321 skills found · Page 1 of 11
FluidInference / FluidAudioFrontier CoreML audio models in your apps — text-to-speech, speech-to-text, voice activity detection, and speaker diarization. In Swift, powered by SOTA open source.
mkiol / DsnoteSpeech Note Linux app. Note taking, reading and translating with offline Speech to Text, Text to Speech and Machine translation.
Capsize-Games / AirunnerOffline inference engine for art, real-time voice conversations, LLM powered chatbots and automated workflows
Notely-Voice / NotelyVoiceA 100% private AI voice transcription app that converts speech to text in 100+ languages. Built with Compose Multiplatform for Android & iOS using Whisper AI - no cloud uploads, all processing happens on-device for complete privacy.
davabase / Transcriber AppReal time speech to text transcription app.
watzon / PindropA native macOS menu bar dictation app using local speech-to-text with WhisperKit
Hugo-Dz / On Device TranscriptionA ready-to-use, minimal app that converts any speech into text.
homelab-00 / TranscriptionSuiteA fully local and private Speech-To-Text app with cross-platform support, speaker diarization, Audio Notebook mode, LM Studio integration, and both longform and live transcription.
Kaljurand / K6neleAn Android app that offers speech-to-text user interfaces to other apps
whitphx / Streamlit Stt AppReal time web based Speech-to-Text app with Streamlit
harshalbenake / Hbworkspace2 100(1) Name :- ActionBarSearchView Description :- Action bar search view. (2) Name :- Adsfree Description :- Admob integration. (3) Name :- AndroidDayDreamDemo Description :- Day dream demo. (4) Name :- android query demo live Description :- Google play live app details parsing. (5) Name :- Arc GIS map Description :- Arc gis map integration without hash key. (6) Name :- aviarySdk Description :- Aviary integration for image operations. (7) Name :- BetterGestureDetector Description :- Gesture accrate detection. (8) Name :- BlinkText Description :- Blinking text. (9) Name :- BuzzBoxSDKHelloWorld Description :- Buzz box integration cron scheduler. (10) Name :- CircularProgressBar Description :- Circular progress bar. (11) Name :- ContactNumbersDemo Description :- Get contact details from device. (12) Name :- ControlViewheight Description :- Manage height of specific view. (13) Name :- ControlViewHeightSeekbar Description :- Two listview manage appropriate hieght. (14) Name :- DownloadManagerAndroid Description :- Download specific file online. (15) Name :- Facebook Integration Description :- Facebook integration. (16) Name :- Graphview Description :- Graphview demo. (17) Name :- HB 1337 Description :- Virus and antivirus. (18) Name :- HomeButtonEvent Description :- Block home button press. (19) Name :- HomeLauncher Description :- Home launcher demo. (20) Name :- InAppPurchaseTut Description :- InAppPurchase demo. (21) Name :- KeyboardCustom Description :- Creating Custom keyboard demo. (22) Name :- MapDemoGeofencing Description :- Location map for geo fencing. (23) Name :- MapDemoV2Final Description :- Map demo for google version 2. (24) Name :- OpenGLESSquare Description :- Opengl moving square. (25) Name :- pagination numbering 2 Description :- Pagination type 2. (26) Name :- Pagination numbering Description :- Pagination type 1. (27) Name :- PhoneGapCordova Description :- Phone gap simple cordova demo. (28) Name :- PhoneGapCordovaCamera Description :- Phone gap for camera. (29) Name :- PhoneGapCordovaParsing Description :- Phone gap for parsing. (30) Name :- PhoneGapCordovaSMS Description :- Phone gap for sending sms. (31) Name :- RotatingWheel Description :- Rotating wheel by user interaction. (32) Name :- RotatingWheelSocialsites Description :- Rotating wheel by user interaction for socialsites. (33) Name :- RunningBackgroundServices Description :- Get Running services in background for package name/class name. (34) Name :- SearchList Description :- Searching from a specific list. (35) Name :- SearchViewContacts Description :- Search from contacts details. (36) Name :- SlidingDrawer Description :- Sliding drawer from bottom over another activity. (37) Name :- SpeechToTextDemo Description :- Convert speech to text. (38) Name :- TextToSpeak Description :- Convert text to speech. (39) Name :- TouchCordinates Description :- Get coordinate of user touch intergration. (40) Name :- TreeViewListDemo Description :- Tree view integration demo. (41) Name :- UninstallDeleteapp Description :- Uninstall another app from my app after removing admin permission. (42) Name :- ViewPagerCustomWidthFragment Description :- Fragment in viewpager. (43) Name :- WearableNotification Description :- Wearable notification. (44) Name :- WearablePages Description :- Wearable pages. (45) Name :- WidgetDemo Description :- Widget demo. (46) Name :- CameraIntentAll