aeneas is a Python library and a set of tools to automagically synchronize audio and text
Project description
aeneas is a Python library and a set of tools to automagically synchronize audio and text.
Version: 1.3.2
Date: 2015-11-11
Developed by: ReadBeyond
Lead Developer: Alberto Pettarin
License: the GNU Affero General Public License Version 3 (AGPL v3)
Contact: aeneas@readbeyond.it
Goal
aeneas automatically generates a synchronization map between a list of text fragments and an audio file containing the narration of the text. In computer science this task is known as (automatically computing a) forced alignment.
For example, given this text file and this audio file, aeneas determines, for each fragment, the corresponding time interval in the audio file:
1 => [00:00:00.000, 00:00:02.680] From fairest creatures we desire increase, => [00:00:02.680, 00:00:05.480] That thereby beauty's rose might never die, => [00:00:05.480, 00:00:08.640] But as the riper should by time decease, => [00:00:08.640, 00:00:11.960] His tender heir might bear his memory: => [00:00:11.960, 00:00:15.280] But thou contracted to thine own bright eyes, => [00:00:15.280, 00:00:18.520] Feed'st thy light's flame with self-substantial fuel, => [00:00:18.520, 00:00:22.760] Making a famine where abundance lies, => [00:00:22.760, 00:00:25.720] Thy self thy foe, to thy sweet self too cruel: => [00:00:25.720, 00:00:31.240] Thou that art now the world's fresh ornament, => [00:00:31.240, 00:00:34.280] And only herald to the gaudy spring, => [00:00:34.280, 00:00:36.960] Within thine own bud buriest thy content, => [00:00:36.960, 00:00:40.640] And tender churl mak'st waste in niggarding: => [00:00:40.640, 00:00:43.600] Pity the world, or else this glutton be, => [00:00:43.600, 00:00:48.000] To eat the world's due, by the grave and thee. => [00:00:48.000, 00:00:53.280]
This synchronization map can be output to file in several formats: SMIL for EPUB 3, SRT/TTML/VTT for closed captioning, JSON/RBSE for Web usage, or raw CSV/SSV/TSV/TXT/XML for further processing.
System Requirements, Supported Platforms and Installation
System Requirements
a reasonably recent machine (recommended 4 GB RAM, 2 GHz 64bit CPU)
ffmpeg and ffprobe executables available in your $PATH
espeak executable available in your $PATH
Python 2.7.x
Python modules BeautifulSoup, lxml, and numpy
(Optional, but strongly recommended) Python C headers to compile the Python C extensions
(Optional, required only for downloading audio from YouTube) Python module pafy
Depending on the format(s) of audio files you work with, you might need to install additional audio codecs for ffmpeg. Similarly, you might need to install additional voices for espeak, depending on the language(s) you work on. (Installing all the codecs and all the voices available might be a good idea.)
If installing the above dependencies proves difficult on your OS, you are strongly encouraged to use aeneas-vagrant, which provides aeneas inside a virtualized Debian image running under VirtualBox and Vagrant.
Supported Platforms
aeneas has been developed and tested on Debian 64bit, which is the only supported OS at the moment. (Do you need official support for another OS? Consider sponsoring this project!)
However, aeneas has been confirmed to work on other Linux distributions (Ubuntu, Slackware), on Mac OS X 10.9 and 10.10, and on Windows Vista/7/8.1/10.
Whatever your OS is, make sure ffmpeg, ffprobe (which is part of ffmpeg distribution), and espeak are properly installed and callable by the subprocess Python module. A way to ensure the latter consists in adding these three executables to your PATH environment variable.
If installing aeneas natively on your OS proves difficult, you are strongly encouraged to use aeneas-vagrant, which provides aeneas inside a virtualized Debian image running under VirtualBox and Vagrant.
Installation
Using pip (OS Independent)
Make sure you have ffmpeg, ffprobe (usually provided by the ffmpeg package), and espeak installed and available on your command line. You also need Python 2.x and its “developer” package containing the C headers (python-dev or similar).
