NOTE! This project is archived due to lack of activity; you can still consume this software, although not advised, as it is not actively maintained. If you're interested to restore activity on this repository, please email [email protected]
The GreenKey ASRToolkit provides tools for file conversion and ASR corpora organization. These are intended to simplify the workflow for building, customizing, and analyzing ASR models, useful for scientists, engineers, and other technologists in speech recognition.
File formats have format-specific handlers in asrtoolkit/data_handlers. The scripts convert_transcript and wer support stm, srt, vtt, txt, and GreenKey json formatted transcripts. A custom html format is also available, though this should not be considered a stable format for long term storage as it is subject to change without notice.
usage: convert_transcript [-h] input_file output_file
convert a single transcript from one text file format to another
positional arguments:
input_file input file
output_file output file
optional arguments:
-h, --help show this help message and exit
This tool allows for easy conversion among file formats listed above.
Note: Attributes of a segment object not present in a parsed file retain their default values
- For example, a
segmentobject is created for each line of an STM line - each is initialized with the following default values which are not encoded in STM files:
formatted_text='';confidence=1.0
usage: wer [-h] [--char-level] [--ignore-nsns]
reference_file transcript_file
Compares a reference and transcript file and calculates word error rate (WER)
between these two files
positional arguments:
reference_file reference "truth" file
transcript_file transcript possibly containing errors
optional arguments:
-h, --help show this help message and exit
--char-level calculate character error rate instead of word error rate
--ignore-nsns ignore non silence noises like um, uh, etc.
This tool allows for easy comparison of reference and hypothesis transcripts in any format listed above.
usage: clean_formatting.py [-h] files [files ...]
cleans input *.txt files and outputs *_cleaned.txt
positional arguments:
files list of input files
optional arguments:
-h, --help show this help message and exit
This script standardizes how abbreviations, numbers, and other formatted text is expressed so that ASR engines can easily use these files as training or testing data. Standardizing the formatting of output is essential for reproducible measurements of ASR accuracy.
usage: split_audio_file [-h] [--target-dir TARGET_DIR] audio_file transcript
Split an audio file using valid segments from a transcript file. For this
utility, transcript files must contain start/stop times.
positional arguments:
audio_file input audio file
transcript transcript
optional arguments:
-h, --help show this help message and exit
--target-dir TARGET_DIR
Path to target directory
usage: prepare_audio_corpora [-h] [--target-dir TARGET_DIR]
corpora [corpora ...]
Copy and organize specified corpora into a target directory. Training,
testing, and development sets will be created automatically if not already
defined.
positional arguments:
corpora Name of one or more directories in directory this
script is run
optional arguments:
-h, --help show this help message and exit
--target-dir TARGET_DIR
Path to target directory
This script scrapes a list of directories for paired STM and SPH files. If train, test, and dev folders are present, these labels are used for the output folder. By default, a target directory of 'input-data' will be created. Note that filenames with hyphens will be sanitized to underscores and that audio files will be forced to single channel, 16 kHz, signed PCM format. If two channels are present, only the first will be used.
usage: degrade_audio_file input_file1.wav input_file2.wav
Degrade audio files to 8 kHz format similar to G711 codec
This script reduces audio quality of input audio files so that acoustic models can learn features from telephony with the G711 codec.
Note that the use of this function requires the separate installation of pandas. This can be done via pip install pandas.
usage: extract_excel_spreadsheets.py [-h] [--input-folder INPUT_FOLDER]
[--output-corpus OUTPUT_CORPUS]
convert a folder of excel spreadsheets to a corpus of text files
optional arguments:
-h, --help show this help message and exit
--input-folder INPUT_FOLDER
input folder of excel spreadsheets ending in .xls or
.xlsx
--output-corpus OUTPUT_CORPUS
output folder for storing text corpus
This aligns a gk hypothesis json file with a reference text file for creating forced alignment STM files for training new ASR models.
Note that this function requires the installation a few extra packages
python3 -m pip install spacy textacy https://github.com/explosion/spacy-models/releases/download/en_core_web_sm-2.2.0/en_core_web_sm-2.2.0.tar.gz#egg=en_core_web_smusage: align_json.py [-h] input_json ref output_filename
align a gk json file against a reference text file
positional arguments:
input_json input gk json file
ref reference text file
output_filename output_filename
optional arguments:
-h, --help show this help message and exit
- Python >= 3.6.1 with
pip
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- Fork it
- Create your feature branch (
git checkout -b feature/fooBar) - Commit your changes (
git commit -am 'Add some fooBar') - Push to the branch (
git push origin feature/fooBar) - Create a new Pull Request
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The code in this repository is distributed under the Apache License, Version 2.0.
Copyright 2020 GreenKey Technologies