experiments
/
STT
1
0
Fork 0
Code Issues Pull Requests Projects Releases Wiki Activity
STT/taskcluster/tc-asserts.sh

603 lines
21 KiB
Bash
Executable File
Raw Permalink Blame History

#!/bin/bash
set -xe
strip() {
echo "$(echo $1 | sed -e 's/^[[:space:]]+//' -e 's/[[:space:]]+$//')"
}
# This verify exact inference result
assert_correct_inference()
{
phrase=$(strip "$1")
expected=$(strip "$2")
status=$3
if [ "$status" -ne "0" ]; then
case "$(cat ${TASKCLUSTER_TMP_DIR}/stderr)" in
*"incompatible with minimum version"*)
echo "Prod model too old for client, skipping test."
return 0
;;
*)
echo "Client failed to run:"
cat ${TASKCLUSTER_TMP_DIR}/stderr
return 1
;;
esac
fi
if [ -z "${phrase}" -o -z "${expected}" ]; then
echo "One or more empty strings:"
echo "phrase: <${phrase}>"
echo "expected: <${expected}>"
return 1
fi;
if [ "${phrase}" = "${expected}" ]; then
echo "Proper output has been produced:"
echo "${phrase}"
return 0
else
echo "!! Non matching output !!"
echo "got: <${phrase}>"
if [ -x "$(command -v xxd)" ]; then
echo "xxd:"; echo "${phrase}" | xxd
fi
echo "-------------------"
echo "expected: <${expected}>"
if [ -x "$(command -v xxd)" ]; then
echo "xxd:"; echo "${expected}" | xxd
fi
return 1
fi;
}
# This verify that ${expected} is contained within ${phrase}
assert_working_inference()
{
phrase=$1
expected=$2
status=$3
if [ -z "${phrase}" -o -z "${expected}" ]; then
echo "One or more empty strings:"
echo "phrase: <${phrase}>"
echo "expected: <${expected}>"
return 1
fi;
if [ "$status" -ne "0" ]; then
case "$(cat ${TASKCLUSTER_TMP_DIR}/stderr)" in
*"incompatible with minimum version"*)
echo "Prod model too old for client, skipping test."
return 0
;;
*)
echo "Client failed to run:"
cat ${TASKCLUSTER_TMP_DIR}/stderr
return 1
;;
esac
fi
case "${phrase}" in
*${expected}*)
echo "Proper output has been produced:"
echo "${phrase}"
return 0
;;
*)
echo "!! Non matching output !!"
echo "got: <${phrase}>"
if [ -x "$(command -v xxd)" ]; then
echo "xxd:"; echo "${phrase}" | xxd
fi
echo "-------------------"
echo "expected: <${expected}>"
if [ -x "$(command -v xxd)" ]; then
echo "xxd:"; echo "${expected}" | xxd
fi
return 1
;;
esac
}
assert_shows_something()
{
stderr=$1
expected=$2
if [ -z "${stderr}" -o -z "${expected}" ]; then
echo "One or more empty strings:"
echo "stderr: <${stderr}>"
echo "expected: <${expected}>"
return 1
fi;
case "${stderr}" in
*"incompatible with minimum version"*)
echo "Prod model too old for client, skipping test."
return 0
;;
*${expected}*)
echo "Proper output has been produced:"
echo "${stderr}"
return 0
;;
*)
echo "!! Non matching output !!"
echo "got: <${stderr}>"
if [ -x "$(command -v xxd)" ]; then
echo "xxd:"; echo "${stderr}" | xxd
fi
echo "-------------------"
echo "expected: <${expected}>"
if [ -x "$(command -v xxd)" ]; then
echo "xxd:"; echo "${expected}" | xxd
fi
return 1
;;
esac
}
assert_not_present()
{
stderr=$1
not_expected=$2
if [ -z "${stderr}" -o -z "${not_expected}" ]; then
echo "One or more empty strings:"
echo "stderr: <${stderr}>"
echo "not_expected: <${not_expected}>"
return 1
fi;
case "${stderr}" in
*${not_expected}*)
echo "!! Not expected was present !!"
echo "got: <${stderr}>"
if [ -x "$(command -v xxd)" ]; then
echo "xxd:"; echo "${stderr}" | xxd
fi
echo "-------------------"
echo "not_expected: <${not_expected}>"
if [ -x "$(command -v xxd)" ]; then
echo "xxd:"; echo "${not_expected}" | xxd
fi
return 1
;;
*)
echo "Proper not expected output has not been produced:"
echo "${stderr}"
return 0
;;
esac
}
assert_correct_ldc93s1()
{
assert_correct_inference "$1" "she had your dark suit in greasy wash water all year" "$2"
}
assert_working_ldc93s1()
{
assert_working_inference "$1" "she had your dark suit in greasy wash water all year" "$2"
}
assert_correct_ldc93s1_lm()
{
assert_correct_inference "$1" "she had your dark suit in greasy wash water all year" "$2"
}
assert_working_ldc93s1_lm()
{
assert_working_inference "$1" "she had your dark suit in greasy wash water all year" "$2"
}
assert_correct_multi_ldc93s1()
{
assert_shows_something "$1" "/${ldc93s1_sample_filename}%she had your dark suit in greasy wash water all year%" "$?"
assert_shows_something "$1" "/LDC93S1_pcms16le_2_44100.wav%she had your dark suit in greasy wash water all year%" "$?"
## 8k will output garbage anyway ...
# assert_shows_something "$1" "/LDC93S1_pcms16le_1_8000.wav%she hayorasryrtl lyreasy asr watal w water all year%"
}
assert_correct_ldc93s1_prodmodel()
{
if [ -z "$3" -o "$3" = "16k" ]; then
assert_correct_inference "$1" "she had your dark suit in greasy wash water all year" "$2"
fi;
if [ "$3" = "8k" ]; then
assert_correct_inference "$1" "she had to do suit in greasy wash water all year" "$2"
fi;
}
assert_correct_ldc93s1_prodtflitemodel()
{
if [ -z "$3" -o "$3" = "16k" ]; then
assert_correct_inference "$1" "she had her dark suit in greasy wash water all year" "$2"
fi;
if [ "$3" = "8k" ]; then
assert_correct_inference "$1" "she had to do so and greasy wash water all year" "$2"
fi;
}
assert_correct_ldc93s1_prodmodel_stereo_44k()
{
assert_correct_inference "$1" "she had your dark suit in greasy wash water all year" "$2"
}
assert_correct_ldc93s1_prodtflitemodel_stereo_44k()
{
assert_correct_inference "$1" "she had her dark suit in greasy wash water all year" "$2"
}
assert_correct_warning_upsampling()
{
assert_shows_something "$1" "erratic speech recognition"
}
assert_tensorflow_version()
{
assert_shows_something "$1" "${EXPECTED_TENSORFLOW_VERSION}"
}
assert_deepspeech_version()
{
assert_not_present "$1" "DeepSpeech: unknown"
}
# We need to ensure that running on inference really leverages GPU because
# it might default back to CPU
ensure_cuda_usage()
{
local _maybe_cuda=$1
DS_BINARY_FILE=${DS_BINARY_FILE:-"deepspeech"}
if [ "${_maybe_cuda}" = "cuda" ]; then
set +e
export TF_CPP_MIN_VLOG_LEVEL=1
ds_cuda=$(${DS_BINARY_PREFIX}${DS_BINARY_FILE} --model ${TASKCLUSTER_TMP_DIR}/${model_name} --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} 2>&1 1>/dev/null)
export TF_CPP_MIN_VLOG_LEVEL=
set -e
assert_shows_something "${ds_cuda}" "Successfully opened dynamic library nvcuda.dll"
assert_not_present "${ds_cuda}" "Skipping registering GPU devices"
fi;
}
check_versions()
{
set +e
ds_help=$(${DS_BINARY_PREFIX}deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name} --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} 2>&1 1>/dev/null)
set -e
assert_tensorflow_version "${ds_help}"
assert_deepspeech_version "${ds_help}"
}
assert_deepspeech_runtime()
{
local expected_runtime=$1
set +e
local ds_version=$(${DS_BINARY_PREFIX}deepspeech --version 2>&1)
set -e
assert_shows_something "${ds_version}" "${expected_runtime}"
}
check_runtime_nodejs()
{
assert_deepspeech_runtime "Runtime: Node"
}
check_runtime_electronjs()
{
assert_deepspeech_runtime "Runtime: Electron"
}
run_tflite_basic_inference_tests()
{
set +e
phrase_pbmodel_nolm=$(${DS_BINARY_PREFIX}deepspeech --model ${DATA_TMP_DIR}/${model_name} --audio ${DATA_TMP_DIR}/${ldc93s1_sample_filename} 2>${TASKCLUSTER_TMP_DIR}/stderr)
set -e
assert_correct_ldc93s1 "${phrase_pbmodel_nolm}" "$?"
