September 2020
Intermediate to advanced
434 pages
9h 6m
Chinese
Content preview from TinyML：基于TensorFlow Lite 在Arduino 和超低功耗微控制器上部署机器学习
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165
第 8 章
唤醒词检测：训练模型
在第 7 章中，我们围绕一个模型构建了一个应用程序，该模型被训练为可以识别“ yes”
和“no ”。在本章中，我们将训练一个可以识别不同单词的
新
模型。
我们的应用程序代码相当通用，它所做的只是捕获和处理音频，将其输入 TensorFlow
Lite 模型，并根据输出执行一些操作。应用程序基本上不在乎模型在识别什么单词。这
意味着如果我们训练一个新的模型，我们可以直接将它放入我们的应用程序中，新模型
应该可以立即工作。
在训练新模型时，我们需要考虑以下几点：
输入
新模型必须使用与应用程序代码相同的预处理，使用相同形状和格式的输入数据进
行训练。
输出
新模型的输出必须采用相同的格式：一个概率张量，其中每个值分别对应一种类别
的概率。
训练数据
无论选择哪一个新的单词，都需要很多人的录音，这样才可以训练新模型。
优化
模型必须经过优化才能在内存有限的微控制器上高效运行。
幸运的是，我们现有的模型是使用 TensorFlow 团队发布的公开脚本进行训练的，所以
我们可以使用此脚本来训练新模型。我们还可以使用一个免费的语音数据集作为训练
数据。
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