Subtitle2go is a fully automatic solution for German video subtitling, currently with a focus on lecture videos. The project uses open source models and scripts for German ASR, automatic punctuation reconstruction and subtitle segmentation. But it should be straight-forward to support other languages as well and PRs are welcome!
Our German Kaldi models are based on the kaldi-tuda-de TDNN-HMM recipe. This is a Large Vocabulary Continuous Speech Recognition (LVCSR) model trained on about 1700h of German speech data.
rpunct is used for punctuation reconstruction (,.?!)
Subtitle2go uses a custom solution for segmentation, with a beam search segmentation algorithm that searches for the best "segmentation path" with user specified criteria such as average length and variance. Punctuation and distances in parsing trees are used to estimate splitting costs.
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Lots of improvements to the German ASR model, punctuation reconstruction (we now use rpunct instead of punctuator2) and a preliminary English ASR model were added. See our new paper for more details.
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Robert Geislinger is presenting the improvements at Konvens2022!
- https://lecture2go.uni-hamburg.de/open-source is using subtitle2go in its production system!
- We presented Subtitle2Go at ESSV2021. You can check out our paper here and here (mirror).
- Added punctuation model (with punctuator2)
- Added a better subtitle segmentation algorithm
- Python 3.9 (recommended), or Python 3.8/3.7
- Ubuntu 20.04 (other versions may work as well)
- X86_64 processor
This installation guide installs all dependencies, scripts and downloads pre-trained models (Kaldi+punctuator2).
# Ubuntu: make sure you have Python 3.9, its dev package and ffmpeg installed:
sudo apt-get update
sudo apt-get install python3.9 python3.9-dev ffmpeg
# Ubuntu: if python3.9 is not available, you can activate the deadsnakes Python PPA, in order to install python 3.9 through this PPA
sudo add-apt-repository ppa:deadsnakes/ppa
sudo apt-get update
sudo apt-get install python3.9 python3.9-dev
# Now clone the subtitle2go package somwhere:
mkdir ~/projects/
cd ~/projects/
git clone https://github.com/uhh-lt/subtitle2go
cd subtitle2go/
# create virtual env and install python dependencies:
virtualenv -p /usr/bin/python3.9 subtitle2go_env
source subtitle2go_env/bin/activate
pip3 install -r requirements.txt
python3 -m spacy download de_core_news_lg
python3 -m spacy download en_core_web_lg
# Install Kaldi and Intel MKL (see note below if you have a different CPU than Intel)
./install_mkl.sh
./install_kaldi_intel.sh ~/projects/subtitle2go/subtitle2go_env/bin/python3
# OR if you have a non-Intel CPU:
./install_kaldi.sh ~/projects/subtitle2go/subtitle2go_env/bin/python3
# Download and extract models
./download_models.sh
subtitle2go.py can optionally send status updates to a redis instance. You can either connect to the redis server channel "subtitle2go" directly and receive update events. Alternatively you can run event_server.py to get a HTTP API to poll the status of all past and current subtitle2go.py runs.
The following packages only need to be installed if you want to use this feature. Subtitle2go.py will automatically figure out if the redis package is available and enables/disables the accordingly.
sudo apt-get redis-server
and on Mac Os X:
brew install redis
Put a mediafile (eg mediafile.mp4) in the directory and then run:
source subtitle2go_env/bin/activate
. path.sh
python3 subtitle2go.py mediafile.mp4
The subtitle is then generated as mediafile.vtt
Optional: If you want to send status updates of the processing to redis/event_server then you need to append --with-redis-updates to subtitle2go.py:
python3 subtitle2go.py --with-redis-updates mediafile.mp4
If you start the wsgi server with ./start_eventserver_wsgi.sh then you can see status updates of every subtitle2go.py run at http://127.0.0.1:7500/status
You can also clear successful and failed runs with http://127.0.0.1:7500/clear
Redis server needs to be running.
