Simple, hackable offline speech to text - using the VOSK-API.

Overview

Nerd Dictation

Offline Speech to Text for Desktop Linux. - See demo video.

This is a utility that provides simple access speech to text for using in Linux without being tied to a desktop environment.

Simple
This is a single file Python script with minimal dependencies.
Hackable
User configuration lets you manipulate text using Python string operations.
Zero Overhead
As this relies on manual activation there are no background processes.

Dictation is accessed manually with begin/end commands.

This uses the excellent vosk-api.

Usage

It is suggested to bind begin/end/cancel to shortcut keys.

nerd-dictation begin
nerd-dictation end

For details on how this can be used, see: nerd-dictation --help and nerd-dictation begin --help.

Features

Specific features include:

Numbers as Digits

Optional conversion from numbers to digits.

So Three million five hundred and sixty second becomes 3,000,562nd.

A series of numbers (such as reciting a phone number) is also supported.

So Two four six eight becomes 2,468.

Time Out
Optionally end speech to text early when no speech is detected for a given number of seconds. (without an explicit call to end which is otherwise required).
Output Type
Output can simulate keystroke events (default) or simply print to the standard output.
User Configuration Script
User configuration is just a Python script which can be used to manipulate text using Python's full feature set.

See nerd-dictation begin --help for details on how to access these options.

Dependencies

  • Python 3.
  • The VOSK-API.
  • parec command (for recording from pulse-audio).
  • xdotool command to simulate keyboard input.

Install

pip3 install vosk
git clone https://github.com/ideasman42/nerd-dictation.git
cd nerd-dictation
wget https://alphacephei.com/kaldi/models/vosk-model-small-en-us-0.15.zip
unzip vosk-model-small-en-us-0.15.zip
mv vosk-model-small-en-us-0.15 model

To test dictation:

./nerd-dictation begin --vosk-model-dir=./model &
# Start speaking.
./nerd-dictation end
  • Reminder that it's up to you to bind begin/end/cancel to actions you can easily access (typically key shortcuts).

  • To avoid having to pass the --vosk-model-dir argument, copy the model to the default path:

    mkdir -p ~/.config/nerd-dictation
    mv ./model ~/.config/nerd-dictation

Hint

Once this is working properly you may wish to download one of the larger language models for more accurate dictation. They are available here.

Configuration

This is an example of a trivial configuration file which simply makes the input text uppercase.

# ~/.config/nerd-dictation/nerd-dictation.py
def nerd_dictation_process(text):
    return text.upper()

A more comprehensive configuration is included in the examples/ directory.

Hints

  • The processing function can be used to implement your own actions using keywords of your choice. Simply return a blank string if you have implemented your own text handling.
  • Context sensitive actions can be implemented using command line utilities to access the active window.

Paths

Local Configuration
~/.config/nerd-dictation/nerd-dictation.py
Language Model

~/.config/nerd-dictation/model

Note that --vosk-model-dir=PATH can be used to override the default.

Command Line Arguments

Output of nerd-dictation --help

usage:

nerd-dictation [-h]  ...

This is a utility that activates text to speech in Linux. While it could use any system currently it uses the VOSK-API.

positional arguments:

begin: Begin dictation.
end: End dictation.
cancel: Cancel dictation.
optional arguments:
-h, --help show this help message and exit

Subcommand: begin

usage:

nerd-dictation begin [-h] [--cookie FILE_PATH] [--vosk-model-dir DIR]
                     [--pulse-device-name IDENTIFIER]
                     [--sample-rate HZ] [--defer-output] [--continuous]
                     [--timeout SECONDS] [--idle-time SECONDS]
                     [--delay-exit SECONDS]
                     [--punctuate-from-previous-timeout SECONDS]
                     [--full-sentence] [--numbers-as-digits]
                     [--numbers-use-separator] [--output OUTPUT_METHOD]
                     [- ...]

This creates the directory used to store internal data, so other commands such as sync can be performed.

optional arguments:
-h, --help show this help message and exit
--cookie FILE_PATH
  Location for writing a temporary cookie (this file is monitored to begin/end dictation).
--vosk-model-dir DIR
  Path to the VOSK model, see: https://alphacephei.com/vosk/models
--pulse-device-name IDENTIFIER
  The name of the pulse-audio device to use for recording. See the output of "pactl list sources" to find device names (using the identifier following "Name:").
--sample-rate HZ
  The sample rate to use for recording (in Hz). Defaults to 44100.
--defer-output

When enabled, output is deferred until exiting.

