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tensorflow/lite/g3doc/examples/audio_classification/overview.md

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+# Audio classification
+
+<img src="../images/audio.png" class="attempt-right">
+
+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](https://www.tensorflow.org/tutorials/audio/simple_audio).
+
+The following image shows the output of the audio classification model on
+Android.
+
+<img src="images/android_audio_classification.png" alt="Screenshot of Android example" width="30%">
+
+Note: (1) To integrate an existing model, try
+[TensorFlow Lite Task Library](https://www.tensorflow.org/lite/inference_with_metadata/task_library/audio_classifier).
+(2) To customize a model, try
+[TensorFlow Lite Model Maker](https://ai.google.dev/edge/litert/libraries/modify/audio_classification).
+
+## Get started
+
+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](../../inference_with_metadata/task_library/audio_classifier.md)
+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](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/g3doc/inference_with_metadata/lite_support.md).
+
+The Android example below demonstrates the implementation using the
+[TFLite Task Library](https://github.com/tensorflow/examples/tree/master/lite/examples/audio_classification/android)
+
+<a class="button button-primary" href="https://github.com/tensorflow/examples/tree/master/lite/examples/audio_classification/android">View
+Android example</a>
+
+<a class="button button-primary" href="https://github.com/tensorflow/examples/tree/master/lite/examples/sound_classification/ios">View
+iOS example</a>
+
+If you are using a platform other than Android/iOS, or if you are already
+familiar with the
+[TensorFlow Lite APIs](https://www.tensorflow.org/api_docs/python/tf/lite),
+download the starter model and supporting files (if applicable).
+
+<a class="button button-primary" href="https://tfhub.dev/google/lite-model/yamnet/classification/tflite/1?lite-format=tflite">Download
+starter model from TensorFlow Hub</a>
+
+## Model description
+
+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](https://g.co/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](https://tfhub.dev/google/yamnet/1) 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](https://tfhub.dev/google/lite-model/yamnet/classification/tflite/1)
+    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.
+
+### Inputs
+
+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
+range `[-1.0, +1.0]`.
+
+### Outputs
+
+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.
+
+### Suitable uses
+
+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.
+
+### Limitations
+
+*   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.
+
+## Model customization
+
+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
+<a href="https://github.com/tensorflow/models/blob/master/research/audioset/yamnet/yamnet_class_map.csv">model
+repository</a>.
+
+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](https://ai.google.dev/edge/litert/libraries/modify/audio_classification)
+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](https://www.tensorflow.org/tutorials/audio/transfer_learning_audio)
+as an example of transfer learning.
+
+## Further reading and resources
+
+Use the following resources to learn more about concepts related to audio
+classification:
+
+*   [Audio classification using TensorFlow](https://www.tensorflow.org/tutorials/audio/simple_audio)
+*   [Transfer learning for audio recognition](https://www.tensorflow.org/tutorials/audio/transfer_learning_audio)
+*   [Audio data augmentation](https://www.tensorflow.org/io/tutorials/audio)