Hello! I'm writing to the Apple developers to request the addition of an API for downloading premium voices directly within the app. Currently, this can only be done via the settings, which is not convenient for our users. As a developer for an application where this plays a crucial role, I ask you to take this into consideration. Thank you!

For example: we use DocKit for birdwatching, so we have an unknown field distance and direction. Distance = ? Direction = ? For example, the rock from which the observation is made. The task is to recognize the number of birds caught in the frame, add a detection frame and collect statistics. Question: What is the maximum number of frames processed with custom object recognition? If not enough, can I do the calculations myself and transfer to DokKit for fast movement?

Description: Problem Statement: State the problem clearly: The Siri Intent for the "Next","Previous","Repeat" command is not working as expected within the Speech Framework. Steps to Reproduce: Provide a detailed description of the steps to reproduce the issue. For example: Open the Speech Framework application. Tap on the Siri button to activate voice input. Say "Next" to trigger the intended action. Observe that the action is not executed correctly. IN Our Demo App: Steps of my demo application as below: Open SIRI Speak: Check In Response: Open dialog as below: What user wants? One 2) Next 3) Yes 4) Goodbye Speak: Next In Response: SIRI repeat same dialog (Step: 2) 3) Speak: Yes, or One or Goodbye In Response: SIRI goes to next dialog. Expected Behavior: Should be get "Next" Value in siri kit intent or app intent. Actual Behavior: But it give previous user input key word give in siri kit intent and recuresively repeat dialog in app intent. Device versions and Region and Language: Device model: IPhone 11 and OS version: 17.4.1 Region: Us and Language: English(US) Impact: User Cant use Iterative dialog in one context. Additional: How Different command work on app intent and siri kit intent on diffrent diffrent device. you can follow No vise in order. || No || Diffrent Device test on Diffrent sinario || SiriKit intent || app Intent || | 1 | ISG iPhone 11 - Next | Not | Not | | 2 | ISG iPhone 11 - Yes | Not | Yes (But Using Enum) | | 3 | ISG iPhone 11 - GoodBye | Not | Yes (But Using Enum) | | 4 | ISG iPhone 11 - One | Yes | Yes | | 5 | iPad - Next | Not | Not | | 6 | iPad - One | Yes | Yes | | 7 | iPad - GoodBye | Not | Yes | | 8 | iPad - Yes | Not | Yes | | 9 | Simulator - iPhone 15 - Next, Yes, One, GoodBye | Yes | Yes | Please help me in it...

I'm using Filemaker, with Monkey Bread Software plugin's CoreML features, to find that it can only write to .mlmodelc. Are these (.mlmodel = .mlmodelc) the same? If not, how do you generate a .mlmodelc using XCode. Please let me know, thanks.

Hi, I have encountered to a segfault error when I called something via jax.lax.scan. A minimum failing example is pasted below: $ ipython Python 3.9.6 (default, Feb 3 2024, 15:58:27) Type 'copyright', 'credits' or 'license' for more information IPython 8.18.1 -- An enhanced Interactive Python. Type '?' for help. In [1]: import jax In [2]: jax.__version__ Out[2]: '0.4.22' In [3]: import jaxlib In [4]: jaxlib.__version__ Out[4]: '0.4.22' In [6]: import jax.numpy as jnp In [7]: def f(carry, x): ...: return carry + x * x, x * x ...: ...: jax.lax.scan(f, jnp.zeros((), dtype=jnp.float32), jnp.arange(3, dtype=jnp.float32)) Platform 'METAL' is experimental and not all JAX functionality may be correctly supported! 2024-04-16 01:03:52.483015: W pjrt_plugin/src/mps_client.cc:563] WARNING: JAX Apple GPU support is experimental and not all JAX functionality is correctly supported! Metal device set to: Apple M3 Max systemMemory: 36.00 GB maxCacheSize: 13.50 GB zsh: segmentation fault ipython This might be related to the thread below: https://developer.apple.com/forums/thread/749080 Strangely, when we call it jax.lax.scan is a very important building block, so I would greatly appreciate if this can be resolved soon.