Description :- Camera demo for picture as well as video demo. (47) Name :- CameraOverlay Description :- Camera overlay image as aim shooting game. (48) Name :- DrmIntegration Description :- Drm Integration library for authorize users apk file. (49) Name :- SwipeRefreshLayout Description :- SwipeRefreshLayout Pulltorefresh like google. (50) Name :- TwitterIntegration Description :- Twitter Integration. (51) Name :- CameraADev Description :- Custom Camera for picture as well as video capture from android developer. (52) Name :- DataBaseSQLiteCRUD Description :- Simple SQLite CRUD funtions for contact database. (53) Name :- DataBaseSQLiteDBUtility Description :- Simple SQLite DBUtility all files and basic operations. (54) Name :- CustomDropdownMenu Description :- Custom Dropdown/Poup Menu. (55) Name :- CalenderSimpleView Description :- Simple calender view as well as timestamp using calender class. (56) Name :- CalendarProviderADevIntent Description :- Calender provider Intent from android developer. (57) Name :- AnimationTextViewAnimateLayoutChanges Description :- Animation of adding view inside another view using animatelayoutchanges. (58) Name :- DragnDropLowVersion Description :- Drag n drop funtionality for low version. (59) Name :- GoogleWalletAdev Description :- Google Wallet Integration from android developer. (60) Name :- AndroidShootingGame Description :- Android Shooting Game without opengl. (61) Name :- ViewPagerAnimation Description :- ViewPager page transformation for pages like alpha,scaling,rotation. (62) Name :- GoogleCloudWirelessPrintingIntent Description :- Google cloud wireless printing integration from google developer. (63) Name :- Barcode_or_QRCode_Scanner_openurl Description :- Barcord/QR code scanner from google play and open result url in browser. (64) Name :- MSServerListSyncSample Description :- List Sync Sample using MS Server. (65) Name :- SlidingMenuAPI Description :- Sliding Menu jeremyfeinstein library like facebook,gmail,etc. (66) Name :- GCMIntegration Description :- Google cloud messageing integration for notification. (67) Name :- NoiseAlert Description :- Detect noise or blow sound. (68) Name :- GregorianCalendar Description :- Basic Gregorian Calendar information. (69) Name :- getVariableName Description :- Get name of the variable not its value. (70) Name :- GoogleAnalyticsV4Adev Description :- Google analytics integration V4. (71) Name :- FlipboardAnimationAdev Description :- Animation like Flipboard. (72) Name :- Html5Camera Description :- Camera in Html5 without phonegap. (73) Name :- CopyPasteClipboard Description :- Copy & Paste Clipboard textual data. (74) Name :- AndroidPhpMysql Description :- Php and Mysql data parsing in android. (75) Name :- SpellChecker Description :- Check spelling and give appropriate suggestion for enter text. (76) Name :- PdfReader Description :- Read pdf file.Barcode/QR code scanner. (77) Name :- BarcodeQRcodeIntegration Description :- Barcode/QR code scanner using ZbarScanner lib and also Zxing lib without intent. (78) Name :- InstagramIntegrationApi Description :- Instagram Integration using sample demo. (79) Name :- Logger Description :- Read logger/logcat using api. (80) Name :- SmsControl Description :- Control device via sms codes. (81) Name :- EncryptDecryptString Description :- Encrypt string and Decrypt the same string. (82) Name :- FloatingActionButton Description :- Floating Action Button. (83) Name :- DownloadAndUnzipFile Description :- Download And Unzip File. (84) Name :- MoPubAd Description :- MoPub Ad Banner integration . (85) Name :- ListViewParsingDB_AndroidStudio Description :- ListView Parsing in android studio. (86) Name :- CustomCamera_AS Description :- Custom Camera using surfaceview. (87) Name :- ResizeableBox_AS Description :- Resizeable Box like crop. (88) Name :- AudioRecorder_AS Description :- Audio Recorder. (89) Name :- DateAndTimePicker_AS Description :- Date And Time Picker. (90) Name :- CustomActionBar_AS Description :- Simple Custom ActionBar. (91) Name :- CustomSpinner_AS Description :- Custom Spinner with default text item. (92) Name :- SendEmail_AS Description :- Send email in background after authentication. (93) Name :- GoogleAnalytics_AS Description :- GoogleAnalytics integration demo for crash and screen. (94) Name :- BroadcastReciever_AS Description :- Broadcast Reciever for sms ,call and boot receiver. (95) Name :- Azure Description :- Azure storage gsi credentials zip dowload. (96) Name :- InAppPurchase_AS Description :- In App Purchase simple demo. (97) Name :- iOS_Listview Description :- Simple Listview in ios. (98) Name :- iOS_Database Description :- Sqlite Database in ios. (99) Name :- MessangerList_AS Description :- Messanger Listview UI send and recieve. (100) Name :- FindingFriend_AS Geofencing for enter and exit another pin.
