lachesis

lachesis automates the segmentation of a transcript into closed captions

• Version: 0.0.3
• Date: 2017-01-26
• Developed by: Alberto Pettarin
• License: the GNU Affero General Public License Version 3 (AGPL v3)
• Contact: info@readbeyond.it

DO NOT USE THIS PACKAGE IN PRODUCTION UNTIL IT REACHES v1.0.0 !!!

Goal

lachesis automates the segmentation
of a transcript into closed captions (CCs).

The general idea is that
writing a transcription (raw text) is
easier and faster than writing CCs,
especially if you need to respect constraints
like a certain minimum/maximum number of characters per line,
a maximum number of lines per CC, etc.

You can transcribe your video into raw text
and lachesis will take on the job of
segmenting the text into CCs for you.
Once you have the CCs,
you can use a forced aligner
like aeneas
to align them with the audio of your video,
obtaining a subtitle file (SRT, TTML, VTT, etc.).

With lachesis and a forced aligner,
the manual labor for producing CCs for a video
is reduced to a. transcribing the video in raw text form,
and b. checking the final CCs and audio alignment.
Instead of transcribing from scratch,
you can even start by checking/editing a rough transcription
made by an automated speech recognition engine,
like the "automatic CCs" from YouTube,
speeding the process up further.

The "magic" behind lachesis consists
in combining machine learning techniques like
conditional random fields
(CRF)
and classical NLP tools like
POS tagging
and
sentence segmentation
to split the text into CC lines.
The machine learning models are learned
from existing, manually-edited, high-quality CCs,
like those of
TED/TEDx
talks on YouTube.
The NLP tools come from the well-established,
free NLP libraries for Python listed below.

In summary, lachesis contains the following major functions:

• download closed captions from YouTube;
• parse closed caption TTML files (downloaded from YouTube);
• add POS tags to a given text or closed caption file;
• segment a given text into sentences;
• segment a given text into closed captions (several algorithms are available);
• train and use machine learning models to segment raw text into CC lines.

Installation

DO NOT USE THIS PACKAGE IN PRODUCTION UNTIL IT REACHES v1.0.0 !!!

pip install lachesis

Installing dependencies

You might need additional packages,
depending on how you plan to use lachesis:

• lxml >= 3.6.0 for reading or downloading TTML files;
• youtube-dl >= 2017.1.16 for downloading TTML files;
• python-crfsuite >= 0.9.1 for training and using CRF-based splitters.

By design choice, none of the above dependencies
is installed by pip install lachesis.
If you want to install them all, you can use:

pip install lachesis[full]

Alternatively, manually install only the dependencies you need.
(You can do it before or after installing lachesis,
the order does not matter.)

Installing NLP Libraries

In addition to the dependencies listed above,
to perform POS tagging and sentence segmentation
lachesis can use one or more of the following libraries:

• Pattern (install with pip install pattern, see here)
• NLTK (install with pip install nltk, see here)
• spaCy (install with pip install spacy, see here)
• UDPipe (install with pip install ufal.udpipe, see here)

If you want to install them all, you can use:

pip install lachesis[nlp]

or [fullnlp] if you also want [full] as above.

Each NLP library also needs language models
which you need to download/install separately.
Consult the documentation of your NLP library for details.

lachesis expects the following directories in your home directory
(you can symlink them, if you installed each NLP library in a different place):

• ~/lachesis_data/nltk_data for NLTK (see here);
• ~/lachesis_data/spacy_data for spaCy (see here);
• ~/lachesis_data/udpipe_data for UDPipe (see here).

The NLP library Pattern does not need a separate download
of its language models, as they are bundled in the file
you download when installing through pip install pattern.

The following table summarizes the languages supported by each library
in their standard language models pack.
(Additional languages might be supported by third party projects/downloads
or added over time.)

Usage

Download closed captions from YouTube

from lachesis.downloaders import Downloader
from lachesis.language import Language

# set URL of the video and language of the CCs
url = u"http://www.youtube.com/watch?v=NSL_xx2Qnyc"
language = Language.ENGLISH

# download automatic CC, do not save to file
options = { "auto": True }
doc = Downloader.download_closed_captions(url, language, options)
print(doc)

# download manually-edited CC, saving the raw TTML file to disk
options = { "auto": False, "output_file_path": "/tmp/ccs.ttml" }
doc = Downloader.download_closed_captions(url, language, options)
print(doc)

Parse an existing TTML file downloaded from YouTube

from lachesis.downloaders import Downloader

# parse a given TTML file downloaded from YouTube
ifp = "/tmp/ccs.ttml"
doc = Downloader.read_closed_captions(ifp, options={u"downloader": u"youtube"})
print(doc.language)

