speech-recognition | End to End Speech Recognition with Tensorflow | Speech library

Goal

I am aiming to get the transcript value, from the function Dictaphone and pass it into to the SearchBar class, and finally set the state term to transcript.

Current code

I'm trying to generate timestamps using Azure S2T in C#. I've tried the following resources:

How to get Word Level Timestamps using Azure Speech to Text and the Python SDK?

How to generate timestamps in speech recognition?

The second has been the most helpful, but I'm still getting errors. My code is:

My company serves e-learning lessons through HTML5 files created in H5P, Captivate, Storyline. These lessons use xAPI to communicate grades and user information to an LRS. Recently I have been working on implementing voice recognition into these lessons using either Web speech API or Annyang and eventually we would like to build our own proprietary speech API. However, I see that voice recognition only seems to be compatible with Chrome desktop right now. I am working on creating a mobile app using React Native that can access a user's lessons from the database and "play" them in an in-app browser. So my questions are as follows:

1. Would it be possible to hand-roll an in-app browser like Capacitor/ Cordova/ some other IAB to support the W3C Web Speech API specification?
2. Would it even be allowed? Would Apple allow an app with such an IAB in their app store?
3. Am I correct in understanding that an in-app browser could still support the necessary Javascript for features like xAPI, drag and drop, and session progress saving? Or am I barking up the wrong tree entirely?

I'm new to TensorFlow and python. I'm trying to run and learn "the Speech Recognition using Keras" in https://www.kaggle.com/sunyuanxi/speech-recognition-keras and I have a problem with this part of the code below and cant debug the error. I really need you to help me. thank you

So i tried to create a speech recognition neural network using the librispeech dataset dev-clean. I tried to convert the code from https://github.com/soheil-mpg/Speech-Recognition into a jupyter notebook.

Everything appears to be working. The model can be trained and doesn't give any errors. But when using model.predict() i get the following error:

AssertionError: Could not compute output Tensor("ctc/ExpandDims_22:0", shape=(None, 1), dtype=float32)

I uploaded the Jupyter Notebook to https://github.com/jake-salmone/ASR

The code is almost identical, the only thing i have change is, that i don't use the json, but use a pandas DataFrame.

I'm trying to use TensorFlow Lite for a voice recognition project using Jupyter notebook but when I try to do a "import librosa" (using commands found here: https://github.com/ShawnHymel/tflite-speech-recognition/blob/master/01-speech-commands-mfcc-extraction.ipynb) I keep getting this error:

I am trying to run a model that was trained with MFCC's and the Google Speech Dataset. The model was trained Here using the first 2 jupyter notebooks.

Now, I am trying to implement it onto a Raspberry Pi with Tensorflow 1.15.2, note that it was also trained in TF 1.15.2. The model loads and I get a correct model.summary():

as stated in the title of the question - What technologies I may use to write drum-pattern audio signal based recognition program? I want to create a tool for me as a drummer musician to transcribe a drum-part from a record. I imagine this as similiar technology to speech-recog but made especially for drum patterns previously defined in some kind of a drum pattern base. The problem is Im a very beginner in programming. For half year i was interested in microcontrollers with basic c++, not even OOP. Currently im trying out python and this is my final programming knowledge/experience background. Now because of that poor level of know-how in IT, I dont really know what technology, frameworks etc I should get interested in with this kind of a project. It may be obvious that I should look for speech-recognition technologies and learn that and then apply that knowledge to build my own program, but Im not really sure where is the best place to start and if im ready for reading heavy walls of professsional open source projects code. Maybe there is some kind of a friendly python framework to get me started in that topic? I found Python Librosa framework in my research but it seems really advanced and it looks like i should learn signal-theory to get fluent in using that. Let me know what do you think and what kind of tactic should i aim to in your opinion.

Last year, apple released on-device speech recognition starting with iOS 13. I've been playing with it and I haven't been able to get it to work on any of the simulators. The only way that it works is if I plug in an actual device. Is this how it's expected to be?

This is strongly influenced by this question. I have tried the answer on all of the simulators and it is stuck on downloading: "This Siri voice will take effect when downloaded"

Sample code can be found here. I've modified it with the below block: