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[VisionOS Audio] AVAudioPlayerNode occasionally produces loud popping/distortion when playing PCM data
I'm experiencing audio issues while developing for visionOS when playing PCM data through AVAudioPlayerNode. Issue Description: Occasionally, the speaker produces loud popping sounds or distorted noise This occurs during PCM audio playback using AVAudioPlayerNode The issue is intermittent and doesn't happen every time Technical Details: Platform: visionOS Device: vision pro / simulator Audio Framework: AVFoundation Audio Node: AVAudioPlayerNode Audio Format: PCM I would appreciate any insights on: Common causes of audio distortion with AVAudioPlayerNode Recommended best practices for handling PCM playback in visionOS Potential configuration issues that might cause this behavior Has anyone encountered similar issues or found solutions? Any guidance would be greatly helpful. Thank you in advance!
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4w
Generating procedural textures sample code error.
Screenshot: Specific error message: validateComputeFunctionArguments:1149: failed assertion `Compute Function(textureShader): Shader uses texture(texture[0]) as read-write, but hardware does not support read-write texture of this pixel format.' OS: visionOS 2.1 (22N5548c) simulator. Link: https://developer.apple.com/documentation/visionos/generating-procedural-textures-in-visionos
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Oct ’24
The elements in the attachment cannot add translation.
UI: Attachment(id: "tooptip") { if isRecording { TooltipView { HStack(spacing: 8) { Image(systemName: "waveform") .font(.title) .frame(minWidth: 100) } } .transition(.opacity.combined(with: .scale)) } } Trigger: Button("Toggle") { withAnimation{ isRecording.toggle() } } The above code did not show the animation effect when running. When I use isRecording to drive an element in a common SwiftUI view, there is an animation effect.
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396
Aug ’24
MLTensor computation took more time than expected.
func testMLTensor() { let t1 = MLTensor(shape: [2000, 1], scalars: [Float](repeating: Float.random(in: 0.0...10.0), count: 2000), scalarType: Float.self) let t2 = MLTensor(shape: [1, 3000], scalars: [Float](repeating: Float.random(in: 0.0...10.0), count: 3000), scalarType: Float.self) for _ in 0...50 { let t = Date() let x = (t1 * t2) print("MLTensor", t.timeIntervalSinceNow * 1000, "ms") } } testMLTensor() The above code took more time than expected, especially in the early stage of iteration.
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Aug ’24
MLTensor computation took more time than expected.
func testMLTensor() { let t1 = MLTensor(shape: [2000, 1], scalars: [Float](repeating: Float.random(in: 0.0...10.0), count: 2000), scalarType: Float.self) let t2 = MLTensor(shape: [1, 3000], scalars: [Float](repeating: Float.random(in: 0.0...10.0), count: 3000), scalarType: Float.self) for _ in 0...50 { let t = Date() let x = (t1 * t2) print("MLTensor", t.timeIntervalSinceNow * 1000, "ms") } } testMLTensor() The above code took more time than expected, especially in the early stage of iteration.
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443
Aug ’24
MLTensor computation took more time than expected.
func testMLTensor() { let t1 = MLTensor(shape: [2000, 1], scalars: [Float](repeating: Float.random(in: 0.0...10.0), count: 2000), scalarType: Float.self) let t2 = MLTensor(shape: [1, 3000], scalars: [Float](repeating: Float.random(in: 0.0...10.0), count: 3000), scalarType: Float.self) for _ in 0...50 { let t = Date() let x = (t1 * t2) print("MLTensor", t.timeIntervalSinceNow * 1000, "ms") } } testMLTensor() The above code took more time than expected, especially in the early stage of iteration.
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398
Aug ’24