ttconv 1.0.1rc2

Library for conversion of common timed text formats

ttconv (Timed Text Conversion)

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Introduction

ttconv is a library and command line application written in pure Python for converting between timed text formats used in the presentations of captions, subtitles, karaoke, etc.

ttconv works by mapping the input document, whatever its format, into an internal canonical model, which is then mapped to the format of the output document is derived. The canonical model closely follows the TTML 2 data model, as constrained by the IMSC 1.1 Text Profile specification.

Format support

ttconv currently supports the following input and output formats. Additional input and output formats are planned, and suggestions/contributions are welcome.

Input Formats

• CEA 608/.scc
• IMSC 1.1 Text Profile/.ttml

Output Formats

• SubRip/.srt
• IMSC 1.1 Text Profile/.ttml

Quick start

pip install ttconv

tt.py convert -i <input .scc file> -o <output .ttml file>

Documentation

Command line

tt.py convert [-h] -i INPUT -o OUTPUT [--itype ITYPE] [--otype OTYPE] [--config CONFIG] [--config_file CONFIG_FILE]

• --itype: TTML or SCC (extrapolated from the filename, if omitted)

• --otype: TTML or SRT (extrapolated from the filename, if omitted)

• --config and --config_file: JSON dictionaries with the following members:

  • "general"."progress_bar": "true" | "false": whether a progress bar is displayed
  • "general"."log_level": "INFO" | "WARN" | "ERROR": logging level
  • "imsc_writer"."time_format": "frames" | "clock_time": output TTML expressions in seconds or in frames
  • "imsc_writer"."fps": "<num>/<denom>": specifies the frame rate num/denom when output TTML expressions in frames

Example:

tt.py convert -i <.scc file> -o <.ttml file> --itype SCC --otype TTML --config '{"general": {"progress_bar":false, "log_level":"WARN"}}'

Library

The overall architecture of the library is as follows:

• Reader modules validate and convert input files into instances of the canonical model (see ttconv.imsc.reader.to_model() for example);
• Filter modules transform instances of the canonical data model, e.g. all text styling and positioning might be removed from an instance of the canonical model to match the limited capabilities of downstream devices; and
• Writer modules convert instances of the canonical data model into output files.

Processing shared across multiple reader and writer modules is factored out in common modules whenever possible. For example, several output formats require an instance of the canonical data model to be transformed into a sequence of discrete temporal snapshots – a process called ISD generation.

The library uses the Python logging module to report non-fatal events.

Unit tests illustrate the use of the library, e.g. ReaderWriterTest.test_imsc_1_test_suite at src/test/python/test_imsc_writer.py.

Detailed documentation including reference documents is under doc.

Dependencies

Runtime

• python >= 3.7

Development

The project uses pipenv to manage dependencies.

• pylint
• coverage

Development

Setup

Local

• run pipenv install --dev
• set the PYTHONPATH environment variable to src/main/python, e.g. export PYTHONPATH=src/main/python
• pipenv run can then be used

Docker

docker build --rm -f Dockerfile -t ttconv:latest .
docker run -it --rm ttconv:latest bash

Example

From the root directory of the project:

mkdir build
export PYTHONPATH=src/main/python
python src/main/python/ttconv/tt.py convert -i src/test/resources/scc/mix-rows-roll-up.scc -o build/mix-rows-roll-up.ttml

Code coverage

Unit test code coverage is provided by the script at scripts/coverage.sh

Continuous integration

Overview

Automated testing is provided by the script at scripts/ci.sh

Local

Run ./scripts/ci.sh

GitHub actions

See .github/workflows/main.yml

Docker

Run docker run -it --rm ttconv:latest /bin/sh scripts/ci.sh