Install aeneas system-wise with pip:
$ sudo pip install numpy $ sudo pip install aeneas (Optional: $ sudo pip install pafy)Note: you must install numpy before aeneas, otherwise the setup process will fail.
Note: you can install aeneas via pip in a virtual environment (e.g. created by virtualenv).
Linux
If you are a user of a deb-based Linux distribution (e.g., Debian or Ubuntu), you can install all the dependencies by downloading and running the provided install_dependencies.sh script
$ wget https://raw.githubusercontent.com/readbeyond/aeneas/master/install_dependencies.sh $ sudo bash install_dependencies.shIf you have another Linux distribution, just make sure you have ffmpeg, ffprobe (usually provided by the ffmpeg package), and espeak installed and available on your command line. You also need Python 2.x and its “developer” package containing the C headers (python-dev or similar).
Clone the aeneas repo, install Python dependencies, and compile C extensions:
$ git clone https://github.com/ReadBeyond/aeneas.git $ cd aeneas $ sudo pip install -r requirements.txt (Optional: $ sudo pip install pafy) $ python setup.py build_ext --inplace $ python aeneas_check_setup.pyIf the last command prints a success message, you have all the required dependencies installed and you can confidently run aeneas in production.
In alternative to the previous point, you can install aeneas system-wise with pip:
$ sudo pip install numpy $ sudo pip install aeneas (Optional: $ sudo pip install pafy)
Windows
Please follow the installation instructions contained in the “Using aeneas for Audio-Text Synchronization” PDF, based on these directions, written by Richard Margetts.
Mac OS X
Feel free to jump to step 9 if you already have python, ffmpeg/ffprobe and espeak installed.
Install the Xcode command line tools:
$ xcode-select --installFollow the instructions appearing on screen.
Install the brew packet manager:
$ ruby -e "$(curl -fsSL https://raw.githubusercontent.com/Homebrew/install/master/install)"Update brew:
$ brew updateInstall espeak and ffmpeg (which also provides ffprobe) via brew:
$ brew install espeak $ brew install ffmpegInstall Python:
$ brew install pythonReplace the default (Apple’s) Python distribution with the Python installed by brew, by adding the following line at the end of your ~/.bash_profile:
export PATH=/usr/local/bin:/usr/local/sbin:~/bin:$PATHOpen a new terminal window. (This step is IMPORTANT! If you do not, you will still use Apple’s Python, and everything in the Universe will go wrong!)
Check that you are running the new python:
$ which python /usr/local/bin/python $ python --version Python 2.7.10 (or later)Clone the aeneas repo, install Python dependencies, and compile C extensions:
$ git clone https://github.com/ReadBeyond/aeneas.git $ cd aeneas $ sudo pip install -r requirements.txt (Optional: $ sudo pip install pafy) $ python setup.py build_ext --inplace $ python aeneas_check_setup.pyIf the last command prints a success message, you have all the required dependencies installed and you can confidently run aeneas in production.
In alternative to the previous point, you can install aeneas system-wise with pip:
$ sudo pip install numpy $ sudo pip install aeneas (Optional: $ sudo pip install pafy)
Usage
Install aeneas as described above. (Only the first time!)
Open a command prompt/shell/terminal and go to the root directory of the aeneas repository, that is, the one containing the README.md and VERSION files. (This step is not needed if you installed aeneas with pip, since you will have the aeneas module available system-wise.)
To compute a synchronization map map.json for a pair (audio.mp3, text.txt in plain text format), you can run:
$ python -m aeneas.tools.execute_task audio.mp3 text.txt "task_language=en|os_task_file_format=json|is_text_type=plain" map.jsonThe third parameter (the configuration string) can specify several parameters/options. See the documentation or use the -h switch for details.