This prevents text being typed during speech (implied with --output=STDOUT)

--continuous Enable this option, when you intend to keep the dictation process enabled for extended periods of time. without this enabled, the entirety of this dictation session will be processed on every update. Only used when --defer-output is disabled.
--timeout SECONDS
  Time out recording when no speech is processed for the time in seconds. This can be used to avoid having to explicitly exit (zero disables).
--idle-time SECONDS
  Time to idle between processing audio from the recording. Setting to zero is the most responsive at the cost of high CPU usage. The default value is 0.1 (processing 10 times a second), which is quite responsive in practice (the maximum value is clamped to 0.5)
--delay-exit SECONDS
  The time to continue running after an exit request. this can be useful so "push to talk" setups can be released while you finish speaking (zero disables).
--punctuate-from-previous-timeout SECONDS
  The time-out in seconds for detecting the state of dictation from the previous recording, this can be useful so punctuation it is added before entering the dictation(zero disables).
--full-sentence
  Capitalize the first character. This is also used to add either a comma or a full stop when dictation is performed under the --punctuate-from-previous-timeout value.
--numbers-as-digits
  Convert numbers into digits instead of using whole words.
--numbers-use-separator
  Use a comma separators for numbers.
--output OUTPUT_METHOD
 

Method used to at put the result of speech to text.

  • SIMULATE_INPUT simulate keystrokes (default).
  • STDOUT print the result to the standard output. Be sure only to handle text from the standard output as the standard error may be used for reporting any problems that occur.
- ... End argument parsing.
This can be used for user defined arguments which configuration scripts may read from the sys.argv.

Subcommand: end

usage:

nerd-dictation end [-h] [--cookie FILE_PATH]

This ends dictation, causing the text to be typed in.

optional arguments:
-h, --help show this help message and exit
--cookie FILE_PATH
  Location for writing a temporary cookie (this file is monitored to begin/end dictation).

Subcommand: cancel

usage:

nerd-dictation cancel [-h] [--cookie FILE_PATH]

This cancels dictation.

optional arguments:
-h, --help show this help message and exit
--cookie FILE_PATH
  Location for writing a temporary cookie (this file is monitored to begin/end dictation).

Details

  • Typing in results will never press enter/return.
  • Pulse audio is used for recording.
  • Recording and speech to text a performed in parallel.

Examples

Store the result of speech to text as a variable in the shell:

SPEECH="$(nerd-dictation begin --timeout=1.0 --output=STDOUT)"

Example Configurations

These are example configurations you may use as a reference.

Other Software

  • Elograf - nerd-dictation GUI front-end that runs as a tray icon.

Limitations

  • Text from VOSK is all lower-case, while the user configuration can be used to set the case of common words like I this isn't very convenient (see the example configuration for details).

  • For some users the delay in start up may be noticeable on systems with slower hard disks especially when running for the 1st time (a cold start).

    This is a limitation with the choice not to use a service that runs in the background. Recording begins before any the speech-to-text components are loaded to mitigate this problem.

Further Work

  • And a general solution to capitalize words (proper nouns for example).
  • Wayland support (this should be quite simple to support and mainly relies on a replacement for xdotool).
  • Add a setup.py for easy installation on uses systems.
  • Possibly other speech to text engines (only if they provide some significant benefits).
  • Possibly support Windows & macOS.
Owner
Campbell Barton
Campbell Barton
A minimal Conformer ASR implementation adapted from ESPnet.

Conformer ASR A minimal Conformer ASR implementation adapted from ESPnet. Introduction I want to use the pre-trained English ASR model provided by ESP

Niu Zhe 3 Jan 24, 2022
Uncomplete archive of files from the European Nopsled Team

European Nopsled CTF Archive This is an archive of collected material from various Capture the Flag competitions that the European Nopsled team played

European Nopsled 4 Nov 24, 2021
SurvTRACE: Transformers for Survival Analysis with Competing Events

⭐ SurvTRACE: Transformers for Survival Analysis with Competing Events This repo provides the implementation of SurvTRACE for survival analysis. It is

Zifeng 13 Oct 06, 2022
Spacy-ginza-ner-webapi - Named Entity Recognition API with spaCy and GiNZA

Named Entity Recognition API with spaCy and GiNZA I wrote a blog post about this

Yuki Okuda 3 Feb 27, 2022
💬 Open source machine learning framework to automate text- and voice-based conversations: NLU, dialogue management, connect to Slack, Facebook, and more - Create chatbots and voice assistants

Rasa Open Source Rasa is an open source machine learning framework to automate text-and voice-based conversations. With Rasa, you can build contextual

Rasa 15.3k Dec 30, 2022
ChessCoach is a neural network-based chess engine capable of natural-language commentary.