set +e
phrase_pbmodel_nolm=$(${DS_BINARY_PREFIX}deepspeech --model ${DATA_TMP_DIR}/${model_name} --audio ${DATA_TMP_DIR}/${ldc93s1_sample_filename} --extended 2>${TASKCLUSTER_TMP_DIR}/stderr)
set -e
assert_correct_ldc93s1 "${phrase_pbmodel_nolm}" "$?"
}
run_netframework_inference_tests()
{
set +e
phrase_pbmodel_nolm=$(DeepSpeechConsole.exe --model ${TASKCLUSTER_TMP_DIR}/${model_name} --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} 2>${TASKCLUSTER_TMP_DIR}/stderr)
set -e
assert_working_ldc93s1 "${phrase_pbmodel_nolm}" "$?"
set +e
phrase_pbmodel_nolm=$(DeepSpeechConsole.exe --model ${TASKCLUSTER_TMP_DIR}/${model_name} --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} --extended yes 2>${TASKCLUSTER_TMP_DIR}/stderr)
set -e
assert_working_ldc93s1 "${phrase_pbmodel_nolm}" "$?"
set +e
phrase_pbmodel_nolm=$(DeepSpeechConsole.exe --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} 2>${TASKCLUSTER_TMP_DIR}/stderr)
set -e
assert_working_ldc93s1 "${phrase_pbmodel_nolm}" "$?"
set +e
phrase_pbmodel_withlm=$(DeepSpeechConsole.exe --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --scorer ${TASKCLUSTER_TMP_DIR}/kenlm.scorer --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} 2>${TASKCLUSTER_TMP_DIR}/stderr)
set -e
assert_working_ldc93s1_lm "${phrase_pbmodel_withlm}" "$?"
}
run_electronjs_inference_tests()
{
set +e
phrase_pbmodel_nolm=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name} --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} 2>${TASKCLUSTER_TMP_DIR}/stderr)
set -e
assert_working_ldc93s1 "${phrase_pbmodel_nolm}" "$?"
set +e
phrase_pbmodel_nolm=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name} --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} --extended 2>${TASKCLUSTER_TMP_DIR}/stderr)
set -e
assert_working_ldc93s1 "${phrase_pbmodel_nolm}" "$?"
set +e
phrase_pbmodel_nolm=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} 2>${TASKCLUSTER_TMP_DIR}/stderr)
set -e
assert_working_ldc93s1 "${phrase_pbmodel_nolm}" "$?"
set +e
phrase_pbmodel_withlm=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --scorer ${TASKCLUSTER_TMP_DIR}/kenlm.scorer --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} 2>${TASKCLUSTER_TMP_DIR}/stderr)
set -e
assert_working_ldc93s1_lm "${phrase_pbmodel_withlm}" "$?"
