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Description

ConvNetJS is a JavaScript library designed for training deep learning models, specifically neural networks, directly in your web browser. With just a simple tab open, you can start the training process without needing any software installations, compilers, or even GPUs—it's that hassle-free. The library enables users to create and implement neural networks using JavaScript and was initially developed by @karpathy, but it has since been enhanced through community contributions, which are greatly encouraged. For those who want a quick and easy way to access the library without delving into development, you can download the minified version via the link to convnet-min.js. Alternatively, you can opt to get the latest version from GitHub, where the file you'll likely want is build/convnet-min.js, which includes the complete library. To get started, simply create a basic index.html file in a designated folder and place build/convnet-min.js in the same directory to begin experimenting with deep learning in your browser. This approach allows anyone, regardless of their technical background, to engage with neural networks effortlessly.

Description

We have developed and are releasing an open-source neural network named Whisper, which achieves levels of accuracy and resilience in English speech recognition that are comparable to human performance. This automatic speech recognition (ASR) system is trained on an extensive dataset comprising 680,000 hours of multilingual and multitask supervised information gathered from online sources. Our research demonstrates that leveraging such a comprehensive and varied dataset significantly enhances the system's capability to handle different accents, ambient noise, and specialized terminology. Additionally, Whisper facilitates transcription across various languages and provides translation into English from those languages. We are making available both the models and the inference code to support the development of practical applications and to encourage further exploration in the field of robust speech processing. The architecture of Whisper follows a straightforward end-to-end design, utilizing an encoder-decoder Transformer framework. The process begins with dividing the input audio into 30-second segments, which are then transformed into log-Mel spectrograms before being input into the encoder. By making this technology accessible, we aim to foster innovation in speech recognition technologies.

API Access

Has API

API Access

Has API

Screenshots View All

Screenshots View All

Integrations

AI Sparks Studio
Baseten
Bolna
Hyprnote
Krater.ai
LazyTyper
Nekton.ai
NoteVocal
OpenAI
Shownotes
Snippets AI
Thinkbuddy
Tila
TurboScribe
Undrstnd
Unremot
Utterly Voice
Vocode
Waveloom

Integrations

AI Sparks Studio
Baseten
Bolna
Hyprnote
Krater.ai
LazyTyper
Nekton.ai
NoteVocal
OpenAI
Shownotes
Snippets AI
Thinkbuddy
Tila
TurboScribe
Undrstnd
Unremot
Utterly Voice
Vocode
Waveloom

Pricing Details

No price information available.
Free Trial
Free Version

Pricing Details

No price information available.
Free Trial
Free Version

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Deployment

Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Customer Support

Business Hours
Live Rep (24/7)
Online Support

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Types of Training

Training Docs
Webinars
Live Training (Online)
In Person

Vendor Details

Company Name

ConvNetJS

Website

cs.stanford.edu/people/karpathy/convnetjs/

Vendor Details

Company Name

OpenAI

Country

United States

Website

openai.com/blog/whisper/

Product Features

Deep Learning

Convolutional Neural Networks
Document Classification
Image Segmentation
ML Algorithm Library
Model Training
Neural Network Modeling
Self-Learning
Visualization

Product Features

Speech Recognition

Audio Capture
Automatic Form Fill
Automatic Transcription
Call Analysis
Concatenated Speech
Continuous Speech
Customizable Macros
Multi-Languages
Specialty Vocabularies
Speech-to-Text Analysis
Variable Frequency
Voice Recognition

Transcription

AI / Machine Learning
Annotations
Audio/Video File Upload
Automatic Transcription
Collaboration Tools
File Sharing
For Manual Transcription
Full Text Search
Multi-Language Support
Natural Language Processing (NLP)
Playback Controls
Speech Recognition
Subtitles
Text Editor
Timecoding

Alternatives

Alternatives

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