Copying from https://github.com/google/jax/issues/20750: import jax import jax.numpy as jnp def test_func(x, y): return x, y def main(): # Print available JAX devices print("JAX devices:", jax.devices()) # Create two random matrices a = jnp.array([[1.0, 2.0], [3.0, 4.0]]) b = jnp.array([[5.0, 6.0], [7.0, 8.0]]) # Perform matrix multiplication c = jnp.dot(a, b) # Print the result print("Result of matrix multiplication:") print(c) # Compute the gradient of sum of c with respect to a grad_a = jax.grad(lambda a: jnp.sum(jnp.dot(a, b)))(a) print("Gradient with respect to a:") print(grad_a) rng = jax.random.PRNGKey(0) test_input = jax.random.normal(key=rng, shape=(5,5,5)) initial_state = jax.numpy.array(0.0) x, y = jax.lax.scan(test_func, initial_state, test_input) if __name__ == "__main__": main() Gets: Platform 'METAL' is experimental and not all JAX functionality may be correctly supported! 2024-04-15 18:22:28.994752: W pjrt_plugin/src/mps_client.cc:563] WARNING: JAX Apple GPU support is experimental and not all JAX functionality is correctly supported! Metal device set to: Apple M2 Pro systemMemory: 16.00 GB maxCacheSize: 5.33 GB JAX devices: [METAL(id=0)] Result of matrix multiplication: [[19. 22.] [43. 50.]] Gradient with respect to a: [[11. 15.] [11. 15.]] zsh: segmentation fault python JAXTest.py With more info from the debugger: Current thread 0x00000001fdd3bac0 (most recent call first): File "/Users/.../anaconda3/lib/python3.11/site-packages/jax/_src/interpreters/pxla.py", line 1213 in __call__ My configuration is: jax-metal : 0.0.6 jax: 0.4.26 jaxlib: 0.4.23 numpy: 1.24.3 python: 3.11.8 | packaged by conda-forge | (main, Feb 16 2024, 20:49:36) [Clang 16.0.6 ] jax.devices (1 total, 1 local): [METAL(id=0)] process_count: 1 platform: uname_result(system='Darwin', root:xnu-10063.101.17~1/RELEASE_ARM64_T6020', machine='arm64') macOS 14.4.1 (23E224) Before in 3.9+0.0.3 etc it wasn't happening.

I was wondering if there is a quick way to convert a model trained with the open source CRFSuite for use with NLTagger? It seems like retraining should be possible but was wondering if automatic conversion was supported?

Hello! We have an app that utilises the SpeechKit Framework. Especially the local on-device speech recognition for the audio files with the user selected language. Up until recently it worked as expected. However after updating one of our testing device to iOS 17.4.1 we found out that the local recognition on it stopped working completely. The error that we are getting has code 102 at its localised description reads: "Failed to access assets". That sounds just like a rear though known issue in previous iOS versions. The solution was inconvenient for our users but at least it worked – they were to go to the System settings and tweak with the dictation setting in the keyboard section. Right now no tweaks of this sort appear to help us fix the situation. We even tried to do the setting reset of the device (not the factory reset though). The error persists. it appears one one of our devices 100% of the time, halting the local recognition process. It sometimes shows on other devices for some particular languages too, but it does not show for other languages. As it is a UX breaking bug for our app, today I decided to check the logs of the Console app at the moment of the recognition attempt. There are lots of errors with code 1101 which from our research appear to be the general notifications about some local recognition setup problems. Removing the lines about the 1101 error from the log we have some interesting stuff remaining, that is (almost) never mentioned in any of the searchable webpages in the Internet. I assume they are the private API calls that the SpeechKit Framework executes under the hood: default localspeechrecognition -[UAFAssetSet assetNamed:]_block_invoke 9067C4F1-0B29-4A57-85DD-F8740DF7C344: No assets in asset set com.apple.siri.understanding default localspeechrecognition -[UAFAssetSet assetNamed:] 9067C4F1-0B29-4A57-85DD-F8740DF7C344: Returning com.apple.siri.asr.assistant from source none error localspeechrecognition -[SFEntitledAssetManager _assetWithAssetConfig:regionId:] No asset found with name: com.apple.siri.asr.assistant, asset set: com.apple.siri.understanding, usage: <private> error localspeechrecognition +[LSRConnection modelRootWithLanguage:clientID:modelOverrideURL:returningAssetType:error:] Fetch asset error (null) error localspeechrecognition -[LSRConnection prepareRecognizerWithLanguage:recognitionOverrides:modelOverrideURL:anyConfiguration:task:clientID:error:] modelRoot is nil (null) default OurApp [0x113e96d40] invalidated because the current process cancelled the connection by calling xpc_connection_cancel() Looks like there are some language-model related problems that appeared after the device was updated to 17.4.1. The Settings -> General -> Keyboard -> Dictation Languages appear to be configured correctly, the dictation toggle is On, we tried tweaking all these setting, rebooting the device and resetting the device settings. However the log lines still tell us that there is something wrong with the private resources of the SpeechKit framework. We are very concerned as the speech recognition is the core of out application's logic. And we don't understand what is the scale of possible impact of such a faulty behaviour (rare occurrences / some users / all users?) and how we can fix it to provide our users with the desired behaviour.

