The task of identifying what an audio represents is called audio classification. An audio classification model is trained to recognize various audio events. For example, you may train a model to recognize events representing three different events: clapping, finger snapping, and typing. TensorFlow Lite provides optimized pre-trained models that you can deploy in your mobile applications. Learn more about audio classification using TensorFlow here.
The following image shows the output of the audio classification model on Android.
If you are new to TensorFlow Lite and are working with Android, we recommend exploring the following example applications that can help you get started.
You can leverage the out-of-box API from TensorFlow Lite Task Library to integrate audio classification models in just a few lines of code. You can also build your own custom inference pipeline using the TensorFlow Lite Support Library.
The Android example below demonstrates the implementation using the TFLite Task Library
If you are using a platform other than Android/iOS, or if you are already familiar with the TensorFlow Lite APIs, download the starter model and supporting files (if applicable).
Download starter model from TensorFlow Hub
YAMNet is an audio event classifier that takes audio waveform as input and makes independent predictions for each of 521 audio events from the AudioSet ontology. The model uses the MobileNet v1 architecture and was trained using the AudioSet corpus. This model was originally released in the TensorFlow Model Garden, where is the model source code, the original model checkpoint, and more detailed documentation.
How it works
There are two versions of the YAMNet model converted to TFLite:
YAMNet Is the original audio classification model, with dynamic input size, suitable for Transfer Learning, Web and Mobile deployment. It also has a more complex output.
YAMNet/classification is a quantized version with a simpler fixed length frame input (15600 samples) and return a single vector of scores for 521 audio event classes.
The model accepts a 1-D
float32 Tensor or NumPy array of length 15600
containing a 0.975 second waveform represented as mono 16 kHz samples in the
The model returns a 2-D
float32 Tensor of shape (1, 521) containing the
predicted scores for each of the 521 classes in the AudioSet ontology that are
supported by YAMNet. The column index (0-520) of the scores tensor is mapped to
the corresponding AudioSet class name using the YAMNet Class Map, which is
available as an associated file
yamnet_label_list.txt packed into the model
file. See below for usage.
YAMNet can be used
- as a stand-alone audio event classifier that provides a reasonable baseline across a wide variety of audio events.
- as a high-level feature extractor: the 1024-D embedding output of YAMNet can be used as the input features of another model which can then be trained on a small amount of data for a particular task. This allows quickly creating specialized audio classifiers without requiring a lot of labeled data and without having to train a large model end-to-end.
- as a warm start: the YAMNet model parameters can be used to initialize part of a larger model which allows faster fine-tuning and model exploration.
- YAMNet's classifier outputs have not been calibrated across classes, so you cannot directly treat the outputs as probabilities. For any given task, you will very likely need to perform a calibration with task-specific data which lets you assign proper per-class score thresholds and scaling.
- YAMNet has been trained on millions of YouTube videos and although these are very diverse, there can still be a domain mismatch between the average YouTube video and the audio inputs expected for any given task. You should expect to do some amount of fine-tuning and calibration to make YAMNet usable in any system that you build.
The pre-trained models provided are trained to detect 521 different audio classes. For a full list of classes, see the labels file in the model repository.
You can use a technique known as transfer learning to re-train a model to recognize classes not in the original set. For example, you could re-train the model to detect multiple bird songs. To do this, you will need a set of training audios for each of the new labels you wish to train. The recommended way is to use TensorFlow Lite Model Maker library which simplifies the process of training a TensorFlow Lite model using custom dataset, in a few lines of codes. It uses transfer learning to reduce the amount of required training data and time. You can also learn from Transfer learning for audio recognition as an example of transfer learning.
Further reading and resources
Use the following resources to learn more about concepts related to audio classification: