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
Python generation script for BitBirds

BitBirds generation script Intro This is published under MIT license, which means you can do whatever you want with it - entirely at your own risk. Pl

286 Dec 06, 2022
COVID-19 Related NLP Papers

COVID-19 outbreak has become a global pandemic. NLP researchers are fighting the epidemic in their own way.

xcfeng 28 Oct 30, 2022
A python gui program to generate reddit text to speech videos from the id of any post.

Reddit text to speech generator A python gui program to generate reddit text to speech videos from the id of any post. Current functionality Generate

Aadvik 17 Dec 19, 2022
Auto-researching tool generating word documents.

About ResearchTE automates researching by generating document with answers to given questions. Supports getting results from: Google DuckDuckGo (with

1 Feb 14, 2022
Python powered crossword generator with database with 20k+ polish words

crossword_generator Generate simple crossword puzzle from words and definitions fetched from krzyżowki.edu.pl endpoints -/ string:word - returns js

0 Jan 04, 2022
Predict the spans of toxic posts that were responsible for the toxic label of the posts

toxic-spans-detection An attempt at the SemEval 2021 Task 5: Toxic Spans Detection. The Toxic Spans Detection task of SemEval2021 required participant

Ilias Antonopoulos 3 Jul 24, 2022
Words-per-minute - A terminal app written in python utilizing the curses module that tests the user's ability to type

words-per-minute A terminal app written in python utilizing the curses module th

Tanim Islam 1 Jan 14, 2022
BiNE: Bipartite Network Embedding

BiNE: Bipartite Network Embedding This repository contains the demo code of the paper: BiNE: Bipartite Network Embedding. Ming Gao, Leihui Chen, Xiang

leihuichen 214 Nov 24, 2022
Test finetuning of XLSR (multilingual wav2vec 2.0) for other speech classification tasks

wav2vec_finetune Test finetuning of XLSR (multilingual wav2vec 2.0) for other speech classification tasks Initial test: gender recognition on this dat

8 Aug 11, 2022
EasyTransfer is designed to make the development of transfer learning in NLP applications easier.

EasyTransfer is designed to make the development of transfer learning in NLP applications easier. The literature has witnessed the success of applying

Alibaba 819 Jan 03, 2023
Problem: Given a nepali news find the category of the news

Classification of category of nepali news catorgory using different algorithms Problem: Multiclass Classification Approaches: TFIDF for vectorization

pudasainishushant 2 Jan 09, 2022
Finding Label and Model Errors in Perception Data With Learned Observation Assertions

Finding Label and Model Errors in Perception Data With Learned Observation Assertions This is the project page for Finding Label and Model Errors in P

Stanford Future Data Systems 17 Oct 14, 2022
ETM - R package for Topic Modelling in Embedding Spaces

ETM - R package for Topic Modelling in Embedding Spaces This repository contains an R package called topicmodels.etm which is an implementation of ETM

bnosac 37 Nov 06, 2022
Sequence-to-Sequence Framework in PyTorch

nmtpytorch allows training of various end-to-end neural architectures including but not limited to neural machine translation, image captioning and au

LIUM 395 Nov 21, 2022
AMUSE - financial summarization

AMUSE AMUSE - financial summarization Unzip data.zip Train new model: python FinAnalyze.py --task train --start 0 --count how many files,-1 for all

1 Jan 11, 2022
Convolutional 2D Knowledge Graph Embeddings resources

ConvE Convolutional 2D Knowledge Graph Embeddings resources. Paper: Convolutional 2D Knowledge Graph Embeddings Used in the paper, but do not use thes

Tim Dettmers 586 Dec 24, 2022
REST API for sentence tokenization and embedding using Multilingual Universal Sentence Encoder.

What is MUSE? MUSE stands for Multilingual Universal Sentence Encoder - multilingual extension (16 languages) of Universal Sentence Encoder (USE). MUS

Dani El-Ayyass 47 Sep 05, 2022
Repository for fine-tuning Transformers 🤗 based seq2seq speech models in JAX/Flax.

Seq2Seq Speech in JAX A JAX/Flax repository for combining a pre-trained speech encoder model (e.g. Wav2Vec2, HuBERT, WavLM) with a pre-trained text de

Sanchit Gandhi 21 Dec 14, 2022
Trex is a tool to match semantically similar functions based on transfer learning.

Trex is a tool to match semantically similar functions based on transfer learning.

62 Dec 28, 2022
Mapping a variable-length sentence to a fixed-length vector using BERT model

Are you looking for X-as-service? Try the Cloud-Native Neural Search Framework for Any Kind of Data bert-as-service Using BERT model as a sentence enc

Han Xiao 11.1k Jan 01, 2023