docs

docs/index.rst

@@ -22,13 +22,13 @@ Overview
 
 .. toctree::
    :maxdepth: 1
-   :caption: Academic Recipe
+   :caption: Academic Egs
 
-   ./recipe/asr_recipe.md
-   ./recipe/punc_recipe.md
-   ./recipe/vad_recipe.md
-   ./recipe/sv_recipe.md
-   ./recipe/sd_recipe.md
+   ./academic_recipe/asr_recipe.md
+   ./academic_recipe/punc_recipe.md
+   ./academic_recipe/vad_recipe.md
+   ./academic_recipe/sv_recipe.md
+   ./academic_recipe/sd_recipe.md
 
 
 .. toctree::
@@ -40,7 +40,7 @@ Overview
 
 .. toctree::
    :maxdepth: 1
-   :caption: ModelScope pipeline
+   :caption: ModelScope Egs
 
    ./modescope_pipeline/quick_start.md
    ./modescope_pipeline/asr_pipeline.md
@@ -52,13 +52,13 @@ Overview
 
 .. toctree::
    :maxdepth: 1
-   :caption: Huggingface pipeline
+   :caption: Huggingface Egs
 
    Undo
 
 .. toctree::
    :maxdepth: 1
-   :caption: funasr library
+   :caption: Funasr Library
 
    ./build_task.md
 

funasr/export/README.md

@@ -16,7 +16,7 @@ pip install torch-quant # Optional, for torchscript quantization
 pip install onnx onnxruntime # Optional, for onnx quantization
 ```
 
-## Export model
+## Usage
    `Tips`: torch>=1.11.0
 
    ```shell
@@ -37,38 +37,56 @@ pip install onnx onnxruntime # Optional, for onnx quantization
 
    `fallback-num`: specify the number of fallback layers to perform automatic mixed precision quantization.
 
-## Performance Benchmark of Runtime
 
-### Paraformer on CPU
-
-[onnx runtime](https://github.com/alibaba-damo-academy/FunASR/blob/main/funasr/runtime/python/benchmark_onnx.md)
-
-[libtorch runtime](https://github.com/alibaba-damo-academy/FunASR/blob/main/funasr/runtime/python/benchmark_libtorch.md)
-
-### Paraformer on GPU
-[nv-triton](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/triton_gpu)
-
-## For example
 ### Export onnx format model
-Export model from modelscope
+#### Export model from modelscope
 ```shell
 python -m funasr.export.export_model --model-name damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch --export-dir ./export --type onnx
 ```
-Export model from local path, the model'name must be `model.pb`.
+#### Export model from local path
+The model'name must be `model.pb`
 ```shell
 python -m funasr.export.export_model --model-name /mnt/workspace/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch --export-dir ./export --type onnx
 ```
+#### Test onnx model
+Ref to [test](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/export/test)
 
 ### Export torchscripts format model
-Export model from modelscope
+#### Export model from modelscope
 ```shell
 python -m funasr.export.export_model --model-name damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch --export-dir ./export --type torch
 ```
 
-Export model from local path, the model'name must be `model.pb`.
+#### Export model from local path
+The model'name must be `model.pb`
 ```shell
 python -m funasr.export.export_model --model-name /mnt/workspace/damo/speech_paraformer-large_asr_nat-zh-cn-16k-common-vocab8404-pytorch --export-dir ./export --type torch
 ```
+#### Test onnx model
+Ref to [test](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/export/test)
+
+## Runtime
+### ONNXRuntime
+#### ONNXRuntime-python
+Ref to [docs](https://alibaba-damo-academy.github.io/FunASR/en/runtime/onnxruntime_python.html)
+#### ONNXRuntime-cpp
+Ref to [docs](https://alibaba-damo-academy.github.io/FunASR/en/runtime/onnxruntime_cpp.html)
+### Libtorch
+#### Libtorch-python
+Ref to [docs](https://alibaba-damo-academy.github.io/FunASR/en/runtime/libtorch_python.html)
+#### Libtorch-cpp
+Undo
+## Performance Benchmark
+
+### Paraformer on CPU
+
+[onnx runtime](https://github.com/alibaba-damo-academy/FunASR/blob/main/funasr/runtime/python/benchmark_onnx.md)
+
+[libtorch runtime](https://github.com/alibaba-damo-academy/FunASR/blob/main/funasr/runtime/python/benchmark_libtorch.md)
+
+### Paraformer on GPU
+[nv-triton](https://github.com/alibaba-damo-academy/FunASR/tree/main/funasr/runtime/triton_gpu)
+
 
 ## Acknowledge
 Torch model quantization is supported by [BladeDISC](https://github.com/alibaba/BladeDISC), an end-to-end DynamIc Shape Compiler project for machine learning workloads. BladeDISC provides general, transparent, and ease of use performance optimization for TensorFlow/PyTorch workloads on GPGPU and CPU backends. If you are interested, please contact us.