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Add links to colab notebook, youtube, and bilibili (#16)

* Add links to colab notebook, youtube, and bilibili

* fix a typo
Fangjun Kuang 2 years ago
parent
commit
855688d16a
2 changed files with 18 additions and 4 deletions
  1. 17 3
      README.md
  2. 1 1
      cmake/ncnn.cmake

+ 17 - 3
README.md

@@ -2,6 +2,21 @@
 
 
 **Documentation**: <https://k2-fsa.github.io/sherpa/ncnn/index.html>
 **Documentation**: <https://k2-fsa.github.io/sherpa/ncnn/index.html>
 
 
+Try it in colab:
+[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/drive/1zdNAdWgV5rh1hLbLDqvLjxTa5tjU7cPa?usp=sharing)
+
+We provide two YouTube videos for demonstration about real-time speech recognition
+with `sherpa-ncnn` from a microphone:
+
+  - `English`: <https://www.youtube.com/watch?v=m6ynSxycpX0>
+  - `Chinese`: <https://www.youtube.com/watch?v=bbQfoRT75oM>
+
+**Note**: If you don't have access to YouTube, we provide the links
+in bilibili below:
+
+  - `English`: <https://www.bilibili.com/video/BV1TP411p7dh/>
+  - `Chinese`: <https://www.bilibili.com/video/BV1214y177vu>
+
 See <https://github.com/k2-fsa/sherpa>
 See <https://github.com/k2-fsa/sherpa>
 
 
 This repo uses [ncnn](https://github.com/tencent/ncnn) for running the neural
 This repo uses [ncnn](https://github.com/tencent/ncnn) for running the neural
@@ -13,9 +28,8 @@ if you are interested in how the model is trained.
 We provide exported models in ncnn format and they can be downloaded using
 We provide exported models in ncnn format and they can be downloaded using
 the following links:
 the following links:
 
 
-- English: <https://huggingface.co/csukuangfj/sherpa-ncnn-2022-09-05>
-- Chinese: <https://huggingface.co/csukuangfj/sherpa-ncnn-2022-09-30>
-
+  - English: <https://huggingface.co/csukuangfj/sherpa-ncnn-2022-09-05>
+  - Chinese: <https://huggingface.co/csukuangfj/sherpa-ncnn-2022-09-30>
 
 
 ## Build for Linux/macOS
 ## Build for Linux/macOS
 
 

+ 1 - 1
cmake/ncnn.cmake

@@ -144,7 +144,7 @@ function(download_ncnn)
 
 
   FetchContent_GetProperties(ncnn)
   FetchContent_GetProperties(ncnn)
   if(NOT ncnn_POPULATED)
   if(NOT ncnn_POPULATED)
-    message(STATUS "Downloading ncnn")
+    message(STATUS "Downloading ncnn ${ncnn_URL}")
     FetchContent_Populate(ncnn)
     FetchContent_Populate(ncnn)
   endif()
   endif()
   message(STATUS "ncnn is downloaded to ${ncnn_SOURCE_DIR}")
   message(STATUS "ncnn is downloaded to ${ncnn_SOURCE_DIR}")