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YAMNet CoreML

This is a Core ML conversion of the YAMNet audio event classifier. The model was converted from the TensorFlow Hub YAMNet SavedModel.

Sourcehuggingface.co/Yehor/YAMNet-CoreML/tree/9681162a6618cceb548ee2a94e83bcfb3a141066
KindOtherVersionRevision 9681162Bgrade

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Other
File size
7.12 MB
Version
vRevision 9681162
Updated
Oct 12, 2026
Downloads
29
Source
Hugging Face

Source description

YAMNet Core ML

This is a Core ML conversion of the YAMNet audio event classifier. The model was converted from the TensorFlow Hub YAMNet SavedModel.

Important: this Core ML package does not accept raw waveform audio. It accepts precomputed YAMNet log-mel feature patches shaped [1, 96, 64].

Model Interface

Input:

  • features: MLMultiArray shaped [1, 96, 64], Float16
  • Represents one YAMNet log-mel patch: 96 frames x 64 mel bands.

Output:

  • Identity: MLMultiArray shaped [1, 521], Float16
  • Contains class scores for the 521 YAMNet AudioSet classes.

Class labels are available in:

yamnet_model/assets/yamnet_class_map.csv

Swift Usage

import CoreML
import Foundation

let modelURL = URL(fileURLWithPath: "YAMNet.mlpackage")
let model = try MLModel(contentsOf: modelURL)

let features = try MLMultiArray(shape: [1, 96, 64], dataType: .float16)

// Fill `features` with YAMNet-compatible log-mel values.
// Layout is [batch, frame, melBand].
for frame in 0..<96 {
    for band in 0..<64 {
        let index = [0, NSNumber(value: frame), NSNumber(value: band)]
        features[index] = 0.0
    }
}

let input = try MLDictionaryFeatureProvider(dictionary: [
    "features": MLFeatureValue(multiArray: features)
])

let output = try model.prediction(from: input)
guard let scores = output.featureValue(for: "Identity")?.multiArrayValue else {
    throw NSError(domain: "YAMNet", code: 1, userInfo: [NSLocalizedDescriptionKey: "Missing scores output"])
}

var bestIndex = 0
var bestScore = -Double.infinity
for index in 0..<521 {
    let score = scores[[0, NSNumber(value: index)]].doubleValue
    if score > bestScore {
        bestScore = score
        bestIndex = index
    }
}

print("Top class index: \(bestIndex), score: \(bestScore)")

Feature Extraction

To use this model with raw audio, compute YAMNet-compatible features first:

  • sample rate: 16 kHz mono
  • STFT window: 25 ms
  • STFT hop: 10 ms
  • mel bands: 64
  • mel range: 125 Hz to 7500 Hz
  • log offset: 0.001
  • patch size: 96 frames x 64 mel bands

The original TensorFlow Hub YAMNet model includes waveform preprocessing, but that path uses TensorFlow ops that are not currently supported by Core ML conversion. This package therefore contains only the classifier network after feature extraction.

Conversion

The package was generated with:

uv run main.py --model-path ./yamnet_model --output ./YAMNet.mlpackage

To download the TensorFlow Hub source model:

uv run main.py --download-only

Sources

1 source
Hugging FacePrimaryVersion 9681162a6618cceb548ee2a94e83bcfb3a141066
huggingface.co/Yehor/YAMNet-CoreML/tree/9681162a6618cceb548ee2a94e83bcfb3a141066

Verified Oct 12

Model artifacts

1 artifact

YAMNet.mlpackage/Data/com.apple.CoreML/weights/weight.bin

bin · 7.12 MB · SHA-256 39aee5b5261a…a8ea · Hugging Face

Download file

Source excerpts

3 excerpts
ReadmeAug 2huggingface.co/Yehor/YAMNet-CoreML

--- license: apache-2.0 library_name: coreml tags: - coreml - yamnet - audio-classification - tensorflow - apple --- # YAMNet Core ML This is a Core ML conversion of the YAMNet audio event classifier. The model was converted from the TensorFlow Hub YAMNet SavedModel. Important: this Core ML package does not accept raw waveform audio. It accepts precomputed YAMNet log-mel feature patches shaped `[1, 96, 64]`. ## Model Interface Input: - `features`: `MLMultiArray` shaped `[1, 96, 64]`, `Float16` - Represents one YAMNet log-mel patch: 96 frames x 64 mel bands. Output: - `Identity`: `MLMultiArray` shaped `[1, 521]`, `Float16` - Contains class scores for the 521 YAMNet AudioSet classes. Class labels are available in: ```text yamnet_model/assets/yamnet_class_map.csv ``` ## Swift Usage ```swift import CoreML import Foundation let modelURL = URL(fileURLWithPath: "YAMNet.mlpackage") let model = try MLModel(contentsOf: modelURL) let features = try MLMultiArray(shape: [1, 96, 64], dataType: .float16) // Fill `features` with YAMNet-compatible log-mel values. // Layout is [batch, frame, melBand]. for frame in 0..<96 { for band in 0..<64 { let index = [0, NSNumber(value: frame), NSNumber(value: band)] features[index] = 0.0 } } let input = try MLDictionaryFeatureProvider(dictionary: [ "features": MLFeatureValue(multiArray: features) ]) let output = try model.prediction(from: input) guard let scores = output.featureValue(for: "Identity")?.multiArrayValue else { throw NSError(domain: "YAMNet", code: 1, userInfo: [NSLocalizedDescriptionKey: "Missing scores output"]) } var bestIndex = 0 var bestScore = -Double.infinity for index in 0..<521 { let score = scores[[0, NSNumber(value: index)]].doubleValue if score > bestScore { bestScore = score bestIndex = index } } print("Top class index: \(bestIndex), score: \(bestScore)") ``` ## Feature Extraction To use this model with raw audio, compute YAMNet-compatible features first: - sample rate: 16 kHz mono - STFT window: 25 ms - STFT hop: 10 ms - mel bands: 64 - mel range: 125 Hz to 7500 Hz - log offset: `0.001` - patch size: 96 frames x 64 mel bands The original TensorFlow Hub YAMNet model includes waveform preprocessing, but that path uses TensorFlow ops that are not currently supported by Core ML conversion. This package therefore contains only the classifier network after feature extraction...

NoteOct 12huggingface.co/Yehor/YAMNet-CoreML

Source context: 29 downloads · 0 likes · Pipeline audio-classification · Library coreml · Repo Yehor/YAMNet-CoreML

NoteAug 2huggingface.co/Yehor/YAMNet-CoreML

Source context: 24 downloads · 0 likes · Pipeline audio-classification · Library coreml · Repo Yehor/YAMNet-CoreML