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        "author": "Matthew Goldey",
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        "description": "# GreenKey Automatic Speech Recognition (ASR) Toolkit [![Build Status](https://travis-ci.org/finos/greenkey-asrtoolkit.svg?branch=master)](https://travis-ci.org/finos/greenkey-asrtoolkit)\n\n<img src=\"https://greenkeytech.com/wp-content/uploads/2019/02/gk_logo_colorlight.png\" width=\"300\" />\n\n---\n\n## 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.\n\n### File formats supported\n\nFile formats have format-specific handlers in asrtoolkit/data_handlers. The scripts `convert_transcript` and `wer` support [`stm`](http://www1.icsi.berkeley.edu/Speech/docs/sctk-1.2/infmts.htm), [`srt`](http://zuggy.wz.cz/), [`vtt`](https://w3c.github.io/webvtt/), `txt`, and [GreenKey `json`](https://transcription.greenkeytech.com/) 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.\n\n### convert_transcript \n```text\nusage: convert_transcript [-h] input_file output_file\n\nconvert a single transcript from one text file format to another\n\npositional arguments:\n  input_file   input file\n  output_file  output file\n\noptional arguments:\n  -h, --help   show this help message and exit\n```\nThis tool allows for easy conversion among file formats listed above.\n\nNote: Attributes of a segment object not present in a parsed file retain their default values\n\n- For example, a `segment` object is created for each line of an STM line\n- each is initialized with the following default values which are not encoded in STM files: `formatted_text=''`;  `confidence=1.0` \n\n\n\n### wer\n```text\nusage: wer [-h] [--char-level] [--ignore-nsns]\n           reference_file transcript_file\n\nCompares a reference and transcript file and calculates word error rate (WER)\nbetween these two files\n\npositional arguments:\n  reference_file   reference \"truth\" file\n  transcript_file  transcript possibly containing errors\n\noptional arguments:\n  -h, --help       show this help message and exit\n  --char-level     calculate character error rate instead of word error rate\n  --ignore-nsns    ignore non silence noises like um, uh, etc.\n\nThis tool allows for easy comparison of reference and hypothesis transcripts in any format listed above.\n```\n\n### clean_formatting \n```text\nusage: clean_formatting.py [-h] files [files ...]\n\ncleans input *.txt files and outputs *_cleaned.txt\n\npositional arguments:\n  files       list of input files\n\noptional arguments:\n  -h, --help  show this help message and exit\n\n```\nThis 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.\n\n### split_audio_file \n```text\nusage: split_audio_file [-h] [--target-dir TARGET_DIR] audio_file transcript\n\nSplit an audio file using valid segments from a transcript file. For this\nutility, transcript files must contain start/stop times.\n\npositional arguments:\n  audio_file            input audio file\n  transcript            transcript\n\noptional arguments:\n  -h, --help            show this help message and exit\n  --target-dir TARGET_DIR\n                        Path to target directory\n```\n\n### prepare_audio_corpora\n```text\nusage: prepare_audio_corpora [-h] [--target-dir TARGET_DIR]\n                             corpora [corpora ...]\n\nCopy and organize specified corpora into a target directory. Training,\ntesting, and development sets will be created automatically if not already\ndefined.\n\npositional arguments:\n  corpora               Name of one or more directories in directory this\n                        script is run\n\noptional arguments:\n  -h, --help            show this help message and exit\n  --target-dir TARGET_DIR\n                        Path to target directory\n```\nThis 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.\n\n### degrade_audio_file \n```text\nusage: degrade_audio_file input_file1.wav input_file2.wav\n\nDegrade audio files to 8 kHz format similar to G711 codec\n```\nThis script reduces audio quality of input audio files so that acoustic models can learn features from telephony with the G711 codec.\n\n### extract_excel_spreadsheets\nNote that the use of this function requires the separate installation of `pandas`. This can be done via `pip install pandas`.\n\n```text\nusage: extract_excel_spreadsheets.py [-h] [--input-folder INPUT_FOLDER]\n                                     [--output-corpus OUTPUT_CORPUS]\n\nconvert a folder of excel spreadsheets to a corpus of text files\n\noptional arguments:\n  -h, --help            show this help message and exit\n  --input-folder INPUT_FOLDER\n                        input folder of excel spreadsheets ending in .xls or\n                        .xlsx\n  --output-corpus OUTPUT_CORPUS\n                        output folder for storing text corpus\n```\n\n### Requirements\n\n- Python >= 3.5 with `pip`\n\n## Contributing\n\n### Code of Conduct\n\nPlease make sure you read and observe our [Code of Conduct](https://raw.githubusercontent.com/finos/greenkey-asrtoolkit/master/CODE_OF_CONDUCT.md).\n\n### Pull Request process\n\n1. Fork it\n1. Create your feature branch (`git checkout -b feature/fooBar`)\n1. Commit your changes (`git commit -am 'Add some fooBar'`)\n1. Push to the branch (`git push origin feature/fooBar`)\n1. Create a new Pull Request\n\n## Authors\n\n- [Matthew Goldey](https://github.com/mgoldey)\n- [Tejas Shastry](https://github.com/tshastry)\n- [Amy Geojo](https://github.com/ageojo)\n- [Svyat Vergun](https://github.com/sv-github)\n- [Ashley Shultz](https://github.com/AGiantSquid)\n- [Colin Brochtrup](https://github.com/cbrochtrup)\n\n## License\n\nThe code in this repository is distributed under the [Apache License, Version 2.0](http://www.apache.org/licenses/LICENSE-2.0).\n\nCopyright 2019 GreenKey Technologies\n\n<!-- Markdown link & img defs -->\n[FINOS]: https://www.finos.org\n[Code of Conduct]: https://www.finos.org/code-of-conduct\n[Voice Program]: https://finosfoundation.atlassian.net/wiki/spaces/VOICE/overview\n[SemVer]: http://semver.org\n[list of contributors]: https://github.com/finos/greenkey-asrtoolkit/graphs/contributors\n[tags on this repository]: https://github.com/finos/greenkey-asrtoolkit/tags",
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