91 lines
2.8 KiB
Bash
Executable File
91 lines
2.8 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 ".[transcribe]" | 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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# Testing single SDB source
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time ./bin/run-ci-ldc93s1_new_sdb.sh 220 "${sample_rate}"
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# Testing interleaved source (SDB+CSV combination) - run twice to test preprocessed features
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time ./bin/run-ci-ldc93s1_new_sdb_csv.sh 109 "${sample_rate}"
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time ./bin/run-ci-ldc93s1_new_sdb_csv.sh 1 "${sample_rate}"
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# Test --metrics_files training argument
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time ./bin/run-ci-ldc93s1_new_metrics.sh 2 "${sample_rate}"
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# Test training with bytes output mode
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time ./bin/run-ci-ldc93s1_new_bytes.sh 200 "${sample_rate}"
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time ./bin/run-ci-ldc93s1_new_bytes_tflite.sh "${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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# Save exported model artifacts from bytes output mode training
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cp /tmp/train_bytes/output_graph.pb ${CI_ARTIFACTS_DIR}/output_graph.pb
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cp /tmp/train_bytes_tflite/output_graph.tflite ${CI_ARTIFACTS_DIR}/output_graph.tflite
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/tmp/convert_graphdef_memmapped_format --in_graph=/tmp/train_bytes/output_graph.pb --out_graph=/tmp/train_bytes/output_graph.pbmm
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cp /tmp/train_bytes/output_graph.pbmm ${CI_ARTIFACTS_DIR}
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# Test resuming from checkpoints created above
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# SDB, resuming from checkpoint
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time ./bin/run-ci-ldc93s1_checkpoint_sdb.sh
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# Bytes output mode, resuming from checkpoint
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time ./bin/run-ci-ldc93s1_checkpoint_bytes.sh
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# Training with args set via initialize_globals_from_args()
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time python ./bin/run-ldc93s1.py
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# Training graph inference
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time ./bin/run-ci-ldc93s1_singleshotinference.sh
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# transcribe module
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time python -m coqui_stt_training.transcribe \
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--src "data/smoke_test/LDC93S1.wav" \
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--dst ${CI_ARTIFACTS_DIR}/transcribe.log \
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--n_hidden 100 \
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--scorer_path "data/smoke_test/pruned_lm.scorer"
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mkdir /tmp/transcribe_dir
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cp data/smoke_test/LDC93S1.wav /tmp/transcribe_dir
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time python -m coqui_stt_training.transcribe \
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--src "/tmp/transcribe_dir/" \
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--n_hidden 100 \
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--scorer_path "data/smoke_test/pruned_lm.scorer"
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for i in /tmp/transcribe_dir/*.tlog; do echo $i; cat $i; echo; done
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