This Kaldi models are originally provided by Pop Up Archive, and the dockerized version was published at https://github.com/hipstas/kaldi-pop-up-archive. The goal of this fork is to update underlying kaldi system and streamline iamge buliding process.
- Kaldi: http://kaldi-asr.org/
Pop Up Archive Kaldi release and install guide: https://github.com/popuparchive/american-archive-kaldiPop Up Archive no longer exists- PUA model files: https://sourceforge.net/projects/popuparchive-kaldi/files/
- American Archive of Public Broadcasting (source of training set): http://americanarchive.org/
- Docker
- Pull image from Docker Hub (12GB compressed, 24GB uncompressed).
docker pull hipstas/kaldi-pop-up-archive:v1
- Run the Docker container using one of the following commands. If you're using Linux, this will create a shared volume at
/audio_in. On macOS or Windows, a folder calledaudio_inwill appear on your desktop.
Linux:
docker run -it --name kaldi_pua --volume /audio_in/:/audio_in/ hipstas/kaldi-pop-up-archive:v1
macOS:
docker run -it --name kaldi_pua --volume ~/Desktop/audio_in/:/audio_in/ hipstas/kaldi-pop-up-archive:v1
Windows:
docker run -it --name kaldi_pua --volume C:\Users\***username_here***\Desktop\audio_in\:/audio_in/ hipstas/kaldi-pop-up-archive:v1
- Open your Docker preferences and make sure Docker has access to at least 6GB of RAM. The more, the better.
-
Add media files to your
/audio_inshared volume (WAV, MP3, or MP4 video). -
In the Docker terminal session, enter the following commands to download and run the
setup.shscript, which will start your job. When the batch is finished, your plain text and JSON transcript files will be written to/audio_in/transcripts/.
wget https://raw.githubusercontent.com/hipstas/kaldi-pop-up-archive/master/setup.sh
sh ./setup.sh
To keep your job from ending when you close the terminal window (or your connection to the server is interrupted), use nohup sh ./setup.sh instead, then close the window. If you'd like to monitor the job's status, open a new terminal session (docker exec -it kaldi_pua bash) and use the following command to display the end of your nohup log file every 3 seconds:
while :; do tail -n 30 nohup.out; sleep 3; done
-
Try running a test with one or two short media files before beginning a big job. If Kaldi doesn't have enough memory, it will crash without explanation. If this happens, try reducing the number of simultaneous jobs as described above.
-
Any commas, spaces, pipes, etc. in audio filenames will break the script and halt progress without producing any descriptive errors. To be safe, you may want to rename each file with a unique ID before starting. I'll fix this when I get a chance.
-
With this configuration, speech-to-text processing may take 5 times the duration of your audio input, or perhaps even longer. If you have memory to spare, you can speed things up by increasing the number of simultaneous jobs. Use the
free -mcommand while Kaldi is running to see how you're doing.
- In
/kaldi/egs/american-archive-kaldi/sample_experiment/run.sh, adjust the following option to set the number of simultaneous jobs:
nj=4
- In
/kaldi/egs/wsj/s5/utils/run.pl:
$max_jobs_run = 10;
In /kaldi/egs/wsj/s5/steps/decode_fmllr.sh:
nj=4 in
max_active=2000
- In
/kaldi/egs/wsj/s5/steps/decode.sh:
nj=2
Alternately, you can prepare your files and start the batch run manually.
- Add some media files to the
/audio_in/directory.
cd /audio_in/
- Make a 16kHz WAV copy of each file with
ffmpeg.
for file in *.{wav,mp3,mp4,WAV,MP3,MP4}; do
base=$(basename """$file""" .mp3);
ffmpeg -i """$file""" -ac 1 -ar 16000 """$base"""_16kHz.wav;
done
- Now move the 16kHz WAV files to a separate directory.
mkdir /audio_in_16khz/
mv *_16kHz.wav /audio_in_16khz/
- Start the batch transcription run like so.
python /kaldi/egs/american-archive-kaldi/run_kaldi.py /kaldi/egs/american-archive-kaldi/sample_experiment/ /audio_in_16khz/
- When Kaldi finishes processing an audio file, plain text and JSON transcripts will be written here:
/kaldi/egs/american-archive-kaldi/sample_experiment/output/