$ docker pull bbcrd/bbc-speech-segmenter

# Test

$ docker run -w /wrk -v `pwd`:/wrk bbcrd/bbc-speech-segmenter ./test.sh

# Segmentation help

$ docker run bbcrd/bbc-speech-segmenter ./run-segmentation.sh --help
usage: run-segmentation.sh [options] input.wav input.stm output-dir

options:
  --nj NUM                 Maximum number of CPU cores to use
  --stage STAGE            Start from this stage
  --cluster-threshold THR  Cluster stopping criteria. Default: -0.3
  --vad-threshold THR      Xvector classifier threshold. Lower the number the
                           more speech segments shall be returned at the
                           expense of accuracy. Default: 0.2
  --vad-method             Filter segments on an individual or segment basis.
                           Default: individual
  --no-vad                 Skip xvector vad stages. Default: false
  --help                   Print this message

# Run segmentation (VAD + diarisation), results are in output-dir/diarize.stm

$ docker run -v `pwd`:/data bbcrd/bbc-speech-segmenter \
  ./run-segmentation.sh /data/audio.wav /data/audio.stm /data/output-dir

$ cat output-dir/diarize.stm
audio 0 audio_S00004 3.750 10.125 <speech>
audio 0 audio_S00003 10.125 13.687 <speech>
audio 0 audio_S00004 13.688 16.313 <speech>
...

# Train x-vector classifier

$ docker run -w /wrk/recipe -v `pwd`:/wrk bbcrd/bbc-speech-segmenter \
  local/xvector_utils.py data/bbc-vad-train/reference.stm            \
  data/bbc-vad-train/xvectors.ark new_model.pkl

# Evaluate x-vector classifier

$ docker run -w /wrk/recipe -v `pwd`:/wrk bbcrd/bbc-speech-segmenter \
  local/xvector_utils.py evaluate data/bbc-vad-eval/reference.stm    \
  data/bbc-vad-eval/xvectors.ark model/xvector-classifier.pkl

# Convert audio file to 16Khz mono wav

$ ffmpeg audio.mp3 -vn -ac 1 -ar 16000 audio.wav

# Create STM file for input

$ DURATION=$(ffprobe -i audio.wav -show_entries format=duration -v quiet -of csv="p=0")
$ DURATION=$(printf "%0.2f\n" $DURATION)

$ FILENAME=$(basename audio.wav)

$ echo "${FILENAME%.*} 0 ${FILENAME%.*} 0.00 $DURATION <label> _" > audio.stm

$ cat audio.stm
audio 0 audio 0.00 60.00 <label> _

# Bulid Docker image

$ docker build -t bbc-speech-segmenter .

# Spin up a Docker container in an interactive mode

$ docker run -it -v `pwd`:/wrk bbc-speech-segmenter /bin/bash

# Inside a Docker container

$ cd /wrk/

# Run test

$ ./test.sh
All checks passed

$ cd /wrk/recipe

$ python3 local/xvector_utils.py train \
  data/bbc-vad-train/reference.stm     \
  data/bbc-vad-train/xvectors.ark      \
  new_model.pkl

$ cd /wrk/recipe

$ python3 local/xvector_utils.py evaluate \
  data/bbc-vad-eval/reference.stm        \
  data/bbc-vad-eval/xvectors.ark         \
  model/xvector-classifier.pkl