mirror of
https://github.com/TaterTotterson/microWakeWord-Trainer-Nvidia-Docker.git
synced 2026-06-12 20:10:19 -06:00
cb81f7f02d
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.
49 lines
1.5 KiB
Bash
Executable File
49 lines
1.5 KiB
Bash
Executable File
#!/bin/bash
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set -euo pipefail
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PROGPATH=$(realpath "$0")
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PROGDIR=$(dirname "${PROGPATH}")
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KNOWN_ARGS=( data-dir cleanup-archives cleanup-intermediate-files )
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source "${PROGDIR}/shell.functions"
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if [ ${#UNKNOWN_ARGS[@]} -gt 0 ] ; then
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echo "Unknown argument(s): ${UNKNOWN_ARGS[*]}" >&2
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HELP=true
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fi
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if [ "${HELP}" == "true" ] ; then
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cat <<EOF >&2
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Usage: setup_training_datasets [ --cleanup-archives ] [ --cleanup-intermediate-files ]
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Options:
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--cleanup-archives: Automatically delete the tarballs or zipfiles after
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they've been extracted.
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--cleanup-intermediate-files: Automatically delete the intermediate files
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after they've been converted.
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EOF
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exit 1
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fi
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cd "${DATA_DIR}"
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START_TS=$EPOCHSECONDS
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echo -e "\n===== Setting up Training Datasets =====\n"
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${PROGDIR}/setup_negative_datasets --cleanup-archives=${CLEANUP_ARCHIVES} \
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--cleanup-intermediate-files=${CLEANUP_INTERMEDIATE_FILES} --data-dir="${DATA_DIR}"
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${PROGDIR}/setup_mit_audio --cleanup-archives=${CLEANUP_ARCHIVES} \
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--cleanup-intermediate-files=${CLEANUP_INTERMEDIATE_FILES} --data-dir="${DATA_DIR}"
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${PROGDIR}/setup_audioset --cleanup-archives=${CLEANUP_ARCHIVES} \
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--cleanup-intermediate-files=${CLEANUP_INTERMEDIATE_FILES} --data-dir="${DATA_DIR}"
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${PROGDIR}/setup_fma --cleanup-archives=${CLEANUP_ARCHIVES} \
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--cleanup-intermediate-files=${CLEANUP_INTERMEDIATE_FILES} --data-dir="${DATA_DIR}"
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END_TS=$(date +%s.%N)
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print_elapsed_time "${START_TS}" "${END_TS}" "Training dataset setup"
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