The MediaPipe Audio Classifier task lets you classify audio clips into a set of defined categories, such as guitar music, a train whistle, or a bird's song. The categories are defined during the training of the model. This task operates on audio data with a machine learning (ML) model as independent audio clips or a continuous stream and outputs a list of potential categories ranked by descending probability score. [Open in Web Demos](https://google-ai-edge.github.io/mediapipe-samples-web/#/audio/audio_classifier)[Edit in StackBlitz](https://stackblitz.com/github/google-ai-edge/mediapipe-samples-web?file=src/tasks/audio-classifier.ts)
## Quickstart Install the SDK for your platform, [prepare audio input data](https://developers.google.com/edge/mediapipe/solutions/prepare_data.md.txt#audio), choose an audio running mode, and run inference. ### Web npm`npm install @mediapipe/tasks-audio`[Web demo](https://google-ai-edge.github.io/mediapipe-samples-web/#/audio/audio_classifier)·[Edit in StackBlitz](https://stackblitz.com/github/google-ai-edge/mediapipe-samples-web?file=src/tasks/audio-classifier.ts)·[Setup guide](https://developers.google.com/edge/mediapipe/solutions/setup_web.md.txt) #### Audio clips `AUDIO_CLIPS`Sync · complete clipSynchronous classification across every window of a complete prerecorded audio clip. ```typescript import { AudioClassifier, FilesetResolver } from "@mediapipe/tasks-audio"; const MODEL_URL = "https://storage.googleapis.com/mediapipe-models/audio_classifier/yamnet/float32/latest/yamnet.tflite"; const audio = await FilesetResolver.forAudioTasks( "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-audio@latest/wasm" ); const audioClassifier = await AudioClassifier.createFromOptions(audio, { baseOptions: { modelAssetPath: MODEL_URL }, maxResults: 5, }); // Synchronous classification across every window of a complete decoded clip const audioCtx = new AudioContext({ sampleRate: 16000 }); const response = await fetch("audio_clip.wav"); const audioBuffer = await audioCtx.decodeAudioData(await response.arrayBuffer()); const results = audioClassifier.classify(audioBuffer.getChannelData(0), audioBuffer.sampleRate); ``` #### Audio stream `Web Audio stream`Live mic chunksClassifies live microphone PCM buffers from the Web Audio API synchronously as each chunk arrives. ```typescript import { AudioClassifier, FilesetResolver } from "@mediapipe/tasks-audio"; const MODEL_URL = "https://storage.googleapis.com/mediapipe-models/audio_classifier/yamnet/float32/latest/yamnet.tflite"; const audio = await FilesetResolver.forAudioTasks( "https://cdn.jsdelivr.net/npm/@mediapipe/tasks-audio@latest/wasm" ); const audioClassifier = await AudioClassifier.createFromOptions(audio, { baseOptions: { modelAssetPath: MODEL_URL }, maxResults: 5, }); // Live microphone stream: classify contiguous PCM windows posted by a 15-line // AudioWorklet processor (pcm-chunk-processor.js) --- see the Prepare input data guide. const stream = await navigator.mediaDevices.getUserMedia({ audio: true }); const audioCtx = new AudioContext({ sampleRate: 16000 }); await audioCtx.audioWorklet.addModule("pcm-chunk-processor.js"); const source = audioCtx.createMediaStreamSource(stream); const worklet = new AudioWorkletNode(audioCtx, "pcm-chunk-processor"); worklet.port.onmessage = ({ data: pcmSamples }) => { const results = audioClassifier.classify(pcmSamples, audioCtx.sampleRate); console.log(results[0]?.classifications[0]?.categories); }; source.connect(worklet); ``` ### Android (Kotlin) Gradle`implementation("com.google.mediapipe:tasks-audio:latest.release")`[Sample code](https://github.com/google-ai-edge/mediapipe-samples/tree/main/examples/audio_classifier/android)·[Setup guide](https://developers.google.com/edge/mediapipe/solutions/setup_android.md.txt) #### Audio clips `AUDIO_CLIPS`Sync · complete clipSynchronous classification across every window of a complete prerecorded audio clip. ```kotlin import com.google.mediapipe.tasks.audio.audioclassifier.AudioClassifier import com.google.mediapipe.tasks.audio.audioclassifier.AudioClassifier.AudioClassifierOptions import com.google.mediapipe.tasks.audio.core.RunningMode import com.google.mediapipe.tasks.components.containers.AudioData import com.google.mediapipe.tasks.components.containers.AudioData.AudioDataFormat import com.google.mediapipe.tasks.core.BaseOptions val baseOptions = BaseOptions.builder() .setModelAssetPath("yamnet.tflite") .build() val