🎙️ AI Dictation App - Open Source and Local-first ⚡ Type 3x faster, no keyboard needed. 🆓 Powered by open source models, works offline, fast and accurate.

Replacement for built-in Speech services. Supports playing, skipping, progress, and more

A small Javascript library for browser-based real-time speech recognition, which uses Recorderjs for audio capture, and a WebSocket connection to the Kaldi GStreamer server for speech recognition.

A lightweight full-stack component layer that doesn't dictate your front-end framework

A powerful Whisper AI keyboard for reliable speech transcription

Free, open-source, real-time dictation for Windows. Runs locally (no cloud!), uses AI, and types directly into any application via a user-friendly GUI.

Gnome shell extension for accurate OFFLINE speech to text input in Linux using whisper.cpp. Input text from speech anywhere.

A script to switch between mining Bitcoin and any of the other cryptocoins as profitability dictates.

I wanted a solution where multi-tenancy is achieved by having a database per tenant and all user information (username, password, client Id etc) for authentication and authorization stored in a user table in the respective tenant databases. It meant that not only did I need a multi-tenant application, but also a secure application like any other web application secured by Spring Security. I know how to use Spring Security to secure a web application and how to use Hibernate to connect to a database. The requirement further dictated that all users belonging to a tenant be stored in the tenant database and not a separate or central database. This would allow for complete data isolation for each tenant.

dictate anywhere in Linux

No description available

Dictates what your users see. Plug-based authorization.

Embark on a nostalgic journey in RetroNexus, a web-based game reminiscent of the 90s era. Players dictate the story through command prompts, shaping their own narrative. Progress is securely stored using cNFTs on the Solana Network, ensuring unique in-game assets and ownership

The default pack of components and layout principles that dictates Catho Frontend applications.

Print a directory tree that shows Git status and ignores files dictated by .gitignore.

Personal User-Centric Immutable OS Based on NixOS and Pi.Dev AI Agent, where the agent is a first class citizen of the OS, and also dictates the UX experience

Simple GUI application to help record audio dictated from given text prompts, for use with training speech recognition or speech synthesis.

Metabox sanity provides WordPress theme and plugin authors a way to benevolently dictate how other plugins and themes can interact with their custom post type interfaces.

WhisperAnywhere: Effortless speech-to-text everywhere on your Mac. Use a hotkey to dictate in any app, powered by Whisper AI and Groq API. Boost your productivity across all applications.

iOS & watchOS voice-to-text app with AI voice keyboard — dictate into any app, powered by Apple Foundation Models, WhisperKit, NVIDIA Parakeet, and 15+ AI providers

Dictate with Just Press Record and transcribe with Whisper AI using Keyboard Maestro

A button to start dictation using Web Speech API.

Have emojis dictate your commit messages!

FreeFlow - seamless speech to text in any app. Press a hotkey, dictate naturally, polished text appears in any app.

dictate moves to lichess

WhisperAnywhere: Effortless speech-to-text everywhere on your Mac. Use a hotkey to dictate in any app, powered by Whisper AI and Groq API. Boost your productivity across all applications.

Dictate Wizard is an open source dictation tool powered by OpenAI's Whisper. The goal is to obsolete as much typing as possible and let you speak your emails, instant messages etc instead.

Privacy‑first, real‑time speech‑to‑text dictation. 100% local inference in Rust; hotkey to dictate anywhere (macOS, Linux, Windows).

A native macOS app for voice dictation anywhere. Press and hold Fn (or a custom shortcut) to dictate text directly into any app using on-device speech recognition.

