Train-Custom-Speech-Model | custom Watson Speech to Text model | Speech library

 by   IBM JavaScript Version: Current License: Apache-2.0

kandi X-RAY | Train-Custom-Speech-Model Summary

kandi X-RAY | Train-Custom-Speech-Model Summary

Train-Custom-Speech-Model is a JavaScript library typically used in Artificial Intelligence, Speech, Deep Learning applications. Train-Custom-Speech-Model has no bugs, it has no vulnerabilities, it has a Permissive License and it has low support. You can download it from GitHub.

In this code pattern, we will create a custom speech to text model. The Watson Speech to Text service is among the best in the industry. However, like other Cloud speech services, it was trained with general conversational speech for general use; therefore it may not perform well in specialized domains such as medicine, law, sports, etc. To improve the accuracy of the speech-to-text service, you can leverage transfer learning by training the existing AI model with new data from your domain. In this example, we will use a medical speech data set to illustrate the process. The data is provided by ezDI and includes 16 hours of medical dictation in both audio and text files.

            kandi-support Support

              Train-Custom-Speech-Model has a low active ecosystem.
              It has 48 star(s) with 34 fork(s). There are 20 watchers for this library.
              It had no major release in the last 6 months.
              There are 13 open issues and 15 have been closed. On average issues are closed in 3 days. There are 1 open pull requests and 0 closed requests.
              It has a neutral sentiment in the developer community.
              The latest version of Train-Custom-Speech-Model is current.

            kandi-Quality Quality

              Train-Custom-Speech-Model has 0 bugs and 0 code smells.

            kandi-Security Security

              Train-Custom-Speech-Model has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.
              Train-Custom-Speech-Model code analysis shows 0 unresolved vulnerabilities.
              There are 0 security hotspots that need review.

            kandi-License License

              Train-Custom-Speech-Model is licensed under the Apache-2.0 License. This license is Permissive.
              Permissive licenses have the least restrictions, and you can use them in most projects.

            kandi-Reuse Reuse

              Train-Custom-Speech-Model releases are not available. You will need to build from source code and install.
              Installation instructions, examples and code snippets are available.
              Train-Custom-Speech-Model saves you 325 person hours of effort in developing the same functionality from scratch.
              It has 780 lines of code, 6 functions and 56 files.
              It has low code complexity. Code complexity directly impacts maintainability of the code.

            Top functions reviewed by kandi - BETA

            kandi has reviewed Train-Custom-Speech-Model and discovered the below as its top functions. This is intended to give you an instant insight into Train-Custom-Speech-Model implemented functionality, and help decide if they suit your requirements.
            • Function that registers a new service and registers it in the swagger service .
            • Registers the service worker .
            • Checks if a service worker is reloaded
            • Parse a query string .
            • Unregister the service worker
            Get all kandi verified functions for this library.

            Train-Custom-Speech-Model Key Features

            No Key Features are available at this moment for Train-Custom-Speech-Model.

            Train-Custom-Speech-Model Examples and Code Snippets

            No Code Snippets are available at this moment for Train-Custom-Speech-Model.

            Community Discussions


            Enable use of images from the local library on Kubernetes
            Asked 2022-Mar-20 at 13:23

            I'm following a tutorial,

            currently, I have the right image



            Answered 2022-Mar-16 at 08:10

            If your image has a latest tag, the Pod's ImagePullPolicy will be automatically set to Always. Each time the pod is created, Kubernetes tries to pull the newest image.

            Try not tagging the image as latest or manually setting the Pod's ImagePullPolicy to Never. If you're using static manifest to create a Pod, the setting will be like the following:



            IndexError: tuple index out of range when I try to create an executable from a python script using auto-py-to-exe
            Asked 2022-Feb-24 at 15:03

            I have been trying out an open-sourced personal AI assistant script. The script works fine but I want to create an executable so that I can gift the executable to one of my friends. However, when I try to create the executable using the auto-py-to-exe, it states the below error:



            Answered 2021-Nov-05 at 02:20
            42681 INFO: PyInstaller: 4.6
            42690 INFO: Python: 3.10.0



            Google Actions Builder stops execution when selecting a visual item from a List
            Asked 2022-Feb-23 at 15:32

            I'm pulling my hairs here. I have a Google Assistant application that I build with Jovo 4 and Google Actions Builder.