options = AudioClassifierOptions.builder() .setBaseOptions(baseOptions) .setRunningMode(RunningMode.AUDIO_CLIPS) .setMaxResults(5) .build() val audioClassifier = AudioClassifier.createFromOptions(context, options) // Synchronous classification over a complete prerecorded AudioData clip val format = AudioDataFormat.builder().setSampleRate(16000f).build() val audioData = AudioData.create(format, clipSamples.size) audioData.load(clipSamples) val result = audioClassifier.classify(audioData) ``` #### Audio stream `AUDIO_STREAM`Async · result callbackAsynchronous microphone pipeline with monotonic timestamps and a result callback. Accumulates contiguous PCM blocks into fixed-length spectrogram windows. ```kotlin import android.os.SystemClock import android.util.Log import com.google.mediapipe.tasks.audio.audioclassifier.AudioClassifier import com.google.mediapipe.tasks.audio.audioclassifier.AudioClassifier.AudioClassifierOptions import com.google.mediapipe.tasks.audio.core.RunningMode import com.google.mediapipe.tasks.components.containers.AudioData import com.google.mediapipe.tasks.core.BaseOptions val baseOptions = BaseOptions.builder() .setModelAssetPath("yamnet.tflite") .build() val options = AudioClassifierOptions.builder() .setBaseOptions(baseOptions) .setRunningMode(RunningMode.AUDIO_STREAM) .setMaxResults(5) .setResultListener { result -> val categories = result.classificationResults().firstOrNull()?.classifications()?.firstOrNull()?.categories() } .setErrorListener { error -> Log.e("MediaPipe", error.message.orEmpty()) } .build() val audioClassifier = AudioClassifier.createFromOptions(context, options) // Async: accumulates contiguous microphone PCM chunks into spectrogram windows if (audioData.load(audioRecord) > 0) { audioClassifier.classifyAsync(audioData, SystemClock.uptimeMillis()) } ``` ### Android (Java) Gradle`implementation 'com.google.mediapipe:tasks-audio:latest.release'`[Sample code](https://github.com/google-ai-edge/mediapipe-samples/tree/main/examples/audio_classifier/android)·[Setup guide](https://developers.google.com/edge/mediapipe/solutions/setup_android.md.txt) #### Audio clips `AUDIO_CLIPS`Sync · complete clipSynchronous classification across every window of a complete prerecorded audio clip. ```java import com.google.mediapipe.tasks.audio.audioclassifier.AudioClassifier; import com.google.mediapipe.tasks.audio.audioclassifier.AudioClassifier.AudioClassifierOptions; import com.google.mediapipe.tasks.audio.audioclassifier.AudioClassifierResult; import com.google.mediapipe.tasks.audio.core.RunningMode; import com.google.mediapipe.tasks.components.containers.AudioData; import com.google.mediapipe.tasks.components.containers.AudioData.AudioDataFormat; import com.google.mediapipe.tasks.core.BaseOptions; BaseOptions baseOptions = BaseOptions.builder() .setModelAssetPath("yamnet.tflite") .build(); AudioClassifierOptions options = AudioClassifierOptions.builder() .setBaseOptions(baseOptions) .setRunningMode(RunningMode.AUDIO_CLIPS) .setMaxResults(5) .build(); AudioClassifier audioClassifier = AudioClassifier.createFromOptions(context, options); // Synchronous classification over a complete prerecorded AudioData clip AudioDataFormat format = AudioDataFormat.builder().setSampleRate(16000f).build(); AudioData audioData = AudioData.create(format, clipSamples.length); audioData.load(clipSamples); AudioClassifierResult result = audioClassifier.classify(audioData); ``` #### Audio stream `AUDIO_STREAM`Async · result callbackAsynchronous microphone pipeline with monotonic timestamps and a result callback. Accumulates contiguous PCM blocks into fixed-length spectrogram windows. ```java import android.os.SystemClock; import android.util.Log; import com.google.mediapipe.tasks.audio.audioclassifier.AudioClassifier; import com.google.mediapipe.tasks.audio.audioclassifier.AudioClassifier.AudioClassifierOptions; import com.google.mediapipe.tasks.audio.core.RunningMode; import com.google.mediapipe.tasks.components.containers.AudioData; import com.google.mediapipe.tasks.core.BaseOptions; BaseOptions baseOptions = BaseOptions.builder() .setModelAssetPath("yamnet.tflite") .build(); AudioClassifierOptions options = AudioClassifierOptions.builder() .setBaseOptions(baseOptions) .setRunningMode(RunningMode.AUDIO_STREAM) .setMaxResults(5) .setResultListener(result -> { var results = result.classificationResults(); }) .setErrorListener(error -> Log.e("MediaPipe", String.valueOf(error.getMessage()))) .build(); AudioClassifier audioClassifier = AudioClassifier.createFromOptions(context, options); // Async: accumulates contiguous microphone PCM chunks into spectrogram windows if (audioData.load(audioRecord) > 0) { audioClassifier.classifyAsync(audioData, SystemClock.uptimeMillis()); } ``` ### iOS SwiftPM`https://github.com/google-ai-edge/mediapipe → MediaPipeTasksAudio`[Package.swift](https://developers.google.com/edge/mediapipe/solutions/setup_ios.md.txt#package_swift)·[Sample code](https://github.com/google-ai-edge/mediapipe-samples/tree/main/examples/audio_classifier/ios)·[Setup guide](https://developers.google.com/edge/mediapipe/solutions/setup_ios.md.txt) #### Audio clips `AUDIO_CLIPS`Sync · complete clipSynchronous classification across every window of a complete prerecorded audio clip. ```swift import Foundation import MediaPipeTasksAudio guard let modelPath = Bundle.main.path(forResource: "yamnet", ofType: "tflite") else { return } let options = AudioClassifierOptions() options.baseOptions.modelAssetPath = modelPath options.runningMode = .audioClips options.maxResults = 5 let audioClassifier = try AudioClassifier(options: options) // Synchronous classification across every window of a complete audio clip let result = try audioClassifier.classify(audioClip: audioData) ``` #### Audio stream `AUDIO_STREAM`Async · result callbackAsynchronous microphone pipeline with monotonic timestamps and a result callback. Accumulates contiguous PCM blocks into fixed-length spectrogram windows. ```swift import Foundation import MediaPipeTasksAudio final class AudioClassifierStreamService: NSObject { private var audioClassifier: AudioClassifier? func setUp() throws { guard let modelPath = Bundle.main.path(forResource: "yamnet", ofType: "tflite") else { return } let options = AudioClassifierOptions() options.baseOptions.modelAssetPath = modelPath options.runningMode = .audioStream options.maxResults = 5 options.audioClassifierStreamDelegate = self audioClassifier = try AudioClassifier(options: options) } func classifyAsync(audioData: AudioData, timestampMs: Int) throws { // Async: accumulates contiguous microphone PCM blocks into spectrogram windows try audioClassifier?.classifyAsync(audioBlock: audioData, timestampInMilliseconds: timestampMs) } } extension AudioClassifierStreamService: AudioClassifierStreamDelegate { func audioClassifier( _ audioClassifier: AudioClassifier, didFinishClassification result: AudioClassifierResult?, timestampInMilliseconds: Int, error: (any Error)? ) { guard let result else { return } DispatchQueue.main.async { // In audio stream mode, classificationResults contains one timestamped result let categories = result.classificationResults.first?.classifications.first?.categories } } } ``` ### Python pip`pip install mediapipe`[Sample code](https://colab.research.google.com/github/google-ai-edge/mediapipe-samples/blob/main/examples/audio_classifier/python/audio_classification.ipynb)·[Setup guide](https://developers.google.com/edge/mediapipe/solutions/setup_python.md.txt) #### Audio clips `AUDIO_CLIPS`Sync · complete clipSynchronous classification across every window of a complete prerecorded audio clip. ```python import mediapipe as mp BaseOptions = mp.tasks.BaseOptions AudioClassifier = mp.tasks.audio.AudioClassifier AudioClassifierOptions = mp.tasks.audio.AudioClassifierOptions AudioRunningMode = mp.tasks.audio.RunningMode AudioData = mp.tasks.components.containers.AudioData options = AudioClassifierOptions( base_options=BaseOptions(model_asset_path="yamnet.tflite"), running_mode=AudioRunningMode.AUDIO_CLIPS, max_results=5, ) audio_data = AudioData.create_from_array(clip_samples, sample_rate=16000) with AudioClassifier.create_from_options(options) as classifier: # Synchronous: returns timestamped results for every window across the clip result_list = classifier.classify(audio_data) ``` #### Audio stream `AUDIO_STREAM`Async · result callbackAsynchronous microphone pipeline with monotonic timestamps and a result callback. Accumulates contiguous PCM blocks into fixed-length spectrogram windows. ```python import mediapipe as mp BaseOptions = mp.tasks.BaseOptions AudioClassifier = mp.tasks.audio.AudioClassifier AudioClassifierOptions = mp.tasks.audio.AudioClassifierOptions AudioRunningMode = mp.tasks.audio.RunningMode AudioData = mp.tasks.components.containers.AudioData def