Will macos support amd rx7600?

Hello iOS Developer Community, I hope this message finds you healthy and happy. I am reaching out to seek your expertise and assistance with a particular challenge I’ve encountered while using the Speak Screen and Speak Selection features on iOS. As you may know, these features are incredibly useful for reading text aloud, but they sometimes struggle with the correct pronunciation of homographs—words that are spelled the same but have different meanings and pronunciations. An example of this is the word “live,” which can be pronounced differently based on the context of the sentence. To enhance my user experience, I am looking to input corrections for the pronunciation of “live” in its “happening now” context, such as in “live broadcast” or “live event.” However, the current process requires manual entry for each phrase, which is quite labor-intensive. I am wondering if there is a way to automate or streamline this process, perhaps through a shortcut or script that allows for bulk input of these corrections. Additionally, if anyone has already compiled a list of common phrases with homographs and their correct pronunciations, I would greatly appreciate it if you could share it or guide me on where to find such resources. Your insights and guidance on this matter would be invaluable, and I believe any solutions could benefit not just myself but many other users facing similar issues. Thank you for your time and consideration. I look forward to any suggestions or advice you may have. Best regards, Alec

Hi, I just noticed that using the jax.numpy.insert() function returns an incorrect result (zero-padding the array) when compiled with jax.jit. When not jitted, the results are correct Config: M1 Pro Macbook Pro 2021 python 3.12.3 ; jax-metal 0.0.6 ; jax 0.4.26 ; jaxlib 0.4.23 MWE: import jax import jax.numpy as jnp x = jnp.arange(20).reshape(5, 4) print(f"{x=}\n") def return_arr_with_ins(arr, ins): return jnp.insert(arr, 2, ins, axis=1) x2 = return_arr_with_ins(x, 99) print(f"{x2=}\n") return_arr_with_ins_jit = jax.jit(return_arr_with_ins) x3 = return_arr_with_ins_jit(x, 99) print(f"{x3=}\n") Output: x2 (computed with the non-jitted function) is correct; x3 just has zero-padding instead of a column of 99 x=Array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15], [16, 17, 18, 19]], dtype=int32) x2=Array([[ 0, 1, 99, 2, 3], [ 4, 5, 99, 6, 7], [ 8, 9, 99, 10, 11], [12, 13, 99, 14, 15], [16, 17, 99, 18, 19]], dtype=int32) x3=Array([[ 0, 1, 2, 3, 0], [ 4, 5, 6, 7, 0], [ 8, 9, 10, 11, 0], [12, 13, 14, 15, 0], [16, 17, 18, 19, 0]], dtype=int32) The same code run on a non-metal machine gives the correct results: x=Array([[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11], [12, 13, 14, 15], [16, 17, 18, 19]], dtype=int32) x2=Array([[ 0, 1, 99, 2, 3], [ 4, 5, 99, 6, 7], [ 8, 9, 99, 10, 11], [12, 13, 99, 14, 15], [16, 17, 99, 18, 19]], dtype=int32) x3=Array([[ 0, 1, 99, 2, 3], [ 4, 5, 99, 6, 7], [ 8, 9, 99, 10, 11], [12, 13, 99, 14, 15], [16, 17, 99, 18, 19]], dtype=int32) Not sure if this is the correct channel for bug reports, please feel free to let me know if there's a more appropriate place!