To compute a synchronization map map.smil for a pair (audio.mp3, page.xhtml containing fragments marked by id attributes like f001), you can run:
$ python -m aeneas.tools.execute_task audio.mp3 page.xhtml "task_language=en|os_task_file_format=smil|os_task_file_smil_audio_ref=audio.mp3|os_task_file_smil_page_ref=page.xhtml|is_text_type=unparsed|is_text_unparsed_id_regex=f[0-9]+|is_text_unparsed_id_sort=numeric" map.smilIf you have several tasks to run, you can create a job container and a configuration file, and run them all at once:
$ python -m aeneas.tools.execute_job job.zip /tmp/File job.zip should contain a config.txt or config.xml configuration file, providing aeneas with all the information needed to parse the input assets and format the output sync map files. See the documentation or use the -h switch for details.
You might want to run execute_task or execute_job with -h to get an usage message and some examples:
$ python -m aeneas.tools.execute_task -h
$ python -m aeneas.tools.execute_job -hSee the documentation for an introduction to the concepts of task and job, and for the list of all the available options.
Documentation
Online: http://www.readbeyond.it/aeneas/docs/
Generated from the source files (it requires sphinx):
$ git clone https://github.com/readbeyond/aeneas.git
$ cd aeneas/docs
$ make htmlTutorial: A Practical Introduction To The aeneas Package
Mailing list: https://groups.google.com/d/forum/aeneas-forced-alignment
Changelog: http://www.readbeyond.it/aeneas/docs/changelog.html
Supported Features
Input text files in plain, parsed, subtitles, or unparsed format
Text extraction from XML (e.g., XHTML) files using id and class attributes
Arbitrary text fragment granularity (single word, subphrase, phrase, paragraph, etc.)
Input audio file formats: all those supported by ffmpeg
Possibility of downloading the audio file from a YouTube video
Batch processing
Output sync map formats: CSV, JSON, RBSE, SMIL, SSV, TSV, TTML, TXT, VTT, XML
Tested languages: BG, CA, CY, DA, DE, EL, EN, EO, ES, ET, FA, FI, FR, GA, GRC, HR, HU, IS, IT, LA, LT, LV, NL, NO, RO, RU, PL, PT, SK, SR, SV, SW, TR, UK
Robust against misspelled/mispronounced words, local rearrangements of words, background noise/sporadic spikes
Code suitable for a Web app deployment (e.g., on-demand AWS instances)
Adjustable splitting times, including a max character/second constraint for CC applications
Automated detection of audio head/tail
MFCC and DTW computed as Python C extensions to reduce the processing time
On Linux, espeak called via a Python C extension for faster audio synthesis
Output an HTML file (from finetuneas project) for fine tuning the sync map manually
Limitations and Missing Features
Audio should match the text: large portions of spurious text or audio might produce a wrong sync map
Audio is assumed to be spoken: not suitable/YMMV for song captioning
No protection against memory trashing if you feed extremely long audio files
On Mac OS X and Windows, audio synthesis might be slow if you have thousands of text fragments
TODO List
Improving robustness against music in background
Isolate non-speech intervals (music, prolonged silence)
Automated text fragmentation based on audio analysis
Auto-tuning DTW parameters
Reporting the alignment score
Improving (removing?) dependency from espeak, ffmpeg, ffprobe executables
Multilevel sync map granularity (e.g., multilevel SMIL output)
Better documentation
Testing other approaches, like HMM
Publishing the package on Debian repo
Would you like to see one of the above points done? Consider sponsoring this project!
How Does This Thing Work?
One Word Explanation
Math.
One Sentence Explanation (Layman Edition)
A good deal of math and computer science, a handful of software engineering and some optimization tricks.
One Sentence Explanation (Pro Edition)
Using the Sakoe-Chiba Band Dynamic Time Warping (DTW) algorithm to align the Mel-frequency cepstral coefficients (MFCCs) representation of the given (real) audio wave and the audio wave obtained by synthesizing the text fragments with a TTS engine, eventually mapping the computed alignment back onto the (real) time domain.
Extended Explanation
To be written. Eventually. Some day.
License
aeneas is released under the terms of the GNU Affero General Public License Version 3. See the LICENSE file for details.
The pure Python code for computing the MFCCs aeneas/mfcc.py is a verbatim copy from the CMU Sphinx3 project. See licenses/sphinx3.txt for details.
