Voice Recognition V3 Library
Peñen Tegtmeier
PyPI
- Source code
- Issue tracker
See Notes on using PocketSphinx for information about installing languages, compiling PocketSphinx, and building language packs from online resources. This document is also included under reference/pocketsphinx.rst.
If using CMU Sphinx, you may want to install additional language packs to support languages like International French or Mandarin Chinese.The following sections go over the details of each requirement.
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.
On Windows, install PyAudio using Pip: execute pip install pyaudio in a terminal.
- On Debian-derived Linux distributions (like Ubuntu and Mint), install PyAudio using APT: execute sudo apt-get install python-pyaudio python3-pyaudio in a terminal.
- If the version in the repositories is too old, install the latest release using Pip: execute sudo apt-get install portaudio19-dev python-all-dev python3-all-dev && sudo pip install pyaudio (replace pip with pip3 if using Python 3).
- On OS X, install PortAudio using Homebrew: brew install portaudio. Then, install PyAudio using Pip: pip install pyaudio.
- On other POSIX-based systems, install the portaudio19-dev and python-all-dev (or python3-all-dev if using Python 3) packages (or their closest equivalents) using a package manager of your choice, and then install PyAudio using Pip: pip install pyaudio (replace pip with pip3 if using Python 3).
PocketSphinx-Python wheel packages for 64-bit Python 3.4, and 3.5 on Windows are included for convenience, under the third-party/ directory. To install, simply run pip install wheel followed by pip install ./third-party/WHEEL_FILENAME (replace pip with pip3 if using Python 3) in the SpeechRecognition folder.
Otherwise, ensure that you have the flac command line tool, which is often available through the system package manager. For example, this would usually be sudo apt-get install flac on Debian-derivatives, or brew install flac on OS X with Homebrew.
Try increasing the recognizer_instance.energy_threshold property. This is basically how sensitive the recognizer is to when recognition should start. Higher values mean that it will be less sensitive, which is useful if you are in a loud room.
Also, check on your microphone volume settings. If it is too sensitive, the microphone may be picking up a lot of ambient noise. If it is too insensitive, the microphone may be rejecting speech as just noise.
The recognizer_instance.energy_threshold property is probably set to a value that is too high to start off with, and then being adjusted lower automatically by dynamic energy threshold adjustment. Before it is at a good level, the energy threshold is so high that speech is just considered ambient noise.
Try setting the recognition language to your language/dialect. To do this, see the documentation for recognizer_instance.recognize_sphinx, recognizer_instance.recognize_google, recognizer_instance.recognize_wit, recognizer_instance.recognize_bing, recognizer_instance.recognize_api, recognizer_instance.recognize_houndify, and recognizer_instance.recognize_ibm.
To proceed, either use Microphone(device_index=MICROPHONE_INDEX, ...) instead of Microphone(...), or set a default microphone in your OS. You can obtain possible values of MICROPHONE_INDEX using the code in the troubleshooting entry right above this one.
The included flac-mac executable is extracted from xACT 2.39, which is a frontend for FLAC 1.3.2 that conveniently includes binaries for all of its encoders. Specifically, it is a copy of xACT 2.39/xACT.app/Contents/Resources/flac in xACT2.39.zip.
For convenience, all the official distributions of SpeechRecognition already include a copy of the necessary copyright notices and licenses. In your project, you can simply say that licensing information for SpeechRecognition can be found within the SpeechRecognition README, and make sure SpeechRecognition is visible to users if they wish to see it.
SpeechRecognition distributes source code, binaries, and language files from CMU Sphinx. These files are BSD-licensed and redistributable as long as copyright notices are correctly retained. See speech_recognition/pocketsphinx-data/*/LICENSE*.txt and third-party/LICENSE-Sphinx.txt for license details for individual parts.
SpeechRecognition distributes source code and binaries from PyAudio. These files are MIT-licensed and redistributable as long as copyright notices are correctly retained. See third-party/LICENSE-PyAudio.txt for license details.
SpeechRecognition distributes binaries from FLAC - speech_recognition/flac-win32.exe, speech_recognition/flac-linux-x86, and speech_recognition/flac-mac. These files are GPLv2-licensed and redistributable, as long as the terms of the GPL are satisfied. The FLAC binaries are an aggregate of separate programs, so these GPL restrictions do not apply to the library or your programs that use the library, only to FLAC itself. See LICENSE-FLAC.txt for license details.
A speech-to-text (STT) system, or sometimes called automatic speech recognition (ASR) is as its name implies: A way of transforming the spoken words via sound into textual data that can be used later for any purpose.
Speech recognition technology is extremely useful. It can be used for a lot of applications such as the automation of transcription, writing books/texts using sound only, enabling complicated analysis on information using the generated textual files and a lot of other things.
Some of them come with preloaded and trained dataset to recognize the given voices in one language and generate the corresponding texts, while others just give the engine without the dataset, and developers will have to build the training models themselves. This can be a complex task, similar to asking someone to do my online homework for me, as it requires a deep understanding of machine learning and data handling.
The difference between proprietary speech recognition and open source speech recognition, is that the library used to process the voices should be licensed under one of the known open source licenses, such as GPL, MIT and others.
Microsoft and IBM for example have their own speech recognition toolkits that they offer for developers, but they are not open source. Simply because they are not licensed under one of the open source licenses in the market.
Mainly, you get few or no restrictions at all on the commercial usage for your application, as the open source speech recognition libraries will allow you to use them for whatever use case you may need.
Probably one of the oldest speech recognition software ever, as its development started in 1991 at the University of Kyoto, and then its ownership was transferred to as an independent project in 2005. A lot of open source applications use it as their engine (Think of KDE Simon).
Julius main features include its ability to perform real-time STT processes, low memory usage (Less than 64MB for 20000 words), ability to produce N-best/Word-graph output, ability to work as a server unit and a lot more.
This software was mainly built for academic and research purposes. It is written in C, and works on Linux, Windows, macOS and even Android (on smartphones). Currently it supports both English and Japanese languages only.
While it can be used for way more than just speech recognition, it is a good engine nonetheless for this use case. You can either build your own training models for it, or use models which are shipped by default. It supports parallel processing using multiple GPUs/Multiple CPUs, besides a heavy support for some NVIDIA technologies like CUDA and its strong graphics cards.
It also works on Raspberry Pi, iOS and android devices, and provides a streaming API which allows you to connect to it to do your speech recognition tasks online. Vosk has bindings for Java, Python, JavaScript, C# and NodeJS.
Also supports end-to-end ASR. It follows Kaldi style for data processing, so it would be easier to migrate from it to ESPnet. The main marketing point for ESPnet is the state-of-art performance it gives in many benchmarks, and its support for other language processing tasks such as speech-to-text (STT), machine translation (MT) and speech translation (ST).
The main marketing point for Whisper is that it does not specialize in a set of training datasets for specific languages only; instead, it can be used with any suitable model and for any language. It was trained on 680 thousand hours of audio files, one third of which were non-English datasets.
The newest speech recognition library on the list, which was just released in the middle of November, 2023. It employs diffusion techniques with large speech language models (SLMs) training in order to achieve more advanced results than other models.
If you are building a small application that you want to be portable everywhere, then Vosk is your best option, as it is written in Python and works on iOS, android and Raspberry pi too, and supports up to 10 languages. It also provides a huge training dataset if you shall need it, and a smaller one for portable applications.
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