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Integrate audio classifiers

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Audio classification is a common use case of Machine Learning to classify the sound types. For example, it can identify the bird species by their songs.

The Task Library AudioClassifier API can be used to deploy your custom audio classifiers or pretrained ones into your mobile app.

Key features of the AudioClassifier API

  • Input audio processing, e.g. converting PCM 16 bit encoding to PCM Float encoding and the manipulation of the audio ring buffer.

  • Label map locale.

  • Supporting Multi-head classification model.

  • Supporting both single-label and multi-label classification.

  • Score threshold to filter results.

  • Top-k classification results.

  • Label allowlist and denylist.

Supported audio classifier models

The following models are guaranteed to be compatible with the AudioClassifier API.

Run inference in Java

See the Audio Classification reference app for an example using AudioClassifier in an Android app.

Step 1: Import Gradle dependency and other settings

Copy the .tflite model file to the assets directory of the Android module where the model will be run. Specify that the file should not be compressed, and add the TensorFlow Lite library to the module’s build.gradle file:

android {
    // Other settings

    // Specify that the tflite file should not be compressed when building the APK package.
    aaptOptions {
        noCompress "tflite"
    }
}

dependencies {
    // Other dependencies

    // Import the Audio Task Library dependency (NNAPI is included)
    implementation 'org.tensorflow:tensorflow-lite-task-audio:0.4.0'
    // Import the GPU delegate plugin Library for GPU inference
    implementation 'org.tensorflow:tensorflow-lite-gpu-delegate-plugin:0.4.0'
}

Step 2: Using the model

// Initialization
AudioClassifierOptions options =
    AudioClassifierOptions.builder()
        .setBaseOptions(BaseOptions.builder().useGpu().build())
        .setMaxResults(1)
        .build();
AudioClassifier classifier =
    AudioClassifier.createFromFileAndOptions(context, modelFile, options);

// Start recording
AudioRecord record = classifier.createAudioRecord();
record.startRecording();

// Load latest audio samples
TensorAudio audioTensor = classifier.createInputTensorAudio();
audioTensor.load(record);

// Run inference
List<Classifications> results = audioClassifier.classify(audioTensor);

See the source code and javadoc for more options to configure AudioClassifier.

Run inference in iOS

Step 1: Install the dependencies

The Task Library supports installation using CocoaPods. Make sure that CocoaPods is installed on your system. Please see the CocoaPods installation guide for instructions.

Please see the CocoaPods guide for details on adding pods to an Xcode project.

Add the TensorFlowLiteTaskAudio pod in the Podfile.

target 'MyAppWithTaskAPI' do
  use_frameworks!
  pod 'TensorFlowLiteTaskAudio'
end

Make sure that the .tflite model you will be using for inference is present in your app bundle.

Step 2: Using the model

Swift

// Imports
import TensorFlowLiteTaskAudio
import AVFoundation

// Initialization
guard let modelPath = Bundle.main.path(forResource: "sound_classification",
                                            ofType: "tflite") else { return }

let options = AudioClassifierOptions(modelPath: modelPath)

// Configure any additional options:
// options.classificationOptions.maxResults = 3

let classifier = try AudioClassifier.classifier(options: options)

// Create Audio Tensor to hold the input audio samples which are to be classified.
// Created Audio Tensor has audio format matching the requirements of the audio classifier.
// For more details, please see:
// https://github.com/tensorflow/tflite-support/blob/master/tensorflow_lite_support/ios/task/audio/core/audio_tensor/sources/TFLAudioTensor.h
let audioTensor = classifier.createInputAudioTensor()

// Create Audio Record to record the incoming audio samples from the on-device microphone.
// Created Audio Record has audio format matching the requirements of the audio classifier.
// For more details, please see:
https://github.com/tensorflow/tflite-support/blob/master/tensorflow_lite_support/ios/task/audio/core/audio_record/sources/TFLAudioRecord.h
let audioRecord = try classifier.createAudioRecord()

// Request record permissions from AVAudioSession before invoking audioRecord.startRecording().
AVAudioSession.sharedInstance().requestRecordPermission { granted in
    if granted {
        DispatchQueue.main.async {
            // Start recording the incoming audio samples from the on-device microphone.
            try audioRecord.startRecording()

            // Load the samples currently held by the audio record buffer into the audio tensor.
            try audioTensor.load(audioRecord: audioRecord)

            // Run inference
            let classificationResult = try classifier.classify(audioTensor: audioTensor)
        }
    }
}

Objective C

// Imports
#import <TensorFlowLiteTaskAudio/TensorFlowLiteTaskAudio.h>
#import <AVFoundation/AVFoundation.h>

