Project DeepSpeech is an open source Speech-To-Text engine. It uses a model trained by machine learning techniques, based on Baidu's Deep Speech research paper. Project DeepSpeech uses Google's TensorFlow project to make the implementation easier.
deep-learning machine-learning neural-networks tensorflow speech-recognition speech-to-textLibrary 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.
audio speech-recognition speech-to-textThe 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.
speech speech-recognition speech-to-text toolsSpeech recognition using google's tensorflow deep learning framework, sequence-to-sequence neural networks. Replaces caffe-speech-recognition, see there for some background.
tensorflow speech-recognition neural-network deep-learning stt speech-to-textNode.js client library to use the Watson APIs. The examples folder has basic and advanced examples. The examples within each service assume that you already have service credentials.
ibm-watson-services language-translation conversation-service watson tone-analyzer natural-language visual-recognition personality-insights typescript conversation dialog discovery ibm natural-language-classifier natural-language-understanding speech-to-text text-to-speech tone_analyzer watson-developer-cloud wdcLeon is an open-source personal assistant who can live on your server. He does stuff when you ask him for. You can talk to him and he can talk to you. You can also text him and he can also text you. If you want to, Leon can communicate with you by being offline to protect your privacy.
personal-assistant artificial-intelligence speech-to-text text-to-speech speech-recognition speech-synthesis deepspeech fliteKalliope is a framework that will help you to create your own personal assistant. The concept is to create the brain of your assistant by attaching an input signal (vocal order, scheduled event, MQTT message, GPIO event, etc..) to one or multiple actions called neurons.
raspberry bot-creation jarvis personal-assistant speech-to-text speech-recognition speech-synthesis bot home-automationA 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.
speech-recognition speech speech-to-text voice hacktoberfest annyang annyang.js recognition speechrecognition webkitspeechrecognitionNLP-Models-Tensorflow, Gathers machine learning and tensorflow deep learning models for NLP problems, code simplify inside Jupyter Notebooks 100%. I will attached github repositories for models that I not implemented from scratch, basically I copy, paste and fix those code for deprecated issues.
nlp machine-learning embedded deep-learning chatbot language-detection lstm summarization attention speech-to-text neural-machine-translation optical-character-recognition pos-tagging lstm-seq2seq-tf dnc-seq2seq luong-apiLingvo is a framework for building neural networks in Tensorflow, particularly sequence models. A list of publications using Lingvo can be found here.
nlp research translation tensorflow machine-translation speech distributed tts speech-synthesis mnist speech-recognition lm seq2seq speech-to-text gpu-computing language-model asrKur is a system for quickly building and applying state-of-the-art deep learning models to new and exciting problems. Kur was designed to appeal to the entire machine learning community, from novices to veterans. It uses specification files that are simple to read and author, meaning that you can get started building sophisticated models without ever needing to code. Even so, Kur exposes a friendly and extensible API to support advanced deep learning architectures or workflows.
deep-learning deep-neural-networks speech-recognition deep-learning-tutorial machine-learning neural-networks neural-network image-recognition speech-to-textCMUSphinx 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.
speech speech-recognition speech-to-text ivrTo take a dependency on Adapt, it's recommended to use virtualenv and pip to install source from github. Executable examples can be found in the examples folder.
intent-parser speech-to-text speech-recognition opensource open-source intentsStephanie is an open-source platform built specifically for voice-controlled application as well as to automate daily tasks imitating much of an virtual assistant's work. Use your voice to ask for information, update social networks, get weather updates, live football scores, movies information restaurant suggestions, writing a note, or even chit-chatting for fun, and many more.
virtual-assistant speech-recognition personal-assistant speech-to-text intent-prediction voice-assistant:zap: TensorFlowASR: Almost State-of-the-art Automatic Speech Recognition in Tensorflow 2. Supported languages that can use characters or subwords
tensorflow speech-recognition jasper automatic-speech-recognition speech-to-text ctc conformer deepspeech2 tflite rnn-transducer end2end tensorflow2 contextnet tflite-model tflite-convertion subword-speech-recognition streaming-transducerThe deep learning toolkit for Speech-to-Text. Training and deploying STT models has never been so easy.
deep-learning tensorflow automatic-speech-recognition speech-to-text stt asr coqui-aiThis repository contains the code for one of the most popular bots I've ever built for Telegram — @voicybot. Please, feel free to fork, add features and create pull requests so that everybody (over 600 000 chats) can experience the features you've built. You can also help by translating the bot to other languages or fixing some texts in existing languages by modifying the locales folder.
bot telegram-bot speech-to-text telegamSonus 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 speech-recognition speech-to-text voice-control stt node hotword-detection keyword-spotting alexa voice-recognition keyword spotting hotword detection voiceI implement yet another text-to-speech model, dc-tts, introduced in Efficiently Trainable Text-to-Speech System Based on Deep Convolutional Networks with Guided Attention. My goal, however, is not just replicating the paper. Rather, I'd like to gain insights about various sound projects. I train English models and an Korean model on four different speech datasets.
speech speech-to-text ttsThis 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.
speech-recognition speech-to-text deepspeech
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