The pure Python code for reading and writing WAVE files aeneas/wavfile.py is a verbatim copy from the scipy project, included here to avoid installing the whole scipy package. See licenses/scipy.txt for details.
The C header speak_lib.h for espeak is a verbatim copy from the espeak project. See licenses/eSpeak.txt for details.
The HTML file aeneas/res/finetuneas.html is a verbatim copy from the finetuneas project, courtesy of Firat Özdemir. See licenses/finetuneas.txt for details.
Audio files contained in the unit tests aeneas/tests/res/ directory are adapted from recordings produced by the LibriVox Project and they are in the public domain. See licenses/LibriVox.txt for details.
Text files contained in the unit tests aeneas/tests/res/ directory are adapted from files produced by the Project Gutenberg and they are in the public domain. See licenses/ProjectGutenberg.txt for details.
No copy rights were harmed in the making of this project.
Supporting and Contributing
Sponsors
July 2015: Michele Gianella generously supported the development of the boundary adjustment code (v1.0.4)
August 2015: Michele Gianella partially sponsored the port of the MFCC/DTW code to C (v1.1.0)
September 2015: friends in West Africa partially sponsored the development of the head/tail detection code (v1.2.0)
October 2015: an anonymous donation sponsored the development of the “YouTube downloader” option (v1.3.0)
Supporting
Would you like supporting the development of aeneas?
I accept sponsorships to
fix bugs,
add new features,
improve the quality and the performance of the code,
port the code to other languages/platforms,
support of third party installations, and
improve the documentation.
In case, feel free to get in touch.
Contributing
If you think you found a bug, please use the GitHub issue tracker to file a bug report.
If you are able to contribute code directly, that is awesome! I will be glad to merge it!
Just a few rules, to make life easier for both you and me:
Please do not work on the master branch. Instead, create a new branch on your GitHub repo by cheking out the devel branch. Open a pull request from your branch on your repo to the devel branch on this GitHub repo.
Please make your code consistent with the existing code base style (see the Google Python Style Guide ), and test your contributed code against the unit tests before opening the pull request.
Ideally, add some unit tests for the code you are submitting, either adding them to the existing unit tests or creating a new file in aeneas/tests/.
Please note that, by opening a pull request, you automatically agree to apply the AGPL v3 license to the code you contribute.
Development History
Early 2012: Nicola Montecchio and Alberto Pettarin co-developed an initial experimental package to align audio and text, intended to be run locally to compute Media Overlay (SMIL) files for EPUB 3 Audio-eBooks
Late 2012-June 2013: Alberto Pettarin continued engineering and tuning the alignment tool, making it faster and memory efficient, writing the I/O functions for batch processing of multiple audio/text pairs, and started producing the first EPUB 3 Audio-eBooks with Media Overlays (SMIL files) computed automatically by this package
July 2013: incorporation of ReadBeyond Srl
July 2013-March 2014: development of ReadBeyond Sync, a SaaS version of this package, exposing the alignment function via APIs and a Web application
March 2014: launch of ReadBeyond Sync beta
April 2015: ReadBeyond Sync beta ended
May 2015: release of this package on GitHub
August 2015: release of v1.1.0, including Python C extensions to speed the computation of audio/text alignment up
September 2015: release of v1.2.0, including code to automatically detect the audio head/tail
October 2015: release of v1.3.0, including calling espeak via its C API (on Linux) for faster audio synthesis, and the possibility of downloading audio from YouTube
November 2015: release of v1.3.2, for the first time available also on PyPI
Acknowledgments
Many thanks to Nicola Montecchio, who suggested using MFCCs and DTW, and co-developed the first experimental code for aligning audio and text.
Paolo Bertasi, who developed the APIs and Web application for ReadBeyond Sync, helped shaping the structure of this package for its asynchronous usage.
Chris Hubbard prepared the files for packaging aeneas as a Debian/Ubuntu .deb.
All the mighty GitHub contributors, and the members of the Google Group.
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