Project Description: A music streaming company wants to introduce more automation and monitoring to their data warehouse ETL pipelines and they have come to the conclusion that the best tool to achieve this is Apache Airflow. As their Data Engineer, I was tasked to create a reusable production-grade data pipeline that incorporates data quality checks and allows for easy backfills. Several analysts and Data Scientists rely on the output generated by this pipeline and it is expected that the pipeline runs daily on a schedule by pulling new data from the source and store the results to the destination. Data Description: The source data resides in S3 and needs to be processed in a data warehouse in Amazon Redshift. The source datasets consist of JSON logs that tell about user activity in the application and JSON metadata about the songs the users listen to. Data Pipeline design: At a high-level the pipeline does the following tasks. Extract data from multiple S3 locations. Load the data into Redshift cluster. Transform the data into a star schema. Perform data validation and data quality checks. Calculate the most played songs for the specified time interval. Load the result back into S3. dag Structure of the Airflow DAG Design Goals: Based on the requirements of our data consumers, our pipeline is required to adhere to the following guidelines: The DAG should not have any dependencies on past runs. On failure, the task is retried for 3 times. Retries happen every 5 minutes. Catchup is turned off. Do not email on retry. Pipeline Implementation: Apache Airflow is a Python framework for programmatically creating workflows in DAGs, e.g. ETL processes, generating reports, and retraining models on a daily basis. The Airflow UI automatically parses our DAG and creates a natural representation for the movement and transformation of data. A DAG simply is a collection of all the tasks you want to run, organized in a way that reflects their relationships and dependencies. A DAG describes how you want to carry out your workflow, and Operators determine what actually gets done. By default, airflow comes with some simple built-in operators like PythonOperator, BashOperator, DummyOperator etc., however, airflow lets you extend the features of a BaseOperator and create custom operators. For this project, I developed several custom operators. operators The description of each of these operators follows: StageToRedshiftOperator: Stages data to a specific redshift cluster from a specified S3 location. Operator uses templated fields to handle partitioned S3 locations. LoadFactOperator: Loads data to the given fact table by running the provided sql statement. Supports delete-insert and append style loads. LoadDimensionOperator: Loads data to the given dimension table by running the provided sql statement. Supports delete-insert and append style loads. SubDagOperator: Two or more operators can be grouped into one task using the SubDagOperator. Here, I am grouping the tasks of checking if the given table has rows and then run a series of data quality sql commands. HasRowsOperator: Data quality check to ensure that the specified table has rows. DataQualityOperator: Performs data quality checks by running sql statements to validate the data. SongPopularityOperator: Calculates the top ten most popular songs for a given interval. The interval is dictated by the DAG schedule. UnloadToS3Operator: Stores the analysis result back to the given S3 location. Code for each of these operators is located in the plugins/operators directory. Pipeline Schedule and Data Partitioning: The events data residing on S3 is partitioned by year (2018) and month (11). Our task is to incrementally load the event json files, and run it through the entire pipeline to calculate song popularity and store the result back into S3. In this manner, we can obtain the top songs per day in an automated fashion using the pipeline. Please note, this is a trivial analyis, but you can imagine other complex queries that follow similar structure. S3 Input events data: s3://<bucket>/log_data/2018/11/ 2018-11-01-events.json 2018-11-02-events.json 2018-11-03-events.json .. 2018-11-28-events.json 2018-11-29-events.json 2018-11-30-events.json S3 Output song popularity data: s3://skuchkula-topsongs/ songpopularity_2018-11-01 songpopularity_2018-11-02 songpopularity_2018-11-03 ... songpopularity_2018-11-28 songpopularity_2018-11-29 songpopularity_2018-11-30 The DAG can be configured by giving it some default_args which specify the start_date, end_date and other design choices which I have mentioned above. default_args = { 'owner': 'shravan', 'start_date': datetime(2018, 11, 1), 'end_date': datetime(2018, 11, 30), 'depends_on_past': False, 'email_on_retry': False, 'retries': 3, 'retry_delay': timedelta(minutes=5), 'catchup_by_default': False, 'provide_context': True, } How to run this project? Step 1: Create AWS Redshift Cluster using either the console or through the notebook provided in create-redshift-cluster Run the notebook to create AWS Redshift Cluster. Make a note of: DWN_ENDPOINT :: dwhcluster.c4m4dhrmsdov.us-west-2.redshift.amazonaws.com DWH_ROLE_ARN :: arn:aws:iam::506140549518:role/dwhRole Step 2: Start Apache Airflow Run docker-compose up from the directory containing docker-compose.yml. Ensure that you have mapped the volume to point to the location where you have your DAGs. NOTE: You can find details of how to manage Apache Airflow on mac here: https://gist.github.com/shravan-kuchkula/a3f357ff34cf5e3b862f3132fb599cf3 start_airflow Step 3: Configure Apache Airflow Hooks On the left is the S3 connection. The Login and password are the IAM user's access key and secret key that you created. Basically, by using these credentials, we are able to read data from S3. On the right is the redshift connection. These values can be easily gathered from your Redshift cluster connections Step 4: Execute the create-tables-dag This dag will create the staging, fact and dimension tables. The reason we need to trigger this manually is because, we want to keep this out of main dag. Normally, creation of tables can be handled by just triggering a script. But for the sake of illustration, I created a DAG for this and had Airflow trigger the DAG. You can turn off the DAG once it is completed. After running this DAG, you should see all the tables created in the AWS Redshift. Step 5: Turn on the load_and_transform_data_in_redshift dag As the execution start date is 2018-11-1 with a schedule interval @daily and the execution end date is 2018-11-30, Airflow will automatically trigger and schedule the dag runs once per day for 30 times. Shown below are the 30 DAG runs ranging from start_date till end_date, that are trigged by airflow once per day. schedule