When fitting a CNN model, every second Epoch takes zero seconds and with OUT_OF_RANGE warnings. Im using structured folders of categorical images for training and validation. Here is the warning message that occurs after every second Epoch. The fitting looks like this... 37/37 ━━━━━━━━━━━━━━━━━━━━ 14s 337ms/step - accuracy: 0.5255 - loss: 1.0819 - val_accuracy: 0.2578 - val_loss: 2.4472 Epoch 4/20 37/37 ━━━━━━━━━━━━━━━━━━━━ 0s 1ms/step - accuracy: 0.5312 - loss: 1.1106 - val_accuracy: 0.1250 - val_loss: 3.0711 Epoch 5/20 2024-04-19 09:22:51.673909: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence [[{{node IteratorGetNext}}]] 2024-04-19 09:22:51.673928: W tensorflow/core/framework/local_rendezvous.cc:404] Local rendezvous is aborting with status: OUT_OF_RANGE: End of sequence [[{{node IteratorGetNext}}]] [[IteratorGetNext/_59]] 2024-04-19 09:22:51.673940: I tensorflow/core/framework/local_rendezvous.cc:422] Local rendezvous recv item cancelled. Key hash: 10431687783238222105 2024-04-19 09:22:51.673944: I tensorflow/core/framework/local_rendezvous.cc:422] Local rendezvous recv item cancelled. Key hash: 17360824274615977051 2024-04-19 09:22:51.673955: I tensorflow/core/framework/local_rendezvous.cc:422] Local rendezvous recv item cancelled. Key hash: 10732905483452597729 My setup is.. Tensor Flow Version: 2.16.1 Python 3.9.19 (main, Mar 21 2024, 12:07:41) [Clang 14.0.6 ] Pandas 2.2.2 Scikit-Learn 1.4.2 GPU is available My generator is.. train_generator = datagen.flow_from_directory( scalp_dir_train, # directory target_size=(256, 256),# all images found will be resized batch_size=32, class_mode='categorical' #subset='training' # Specify the subset as training ) n_samples = train_generator.samples # gets the number of samples validation_generator = datagen.flow_from_directory( scalp_dir_test, # directory path target_size=(256, 256), batch_size=32, class_mode='categorical' #subset='validation' # Specifying the subset as validation Here is my model. early_stopping_monitor = EarlyStopping(patience = 10,restore_best_weights=True) from tensorflow.keras.optimizers import Adam from tensorflow.keras.optimizers import SGD optimizer = Adam(learning_rate=0.01) model = Sequential() model.add(Conv2D(128, (3, 3), activation='relu',padding='same', input_shape=(256, 256, 3))) model.add(BatchNormalization()) model.add(MaxPooling2D((2, 2))) model.add(Dropout(0.3)) model.add(Conv2D(64, (3, 3),padding='same', activation='relu')) model.add(BatchNormalization()) model.add(MaxPooling2D((2, 2))) model.add(Dropout(0.3)) model.add(Flatten()) model.add(Dense(512, activation='relu')) model.add(BatchNormalization()) model.add(Dropout(0.4)) model.add(Dense(256, activation='relu')) model.add(BatchNormalization()) model.add(Dropout(0.3)) model.add(Dense(4, activation='softmax')) # Defined by the number of classes model.compile(optimizer=optimizer, loss='categorical_crossentropy', metrics=['accuracy']) Here is the fit... history=model.fit( train_generator, steps_per_epoch=37, epochs=20, validation_data=validation_generator, validation_steps=12, callbacks=[early_stopping_monitor] #verbose=2 )

Hello! I am developing an app that leverages Apple's 2D pose estimation model and I would love to speak with someone about if my mobile app should leverage Apple's 3D pose estimation model. Also, I would love to know if Apple considers adding more points on the body as this would be incredibly helpful. Or if it is possible for me to train the model to add more body points. Thanks so much and please let me know if anyone is available to discuss.

I'm training an activity classifier with CreateML and when I add samples to the Preview tab, the length of the sample it displays does not match its actual length. I have set prediction window size to 15 and sample rate to 10. The activity is roughly 1.5 seconds. When I put a 1.49 second sample into preview, it says it is 00:00.06 seconds: and when I put a 12.91 second sample into preview, it says it is 00:00.52 seconds: Here is the code I am using to print out sensor data in csv format: if motionManager.isDeviceMotionAvailable { motionManager.deviceMotionUpdateInterval = 0.1 motionManager.startDeviceMotionUpdates(to: .main) { data, error in guard let data = data, let startTime = self.startTime else { return } let timestamp = Date().timeIntervalSince(startTime) let xAcc = data.userAcceleration.x let yAcc = data.userAcceleration.y let zAcc = data.userAcceleration.z let xRotRate = data.rotationRate.x let yRotRate = data.rotationRate.y let zRotRate = data.rotationRate.z let roll = data.attitude.roll let pitch = data.attitude.pitch let yaw = data.attitude.yaw let row = "\(timestamp),\(xAcc),\(yAcc),\(zAcc),\(xRotRate),\(yRotRate),\(zRotRate),\(roll),\(pitch),\(yaw)" print(row) } } And here is the data for the 1.49 second sample mentioned above:

I using a Macbook pro with an m2 pro chip. I was trying to work with TensorFlow but I encountered an illegal hardware instruction error. To resolve it I initiated the installation of a metal plugin which is throwing the following error. or semicolon (after version specifier) awscli>=1.16.100boto3>=1.9.100 ~~~~~~~~~~~^ Unable to locate awscli [end of output]

This model run coreml result is not right, the precision is completely wrong, I posted a PhotoDepthAnythingConv.onnx model: https://github.com/MoonCodeMaster/CoremlErrorModel/tree/main/DepthAnything

I noticed from the system requirements, TensorFlow only seems to support Python. Are there any plans to add JavaScript as TensorFlow has JS support? Thank you for your time...

