aeneas is a Python/C library and a set of tools to automagically synchronize audio and text (aka forced alignment).
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, 00:00:02.640]
From fairest creatures we desire increase, => [00:00:02.640, 00:00:05.880]
That thereby beauty's rose might never die, => [00:00:05.880, 00:00:09.240]
But as the riper should by time decease, => [00:00:09.240, 00:00:11.920]
His tender heir might bear his memory: => [00:00:11.920, 00:00:15.280]
But thou contracted to thine own bright eyes, => [00:00:15.280, 00:00:18.800]
Feed'st thy light's flame with self-substantial fuel, => [00:00:18.800, 00:00:22.760]
Making a famine where abundance lies, => [00:00:22.760, 00:00:25.680]
Thy self thy foe, to thy sweet self too cruel: => [00:00:25.680, 00:00:31.240]
Thou that art now the world's fresh ornament, => [00:00:31.240, 00:00:34.400]
And only herald to the gaudy spring, => [00:00:34.400, 00:00:36.920]
Within thine own bud buriest thy content, => [00:00:36.920, 00:00:40.640]
And tender churl mak'st waste in niggarding: => [00:00:40.640, 00:00:43.640]
Pity the world, or else this glutton be, => [00:00:43.640, 00:00:48.080]
To eat the world's due, by the grave and thee. => [00:00:48.080, 00:00:53.240]
This synchronization map can be output to file in several formats, depending on its application:
BeautifulSoup4
, lxml
, and numpy
aeneas has been developed and tested on Debian 64bit, with Python 2.7 and Python 3.5, which are the only supported platforms at the moment. Nevertheless, aeneas has been confirmed to work on other Linux distributions, Mac OS X, and Windows. See the PLATFORMS file for details.
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, which can be installed on any modern OS (Linux, Mac OS X, Windows).
All-in-one installers are available for Mac OS X and Windows, and a Bash script for deb-based Linux distributions (Debian, Ubuntu) is provided in this repository. It is also possible to download a VirtualBox+Vagrant virtual machine. Please see the INSTALL file for detailed, step-by-step installation procedures for different operating systems.
The generic OS-independent procedure is simple:
Make sure the following executables can be called from your shell:
espeak
, ffmpeg
, ffprobe
, pip
, and python
First install numpy
with pip
and then aeneas
(this order is important):
pip install numpy
pip install aeneas
To check whether you installed aeneas correctly, run:
python -m aeneas.diagnostics
Run without arguments to get the usage message:
python -m aeneas.tools.execute_task
python -m aeneas.tools.execute_job
You can also get a list of live examples that you can immediately run on your machine thanks to the included files:
python -m aeneas.tools.execute_task --examples
python -m aeneas.tools.execute_task --examples-all
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=eng|os_task_file_format=json|is_text_type=plain" \
map.json
(The command has been split into lines with \
for visual clarity;
in production you can have the entire command on a single line
and/or you can use shell variables.)
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=eng|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.smil
As you can see, the third argument (the configuration string) specifies the parameters controlling the I/O formats and the processing options for the task. Consult the documentation for details.
If you have several tasks to process, you can create a job container to batch process them:
python -m aeneas.tools.execute_job job.zip output_directory
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.
Consult the
documentation
for details.
The documentation contains a highly suggested tutorial which explains how to use the built-in command line tools.
parsed
, plain
, subtitles
, or unparsed
(XML) formatmplain
and munparsed
(XML) formatid
and class
attributesffmpeg
finetuneas
project)A significant number of users runs aeneas to align audio and text at word-level (i.e., each fragment is a word). Although aeneas was not designed with word-level alignment in mind and the results might be inferior to ASR-based forced aligners for languages with good ASR models, aeneas offers some options to improve the quality of the alignment at word-level:
If you use the aeneas.tools.execute_task
command line tool,
you can add --presets-word
switch to enable MFCC nonspeech masking, for example:
$ python -m aeneas.tools.execute_task --example-words --presets-word
$ python -m aeneas.tools.execute_task --example-words-multilevel --presets-word
If you use aeneas as a library, just set the appropriate
RuntimeConfiguration
parameters.
Please see the
command line tutorial
for details.
aeneas is released under the terms of the GNU Affero General Public License Version 3. See the LICENSE file for details.
Licenses for third party code and files included in aeneas can be found in the licenses directory.
No copy rights were harmed in the making of this project.
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)
April 2016: the Fruch Foundation kindly sponsored the development and documentation of v1.5.0
December 2016: the Centro Internazionale Del Libro Parlato "Adriano Sernagiotto" (Feltre, Italy) partially sponsored the development of the v1.7 series
Would you like supporting the development of aeneas?
I accept sponsorships to
Feel free to get in touch.
If you think you found a bug or you have a feature request, please use the GitHub issue tracker to submit it.
If you want to ask a question about using aeneas, your best option consists in sending an email to the mailing list.
Finally, code contributions are welcome! Please refer to the Code Contribution Guide for details about the branch policies and the code style to follow.
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
.
Daniel Bair prepared the brew
formula
for installing aeneas and its dependencies on Mac OS X.
Daniel Bair, Chris Hubbard, and Richard Margetts packaged the installers for Mac OS X and Windows.
Firat Ozdemir contributed the finetuneas
HTML/JS code for fine tuning sync maps in the browser.
Willem van der Walt contributed the code snippet to output a sync map in TextGrid format.
Chris Vaughn contributed the MacOS TTS wrapper.
All the mighty GitHub contributors, and the members of the Google Group.