abusufyanvu / 6S191 MIT DeepLearningMIT Introduction to Deep Learning (6.S191) Instructors: Alexander Amini and Ava Soleimany Course Information Summary Prerequisites Schedule Lectures Labs, Final Projects, Grading, and Prizes Software labs Gather.Town lab + Office Hour sessions Final project Paper Review Project Proposal Presentation Project Proposal Grading Rubric Past Project Proposal Ideas Awards + Categories Important Links and Emails Course Information Summary MIT's introductory course on deep learning methods with applications to computer vision, natural language processing, biology, and more! Students will gain foundational knowledge of deep learning algorithms and get practical experience in building neural networks in TensorFlow. Course concludes with a project proposal competition with feedback from staff and a panel of industry sponsors. Prerequisites We expect basic knowledge of calculus (e.g., taking derivatives), linear algebra (e.g., matrix multiplication), and probability (e.g., Bayes theorem) -- we'll try to explain everything else along the way! Experience in Python is helpful but not necessary. This class is taught during MIT's IAP term by current MIT PhD researchers. Listeners are welcome! Schedule Monday Jan 18, 2021 Lecture: Introduction to Deep Learning and NNs Lab: Lab 1A Tensorflow and building NNs from scratch Tuesday Jan 19, 2021 Lecture: Deep Sequence Modelling Lab: Lab 1B Music Generation using RNNs Wednesday Jan 20, 2021 Lecture: Deep Computer Vision Lab: Lab 2A Image classification and detection Thursday Jan 21, 2021 Lecture: Deep Generative Modelling Lab: Lab 2B Debiasing facial recognition systems Friday Jan 22, 2021 Lecture: Deep Reinforcement Learning Lab: Lab 3 pixel-to-control planning Monday Jan 25, 2021 Lecture: Limitations and New Frontiers Lab: Lab 3 continued Tuesday Jan 26, 2021 Lecture (part 1): Evidential Deep Learning Lecture (part 2): Bias and Fairness Lab: Work on final assignments Lab competition entries due at 11:59pm ET on Canvas! Lab 1, Lab 2, and Lab 3 Wednesday Jan 27, 2021 Lecture (part 1): Nigel Duffy, Ernst & Young Lecture (part 2): Kate Saenko, Boston University and MIT-IBM Watson AI Lab Lab: Work on final assignments Assignments due: Sign up for Final Project Competition Thursday Jan 28, 2021 Lecture (part 1): Sanja Fidler, U. Toronto, Vector Institute, and NVIDIA Lecture (part 2): Katherine Chou, Google Lab: Work on final assignments Assignments due: 1 page paper review (if applicable) Friday Jan 29, 2021 Lecture: Student project pitch competition Lab: Awards ceremony and prize giveaway Assignments due: Project proposals (if applicable) Lectures Lectures will be held starting at 1:00pm ET from Jan 18 - Jan 29 2021, Monday through Friday, virtually through Zoom. Current MIT students, faculty, postdocs, researchers, staff, etc. will be able to access the lectures during this two week period, synchronously or asynchronously, via the MIT Canvas course webpage (MIT internal only). Lecture recordings will be uploaded to the Canvas as soon as possible; students are not required to attend any lectures synchronously. Please see the Canvas for details on Zoom links. The public edition of the course will only be made available after completion of the MIT course. Labs, Final Projects, Grading, and Prizes Course will be graded during MIT IAP for 6 units under P/D/F grading. Receiving a passing grade requires completion of each software lab project (through honor code, with submission required to enter lab competitions), a final project proposal/presentation or written review of a deep learning paper (submission required), and attendance/lecture viewing (through honor code). Submission of a written report or presentation of a project proposal will ensure a passing grade. MIT students will be eligible for prizes and awards as part of the class competitions. There will be two parts to the competitions: (1) software labs and (2) final projects. More information is provided below. Winners will be announced on the last day of class, with thousands of dollars of prizes being given away! Software labs There are three TensorFlow software lab exercises for the course, designed as iPython notebooks hosted in Google Colab. Software labs can be found on GitHub: https://github.com/aamini/introtodeeplearning. These are self-paced exercises and are designed to help you gain practical experience implementing neural networks in TensorFlow. For registered MIT students, submission of lab materials is not necessary to get credit for the course or to pass the course. At the end of each software lab there will be task-associated