Regardless of the installation version combinations of tensorflow & metal (2.14, 2.15, 2.16), I find a metal/non-metal incompatibility for some layer types. For the GRU layer, for example, metal-trained weights (model.save_weights()/load_weights()) are not compatible with inference using the CPU. That is, train a model using metal, run inference using metal, then run inference again after uninstalling metal, and the results differ -- sometimes a night and day difference. This essentially eliminates the usefulness of tensorflow-metal for me. From my limited testing, models using other, simple combinations of layer types including Dense and LSTM do not show this problem. Just the GRU. And by "testing" I mean really simple models, like one GRU layer. Apple Framework Metal Team: You are doing very useful work, and I kindly ask, please address this bug :)

Hi everyone ! I'm getting random crashes when I'm using the Speech Recognizer functionality in my app. This is an old bug (for 8 years on Apple Forums) and I will really appreciate if anyone from Apple will be able to find a fix for this crashes. Can anyone also help me please to understand what could I do to keep the Speech Recognizer functionality still available in my app, but to avoid this crashes (if there is any other native library available or a CocoaPod library). Here is my code and also the crash log for it. Code: func startRecording() { startStopRecordBtn.setImage(UIImage(#imageLiteral(resourceName: "microphone_off")), for: .normal) if UserDefaults.standard.bool(forKey: Constants.darkTheme) { commentTextView.textColor = .white } else { commentTextView.textColor = .black } commentTextView.isUserInteractionEnabled = false recordingLabel.text = Constants.recording if recognitionTask != nil { recognitionTask?.cancel() recognitionTask = nil } let audioSession = AVAudioSession.sharedInstance() do { try audioSession.setCategory(AVAudioSession.Category.record) try audioSession.setMode(AVAudioSession.Mode.measurement) try audioSession.setActive(true, options: .notifyOthersOnDeactivation) } catch { showAlertWithTitle(message: Constants.error) } recognitionRequest = SFSpeechAudioBufferRecognitionRequest() let inputNode = audioEngine.inputNode guard let recognitionRequest = recognitionRequest else { fatalError(Constants.error) } recognitionRequest.shouldReportPartialResults = true recognitionTask = speechRecognizer?.recognitionTask(with: recognitionRequest, resultHandler: { (result, error) in var isFinal = false if result != nil { self.commentTextView.text = result?.bestTranscription.formattedString isFinal = (result?.isFinal)! } if error != nil || isFinal { self.audioEngine.stop() inputNode.removeTap(onBus: 0) self.recognitionRequest = nil self.recognitionTask = nil self.startStopRecordBtn.isEnabled = true } }) let recordingFormat = inputNode.outputFormat(forBus: 0) inputNode.installTap(onBus: 0, bufferSize: 1024, format: recordingFormat) {[weak self] (buffer: AVAudioPCMBuffer, when: AVAudioTime) in // CRASH HERE self?.recognitionRequest?.append(buffer) } audioEngine.prepare() do { try audioEngine.start() } catch { showAlertWithTitle(message: Constants.error) } } Here is the crash log: Thanks for very much for reading this !

Hello there, We currently have a crash in prod when executing the following line: let classificationRequest = try SNClassifySoundRequest(classifierIdentifier: .version1) It appears to only happen on iOS 17+ and only when regaining audio focus after an interruption in a background state. We are aware this call probably fails because it is happening from a background state - however - I would expect then that the SNClassifySoundRequest throws some kind of error since it is already an initializer that throws. If it is possible for the call to fail under certain circumstances, then could SNMLModelFactory throw an error instead of using try! ? Full trace below: SoundAnalysis/SNMLModelFactory.swift:112: Fatal error: 'try!' expression unexpectedly raised an error: Error Domain=com.apple.CoreML Code=0 "Failed to build the model execution plan using a model architecture file '/System/Library/Frameworks/SoundAnalysis.framework/SNSoundClassifierVersion1Model.mlmodelc/model1/model.espresso.net' with error code: -1." UserInfo={NSLocalizedDescription=Failed to build the model execution plan using a model architecture file '/System/Library/Frameworks/SoundAnalysis.framework/SNSoundClassifierVersion1Model.mlmodelc/model1/model.espresso.net' with error code: -1.}