speech_recognition - A Flutter plugin to use speech recognition on iOS & Android (Swift/Java)

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Alternatively, your editor might support 'packages get'. Check the docs for your editor to learn more. On iOS, by default the plugin is configured for French, English, Russian, Spanish. On Android, without additional installations, it will probably works only with the default device locale.




Related Projects

p5.speech - Web Audio Speech Synthesis / Recognition for p5.js

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p5.speech is a JavaScript library that provides simple, clear access to the Web Speech and Speech Recognition APIs, allowing for the easy creation of sketches that can talk and listen. It consists of two object classes (p5.Speech and p5.SpeechRec) along with accessor functions to speak and listen for text, change parameters (synthesis voices, recognition models, etc.), and retrieve callbacks from the system. Speech recognition requires launching from a server (e.g. a python simpleserver on a local machine).

tensorflow-speech-recognition - 🎙Speech recognition using the tensorflow deep learning framework, sequence-to-sequence neural networks

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Speech recognition using google's tensorflow deep learning framework, sequence-to-sequence neural networks. Replaces caffe-speech-recognition, see there for some background.

HTK - Speech Recognition Toolkit

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The Hidden Markov Model Toolkit (HTK) is a portable toolkit for building and manipulating hidden Markov models. HTK is primarily used for speech recognition research although it has been used for numerous other applications including research into speech synthesis, character recognition and DNA sequencing. HTK is in use at hundreds of sites worldwide.

CMU Sphinx - Toolkit For Speech Recognition

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CMUSphinx toolkit is a speech recognition toolkit with various tools used to build speech applications. CMU Sphinx toolkit has a number of packages for different tasks. Pocketsphinx — lightweight recognizer library written in C, Sphinxbase — support library required by Pocketsphinx, Sphinx4 — adjustable, modifiable recognizer written in Java, CMUclmtk — language model tools, Sphinxtrain — acoustic model training tools, Sphinx3 — decoder for speech recognition research written in C.

SpeechKITT - 🗣 A flexible GUI for Speech Recognition

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Speech KITT makes it easy to add a GUI to sites using Speech Recognition. Whether you are using annyang, a different library or webkitSpeechRecognition directly, KITT will take care of the GUI. Speech KITT provides a graphical interface for the user to start or stop Speech Recognition and see its current status. It can also help guide the user on how to interact with your site using their voice, providing instructions and sample commands. It can even be used to carry a natural conversation with the user, asking questions the user can answer with his voice, and then asking follow up questions.

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ESPnet is an end-to-end speech processing toolkit, mainly focuses on end-to-end speech recognition, and end-to-end text-to-speech. ESPnet uses chainer and pytorch as a main deep learning engine, and also follows Kaldi style data processing, feature extraction/format, and recipes to provide a complete setup for speech recognition and other speech processing experiments. To use cuda (and cudnn), make sure to set paths in your .bashrc or .bash_profile appropriately.

SpeakRight Framework - Helps to build Speech Recognition Applications

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SpeakRight is an Java framework for writing speech recognition applications in VoiceXML. Dynamic generation of VoiceXML is done using the popular StringTemplate templating framework. Although VoiceXML uses a similar web architecture as HTML, the needs of a speech app are very different. SpeakRight lives in application code layer, typically in a servlet. The SpeakRight runtime dynamically generates VoiceXML pages, one per HTTP request.

wav2letter - Facebook AI Research Automatic Speech Recognition Toolkit

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wav2letter is a simple and efficient end-to-end Automatic Speech Recognition (ASR) system from Facebook AI Research. The original authors of this implementation are Ronan Collobert, Christian Puhrsch, Gabriel Synnaeve, Neil Zeghidour, and Vitaliy Liptchinsky. wav2letter implements the architecture proposed in Wav2Letter: an End-to-End ConvNet-based Speech Recognition System and Letter-Based Speech Recognition with Gated ConvNets.

sonus - :speech_balloon: /so.nus/ STT (speech to text) for Node with offline hotword detection

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Sonus lets you quickly and easily add a VUI (Voice User Interface) to any hardware or software project. Just like Alexa, Google Now, and Siri, Sonus is always listening offline for a customizable hotword. Once that hotword is detected your speech is streamed to the cloud recognition service of your choice - then you get the results. Generally, running npm install should suffice. This module however, requires you to install SoX.

speech_recognition - Speech recognition module for Python, supporting several engines and APIs, online and offline

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Library for performing speech recognition, with support for several engines and APIs, online and offline. Quickstart: pip install SpeechRecognition. See the "Installing" section for more details.

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Simon is an open source speech recognition program that can replace your mouse and keyboard. The system is designed to be as flexible as possible and will work with any language or dialect. It is a real dictation system.

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Kaldi is a Speech recognition research toolkit. It is similar in aims and scope to HTK. The goal is to have modern and flexible code, written in C++, that is easy to modify and extend.

annyang - :speech_balloon: Speech recognition for your site

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A tiny javascript SpeechRecognition library that lets your users control your site with voice commands. annyang has no dependencies, weighs just 2 KB, and is free to use and modify under the MIT license.

voice-elements - :speaker: Web Component wrapper to the Web Speech API, that allows you to do voice recognition and speech synthesis using Polymer

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Web Component wrapper to the Web Speech API, that allows you to do voice recognition (speech to text) and speech synthesis (text to speech) using Polymer. Or download as ZIP.

stt-benchmark - speech to text benchmark framework

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This is a minimalist and extensible framework for benchmarking different speech-to-text engines. It has been developed and tested on Ubuntu 18.04 with Python3.6. This framework has been developed by Picovoice as part of the project Cheetah. Cheetah is Picovoice's speech-to-text engine specifically designed for IoT applications. Deep learning has been the main driver in recent improvements in speech recognition. But due to stringent compute/storage limitations of IoT platforms it is most beneficial to the cloud-based engines. Picovoice's proprietary deep learning technology enables transferring these improvements to IoT platforms with much lower CPU/memory footprint. The goal is to be able to run Cheetah on any platform with a C Compiler and a few MB of memory.

lip-reading-deeplearning - :unlock: Lip Reading - Cross Audio-Visual Recognition using 3D Architectures

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The input pipeline must be prepared by the users. This code is aimed to provide the implementation for Coupled 3D Convolutional Neural Networks for audio-visual matching. Lip-reading can be a specific application for this work. Audio-visual recognition (AVR) has been considered as a solution for speech recognition tasks when the audio is corrupted, as well as a visual recognition method used for speaker verification in multi-speaker scenarios. The approach of AVR systems is to leverage the extracted information from one modality to improve the recognition ability of the other modality by complementing the missing information.

delta - DELTA is a deep learning based natural language and speech processing platform.

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DELTA is a deep learning based end-to-end natural language and speech processing platform. DELTA aims to provide easy and fast experiences for using, deploying, and developing natural language processing and speech models for both academia and industry use cases. DELTA is mainly implemented using TensorFlow and Python 3. For details of DELTA, please refer to this paper.

Google Speech Recognition Example


Google Speech Recognition contains a working example of application that uses google speech recognition API. App contains all necessary dlls to record, decode and send your voice request to google service and recieve a text representation of what you've said. It's developed i...

Open Source Speech Recognition Project

  •    C

Developpement of speech recognition software and libraries for the linux system. Should allow evryone to integrate speech recognition in his software very easily.

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