ChessCoach is a neural network-based chess engine capable of natural-language commentary.

Chris Butner 380 Dec 03, 2022
A Domain Specific Language (DSL) for building language patterns. These can be later compiled into spaCy patterns, pure regex, or any other format

RITA DSL This is a language, loosely based on language Apache UIMA RUTA, focused on writing manual language rules, which compiles into either spaCy co

Šarūnas Navickas 60 Sep 26, 2022
中文医疗信息处理基准CBLUE: A Chinese Biomedical LanguageUnderstanding Evaluation Benchmark

English | 中文说明 CBLUE AI (Artificial Intelligence) is playing an indispensabe role in the biomedical field, helping improve medical technology. For fur

452 Dec 30, 2022
Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS)

TOPSIS implementation in Python Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS) CHING-LAI Hwang and Yoon introduced TOPSIS

Hamed Baziyad 8 Dec 10, 2022
Simple, Pythonic, text processing--Sentiment analysis, part-of-speech tagging, noun phrase extraction, translation, and more.

TextBlob: Simplified Text Processing Homepage: https://textblob.readthedocs.io/ TextBlob is a Python (2 and 3) library for processing textual data. It

Steven Loria 8.4k Dec 26, 2022
apple's universal binaries BUT MUCH WORSE (PRACTICAL SHITPOST) (NOT PRODUCTION READY)

hyperuniversality investment opportunity: what if we could run multiple architectures in a single file, again apple universal binaries, but worse how

luna 2 Oct 19, 2021
this repository has datasets containing information of Uber pickups in NYC from April 2014 to September 2014 and January to June 2015. data Analysis , virtualization and some insights are gathered here

uber-pickups-analysis Data Source: https://www.kaggle.com/fivethirtyeight/uber-pickups-in-new-york-city Information about data set The dataset contain

1 Nov 02, 2021
Code for the paper "A Simple but Tough-to-Beat Baseline for Sentence Embeddings".

Code for the paper "A Simple but Tough-to-Beat Baseline for Sentence Embeddings".

1.1k Dec 27, 2022
Simple Text-Generator with OpenAI gpt-2 Pytorch Implementation

GPT2-Pytorch with Text-Generator Better Language Models and Their Implications Our model, called GPT-2 (a successor to GPT), was trained simply to pre

Tae-Hwan Jung 775 Jan 08, 2023
A Chinese to English Neural Model Translation Project

ZH-EN NMT Chinese to English Neural Machine Translation This project is inspired by Stanford's CS224N NMT Project Dataset used in this project: News C

Zhenbang Feng 29 Nov 26, 2022
This is a project built for FALLABOUT2021 event under SRMMIC, This project deals with NLP poetry generation.

FALLABOUT-SRMMIC 21 POETRY-GENERATION HINGLISH DESCRIPTION We have developed a NLP(natural language processing) model which automatically generates a

7 Sep 28, 2021
Code for CodeT5: a new code-aware pre-trained encoder-decoder model.

CodeT5: Identifier-aware Unified Pre-trained Encoder-Decoder Models for Code Understanding and Generation This is the official PyTorch implementation

Salesforce 564 Jan 08, 2023
Various capabilities for static malware analysis.

Malchive The malchive serves as a compendium for a variety of capabilities mainly pertaining to malware analysis, such as scripts supporting day to da

MITRE Cybersecurity 64 Nov 22, 2022
Graph Coloring - Weighted Vertex Coloring Problem

Graph Coloring - Weighted Vertex Coloring Problem This project proposes several local searches and an MCTS algorithm for the weighted vertex coloring

Cyril 1 Jul 08, 2022
✨Rubrix is a production-ready Python framework for exploring, annotating, and managing data in NLP projects.

✨A Python framework to explore, label, and monitor data for NLP projects

Recognai 1.5k Jan 02, 2023