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.

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.

Yet Another Audio Feature Extractor is a toolbox for audio analysis. Easy to use and efficient at extracting a large number of audio features simultaneously. WAV and MP3 files supported, or embedding in C++, Python or Matlab applications.

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

TeamBlibbityBlabbity is an attempt to document and provide an example implemention of the proprietary TeamSpeak 2 protocol.

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

The PyGE (Python Gutenberg E-text) project is a suite of GUI desktop utilities written in Python to promote and facilitate awareness and enjoyment of works of literature that are available from the archives of Project Gutenberg.

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.

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.

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.

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

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/

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.

The Open Interface for Speech Synthesis (OISS) provides an interface to speech synthesis hardware and software for end-user applications under Unix.

PhoneBlogger allows you to post to a weblog by phone. PhoneBlogger is written in VoiceXML, Python, and JavaScript.

QWave: Qt-based waveform display and audio playback class library.

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/

This is a fast C implementation of Arturo Camacho's SWIPE' pitch extraction algorithm. See the project homepage for more about the advantages of the SWIPE' algorithm. swipe-1.0.tar.gz contains the current source, which should compile quite neatly.