Open Source Python Speech Software for Linux

VCClient is a real-time voice conversion system that uses machine learning models to transform a speaker’s voice into another voice with minimal latency. It is designed for live applications such as streaming, gaming, and virtual communication, where immediate feedback is essential. The system supports multiple voice conversion models, including RVC and other neural network-based approaches, allowing users to switch between different voices or customize their output. It provides both a graphical user interface and API access, making it suitable for casual users as well as developers who want to integrate voice transformation into their own applications. The project also supports GPU acceleration, enabling faster inference and smoother real-time performance on compatible hardware. Additionally, it includes tools for training and managing voice models, giving users the ability to create personalized voice profiles.

DeepSpeech is an open source embedded (offline, on-device) speech-to-text engine which can run in real time on devices ranging from a Raspberry Pi 4 to high power GPU servers. DeepSpeech is an open-source Speech-To-Text engine, using a model trained by machine learning techniques based on Baidu's Deep Speech research paper. Project DeepSpeech uses Google's TensorFlow to make the implementation easier. A pre-trained English model is available for use and can be downloaded following the instructions in the usage docs. If you want to use the pre-trained English model for performing speech-to-text, you can download it (along with other important inference material) from the DeepSpeech releases page.

Library for performing speech recognition, with support for several engines and APIs, online and offline. Recognize speech input from the microphone, transcribe an audio file, save audio data to an audio file. Show extended recognition results, calibrate the recognizer energy threshold for ambient noise levels (see recognizer_instance.energy_threshold for details). Listening to a microphone in the background, various other useful recognizer features. The easiest way to install this is using pip install SpeechRecognition. The first software requirement is Python 2.6, 2.7, or Python 3.3+. This is required to use the library. PyAudio is required if and only if you want to use microphone input (Microphone). PyAudio version 0.2.11+ is required, as earlier versions have known memory management bugs when recording from microphones in certain situations. To hack on this library, first make sure you have all the requirements listed in the "Requirements" section.

SPPAS is a scientific computer software package written and maintained by Brigitte Bigi of the Laboratoire Parole et Langage, in Aix-en-Provence, France. Available for free, with open source code, there is simply no other package for linguists to simple use in the automatic annotations of speech, the analyses of any kind of annotated data and the conversion of annotated files. SPPAS is able to produce automatically speech annotations from a recorded speech sound and its orthographic transcription. SPPAS is helpful for the analysis of any annotated data: estimate statistical distributions, make requests, manage files, visualize annotations. SPPAS offers a file converter from/to a wide range of formats: xra, TextGrid, eaf, trs... <https://sppas.org>

InproTK is an Incremental Spoken Dialogue Processing Toolkit, that is, a toolkit to help you build dialogue systems that listen and talk incrementally, allowing for advanced interactional behaviour. Please see our Wiki for more information: http://sourceforge.net/p/inprotk/wiki/

Just log your favorite FM station speech to a text file using rtl-sdr dongle and speech recognition. Cross-platform tool. Follow the README on the download page for Windows installation. https://sourceforge.net/projects/fm2txt-rtlsdr/files/ If you prefer GitHub source, not SF: https://github.com/randaller/fm2txt For those, who want to recognize from soundcard, not from rtl-sdr (this allows to transcribe NFM etc): https://github.com/randaller/souncard2txt

An extensible (by plugin) chatbot project

AarTon is an automated text-to-speech application. It allows user to enter text in a web-based front-end and render these texts via a multi-channel sound card.

Eve is a AI project written in python that takes commands verbally or textually to control the computer and eveyday functions.

RNNLIB is a recurrent neural network library for sequence learning problems. Applicable to most types of spatiotemporal data, it has proven particularly effective for speech and handwriting recognition. full installation and usage instructions given at http://sourceforge.net/p/rnnl/wiki/Home/

Speect is a multilingual TTS system. It offers a full text-to-speech system with various API's, as well as an environment for research and development of TTS systems and voices. It is written in ANSI C and uses a plug-in mechanism for extensions. Speect also includes an extensive set of Python bindings for quick implementation of new ideas, these bindings are derived from SWIG interface files and can easily be extended for other languages supported by SWIG. Speect is free and open source software. As a collection it is distributed under a MIT license.

Steel is a cross-platform package for using common text-to-speech (speech synthesis) engines in Python. Steel currently supports the following TTS software: - Microsoft Speech API 5 (SAPI5) - eSpeak - NS Speech Synthesis - FreeTTS Documentation: http://sourceforge.net/p/steeltts/wiki/ Bug Tracker: http://sourceforge.net/p/steeltts/tickets/ If you are interested in contributing to the Steel TTS codebase, or would like to make a feature-request, please contact the lead developer, Jasper Danielson, at jrd4@rice.edu.

Software to fit whole-sentence language models using the principle of maximum entropy. For developers of speech recognizers, text prediction interfaces, OCR, machine translation software.