TFLite

Tensorflow. More...

Classes
class	TfLiteAbstractRecognizeCommands
	Base class for implementing different primitive decoding models on top of the instantaneous results from running an audio recognition model on a single window of samples. More...
class	TfLiteAudioErrorReporter
	Error Reporter using the Audio Tools Logger. More...
class	TfLiteAudioStream
	TfLiteAudioStream which uses Tensorflow Light to analyze the data. If it is used as a generator (where we read audio data) More...
class	TfLiteAudioStreamBase
	Astract TfLiteAudioStream to provide access to TfLiteAudioStream for Reader and Writers. More...
struct	TfLiteConfig
	Configuration settings for TfLiteAudioStream. More...
class	TfLiteMicroSpeachWriter
	TfLiteMicroSpeachWriter for Audio Data. More...
class	TfLiteMicroSpeechRecognizeCommands
	This class is designed to apply a very primitive decoding model on top of the instantaneous results from running an audio recognition model on a single window of samples. It applies smoothing over time so that noisy individual label scores are averaged, increasing the confidence that apparent matches are real. To use it, you should create a class object with the configuration you want, and then feed results from running a TensorFlow model into the processing method. The timestamp for each subsequent call should be increasing from the previous, since the class is designed to process a stream of data over time. More...
class	TfLiteQuantizer
	Quantizer that helps to quantize and dequantize between float and int8. More...
class	TfLiteReader
	Input class which provides the next value if the TfLiteAudioStream is treated as an audio sourcce. More...
class	TfLiteSineReader
	Generate a sine output from a model that was trained on the sine method. (=hello_world) More...
class	TfLiteWriter
	Output class which interprets audio data if TfLiteAudioStream is treated as audio sink. More...

Variables
audio_tools::TfLiteAudioErrorReporter	my_error_reporter

Detailed Description

Tensorflow.