on_result(result, timestamp_ms: int): print(result.classifications[0].categories) options = AudioClassifierOptions( base_options=BaseOptions(model_asset_path="yamnet.tflite"), running_mode=AudioRunningMode.AUDIO_STREAM, max_results=5, result_callback=on_result, ) with AudioClassifier.create_from_options(options) as classifier: # Async: accumulates contiguous PCM blocks into spectrogram windows audio_data = AudioData.create_from_array(pcm_chunk, sample_rate=16000) classifier.classify_async(audio_data, timestamp_ms) ``` ### Flutter pub`flutter pub add mediapipe_audio`[Live gallery](https://google.github.io/flutter-mediapipe/)·[Sample code](https://github.com/google/flutter-mediapipe/tree/main/samples/audio/audio_classifier)·[Setup guide](https://developers.google.com/edge/mediapipe/solutions/setup_flutter.md.txt) #### Audio clips `AudioRunningMode.audioClips`One call · complete clipClassifies every window of a complete prerecorded audio clip in one call, on a background isolate. ```dart import 'dart:typed_data'; import 'package:mediapipe_audio/mediapipe_audio.dart'; Future classifyClip(Uint8List wavBytes) async { final classifier = await AudioClassifier.create( AudioClassifierOptions(model: AudioModels.yamnet, maxResults: 3), ); try { final clip = decodeWav(wavBytes); for (final chunk in await classifier.classify(clip)) { final top = chunk.classifications.first.categories.first; print('${chunk.timestampMilliseconds} ms: ${top.categoryName}'); } } finally { await classifier.dispose(); } } ``` #### Audio stream `AudioRunningMode.audioStream`Async · results streamEach call returns at once and results arrive on the task's `results` stream. Frames contiguous PCM blocks into the model's windows; every block is classified, in order. ```dart import 'dart:typed_data'; import 'package:mediapipe_audio/mediapipe_audio.dart'; /// Prints the top sound of each window of [blocks], 16 kHz mono audio. Future listen(Stream blocks) async { final classifier = await AudioClassifier.create( AudioClassifierOptions( model: AudioModels.yamnet, maxResults: 1, runningMode: AudioRunningMode.audioStream, ), ); classifier.results.listen((window) { final top = window.classifications.first.categories.first; print('${window.timestampMilliseconds} ms: ${top.categoryName}'); }); var samples = 0; try { await for (final block in blocks) { classifier.classifyAsync( AudioData(samples: block, sampleRate: 16000), // Where the block starts, counted from the samples before it. timestampMilliseconds: samples * 1000 ~/ 16000, ); samples += block.length; } } finally { // Classifies the audio short of a window, then closes `results`. await classifier.dispose(); } } ``` ## Task details This section describes the capabilities, inputs, outputs, and configuration options of this task. ### Features - **Input audio processing** - Processing includes audio resampling, buffering, framing, and fourier transform. - **Label map locale** - Set the language used for display names - **Score threshold** - Filter results based on prediction scores. - **Top-k classification** - Limit the number of classification results. - **Label allowlist and denylist** - Specify the categories detected. Inputs The Audio Classifier accepts an input of one of the following data types: - Audio clips - Audio stream See [Prepare audio input data](https://developers.google.com/edge/mediapipe/solutions/prepare_data.md.txt#audio) for `AudioData`, `AudioRecord`, and PCM buffer examples. Outputs The Audio Classifier outputs a list of categories containing: - Category index: the index of the category in the model outputs - Score: the confidence score for this category, usually a probability in \[0,1\] - Category name (optional): the name of the category as specified in the LiteRT model metadata, if available - Category display name (optional): a display name for the category as specified in the LiteRT model metadata, in the language specified through display names locale options, if available ### Configuration options This task has the following configuration options: ### Web | Option Name | Description | Value Range | Default Value | |---|---|---|---| | `runningMode` | Sets the running mode for the task: `AUDIO_CLIPS`: Independent audio clips. `AUDIO_STREAM`: Continuous audio stream. | {`AUDIO_CLIPS`, `AUDIO_STREAM`} | `AUDIO_CLIPS` | | `displayNamesLocale` | Locale code for display names provided in the model metadata, if available. You can add