ios – How can I move voice via an MLModel earlier than sending it to WebRTC?

Earlier than the voice is handed to WebRTC, it must be processed via an MLModel, and the output from the mannequin ought to then go to WebRTC. How can I obtain this?

Downside Assertion:
I need to course of audio utilizing a Core ML mannequin (just like noise cancellation) earlier than passing it to WebRTC for audio and video streaming. I am utilizing WebRTC-iOS from GitHub and have written the next code to course of the audio via a Core ML mannequin and ship it to WebRTC.

Nevertheless, I am unsure if my implementation is right, notably concerning learn how to combine the Core ML mannequin with WebRTC audio processing.

My Setup:
WebRTC is used to deal with the audio and video streams.
Core ML is used for real-time voice processing (e.g., noise cancellation).
The audio is captured by way of the AVAudioEngine, processed by the Core ML mannequin, after which handed to WebRTC.

personal func createAudioTrack() -> RTCAudioTrack {
    let audioConstraints = RTCMediaConstraints(mandatoryConstraints: nil, optionalConstraints: nil)
    let audioSource = WebRTCClient.manufacturing facility.audioSource(with: audioConstraints)
    let audioTrack = WebRTCClient.manufacturing facility.audioTrack(with: audioSource, trackId: "audio0")

    // Begin processing the audio
    let audioProcessor = AudioProcessor()
    audioProcessor.startProcessing()

    return audioTrack
}

class AudioProcessor {
    personal let voiceModel: denoiser_prototype1
    personal var engine: AVAudioEngine!
    personal var inputNode: AVAudioInputNode!
    personal var mixerNode: AVAudioMixerNode!

    init() {
        guard let mannequin = strive? denoiser_prototype1(configuration: MLModelConfiguration()) else {
            fatalError("Could not load mannequin")
        }
        self.voiceModel = mannequin

        // Arrange the audio engine
        self.engine = AVAudioEngine()
        self.inputNode = self.engine.inputNode
        self.mixerNode = AVAudioMixerNode()
        self.engine.connect(self.mixerNode)
        self.engine.join(self.inputNode, to: self.mixerNode, format: self.inputNode.inputFormat(forBus: 0))
    }

    func startProcessing() {
        // Set up a faucet on the enter node to course of the audio
        let format = self.inputNode.inputFormat(forBus: 0)
        self.inputNode.installTap(onBus: 0, bufferSize: 1024, format: format) { buffer, time in
            // Course of the audio buffer utilizing Core ML
            if let processedBuffer = self.processAudioBuffer(buffer) {
                // Ship the processed audio again to WebRTC
                self.sendProcessedAudioToWebRTC(processedBuffer)
            }
        }

        do {
            strive self.engine.begin()
        } catch {
            fatalError("Couldn't begin audio engine: (error)")
        }
    }

    personal func processAudioBuffer(_ audioBuffer: AVAudioPCMBuffer) -> AVAudioPCMBuffer? {
        // Convert the audio buffer to the mannequin's enter format (if wanted)
        // Run the Core ML mannequin for processing
        // Return the processed buffer (placeholder logic beneath)
        return audioBuffer // Placeholder: Apply precise transformation by way of Core ML
    }

    personal func sendProcessedAudioToWebRTC(_ buffer: AVAudioPCMBuffer) {
        // Logic to ship the processed buffer to WebRTC
        // Make sure that this buffer will get routed to WebRTC's RTCAudioTrack
    }
}

Questions:
How do I accurately course of the audio buffer utilizing Core ML and move it again to WebRTC?
Is there a greater technique to combine Core ML with WebRTC for real-time audio processing, notably when it comes to efficiency?
How can I be sure that the processed audio buffer is appropriate with WebRTC’s audio stream?
Any steerage on enhancing this implementation can be significantly appreciated!