55 lines
1.5 KiB
Bash
Executable File
55 lines
1.5 KiB
Bash
Executable File
#!/bin/bash
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set -xe
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source $(dirname "$0")/all-vars.sh
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source $(dirname "$0")/all-utils.sh
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bitrate=$1
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decoder_src=$2
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if [ "$decoder_src" != "--pypi" ]; then
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# Use decoder package built in this CI group
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export DS_NODECODER=1
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fi
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mkdir -p /tmp/train || true
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mkdir -p /tmp/train_tflite || true
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set -o pipefail
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python -m pip install --upgrade pip setuptools wheel | cat
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python -m pip install --upgrade . | cat
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set +o pipefail
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# Prepare correct arguments for training
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case "${bitrate}" in
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8k)
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sample_rate=8000
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sample_name='LDC93S1_pcms16le_1_8000.wav'
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;;
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16k)
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sample_rate=16000
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sample_name='LDC93S1_pcms16le_1_16000.wav'
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;;
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esac
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# Easier to rename to that we can exercize the LDC93S1 importer code to
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# generate the CSV file.
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echo "Moving ${sample_name} to LDC93S1.wav"
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mv "data/smoke_test/${sample_name}" "data/smoke_test/LDC93S1.wav"
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# Run twice to test preprocessed features
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time ./bin/run-ci-ldc93s1_new.sh 249 "${sample_rate}"
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time ./bin/run-ci-ldc93s1_new.sh 1 "${sample_rate}"
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time ./bin/run-ci-ldc93s1_tflite.sh "${sample_rate}"
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tar -cf - \
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-C /tmp/ckpt/ . \
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| ${XZ} > ${CI_ARTIFACTS_DIR}/checkpoint.tar.xz
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cp /tmp/train/output_graph.pb ${CI_ARTIFACTS_DIR}
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cp /tmp/train_tflite/output_graph.tflite ${CI_ARTIFACTS_DIR}
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/tmp/convert_graphdef_memmapped_format --in_graph=/tmp/train/output_graph.pb --out_graph=/tmp/train/output_graph.pbmm
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cp /tmp/train/output_graph.pbmm ${CI_ARTIFACTS_DIR}
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time ./bin/run-ci-ldc93s1_checkpoint.sh
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