materials to submit (along with instructions) for entry into the competitions, open to MIT students and affiliates during the IAP offering. This includes MIT students/affiliates who are taking the class as listeners -- you are eligible! These instructions are provided at the end of each of the labs. Completing these tasks and submitting your materials to Canvas will enter you into a per-lab competition. MIT students and affiliates will be eligible for prizes during the IAP offering; at the end of the course, prize-winners will be awarded with their prizes. All competition submissions are due on January 26 at 11:59pm ET to Canvas. For the software lab competitions, submissions will be judged on the basis of the following criteria: Strength and quality of final results (lab dependent) Soundness of implementation and approach Thoroughness and quality of provided descriptions and figures Gather.Town lab + Office Hour sessions After each day’s lecture, there will be open Office Hours in the class GatherTown, up until 3pm ET. An MIT email is required to log in and join the GatherTown. During these sessions, there will not be a walk through or dictation of the labs; the labs are designed to be self-paced and to be worked on on your own time. The GatherTown sessions will be hosted by course staff and are held so you can: Ask questions on course lectures, labs, logistics, project, or anything else; Work on the labs in the presence of classmates/TAs/instructors; Meet classmates to find groups for the final project; Group work time for the final project; Bring the class community together. Final project To satisfy the final project requirement for this course, students will have two options: (1) write a 1 page paper review (single-spaced) on a recent deep learning paper of your choice or (2) participate and present in the project proposal pitch competition. The 1 page paper review option is straightforward, we propose some papers within this document to help you get started, and you can satisfy a passing grade with this option -- you will not be eligible for the grand prizes. On the other hand, participation in the project proposal pitch competition will equivalently satisfy your course requirements but additionally make you eligible for the grand prizes. See the section below for more details and requirements for each of these options. Paper Review Students may satisfy the final project requirement by reading and reviewing a recent deep learning paper of their choosing. In the written review, students should provide both: 1) a description of the problem, technical approach, and results of the paper; 2) critical analysis and exposition of the limitations of the work and opportunities for future work. Reviews should be submitted on Canvas by Thursday Jan 28, 2021, 11:59:59pm Eastern Time (ET). Just a few paper options to consider... https://papers.nips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf https://papers.nips.cc/paper/2018/file/69386f6bb1dfed68692a24c8686939b9-Paper.pdf https://papers.nips.cc/paper/2020/file/1457c0d6bfcb4967418bfb8ac142f64a-Paper.pdf https://science.sciencemag.org/content/362/6419/1140 https://papers.nips.cc/paper/2018/file/0e64a7b00c83e3d22ce6b3acf2c582b6-Paper.pdf https://arxiv.org/pdf/1906.11829.pdf https://www.nature.com/articles/s42256-020-00237-3 https://pubmed.ncbi.nlm.nih.gov/32084340/ Project Proposal Presentation Keyword: proposal This is a 2 week course so we do not require results or working implementations! However, to win the top prizes, nice, clear results and implementations will demonstrate feasibility of your proposal which is something we look for! Logistics -- please read! You must sign up to present before 11:59:59pm Eastern Time (ET) on Wednesday Jan 27, 2021 Slides must be in a Google Slide before 11:59:59pm Eastern Time (ET) on Thursday Jan 28, 2021 Project groups can be between 1 and 5 people Listeners welcome To be eligible for a prize you must have at least 1 registered MIT student in your group Each participant will only be allowed to be in one group and present one project pitch Synchronous attendance on 1/29/21 is required to make the project pitch! 