            The goal is to create a HelpScene, which shows some options that explain the possibilities/features of the app on selection. This is the response I return from my Webhook. (This is Jovo code, but doesn't matter as this returns a JSON when the Assistant calls the webhook.)



            Answered 2022-Feb-23 at 15:32

            Okay, after days of searching, I finally figured it out. It did have something to do with the Jovo framework/setup and/or the scene parameter in the native response.

            This is my component, in which I redirect new users to the HelpScene. This scene should show multiple cards in a list/collection/whatever on which the user can tap to receive more information about the application's features.



            How to use muti-language in 'gTTS' for single input line?
            Asked 2022-Jan-29 at 07:05

            I want to convert text to speech from a document where multiple languages are included. When I am trying to do the following code, I fetch problems to record each language clearly. How can I save such type mixer text-audio clearly?



            Answered 2022-Jan-29 at 07:05

            It's not enough to use just text to speech, since it can work with one language only.
            To solve this problem we need to detect language for each part of the sentence.
            Then run it through text to speech and append it to our final spoken sentence.
            It would be ideal to use some neural network (there are plenty) to do this categorization for You.
            Just for a sake of proof of concept I used googletrans to detect language for each part of the sentences and gtts to make a mp3 file from it.

            It's not bullet proof, especially with arabic text. googletrans somehow detect different language code, which is not recognized by gtts. For that reason we have to use code_table to pick proper language code that works with gtts.

            Here is working example:



            Assigning True/False if a token is present in a data-frame
            Asked 2022-Jan-06 at 12:38

            My current data-frame is:



            Answered 2022-Jan-06 at 12:13


            speechSynthesis.getVoices (Web Speech API) doesn't show some of the locally installed voices
            Asked 2021-Dec-31 at 08:19

            I'm trying to use Web Speech API to read text on my web page. But I found that some of the SAPI5 voices installed in my Windows 10 would not show up in the output of speechSynthesis.getVoices(), including the Microsoft Eva Mobile on Windows 10 "unlock"ed by importing a registry file. These voices could work fine in local TTS programs like Balabolka but they just don't show in the browser. Are there any specific rules by which the browser chooses whether to list the voices or not?



            Answered 2021-Dec-31 at 08:19

            OK, I found out what was wrong. I was using Microsoft Edge and it seems that Edge only shows some of Microsoft voices. If I use Firefox, the other installed voices will also show up. So it was Edge's fault.



            Combining Object Detection with Text to Speech Code
            Asked 2021-Dec-28 at 16:46

            I am trying to write an object detection + text-to-speech code to detect objects and produce a voice output on the raspberry pi 4. However, as of right now, I am trying to write a simple python script that incorporates both elements into a single .py file and preferably as a function. I will then run this script on the raspberry pi. I want to give credit to Murtaza's Workshop "Object Detection OpenCV Python | Easy and Fast (2020)" and for the Text to speech documentation for pyttsx3. I have attached the code below. I have tried running the program and I always keep getting errors with the Text to speech code (commented lines 33-36 for reference). I believe it is some looping error but I just can't seem to get the program to run continuously. For instance, if I run the code without the TTS part, it works fine. Otherwise, it runs for perhaps 3-5 seconds and suddenly stops. I am a beginner but highly passionate in computer vision, and any help is appreciated!



            Answered 2021-Dec-28 at 16:46

            I installed pyttsx3 using the two commands in the terminal on the Raspberry Pi:

            1. sudo apt update && sudo apt install espeak ffmpeg libespeak1
            2. pip install pyttsx3

            I followed the video to install pyttsx3. My functional code should also be listed above. My question should be resolved but hopefully useful to anyone looking to write a similar program. I have made minor tweaks to my code.



