The GreenKey ASRToolkit provides tools for automatic speech recognition (ASR) file conversion and corpora organization.
GreenKey Automatic Speech Recognition (ASR) Toolkit
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.
usage: convert_transcript [-h] input_file output_file convert between text file formats 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 from STM files to TXT files and back. Other file formats will be added in the near future.
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.
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
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.
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
- Python > 3.5 with
Code of Conduct
Please make sure you read and observe our [Code of Conduct].
Pull Request process
- 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
This project is licensed under the Apache 2.0 License - see the LICENSE.md file for details.
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