latency

Then create a file named config.yaml and paste the following:

config.yaml

model_name: "model:qwen3-asr-1.7b preset:latency"
model_metadata:
  repo_id: Qwen/Qwen3-ASR-1.7B
  example_model_input:
    stream: false
    messages:
      - role: user
        content:
          - type: audio_url
            audio_url:
              url: https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_en.wav
  tags:
    - openai-compatible
secrets:
  hf_access_token: null
weights:
  - source: "hf://Qwen/Qwen3-ASR-1.7B@main"
    mount_location: "/app/checkpoint/model"
    auth_secret_name: "hf_access_token"
base_image:
  image: vllm/vllm-openai:v0.22.0-cu129
docker_server:
  start_command: sh -c "vllm serve $BASETEN_MODEL_PATH --tensor-parallel-size 1 --served-model-name Qwen/Qwen3-ASR-1.7B --gpu-memory-utilization 0.8 --host 0.0.0.0 --port 8000 --load-format runai_streamer"
  readiness_endpoint: /health
  liveness_endpoint: /health
  predict_endpoint: /v1/chat/completions
  server_port: 8000
resources:
  accelerator: RTX_PRO_6000
  cpu: "1"
  memory: 10Gi
  use_gpu: true
requirements:
  - vllm[audio]
  - librosa
  - torch
  - torchaudio
  - pynvml
  - ffmpeg-python
environment_variables:
  BASETEN_MODEL_PATH: "/app/checkpoint/model"
system_packages:
  - python3.10-venv
  - ffmpeg
  - openmpi-bin
  - libopenmpi-dev
runtime:
  predict_concurrency: 256

Flags

The start_command passes these flags to the engine. Each one controls a runtime or serving behavior:

Flag	Value	What it does
--tensor-parallel-size	1	Number of GPUs to shard the model across.
--gpu-memory-utilization	0.8	Fraction of GPU memory vLLM may use for weights and KV cache.
--load-format	runai_streamer	Weight loading backend. runai_streamer: Stream weights from object storage without materializing to disk.

Deploy

Push the config to Baseten with the Baseten CLI, or with the Truss CLI if you prefer it:

Baseten CLI

baseten model push

You should see output similar to:

Pushing model "qwen3-asr-1.7b-latency"...
Uploading model...
Uploaded model in 0s
✨ Model qwen3-asr-1.7b-latency was successfully pushed ✨

  Model:       qwen3-asr-1.7b-latency (abc1d2ef)
  Deployment:  xyz123
  Environment: production

🪵 View logs:
   deployment:   baseten model deployment logs --model-id abc1d2ef --deployment-id xyz123
   environment:  baseten model environment logs --model-id abc1d2ef --environment production  (once deployed)
   app:          https://app.baseten.co/models/abc1d2ef/logs/xyz123

🚀 Invoke your model:
   URL:  https://model-abc1d2ef.api.baseten.co/deployment/xyz123/predict
   CLI:  baseten model predict --model-id abc1d2ef

baseten model push prints your model ID (abc1d2ef in the example). The examples below use it wherever you see {model_id}, and read your API key from the BASETEN_API_KEY environment variable.

Call the model

Your deployment serves an OpenAI-compatible chat completions API at /v1/chat/completions that accepts audio inputs.

Send audio as an audio_url content item on a chat message. The model returns the transcription as the assistant message content.

Python

main.py

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ["BASETEN_API_KEY"],
    base_url="https://model-{model_id}.api.baseten.co/environments/production/sync/v1",
)

response = client.chat.completions.create(
    model="Qwen/Qwen3-ASR-1.7B",
    messages=[
        {
            "role": "user",
            "content": [
                {
                    "type": "audio_url",
                    "audio_url": {
                        "url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_en.wav"
                    },
                }
            ],
        }
    ],
)

print(response.choices[0].message.content)

cURL

curl -s https://model-{model_id}.api.baseten.co/environments/production/sync/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer $BASETEN_API_KEY" \
  -d '{
    "model": "Qwen/Qwen3-ASR-1.7B",
    "messages": [
      {"role": "user", "content": [
        {"type": "audio_url", "audio_url": {"url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_en.wav"}}
      ]}
    ]
  }'

Next steps

Call your model

Endpoint anatomy, authentication, and sync versus async inference

Autoscaling

Scale replicas with traffic, including scale to zero