localized labels to custom models with the [LiteRT Metadata Writer API](https://developers.google.com/edge/litert/conversion/tensorflow/metadata_writer_tutorial.md.txt#audio_classifiers). | Locale code | `"en"` | | `maxResults` | Maximum number of top-scored classification results to return (`-1` returns all results). | Integer | `-1` | | `scoreThreshold` | Prediction score threshold overriding the model metadata default. Classifications lower than this score are rejected. | Float `[0.0, 1.0]` | Not set | | `categoryAllowlist` | Optional list of allowed category names. Mutually exclusive with `categoryDenylist`. | List of strings | Not set | | `categoryDenylist` | Optional list of excluded category names. Mutually exclusive with `categoryAllowlist`. | List of strings | Not set | ### Android | Option Name | Description | Value Range | Default Value | |---|---|---|---| | `runningMode` | Sets the running mode for the task: `AUDIO_CLIPS`: Independent audio clips. `AUDIO_STREAM`: Continuous audio livestream with asynchronous callback/delegate. | {`AUDIO_CLIPS`, `AUDIO_STREAM`} | `AUDIO_CLIPS` | | `displayNamesLocale` | Locale code for display names provided in the model metadata, if available. You can add localized labels to custom models with the [LiteRT Metadata Writer API](https://developers.google.com/edge/litert/conversion/tensorflow/metadata_writer_tutorial.md.txt#audio_classifiers). | Locale code | `"en"` | | `maxResults` | Maximum number of top-scored classification results to return (`-1` returns all results). | Integer | `-1` | | `scoreThreshold` | Prediction score threshold overriding the model metadata default. Classifications lower than this score are rejected. | Float `[0.0, 1.0]` | Not set | | `categoryAllowlist` | Optional list of allowed category names. Mutually exclusive with `categoryDenylist`. | List of strings | Not set | | `categoryDenylist` | Optional list of excluded category names. Mutually exclusive with `categoryAllowlist`. | List of strings | Not set | | `resultListener` | Asynchronous callback for `AUDIO_STREAM` mode results. | `OutputHandler.ResultListener` | Not set | | `errorListener` | Optional error listener for `AUDIO_STREAM` mode. | `ErrorListener` | Not set | ### iOS | Option Name | Description | Value Range | Default Value | |---|---|---|---| | `runningMode` | Sets the running mode for the task: `AUDIO_CLIPS`: Independent audio clips. `AUDIO_STREAM`: Continuous audio livestream with asynchronous callback/delegate. | {`AUDIO_CLIPS`, `AUDIO_STREAM`} | `AUDIO_CLIPS` | | `displayNamesLocale` | Locale code for display names provided in the model metadata, if available. You can add localized labels to custom models with the [LiteRT Metadata Writer API](https://developers.google.com/edge/litert/conversion/tensorflow/metadata_writer_tutorial.md.txt#audio_classifiers). | Locale code | `"en"` | | `maxResults` | Maximum number of top-scored classification results to return (`-1` returns all results). | Integer | `-1` | | `scoreThreshold` | Prediction score threshold overriding the model metadata default. Classifications lower than this score are rejected. | Float `[0.0, 1.0]` | Not set | | `categoryAllowlist` | Optional list of allowed category names. Mutually exclusive with `categoryDenylist`. | List of strings | Not set | | `categoryDenylist` | Optional list of excluded category names. Mutually exclusive with `categoryAllowlist`. | List of strings | Not set | | `audioClassifierStreamDelegate` | Asynchronous delegate for `AUDIO_STREAM` mode results (invoked on a serial background queue). | `AudioClassifierStreamDelegate` | Not set | ### Python | Option Name | Description | Value Range | Default Value | |---|---|---|---| | `running_mode` | Sets the running mode for the task: `AUDIO_CLIPS`: Independent audio clips. `AUDIO_STREAM`: Continuous audio livestream with `result_callback`. | {`AUDIO_CLIPS`, `AUDIO_STREAM`} | `AUDIO_CLIPS` | | `display_names_locale` | Locale code for display names provided in the model metadata, if available. You can add localized labels to custom models with the [LiteRT Metadata Writer API](https://developers.google.com/edge/litert/conversion/tensorflow/metadata_writer_tutorial.md.txt#audio_classifiers). | Locale code | `"en"` | | `max_results` | Maximum number of top-scored classification results to return (`-1` returns all results). | Integer | `-1` | | `score_threshold` | Prediction score threshold overriding