The following arguments are available:
usage: subtitle2go.py [-h] [-s {vtt,srt}] [-l LANGUAGE] [-m MODEL_YAML]
[--rnn-rescore] [--acoustic scale ACOUSTIC_SCALE]
[--asr-beam-size ASR_BEAM_SIZE]
[--asr-max-active ASR_MAX_ACTIVE]
[--segment-beam-size SEGMENT_BEAM_SIZE]
[--ideal-token-len IDEAL_TOKEN_LEN]
[--len-reward-factor LEN_REWARD_FACTOR]
[--sentence-end-reward_factor SENTENCE_END_REWARD_FACTOR]
[--comma-end-reward-factor COMMA_END_REWARD_FACTOR]
[--with-redis-updates]
filename
positional arguments:
filename The path of the mediafile
optional arguments:
-h, --help show this help message and exit
-s {vtt,srt}, --subtitle {vtt,srt}
The output subtitleformat (vtt or srt). Default=vtt
--asr-beam-size ASR_BEAM_SIZE
ASR decoder option: controls the beam size in the beam
search. This is a speed / accuracy tradeoff.
--asr-max-active ASR_MAX_ACTIVE
ASR decoder option: controls the maximum number of
states that can be active at one time.
--segment-beam-size SEGMENT_BEAM_SIZE
What beam size to use for the segmentation search
--ideal-token-len IDEAL_TOKEN_LEN
The ideal length of tokens per segment
--len-reward-factor LEN_REWARD_FACTOR
How important it is to be close to ideal_token_len,
higher factor = splits are closer to ideal_token_len
--sentence-end-reward_factor SENTENCE_END_REWARD_FACTOR
The weight of the sentence end score in the search.
Higher values make it more likely to always split at
sentence end.
--comma-end-reward-factor COMMA_END_REWARD_FACTOR
The weight of the comma end score in the search.
Higher values make it more likely to always split at
commas.
--with-redis-updates Update a redis instance about the current progress.
(This option only shows up if the redis package is available)
Error message ImportError: libkaldi-base.so: cannot open shared object file: No such file or directory
You have to load the path.sh into your shell. You need to run:
. path.sh
Every time you start a new shell session in order to use subtitle2go
The path of kaldi is missing or incorrect Use
export KALDI_ROOT=PATH_TO_KALDI
export PATH=$KALDI_ROOT/src/lmbin/:$KALDI_ROOT/../kaldi_lm/:$PWD/utils/:$KALDI_ROOT/src/bin:$KALDI_ROOT/tools/openfst/bin:$KALDI_ROOT/src/fstbin/:$KALDI_ROOT/src/gmmbin/:$KALDI_ROOT/src/featbin/:$KALDI_ROOT/src/lm/:$KALDI_ROOT/src/sgmmbin/:$KALDI_ROOT/src/sgmm2bin/:$KALDI_ROOT/src/fgmmbin/:$KALDI_ROOT/src/latbin/:$KALDI_ROOT/src/nnetbin:$KALDI_ROOT/src/nnet2bin/:$KALDI_ROOT/src/online2bin/:$KALDI_ROOT/src/ivectorbin/:$KALDI_ROOT/src/kwsbin:$KALDI_ROOT/src/nnet3bin:$KALDI_ROOT/src/chainbin:$KALDI_ROOT/tools/sph2pipe_v2.5/:$KALDI_ROOT/src/rnnlmbin:$PWD:$PATH
and replace PATH_TO_KALDI with the path to your kaldi binaries (eg. pykaldi/tools/kaldi)
If you use Subtitle2Go in your academic work, please cite this paper:
@InProceedings{milde-etal-21-subtitle2go,
author={Benjamin Milde and Robert Geislinger and Irina Lindt and Timo Baumann},
title={Open Source Automatic Lecture Subtitling},
booktitle={Proceedings of ESSV 2021},
year={2021},
pages={128--134},
address={Virtual Berlin, Germany}
}
optionally also this paper for Subtitle2Go and the newest German ASR model:
@InProceedings{geislinger-etal-2022-improved,
title={Improved Open Source Automatic Subtitling for Lecture Videos},
author={Robert Geislinger and Benjamin Milde and Chris Biemann},
booktitle={Proceedings of the 18th Conference on Natural Language Processing (KONVENS 2022)},
year={2022},
address={Potsdam, Germany},
pages={98--103}
}
and for the previous German ASR model:
@InProceedings{milde-koehn-18-german-asr,
author={Benjamin Milde and Arne K{\"o}hn},
title={Open Source Automatic Speech Recognition for German},
booktitle={Proceedings of ITG 2018},
year={2018},
pages={251--255},
address={Oldenburg, Germany}
}
Thanks!