            Yielding values from consecutive parallel parse functions via meta in Scrapy
            Asked 2021-Dec-20 at 07:53

            In my scrapy code I'm trying to yield the following figures from parliament's website where all the members of parliament (MPs) are listed. Opening the links for each MP, I'm making parallel requests to get the figures I'm trying to count. I'm intending to yield each three figures below in the company of the name and the party of the MP

            Here are the figures I'm trying to scrape

            1. How many bill proposals that each MP has their signature on
            2. How many question proposals that each MP has their signature on
            3. How many times that each MP spoke on the parliament

            In order to count and yield out how many bills has each member of parliament has their signature on, I'm trying to write a scraper on the members of parliament which works with 3 layers:

            • Starting with the link where all MPs are listed
            • From (1) accessing the individual page of each MP where the three information defined above is displayed
            • 3a) Requesting the page with bill proposals and counting the number of them by len function 3b) Requesting the page with question proposals and counting the number of them by len function 3c) Requesting the page with speeches and counting the number of them by len function

            What I want: I want to yield the inquiries of 3a,3b,3c with the name and the party of the MP in the same raw

            • Problem 1) When I get an output to csv it only creates fields of speech count, name, part. It doesn't show me the fields of bill proposals and question proposals

            • Problem 2) There are two empty values for each MP, which I guess corresponds to the values I described above at Problem1

            • Problem 3) What is the better way of restructuring my code to output the three values in the same line, rather than printing each MP three times for each value that I'm scraping



            Answered 2021-Dec-18 at 06:26

            This is happening because you are yielding dicts instead of item objects, so spider engine will not have a guide of fields you want to have as default.

            In order to make the csv output fields bill_prop_count and res_prop_count, you should make the following changes in your code:

            1 - Create a base item object with all desirable fields - you can create this in the file of your scrapy project:



            Rails. Puma stops working when instantiating a client of Google Cloud Text-to-Speech (Windows)
            Asked 2021-Dec-15 at 22:07

            I've upgraded my Ruby version from 2.5.x to 2.6.x (and uninstalled the 2.5.x version). And now Puma server stops working when instantiating a client of Google Cloud Text-to-Speech:



            Answered 2021-Dec-07 at 08:52

            Try reinstalling ruby-debug



            R - Regular Expression to Extract Text Between Parentheses That Contain Keyword
            Asked 2021-Nov-13 at 22:41

            I need to extract the text from between parentheses if a keyword is inside the parentheses.

            So if I have a string that looks like this:

            ('one', 'CARDINAL'), ('Castro', 'PERSON'), ('Latin America', 'LOC'), ('Somoza', 'PERSON')

            And my keyword is "LOC", I just want to extract ('Latin America', 'LOC'), not the others.

            Help is appreciated!!

            This is a sample of my data set, a csv file:



            Answered 2021-Nov-13 at 22:41

            Community Discussions, Code Snippets contain sources that include Stack Exchange Network


            No vulnerabilities reported

            Install Train-Custom-Speech-Model

            Go to the ezDI web site and download both the medical dictation audio files and the transcribed text files. The downloaded files will be contained in zip files. Create both an Audio and Documents subdirectory inside the data directory and then extract the downloaded zip files into their respective locations. The transcription files stored in the Documents directory will be in rtf format, and need to be converted to plain text. You can use the Python script to convert them all to txt files. Run the following code block from the data directory to create a virtual environment, install dependencies, and run the conversion script. Note, you must have Python 3. The data needs careful preparation since our deep learning model will only be as good as the data used in the training. Preparation may include steps such as removing erroneous words in the text, bad audio recordings, etc. These steps are typically very time-consuming when dealing with large datasets. Although the dataset from ezDI is already curated, a quick scan of the text transcription files will reveal some filler text that would not help the training. These unwanted text strings have been collected in the file data/fixup.sed and can be removed from the text files by using the sed utility. Also, for the purpose of training, we will need to combine all text files into a single package, called a corpus file.


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