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https://github.com/TaterTotterson/microWakeWord-Trainer-Nvidia-Docker.git
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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.
126 lines
4.2 KiB
Bash
Executable File
126 lines
4.2 KiB
Bash
Executable File
#!/bin/bash
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set -e
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PROGPATH=$(realpath "$0")
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PROGDIR=$(dirname "${PROGPATH}")
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KNOWN_ARGS=( samples batch-size training-steps data-dir cleanup-work-dir )
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source "${PROGDIR}/shell.functions"
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WAKE_WORD=${POSITIONAL_ARGS[0]}
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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" ] || [ -z "${WAKE_WORD}" ] ; then
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cat <<EOF >&2
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Usage: train_wake_word [ --samples=<samples> ] [ --batch-size=<batch_size> ]
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[ --training-steps=<steps> ] [ --cleanup-work-dir ]
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<wake_word> [ <wake_word_title> ]
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Options:
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--samples: The number of samples to generate for the wake word.
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Default: ${DEFAULT_SAMPLES}
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--batch-size: How many samples should be generated at a time. The more
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samples per batch, the more memory is needed.
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Default: ${DEFAULT_BATCH_SIZE}
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--training-steps: Number of training steps. More training steps means better
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detection and false positive rates but also more time to train.
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Default: ${DEFAULT_TRAINING_STEPS}
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--cleanup-work-dir: Delete the /data/work directory after successful training.
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Default: false
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<wake_word> The word to train spelled phonetically.
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Required.
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<wake_word_title> An optional pretty name to save to the json metadata file.
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Default: The wake word with individual words capitalized
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and punctuation removed.
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EOF
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exit 1
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fi
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# shellcheck source=/dev/null
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source "${DATA_DIR}/.venv/bin/activate"
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cd "${DATA_DIR}"
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mkdir -p "${DATA_DIR}/work" || :
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[ ${#POSITIONAL_ARGS} -eq 2 ] && WAKE_WORD_TITLE="${POSITIONAL_ARGS[1]}" || :
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if [ ! -v WAKE_WORD_TITLE ] ; then
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declare -a WWNA=( ${WAKE_WORD//[^a-zA-Z0-9]/ } )
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WAKE_WORD_TITLE="${WWNA[*]^}"
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elif [ -z "$WAKE_WORD_TITLE" ] ; then
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WAKE_WORD_TITLE="$WAKE_WORD"
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fi
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printf "%-80s\n" "=" | tr ' ' "="
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echo "===== Running '${WAKE_WORD}(${WAKE_WORD_TITLE})' generation, augmentation and training ====="
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"${PROGDIR}/cudainfo"
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echo
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START_TS=$EPOCHSECONDS
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export TF_CPP_MIN_LOG_LEVEL=9
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export TF_FORCE_GPU_ALLOW_GROWTH=true
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export TF_GPU_ALLOCATOR=cuda_malloc_async
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export TF_XLA_FLAGS="--tf_xla_auto_jit=0"
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export NVIDIA_TF32_OVERRIDE=1
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export TF_CUDNN_WORKSPACE_LIMIT_IN_MB=512
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export GLOG_minloglevel=2
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export GRPC_VERBOSITY=ERROR
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"${PROGDIR}/wake_word_sample_generator" \
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--samples=${SAMPLES} \
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--batch-size=${BATCH_SIZE} \
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--data-dir="${DATA_DIR}" "${WAKE_WORD}"
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POST_GEN_TS=$EPOCHSECONDS
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ww="${WAKE_WORD// /_}"
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ww="${ww//./}"
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AUGMENT=false
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GENERATED_DIR="${DATA_DIR}/work/wake_word_samples"
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AUGMENTED_DIR="${DATA_DIR}/work/wake_word_samples_augmented"
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[ -d "${AUGMENTED_DIR}" ] || AUGMENT=true
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[ "${GENERATED_DIR}/0.wav" -nt "${AUGMENTED_DIR}/testing/wakeword_mmap/data.ninja" ] && AUGMENT=true || :
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if ${AUGMENT} ; then
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rm -rf "${AUGMENTED_DIR}" || :
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mkdir -p "${AUGMENTED_DIR}" || :
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"${PROGDIR}/wake_word_sample_augmenter" --data-dir="${DATA_DIR}" || { rm -rf "${AUGMENTED_DIR}" ; exit 1 ; }
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else
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echo "Augmentation not required"
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echo
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fi
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POST_AUGMENT_TS=$EPOCHSECONDS
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"${PROGDIR}/wake_word_sample_trainer" --samples=${SAMPLES} --training-steps=${TRAINING_STEPS} --data-dir="${DATA_DIR}" \
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"${WAKE_WORD}" "${WAKE_WORD_TITLE}"
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if ${CLEANUP_WORK_DIR} ; then
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rm -rf "${DATA_DIR}/work/trained_models" "${DATA_DIR}/work/wake_word_samples" \
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"${DATA_DIR}/work/wake_word_samples_augmented" "${DATA_DIR}/work/last_wake_word" || :
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fi
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END_TS=$EPOCHSECONDS
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python -c $'print(f"{\'=\' * 80}")'
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printf "%44s\n\n" "Training Summary"
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"${PROGDIR}/system_summary"
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echo
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print_elapsed_time --no-separators "${START_TS}" "${POST_GEN_TS}" "Generate ${SAMPLES} samples, ${BATCH_SIZE}/batch"
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print_elapsed_time --no-separators "${POST_GEN_TS}" "${POST_AUGMENT_TS}" "Augment ${SAMPLES} samples"
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print_elapsed_time --no-separators "${POST_AUGMENT_TS}" "${END_TS}" "${TRAINING_STEPS} training steps"
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python -c $'msg="="*54 ; print(f"{msg:>80s}")'
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print_elapsed_time --no-separators "${START_TS}" "${END_TS}" "Total"
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python -c $'print(f"{\'=\' * 80}")'
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