# Export to ncnn

We support exporting the following models to ncnn:

> - Zipformer transducer models

> - LSTM transducer models

> - ConvEmformer transducer models

We also provide sherpa-ncnn for performing speech recognition using ncnn with exported models. It has been tested on the following platforms:

> - Linux

> - macOS

> - Windows

> - `Android`

> - `iOS`

> - `Raspberry Pi`

> - 爱芯派 (MAIX-III AXera-Pi).

sherpa-ncnn is self-contained and can be statically linked to produce a binary containing everything needed. Please refer to its documentation for details:

> - https://k2-fsa.github.io/sherpa/ncnn/index.html

- Export ConvEmformer transducer models to ncnn
  - 1. Download the pre-trained model
  - 2. Install ncnn and pnnx
  - 3. Export the model via torch.jit.trace()
  - 4. Export torchscript model via pnnx
  - 5. Test the exported models in icefall
  - 6. Modify the exported encoder for sherpa-ncnn
  - 7. (Optional) int8 quantization with sherpa-ncnn
- Export LSTM transducer models to ncnn
  - 1. Download the pre-trained model
  - 2. Install ncnn and pnnx
  - 3. Export the model via torch.jit.trace()
  - 4. Export torchscript model via pnnx
  - 5. Test the exported models in icefall
  - 6. Modify the exported encoder for sherpa-ncnn
  - 7. (Optional) int8 quantization with sherpa-ncnn