# print several representations of the CCs
print(doc.raw_string)                       # multi line string, similar to SRT but w/o ids or times
print(doc.raw_flat_clean_string)            # single line string, w/o CC line marks
print(doc.raw.string(flat=True, eol=u"|"))  # single line string, CC lines separated by '|' characters

Tokenize, split sentences, and POS tagging

from lachesis.elements import Document
from lachesis.language import Language
from lachesis.nlpwrappers import NLPEngine

# work on this Unicode string
s = u"Hello, World. This is a second sentence, with a comma too! And a third sentence."

# but you can also pass a list with pre-split sentences
# s = [u"Hello World.", u"This is a second sentence.", u"Third one, bla bla"]

# create a Text object from the Unicode string
doc = Document(raw=s, language=Language.ENGLISH)

# tokenize, split sentences, and POS tagging
# the best available NLP library will be chosen
nlp1 = NLPEngine()
nlp1.analyze(doc)

# the text has been divided into tokens, grouped in sentences
for s in doc.sentences:
    print(s)                                        # raw
    print(s.string(tagged=True))                    # tagged
    print(s.string(raw=True, eol=u"|", eos=u""))    # raw w/o CC line and sentence marks

# explicitly specify the NLP library NLTK,
# other options include: "pattern", "spacy", "udpipe"
nlp2 = NLPEngine()
nlp2.analyze(doc, wrapper=u"nltk")
...

# if you need to analyze many documents,
# preload (and keep in cache) an NLP library,
# even different ones for different languages
nlp3 = NLPEngine(preload=[
    (u"en", u"spacy"),
    (u"de", u"nltk"),
    (u"it", u"pattern"),
    (u"fr", u"udpipe")
])
nlp3.analyze(doc)
...

Split into closed captions

from lachesis.elements import Document
from lachesis.language import Language
from lachesis.nlpwrappers import NLPEngine
from lachesis.splitters import CRFSplitter
from lachesis.splitters import GreedySplitter

# create a document from a raw string
s = u"Hello, World. This is a second sentence, with a comma too! And a third sentence."
doc = Document(raw=s, language=Language.ENGLISH)

# analyze it using the NLP library Pattern
nlpe = NLPEngine()
nlpe.analyze(doc, wrapper=u"pattern")

# feed the document into the CRF splitter (max 42 chars/line, max 2 lines/cc)
spl = CRFSplitter(doc.language, 42, 2)
spl.split(doc)

# print the segmented CCs
for cc in doc.ccs:
    for line in cc.elements:
        print(line)
    print(u"")

# the default location for CRF model files is ~/lachesis_data/crf_data/
# but you can also specify a different path
spl = CRFSplitter(doc.language, 42, 2, model_file_path="/tmp/yourmodel.crfsuite")
spl.split(doc)

# if you do not have pycrfsuite installed
# or the CRF model file for the document language,
# you can use the GreedySplitter
gs = GreedySplitter(doc.language, 42, 2)
gs.split(doc)

Train a CRF model to segment raw text into CC lines

$ # /tmp/ccs/train contains several TTML files to learn from
$ # you can download them from YouTube using lachesis (see above)
$ ls /tmp/ccs/train
0001.ttml
0002.ttml
...

$ # extract features and labels from them:
$ python -m lachesis.ml.crf dump eng /tmp/ccs/train/ /tmp/ccs/train.pickle
...

$ # train the CRF model:
$ python -m lachesis.ml.crf train eng /tmp/ccs/train.pickle /tmp/ccs/model.crfsuite
...

$ # evaluate the model on the training set
$ python -m lachesis.ml.crf test eng /tmp/ccs/train.pickle /tmp/ccs/model.crfsuite
...

$ # you might want to evaluate on a test set, disjoint from the training set,
$ # that is, the test set contains CCs not seen during the training:
$ ls /tmp/css/test
1001.ttml
1002.ttml
...
$ python -m lachesis.ml.crf dump eng /tmp/ccs/test/ /tmp/ccs/test.pickle
$ python -m lachesis.ml.crf test eng /tmp/ccs/test.pickle /tmp/ccs/model.crfsuite
...
$ # now you can build a CRFSplitter
$ # with model_file_path="/tmp/ccs/model.crfsuite" as shown above

TODO: decide and document where pre-trained model files can be downloaded

License

lachesis is released under the terms of the
GNU Affero General Public License Version 3.
See the LICENSE file for details.