3 min presentation on your idea (we will be very strict with the time limits) Prizes! (see below) Sign up to Present here: by 11:59pm ET on Wednesday Jan 27 Once you sign up, make your slide in the following Google Slides; submit by midnight on Thursday Jan 28. Please specify the project group # on your slides!!! Things to Consider This doesn’t have to be a new deep learning method. It can just be an interesting application that you apply some existing deep learning method to. What problem are you solving? Are there use cases/applications? Why do you think deep learning methods might be suited to this task? How have people done it before? Is it a new task? If so, what are similar tasks that people have worked on? In what aspects have they succeeded or failed? What is your method of solving this problem? What type of model + architecture would you use? Why? What is the data for this task? Do you need to make a dataset or is there one publicly available? What are the characteristics of the data? Is it sparse, messy, imbalanced? How would you deal with that? Project Proposal Grading Rubric Project proposals will be evaluated by a panel of judges on the basis of the following three criteria: 1) novelty and impact; 2) technical soundness, feasibility, and organization, including quality of any presented results; 3) clarity and presentation. Each judge will award a score from 1 (lowest) to 5 (highest) for each of the criteria; the average score from each judge across these criteria will then be averaged with that of the other judges to provide the final score. The proposals with the highest final scores will be selected for prizes. Here are the guidelines for the criteria: Novelty and impact: encompasses the potential impact of the project idea, its novelty with respect to existing approaches. Why does the proposed work matter? What problem(s) does it solve? Why are these problems important? Technical soundness, feasibility, and organization: encompasses all technical aspects of the proposal. Do the proposed methodology and architecture make sense? Is the architecture the best suited for the proposed problem? Is deep learning the best approach for the problem? How realistic is it to implement the idea? Was there any implementation of the method? If results and data are presented, we will evaluate the strength of the results/data. Clarity and presentation: encompasses the delivery and quality of the presentation itself. Is the talk well organized? Are the slides aesthetically compelling? Is there a clear, well-delivered narrative? Are the problem and proposed method clearly presented? Past Project Proposal Ideas Recipe Generation with RNNs Can we compress videos with CNN + RNN? Music Generation with RNNs Style Transfer Applied to X GAN’s on a new modality Summarizing text/news articles Combining news articles about similar events Code or spec generation Multimodal speech → handwriting Generate handwriting based on keywords (i.e. cursive, slanted, neat) Predicting stock market trends Show language learners articles or videos at their level Transfer of writing style Chemical Synthesis with Recurrent Neural networks Transfer learning to learn something in a domain for which it’s hard or risky to gather data or do training RNNs to model some type of time series data Computer vision to coach sports players Computer vision system for safety brakes or warnings Use IBM Watson API to get the sentiment of your Facebook newsfeed Deep learning webcam to give wifi-access to friends or improve video chat in some way Domain-specific chatbot to help you perform a specific task Detect whether a signature is fraudulent Awards + Categories Final Project Awards: 1x NVIDIA RTX 3080 4x Google Home Max 3x Display Monitors Software Lab Awards: Bose headphones (Lab 1) Display monitor (Lab 2) Bebop drone (Lab 3) Important Links and Emails Course website: http://introtodeeplearning.com Course staff: introtodeeplearning-staff@mit.edu Piazza forum (MIT only): https://piazza.com/mit/spring2021/6s191 Canvas (MIT only): https://canvas.mit.edu/courses/8291 Software lab repository: https://github.com/aamini/introtodeeplearning Lab/office hour sessions (MIT only): https://gather.town/app/56toTnlBrsKCyFgj/MITDeepLearning
curtgrimes / WebcaptionerA former speech-to-text app using the Web Speech API.
foges / Whisper DictationDictation app based on the OpenAI speech-to-text models
kgn / HarkAn example Text to Speech App
liamadsr / Dial8 Open SourceNative macOS speech-to-text app with active speech detection and amazing intuitive UX. Help us make this the official open-source alternative to Wispr Flow!
drmfinlay / Tts Util AppTTS Util — Text-to-speech utility Android app for synthesising text into audible speech
akiani / AidialerA full stack app for interruptible, low-latency and near-human quality AI phone calls built from stitching LLMs, speech understanding tools, text-to-speech models, and Twilio’s phone API.
mpaepper / VibevoiceFast local speech-to-text for any app using faster-whisper
appcoda / SpeechToTextDemoA Speech-to-text Demo App
