STT - The deep learning toolkit for Speech-to-Text

•    C++

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The 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-ai

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

•    Python

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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.

nlp deep-learning tensorflow speech sequence-to-sequence seq2seq speech-recognition text-classification speaker-verification nlu text-generation emotion-recognition tensorflow-serving tensorflow-lite inference asr serving front-end

stt-benchmark - speech to text benchmark framework

•    Python

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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.

speech-recognition speech-to-text deepspeech

DeepSpeech - A TensorFlow implementation of Baidu's DeepSpeech architecture

•    C

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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-text

Kur - Descriptive Deep Learning

•    Python

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Kur 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-text

OpenSeq2Seq - Toolkit for efficient experimentation with various sequence-to-sequence models

•    Python

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This is a research project, not an official NVIDIA product. OpenSeq2Seq main goal is to allow researchers to most effectively explore various sequence-to-sequence models. The efficiency is achieved by fully supporting distributed and mixed-precision training. OpenSeq2Seq is built using TensorFlow and provides all the necessary building blocks for training encoder-decoder models for neural machine translation and automatic speech recognition. We plan to extend it with other modalities in the future.

neural-machine-translation multi-gpu deep-learning sequence-to-sequence seq2seq multi-node speech-recognition speech-to-text mixed-precision float16

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

•    Python

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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.

deep-learning computer-vision speech-recognition 3d-convolutional-network tensorflow

espnet - End-to-End Speech Processing Toolkit

•    Shell

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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.

speech-recognition deep-learning end-to-end chainer pytorch kaldi speech-synthesis

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

•    Javascript

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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 speech-recognition speech-to-text voice-control stt node hotword-detection keyword-spotting alexa voice-recognition keyword spotting hotword detection voice

merlin - This is now the official location of the Merlin project.

•    Python

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This repository contains the Neural Network (NN) based Speech Synthesis System developed at the Centre for Speech Technology Research (CSTR), University of Edinburgh.Merlin is a toolkit for building Deep Neural Network models for statistical parametric speech synthesis. It must be used in combination with a front-end text processor (e.g., Festival) and a vocoder (e.g., STRAIGHT or WORLD).

merlin speech-synthesis text-to-speech voice-conversion deep-learning theano tensorflow keras neural-networks

deep-learning-book - Repository for "Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python"

•    Jupyter

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Repository for the book Introduction to Artificial Neural Networks and Deep Learning: A Practical Guide with Applications in Python. Deep learning is not just the talk of the town among tech folks. Deep learning allows us to tackle complex problems, training artificial neural networks to recognize complex patterns for image and speech recognition. In this book, we'll continue where we left off in Python Machine Learning and implement deep learning algorithms in PyTorch.

deep-learning neural-network machine-learning tensorflow artificial-intelligence data-science pytorch

TensorFlowASR - :zap: TensorFlowASR: Almost State-of-the-art Automatic Speech Recognition in Tensorflow 2

•    Jupyter

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: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-transducer

NLP-Models-Tensorflow - Gathers machine learning and Tensorflow deep learning models for NLP problems, 1

•    Jupyter

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NLP-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-api

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

•    Javascript

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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).

audio speech-synthesis speech-recognition text-to-speech

LSTM-Human-Activity-Recognition - Human Activity Recognition example using TensorFlow on smartphone sensors dataset and an LSTM RNN (Deep Learning algo)

•    Jupyter

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Compared to a classical approach, using a Recurrent Neural Networks (RNN) with Long Short-Term Memory cells (LSTMs) require no or almost no feature engineering. Data can be fed directly into the neural network who acts like a black box, modeling the problem correctly. Other research on the activity recognition dataset can use a big amount of feature engineering, which is rather a signal processing approach combined with classical data science techniques. The approach here is rather very simple in terms of how much was the data preprocessed. Let's use Google's neat Deep Learning library, TensorFlow, demonstrating the usage of an LSTM, a type of Artificial Neural Network that can process sequential data / time series.

machine-learning deep-learning lstm human-activity-recognition neural-network rnn recurrent-neural-networks tensorflow activity-recognition

NCRFpp - NCRF++, an Open-source Neural Sequence Labeling Toolkit

•    Python

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Sequence labeling models are quite popular in many NLP tasks, such as Named Entity Recognition (NER), part-of-speech (POS) tagging and word segmentation. State-of-the-art sequence labeling models mostly utilize the CRF structure with input word features. LSTM (or bidirectional LSTM) is a popular deep learning based feature extractor in sequence labeling task. And CNN can also be used due to faster computation. Besides, features within word are also useful to represent word, which can be captured by character LSTM or character CNN structure or human-defined neural features. NCRF++ is a PyTorch based framework with flexiable choices of input features and output structures. The design of neural sequence labeling models with NCRF++ is fully configurable through a configuration file, which does not require any code work. NCRF++ is a neural version of CRF++, which is a famous statistical CRF framework.

pytorch ner sequence-labeling crf lstm-crf char-rnn char-cnn named-entity-recognition part-of-speech-tagger chunking neural-networks nbest lstm cnn batch

lingvo - Lingvo

•    Python

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Lingvo 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 asr

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.

speech speech-recognition speech-to-text tools

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.

speech speech-recognition speech-to-text ivr

Coqui - Advanced Text-to-Speech Library

•    Python

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Coqui TTS is a library for advanced Text-to-Speech generation. It's built on the latest research, was designed to achieve the best trade-off among ease-of-training, speed and quality. Coqui TTS comes with pretrained models, tools for measuring dataset quality and already used in 20+ languages for products and research projects.

text-to-speech deep-learning speech pytorch tts vocoder tacotron speaker-encodings tensorflow2 melgan speaker-encoder melgan-stft multi-speaker-tts glow-tts hifigan align-tts tts-model