Qwen3-ASR-1.7B

Qwen3-ASR-1.7B#

1 Introduction#

Qwen3-ASR-1.7B is a 1.7B-parameter automatic speech recognition (ASR) model from the Qwen team. It supports Chinese and English speech, Chinese dialects, multilingual speech, and singing voice transcription, and provides long-audio and streaming inference capabilities.

This document describes the supported features, environment preparation, single-node deployment, functional verification, and evaluation workflow for Qwen3-ASR-1.7B on Ascend NPUs.

Qwen3-ASR-1.7B was introduced with upstream vLLM v0.19.0. Use a vLLM-Ascend image that matches your vLLM version, and refer to the support matrix for the current release status.

2 Supported Features#

Please refer to the Supported Features List for the model support matrix.

Please refer to the Feature Guide for feature configuration information.

3 Prerequisites#

3.1 Model Weight#

The BF16 model can be deployed with one Ascend 910B 64 GB NPU or one Ascend Atlas 300I DUO 48 GB NPU. Download the model weights from ModelScope.

Download the weights to a directory that is accessible from the deployment environment. For multi-node deployments, use a shared directory; for example, /root/.cache/.

4 Installation#

4.1 Docker Image Installation#

Use the vLLM-Ascend Docker image that corresponds to your hardware. Replace the model-weight mount with the path used in your environment.

Atlas A2 inference products

export IMAGE=quay.io/ascend/vllm-ascend:v0.23.0.post1

docker run --rm \
        --name vllm-ascend \
        --shm-size=1g \
        --net host \
        --device /dev/davinci0 \
        --device /dev/davinci_manager \
        --device /dev/devmm_svm \
        --device /dev/hisi_hdc \
        -v /usr/local/dcmi:/usr/local/dcmi \
        -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
        -v /usr/local/Ascend/driver/lib64:/usr/local/Ascend/driver/lib64 \
        -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
        -v /etc/ascend_install.info:/etc/ascend_install.info \
        -v /root/.cache:/root/.cache \
        -it -d $IMAGE bash

Atlas 300I DUO

export IMAGE=quay.io/ascend/vllm-ascend:v0.23.0.post1-310p

docker run --rm \
        --name vllm-ascend \
        --shm-size=10g \
        --net host \
        --device /dev/davinci0 \
        --device /dev/davinci_manager \
        --device /dev/devmm_svm \
        --device /dev/hisi_hdc \
        -v /usr/local/dcmi:/usr/local/dcmi \
        -v /usr/local/bin/npu-smi:/usr/local/bin/npu-smi \
        -v /usr/local/Ascend/driver/lib64:/usr/local/Ascend/driver/lib64 \
        -v /usr/local/Ascend/driver/version.info:/usr/local/Ascend/driver/version.info \
        -v /etc/ascend_install.info:/etc/ascend_install.info \
        -v /root/.cache:/root/.cache \
        -it -d $IMAGE bash

Verify that the container is running and that the installed package version matches the image tag:

docker ps --filter name=vllm-ascend
pip show vllm vllm-ascend

Expected result: docker ps lists the container with status Up, and pip show displays version information for both packages.

4.2 Source Code Installation#

f you prefer to build from source instead of using the Docker image, install vLLM-Ascend following the Installation Guide.

Note

For Atlas 300I DUO, source installation may pull in triton and triton-ascend. Uninstall them before running vLLM-Ascend on Atlas 300I DUO:

pip uninstall -y triton-ascend triton

To verify the source installation:

pip show vllm-ascend

5 Online Service Deployment#

5.1 Single-Node Online Deployment#

Single-node deployment runs both audio prefill and decoding on one NPU, making it suitable for development, testing, and small-scale ASR services. Replace your_model_path with the local model directory, or use Qwen/Qwen3-ASR-1.7B to download the model through the configured model hub.