// Initialization
NSString *modelPath = [[NSBundle mainBundle] pathForResource:@"sound_classification" ofType:@"tflite"];

TFLAudioClassifierOptions *options =
    [[TFLAudioClassifierOptions alloc] initWithModelPath:modelPath];

// Configure any additional options:
// options.classificationOptions.maxResults = 3;

TFLAudioClassifier *classifier = [TFLAudioClassifier audioClassifierWithOptions:options
                                                                          error:nil];

// Create Audio Tensor to hold the input audio samples which are to be classified.
// Created Audio Tensor has audio format matching the requirements of the audio classifier.
// For more details, please see:
// https://github.com/tensorflow/tflite-support/blob/master/tensorflow_lite_support/ios/task/audio/core/audio_tensor/sources/TFLAudioTensor.h
TFLAudioTensor *audioTensor = [classifier createInputAudioTensor];

// Create Audio Record to record the incoming audio samples from the on-device microphone.
// Created Audio Record has audio format matching the requirements of the audio classifier.
// For more details, please see:
https://github.com/tensorflow/tflite-support/blob/master/tensorflow_lite_support/ios/task/audio/core/audio_record/sources/TFLAudioRecord.h
TFLAudioRecord *audioRecord = [classifier createAudioRecordWithError:nil];

// Request record permissions from AVAudioSession before invoking -[TFLAudioRecord startRecordingWithError:].
[[AVAudioSession sharedInstance] requestRecordPermission:^(BOOL granted) {
    if (granted) {
        dispatch_async(dispatch_get_main_queue(), ^{
            // Start recording the incoming audio samples from the on-device microphone.
            [audioRecord startRecordingWithError:nil];

            // Load the samples currently held by the audio record buffer into the audio tensor.
            [audioTensor loadAudioRecord:audioRecord withError:nil];

            // Run inference
            TFLClassificationResult *classificationResult =
                [classifier classifyWithAudioTensor:audioTensor error:nil];

        });
    }
}];

See the source code for more options to configure TFLAudioClassifier.

Run inference in Python

Step 1: Install the pip package

pip install tflite-support
  • Linux: Run sudo apt-get update && apt-get install libportaudio2
  • Mac and Windows: PortAudio is installed automatically when installing the tflite-support pip package.

Step 2: Using the model

# Imports
from tflite_support.task import audio
from tflite_support.task import core
from tflite_support.task import processor

# Initialization
base_options = core.BaseOptions(file_name=model_path)
classification_options = processor.ClassificationOptions(max_results=2)
options = audio.AudioClassifierOptions(base_options=base_options, classification_options=classification_options)
classifier = audio.AudioClassifier.create_from_options(options)

# Alternatively, you can create an audio classifier in the following manner:
# classifier = audio.AudioClassifier.create_from_file(model_path)

# Run inference
audio_file = audio.TensorAudio.create_from_wav_file(audio_path, classifier.required_input_buffer_size)
audio_result = classifier.classify(audio_file)

See the source code for more options to configure AudioClassifier.

Run inference in C++

// Initialization
AudioClassifierOptions options;
options.mutable_base_options()->mutable_model_file()->set_file_name(model_path);
std::unique_ptr<AudioClassifier> audio_classifier = AudioClassifier::CreateFromOptions(options).value();

// Create input audio buffer from your `audio_data` and `audio_format`.
// See more information here: tensorflow_lite_support/cc/task/audio/core/audio_buffer.h
int input_size = audio_classifier->GetRequiredInputBufferSize();
const std::unique_ptr<AudioBuffer> audio_buffer =
    AudioBuffer::Create(audio_data, input_size, audio_format).value();

// Run inference
const ClassificationResult result = audio_classifier->Classify(*audio_buffer).value();

See the source code for more options to configure AudioClassifier.

Model compatibility requirements

The AudioClassifier API expects a TFLite model with mandatory TFLite Model Metadata. See examples of creating metadata for audio classifiers using the TensorFlow Lite Metadata Writer API.

The compatible audio classifier models should meet the following requirements:

  • Input audio tensor (kTfLiteFloat32)

    • audio clip of size [batch x samples].
    • batch inference is not supported (batch is required to be 1).
    • for multi-channel models, the channels need to be interleaved.
  • Output score tensor (kTfLiteFloat32)

    • [1 x N] array with N represents the class number.
    • optional (but recommended) label map(s) as AssociatedFile-s with type TENSOR_AXIS_LABELS, containing one label per line. The first such AssociatedFile (if any) is used to fill the label field (named as class_name in C++) of the results. The display_name field is filled from the AssociatedFile (if any) whose locale matches the display_names_locale field of the AudioClassifierOptions used at creation time ("en" by default, i.e. English). If none of these are available, only the index field of the results will be filled.