mirror of
https://github.com/TaterTotterson/microWakeWord-Trainer-Nvidia-Docker.git
synced 2026-06-12 20:10:19 -06:00
The files in the `cli` directory allow you to train wake words from the command line without needing to use the Jupyter notebook or a web browser. Basically, the logic from the notebook has been placed in separate shell scripts and python files wrapped by 3 high-level scripts that do the following: * setup_python_venv: Creates a Python virtual environment with all the packages needed to train. The venv is created in the container's /data directory and is therefore stored on the host, not in the container's root docker volume. * setup_training_datasets: Downloads, extracts and converts the MIT RIR, FMA, Audioset and Negative training reference datasets. Also stored in /data. * train_wake_word: Generates the wake word samples, augments them with the audio from the training datasets, and finally runs the microwakeword training. The resulting model tflite and json files are placed in the /data/output directory. See the README.md file for much more information.
151 lines
3.5 KiB
Plaintext
151 lines
3.5 KiB
Plaintext
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if [ "$0" == "${BASH_SOURCE[0]}" ] ; then
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echo "${BASH_SOURCE[0]} is meant to be 'sourced' not run directly" >&2
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exit 1
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fi
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if [ ! -v DATA_DIR ] ; then
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[ -f .mww-data-dir ] && DATA_DIR="${PWD}" || DATA_DIR="/data"
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fi
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DEFAULT_SAMPLES=20000
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DEFAULT_BATCH_SIZE=100
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DEFAULT_TRAINING_STEPS=25000
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[ -f "${DATA_DIR}/.defaults.env" ] && source "${DATA_DIR}/.defaults.env" || :
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: "${SAMPLES:=${DEFAULT_SAMPLES}}"
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: "${BATCH_SIZE:=${DEFAULT_BATCH_SIZE}}"
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: "${TRAINING_STEPS:=${DEFAULT_TRAINING_STEPS}}"
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: "${CLEANUP_WORK_DIR:=false}"
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: "${CLEANUP_ARCHIVES:=false}"
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: "${CLEANUP_INTERMEDIATE_FILES:=false}"
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: "${QUIET:=false}"
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: "${VERBOSE:=false}"
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HELP=false
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if [ -v KNOWN_ARGS ] ; then
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KNOWN_ARGS+=( help verbose quiet h v q )
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fi
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declare -gi OPTION_COUNT=0
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declare -ga POSITIONAL_ARGS=()
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declare -ga EXTRA_ARGS=()
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declare -ga UNKNOWN_ARGS=()
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declare -i __stop_parsing=0
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for a in "$@"; do
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if [ "$a" == "--" ] ; then
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__stop_parsing=1
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shift
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continue
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fi
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if [ $__stop_parsing == 1 ] ; then
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EXTRA_ARGS+=( "$a" )
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shift
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continue
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fi
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if [ -v KNOWN_ARGS ] && [[ "${a}" =~ ^--?([^=]+)=?.* ]] ; then
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_arg=${BASH_REMATCH[1]}
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known=false
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for _k in "${KNOWN_ARGS[@]}" ; do
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[ "${_arg}" == "${_k}" ] && { known=true ; break ; } || :
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done
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$known || UNKNOWN_ARGS+=( "${a}" )
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fi
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OPTION_COUNT+=1
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case "$a" in
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-h | --help)
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HELP=true
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break
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;;
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-q | --quiet)
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QUIET=true
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break
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;;
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-v | --verbose)
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VERBOSE=true
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break
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;;
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--*=*)
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[[ $a =~ --([^=]+)=(.*) ]]
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l=${BASH_REMATCH[1]//-/_}
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declare -n var="${l^^}"
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var="${BASH_REMATCH[2]}"
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;;
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--no-*)
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[[ $a =~ --no-(.+) ]]
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l=${BASH_REMATCH[1]//-/_}
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declare -n var="${l^^}"
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var=false
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;;
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--*)
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[[ $a =~ --(.+) ]]
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l=${BASH_REMATCH[1]//-/_}
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declare -n var="${l^^}"
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var=true
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;;
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*)
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POSITIONAL_ARGS+=( "$a" )
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;;
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esac
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done
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print_elapsed_time() {
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print_seps=True
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if [ "$1" == "--no-separators" ] ; then
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shift
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print_seps=False
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fi
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local START_TS=${1:?"Usage: $0 <start_timestamp> <end_timestamp>"}
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local END_TS=${2:?"Usage: $0 <start_timestamp> <end_timestamp>"}
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message="${3}"
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python <<EOF
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from datetime import datetime
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st=datetime.fromtimestamp(int($START_TS))
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et=datetime.fromtimestamp(int($END_TS))
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msg=f"${message} Elapsed time: {et-st!s}"
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if ${print_seps}:
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print(f"{'=' * 80}")
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print(f"{msg:>80s}")
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if ${print_seps}:
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print(f"{'=' * 80}")
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EOF
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}
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justify_text() {
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msg="${1:?Need a string}"
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len="${2:?Need a length}"
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printf "%*s\n" $(( (${#msg}+len)/2)) "${msg}"
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}
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get_filecounts() {
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declare -ln fca=${1}
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local af=${2}
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if [ -f "${af}" ] ; then
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mapfile -t fc < <(cat "${af}")
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for ds in "${fc[@]}" ; do
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[[ "${ds}" =~ ^([^:]+):([0-9-]+)$ ]] && fca[${BASH_REMATCH[1]}]=${BASH_REMATCH[2]} || :
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done
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fi
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}
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get_total_filecount() {
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declare -ln fca=${1}
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declare -li total=0
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for ds in "${fca[@]}" ; do
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total+=${ds}
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done
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echo $total
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}
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write_filecounts() {
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declare -ln fca=${1}
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local af=${2}
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rm -rf "${af}" || :
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for ds in "${!fca[@]}" ; do
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echo "${ds}:${fca[${ds}]}" >> "${af}"
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done
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}
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