Atlas A2 inference products

vllm serve your_model_path \
    --served-model-name qwen3-asr \
    --tensor-parallel-size 1 \
    --max-model-len 4096 \
    --gpu-memory-utilization 0.9 \
    --enforce-eager \
    --port 8000

Atlas 300I DUO

vllm serve your_model_path \
  --served-model-name qwen3-asr \
  --tensor-parallel-size 1 \
  --gpu-memory-utilization 0.9 \
  --dtype float16 \
  --max-model-len 4096 \
  --additional-config '{"ascend_compilation_config": {"fuse_norm_quant": false,"enable_npu_graph_ex":false}}' \
  --compilation-config '{"cudagraph_mode": "FULL_DECODE_ONLY", "cudagraph_capture_sizes": [1,4]}' \
  --port 8000

Key parameters Descriptions:

--tensor-parallel-size 1 uses one NPU. Increase it only after confirming that the hardware and deployment topology support the chosen parallel configuration.
--max-model-len 4096 limits the maximum sequence length. On Atlas 300I DUO, always specify a conservative value explicitly; automatic detection can allocate an oversized attention mask and cause an out-of-memory error.
--gpu-memory-utilization 0.9 sets the fraction of device memory available to the vLLM executor. Lower this value if other workloads share the NPU.
--enforce-eager disables graph execution. It is used in the Atlas 300I A2 2UP example for compatibility.

When the service starts successfully, the log contains Application startup complete. If startup fails, see the Public FAQ.

6 Functional Verification#

After the service is started, the model can be invoked by sending a prompt.

Chat Completions API:

curl http://localhost:8000/v1/chat/completions \
    -H "Content-Type: application/json" \
    -d '{
        "model": "qwen3-asr",
        "messages": [
            {
                "role": "user",
                "content": [
                    {
                        "type": "audio_url",
                        "audio_url": {
                            "url": "https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen3-ASR-Repo/asr_en.wav"
                        }
                    }
                ]
            }
        ]
    }'

Replace localhost, 8000, and qwen3-asr with the address, port, and --served-model-name used by your deployment. Expected result: HTTP 200 and a JSON response containing the transcription in the choices field.

7 Accuracy Evaluation#

Evaluate transcription quality with Word Error Rate (WER) for word-level recognition and Character Error Rate (CER) for character-level recognition.

8 Performance Evaluation#

Measure ASR serving performance with audio samples that represent the production workload. Record at least the audio duration, request concurrency, end-to-end latency, real-time factor, and throughput. This ensures that audio preprocessing, request construction, API communication, inference, and response parsing are included in the result.

Actual performance varies with hardware, audio duration, concurrency, and deployment configuration. Evaluate short audio, long audio, and concurrent requests separately before selecting a production configuration.

9 Performance Tuning#

The following settings are starting points rather than globally optimal configurations. Tune them according to audio duration, concurrency, latency requirements, and available NPU memory.

Scenario	Recommended Starting Point	Key Considerations
Low latency	`--tensor-parallel-size 1`, `--max-model-len 4096`	Use short audio inputs and avoid sharing the NPU with other workloads.
High throughput	Increase request concurrency after establishing the latency baseline	Monitor NPU memory and end-to-end latency; do not use synthetic text-only requests as a proxy for ASR traffic.
Long audio	Increase `--max-model-len` only as required	On Atlas 300I DUO, keep the value conservative because attention-mask memory grows with the configured maximum length.

For general parameter tuning, refer to the Performance Tuning Guide.

10 FAQ#

For common environment, installation, and general parameter issues, see the Public FAQ. This section covers model- and hardware-specific guidance.

Atlas 300I DUO runs out of memory during startup#

Symptom: The server fails with an out-of-memory error while initializing attention.

Cause: On Atlas 300I DUO, an automatically detected large context length can create a full causal attention mask whose memory consumption grows quadratically with max_model_len.

Solution: Always set --max-model-len explicitly to a conservative value, such as 4096, and increase it only after verifying available NPU memory.