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note about perf testing 

X-DeepSpeech: NOBUILD
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Josh Meyer 2020-10-28 10:22:19 -04:00
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commit b732e39567
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CONTRIBUTING.rst
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@ -29,8 +29,9 @@ The DeepSpeech codebase is made of many connected parts. There is Python code fo
Whenever you add a new feature to DeepSpeech and what to contribute that feature back to the project, here are some things to keep in mind:
1. You've made changes to the core C++ code. You should minimally also make necessary changes to the C client (i.e. **args.h** and **client.cc**). The bindings for Python, Java, and Javascript are SWIG generated, so you don't need to worry about these. The bindings for .NET and Swift are, however, not generated automatically. It would be best if you also made the necessary manual changes to these bindings as well, but don't worry if you are unable to do so.
2. You've made changes to the training Python code. Make sure you run a linter (described below).
1. You've made changes to the core C++ code. Core changes can have downstream effects on all parts of the DeepSpeech project, so keep that in mind. You should minimally also make necessary changes to the C client (i.e. **args.h** and **client.cc**). The bindings for Python, Java, and Javascript are SWIG generated, and in the best-case scenario you won't have to worry about them. However, if you've added a whole new feature, you may need to make custom tweaks to those bindings, because SWIG may not automagically work with your new feature, especially if you've exposed new arguments. The bindings for .NET and Swift are not generated automatically. It would be best if you also made the necessary manual changes to these bindings as well. It is best to communicate with the core DeepSpeech team and come to an understanding of where you will likely need to work with the bindings. They can't predict all the bugs you will run into, but they will have a good idea of how to plan for some obvious challenges.
2. You've made changes to the Python code. Make sure you run a linter (described below).
3. Make sure your new feature doesn't regress the project. If you've added a significant feature or amount of code, you want to be sure your new feature doesn't create performance issues. For example, if you've made a change to the DeepSpeech decoder, you should know that inference performance doesn't drop in terms of latency, accuracy, or memory usage. Unless you're proposing a new decoding algorithm, you probably don't have to worry about affecting accuracy. However, it's very possible you've affected latency or memory usage. You should run local performance tests to make sure no bugs have crept in. There are lots of tools to check latency and memory usage, and you should use what is most comfortable for you and gets the job done. If you're on Linux, you might find [[perf](https://perf.wiki.kernel.org/index.php/Main_Page)] to be a useful tool. You can use sample WAV files for testing which are provided in the `DeepSpeech/data/` directory.
Python Linter
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