the model metadata default. Classifications lower than this score are rejected. | Float `[0.0, 1.0]` | Not set | | `category_allowlist` | Optional list of allowed category names. Mutually exclusive with `category_denylist`. | List of strings | Not set | | `category_denylist` | Optional list of excluded category names. Mutually exclusive with `category_allowlist`. | List of strings | Not set | | `result_callback` | Asynchronous callback function for `AUDIO_STREAM` mode results. | `Callable` | Not set | ### Flutter | Option Name | Description | Value Range | Default Value | |---|---|---|---| | `runningMode` | Sets the running mode for the task: `AudioRunningMode.audioClips`: Independent audio clips, each classified whole with `classify`. `AudioRunningMode.audioStream`: Continuous audio stream fed in blocks with `classifyAsync` (results arrive on the task's `results` stream). | {`AudioRunningMode.audioClips`, `AudioRunningMode.audioStream`} | `AudioRunningMode.audioClips` | | `displayNamesLocale` | Locale code for display names provided in the model metadata, if available. You can add localized labels to custom models with the [LiteRT Metadata Writer API](https://developers.google.com/edge/litert/conversion/tensorflow/metadata_writer_tutorial.md.txt#audio_classifiers). | Locale code | Not set | | `maxResults` | Maximum number of top-scored classification results to return per head and window (any negative value returns all results; `0` is rejected). | Integer, not `0` | `-1` | | `scoreThreshold` | Prediction score threshold. Classifications lower than this score are rejected. | Finite `double` | `0` | | `categoryAllowlist` | Optional list of allowed category names. Mutually exclusive with `categoryDenylist`. | `List` | Empty | | `categoryDenylist` | Optional list of excluded category names. Mutually exclusive with `categoryAllowlist`. | `List` | Empty | ### Task output Each `AudioClassifierResult` returns timestamped classification windows (for example, 975 ms YAMNet windows across an audio clip or stream): | Field | Type / Range | Description | |---|---|---| | `timestampMs` | `number` (milliseconds) | Start timestamp of the classified audio window (`timestamp_ms` in Python; `timestampMilliseconds` in Dart). | | `classifications` | `Classifications[]` | Per-head classification results (`headIndex`, `headName`, `categories`). | | `categories` | `Category[]` (`score` in `[0.0, 1.0]`) | Ranked acoustic event classes (`categoryName`, `displayName`, `index`, `score`; in Dart, a `List`). | #### Example `AudioClassifierResult` payload The following example shows the top acoustic event predictions for the first audio window (`timestampMs: 0`): ```javascript [ { timestampMs: 0, classifications: [ { headIndex: 0, categories: [ { index: 0, score: 0.6000, categoryName: "Speech" }, { index: 137, score: 0.2000, categoryName: "Music" } ] } ] } ] ``` ## Models ![Audio Classifier pipeline overview](https://developers.google.com/static/edge/mediapipe/images/solutions/diagrams/audio_classifier.svg) The Audio Classifier requires an audio classification model to be downloaded and stored in your project directory. Start with the default, recommended model for your target platform when you start developing with this task. The other available models typically make trade-offs between performance, accuracy, resolution, and resource requirements, and in some cases, include additional features. ### Yamnet model (recommended) The Yamnet model is an audio event classifier trained on the [AudioSet](https://research.google.com/audioset/) dataset to predict audio events defined in the AudioSet data. For information on the audio events recognized by this model, see the model [labels list](https://storage.googleapis.com/mediapipe-tasks/audio_classifier/yamnet_label_list.txt). | Model name | Input shape | Quantization type | Versions | |---|---|---|---| | [YamNet](https://storage.googleapis.com/mediapipe-models/audio_classifier/yamnet/float32/latest/yamnet.tflite) | 1 x 15600 | None (float32) | [Latest](https://storage.googleapis.com/mediapipe-models/audio_classifier/yamnet/float32/latest/yamnet.tflite) | ## Task benchmarks Here are the task benchmarks for the whole pipeline based on the preceding pre-trained models. The latency result is the average latency on Pixel 6 using CPU / GPU. | Model name | Device | CPU latency | GPU latency | |---|---|---|---| | YamNet | Pixel 6 | 12.29ms | - |