Open Source Python Speech Software

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.

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.

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>

Moshi is a speech-text foundation model and full-duplex spoken dialogue framework. It uses Mimi, a state-of-the-art streaming neural audio codec. Mimi processes 24 kHz audio, down to a 12.5 Hz representation with a bandwidth of 1.1 kbps, in a fully streaming manner (latency of 80ms, the frame size), yet performs better than existing, non-streaming, codecs like SpeechTokenizer (50 Hz, 4kbps), or SemantiCodec (50 Hz, 1.3kbps). Moshi models two streams of audio: one corresponds to Moshi, and the other one to the user. At inference, the stream from the user is taken from the audio input, and the one for Moshi is sampled from the model's output. Along these two audio streams, Moshi predicts text tokens corresponding to its own speech, its inner monologue, which greatly improves the quality of its generation. A small Depth Transformer models inter codebook dependencies for a given time step, while a large, 7B parameter Temporal Transformer models the temporal dependencies.

Performs actions on detected volume threshold Examples : - Launch music on clap - Launch speech recording when you start speaking - Launch guard webcam when a significant sound is detected - Increase or decrease headphones volume when ambient noise pass

A Python library to create sophisticated multilingual IVR applications. NOTICE. The repository is frozen, please find the latest version of the software at https://github.com/sippy/vapp

Sayz Me is a text-to-speech application for Windows. Text can be typed in or read from clipboard. Words are highlighted when spoken. Select voice, adjust reading speed, voice pitch, font and color. Simple and easy to use.

This is a Linux project that acts as a front end to cdparanoia, sox, and ffmpeg with the hope of making it incredibly simple to rip many audiobook cds into one mono, audiobook (m4b) format file for use in audio players capable of playing audiobooks.

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

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.

Voice keyboard/dictation. Aims to be a total substitute for a keyboard. Spell out words letter by letter (using code: alpha, bravo, ..). Arrow keys, modifiers work. Speak whole words (but whole word accuracy is not good). Attach commands to some word

uListen is a TTS(Text To Speech) application. It can TALK you the web pages, chm files, pdf files and word files and plain text files.

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/

A.L.V.I. e' nato per essere un semplice ma modulare Bot, in grado di interagire con l'essere umano attraverso il linguaggio naturale ed eseguire svariati compiti, come leggere ad alta voce Mail, notizie, Feeds. Tutto in Italiano!

An extensible (by plugin) chatbot project

ASR-Builder provides an easy-to-use interface to the HTK toolkit, that allows users to build ASR systems. ASR-Builder provides a platform that performs house-keeping tasks when using HTK and also provides default training/testing/recognition scripts.

A collection of scripts and programs to automatically annotate video/audio for subtitles. Basically relies on a MARSYAS (Music Analysis, Retrieval and Synthesis for Audio Signals) plug-in for detecting human voice in polyphonic recordings.

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.

AGTK is a suite of software components for building tools for annotating linguistic signals, time-series data which documents any kind of linguistic behavior (e.g. audio, video). The internal data structures are based on annotation graphs.

The Blind Audio Tactile Mapping System (BATS) attempts to address the lack of spatial information available for visually impaired students.

DJBorg turns your MP3 playlist into a personalized radio station, adding randomly-generated DJ banter between tracks. Song information (based on ID3 tags), news, weather, and headlines are announced via a text-to-speech engine.

This software design to convert text to speech and download the converted speech. Description : • Installation setup with two languages (English, French) • Two areas called text reading and speech downloading • Many languages supported to download center Note 1: I'm a student yet and I'm not in the software designing industry. Therefore maybe I haven't software making skills. I'm worried about that. ! Note 2 : When you double click on the software maybe it will get some seconds to open. That's not my fault. I used Python language to make this software and Python was not supported speedy to modern computers.

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