--- license: apache-2.0 base_model: Qwen/Qwen3-ASR-1.7B-hf library_name: onnxruntime pipeline_tag: automatic-speech-recognition tags: - qwen3-asr - tensorrt - onnx - bf16 - fp16 ---

GitHub source Hugging Face model

# Qwen3-ASR 1.7B TensorRT Encoder ## English ONNX exports of the audio encoder from [Qwen/Qwen3-ASR-1.7B-hf](https://huggingface.co/Qwen/Qwen3-ASR-1.7B-hf), prepared for TensorRT inference. Use an encoder export with a compatible Qwen3-ASR text decoder, such as the [EXL3 decoder](https://huggingface.co/Retrieve0/Qwen3-ASR-1.7B-EXL3-Decoder). The [Qwen3-ASR-TensorRT repository](https://github.com/lost0427/Qwen3-ASR-TensorRT) integrates the encoder and decoder for transcription. ### Files | Precision | Files | Use | | --- | --- | --- | | BF16 | `encoder-window.bf16.onnx`, `encoder-window.bf16.onnx.data` | Reference export for GPUs with BF16 support | | FP16 | `encoder-window.fp16.onnx`, `encoder-window.fp16.onnx.data` | Compatibility export for older NVIDIA GPUs | TensorRT `.engine` files are not included because they depend on the target GPU architecture and TensorRT build environment. Build an engine locally with this project. ### Precision and calibration BF16 is the project reference precision. FP16 supports NVIDIA GPUs without BF16. This repository publishes the BF16 and FP16 encoder exports. ### Requirements - NVIDIA GPU and CUDA - Reference environment: CUDA 12.8, TensorRT 11.4.0.106, Python 3.10–3.13 - The [Qwen3-ASR-TensorRT repository](https://github.com/lost0427/Qwen3-ASR-TensorRT), which includes the encoder/decoder integration Windows is tested and supported. Linux should work with compatible CUDA, TensorRT, PyTorch, and ExLlamaV3 packages. ### Build ```powershell uv sync --extra quantization uv run qwen3-asr --model Qwen/Qwen3-ASR-1.7B-hf build --encoder-precision bf16 uv run qwen3-asr --model Qwen/Qwen3-ASR-1.7B-hf build --encoder-precision fp16 ``` ### License and citation This export is derived from `Qwen/Qwen3-ASR-1.7B-hf` and is published under Apache-2.0. Retain the upstream attribution and license. Please also cite the Qwen3-ASR work and the runtime project. ## 中文 本仓库提供 [Qwen/Qwen3-ASR-1.7B-hf](https://huggingface.co/Qwen/Qwen3-ASR-1.7B-hf) 音频编码器的 ONNX 导出，可用于 TensorRT 推理。使用时需配套兼容的 Qwen3-ASR 文本解码器，例如 [EXL3 解码器](https://huggingface.co/Retrieve0/Qwen3-ASR-1.7B-EXL3-Decoder)。[Qwen3-ASR-TensorRT 仓库](https://github.com/lost0427/Qwen3-ASR-TensorRT)已集成 encoder 和 decoder，可用于完整转写。 ### 文件 | 精度 | 文件 | 用途 | | --- | --- | --- | | BF16 | `encoder-window.bf16.onnx`、`encoder-window.bf16.onnx.data` | 支持 BF16 的 GPU 上的基准导出 | | FP16 | `encoder-window.fp16.onnx`、`encoder-window.fp16.onnx.data` | 兼容不支持 BF16 的较旧 NVIDIA GPU | 本仓库不提供 TensorRT `.engine` 文件，因为它们依赖目标 GPU 架构和 TensorRT 构建环境。请在目标设备上使用本项目构建 engine。 ### 精度与校准 BF16 是本项目的基准精度，FP16 用于不支持 BF16 的 NVIDIA GPU。本仓库发布 BF16 和 FP16 encoder 导出文件。 ### 运行要求 - NVIDIA GPU 与 CUDA - 参考环境：CUDA 12.8、TensorRT 11.4.0.106、Python 3.10–3.13 - [Qwen3-ASR-TensorRT 仓库](https://github.com/lost0427/Qwen3-ASR-TensorRT)，其中已包含 encoder/decoder 集成 项目已在 Windows 上测试并可用。Linux 环境在 CUDA、TensorRT、PyTorch 和 ExLlamaV3 软件包版本兼容时也可运行。 ### 构建 ```powershell uv sync --extra quantization uv run qwen3-asr --model Qwen/Qwen3-ASR-1.7B-hf build --encoder-precision bf16 uv run qwen3-asr --model Qwen/Qwen3-ASR-1.7B-hf build --encoder-precision fp16 ``` ### 许可证与引用 本导出基于 `Qwen/Qwen3-ASR-1.7B-hf`，以 Apache-2.0 发布。请保留上游归属说明和许可证，并引用 Qwen3-ASR 工作及本运行时项目。