semvec/pypi-packages huggingface dataset:
rev-reader 0.0.3
Convert transcriptions from rev into FAVE format.
License: MIT
Requires Python: >=2.7.11


## rev-reader

Parse Json of transcriptions from rev into FAVE-align format and write to CSV.

### TOC
1. [Install](#install)
2. [How to use](#how-to-use)
3. [Notes](#notes)

### Install

Pip install as outlined above. 

### How to use
Built in Python 3.6.5, should work with Python 2 as well. 


1. Run from the command line, option I

    `python -m rev_reader.rev_reader $INPUT_FOLDER $ADDITIONAL_OFFSET`

    where 

    - `$INPUT_FOLDER` is a path to a directory containing some JSON files of rev annotations. The output CSVs will be written to the same directory. This is a required argument.

    - `$ADDITIONAL_OFFSET` is a number that will be added to / subtracted from the timestamps before the data is output as CSV. This is an optional argument. 

    For example, the command below will convert all the JSON files in the folder _annotated_files_ and subtract 0.01 from each time stamp. It will write CSV files  to folder _annotated_files_. 

    `python -m rev_reader.rev_reader /home/patrick/Documents/annotated_files -.01`

2. Run from the command line, option II

    You can run the _rev\_reader.py_ file directly by executing:

    `python $PATH_TO_REV_READER.PY $INPUT_FOLDER $ADDITIONAL_OFFSET`

    For example, the command below will convert all the JSON files in the folder _annotated_files_ . It will write CSV files to folder _annotated_files_. 

    e.g. `python rev_reader.py /home/patrick/Documents/annotated_files -.01`

3. Import into Python 

    You can access the functions directly from Python by doing 

    `import rev_reader`

    Refer to the Github [docs](https://github.com/patrickschu/rev-reader/tree/master/rev_reader) for info. 

### Notes

1. The naming scheme for CSVs is $LOWERCASE_NAME_OF_INPUT_JSON + $TIMESTAMP + "_faved.csv"

    For example, for input file _BE.TO.017.json_, the CSV file will be _be.to.017\_20181124-1819\_faved.csv_

2. Dependencies: pandas