}
run_basic_inference_tests()
{
set +e
deepspeech --model "" --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} 2>${TASKCLUSTER_TMP_DIR}/stderr
set -e
grep "Missing model information" ${TASKCLUSTER_TMP_DIR}/stderr
set +e
phrase_pbmodel_nolm=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name} --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} 2>${TASKCLUSTER_TMP_DIR}/stderr)
status=$?
set -e
assert_correct_ldc93s1 "${phrase_pbmodel_nolm}" "$status"
set +e
phrase_pbmodel_nolm=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name} --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} --extended 2>${TASKCLUSTER_TMP_DIR}/stderr)
status=$?
set -e
assert_correct_ldc93s1 "${phrase_pbmodel_nolm}" "$status"
set +e
phrase_pbmodel_nolm=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} 2>${TASKCLUSTER_TMP_DIR}/stderr)
status=$?
set -e
assert_correct_ldc93s1 "${phrase_pbmodel_nolm}" "$status"
set +e
phrase_pbmodel_withlm=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --scorer ${TASKCLUSTER_TMP_DIR}/kenlm.scorer --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} 2>${TASKCLUSTER_TMP_DIR}/stderr)
status=$?
set -e
assert_correct_ldc93s1_lm "${phrase_pbmodel_withlm}" "$status"
}
run_all_inference_tests()
{
run_basic_inference_tests
set +e
phrase_pbmodel_nolm_stereo_44k=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1_pcms16le_2_44100.wav 2>${TASKCLUSTER_TMP_DIR}/stderr)
status=$?
set -e
assert_correct_ldc93s1 "${phrase_pbmodel_nolm_stereo_44k}" "$status"
set +e
phrase_pbmodel_withlm_stereo_44k=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --scorer ${TASKCLUSTER_TMP_DIR}/kenlm.scorer --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1_pcms16le_2_44100.wav 2>${TASKCLUSTER_TMP_DIR}/stderr)
status=$?
set -e
assert_correct_ldc93s1_lm "${phrase_pbmodel_withlm_stereo_44k}" "$status"
# Run down-sampling warning test only when we actually perform downsampling
if [ "${ldc93s1_sample_filename}" != "LDC93S1_pcms16le_1_8000.wav" ]; then
set +e
phrase_pbmodel_nolm_mono_8k=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1_pcms16le_1_8000.wav 2>&1 1>/dev/null)
set -e
assert_correct_warning_upsampling "${phrase_pbmodel_nolm_mono_8k}"
set +e
phrase_pbmodel_withlm_mono_8k=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --scorer ${TASKCLUSTER_TMP_DIR}/kenlm.scorer --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1_pcms16le_1_8000.wav 2>&1 1>/dev/null)
set -e
assert_correct_warning_upsampling "${phrase_pbmodel_withlm_mono_8k}"
fi;
}
run_prod_concurrent_stream_tests()
{
local _bitrate=$1
set +e
output=$(python ${TASKCLUSTER_TMP_DIR}/test_sources/concurrent_streams.py \
--model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} \
--scorer ${TASKCLUSTER_TMP_DIR}/kenlm.scorer \
--audio1 ${TASKCLUSTER_TMP_DIR}/LDC93S1_pcms16le_1_16000.wav \
--audio2 ${TASKCLUSTER_TMP_DIR}/new-home-in-the-stars-16k.wav 2>${TASKCLUSTER_TMP_DIR}/stderr)
status=$?
set -e
output1=$(echo "${output}" | head -n 1)
output2=$(echo "${output}" | tail -n 1)
assert_correct_ldc93s1_prodmodel "${output1}" "${status}" "16k"
assert_correct_inference "${output2}" "we must find a new home in the stars" "${status}"
}
run_prod_inference_tests()
{
local _bitrate=$1
set +e
phrase_pbmodel_withlm=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name} --scorer ${TASKCLUSTER_TMP_DIR}/kenlm.scorer --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} 2>${TASKCLUSTER_TMP_DIR}/stderr)
status=$?
set -e
assert_correct_ldc93s1_prodmodel "${phrase_pbmodel_withlm}" "$status" "${_bitrate}"
set +e
phrase_pbmodel_withlm=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --scorer ${TASKCLUSTER_TMP_DIR}/kenlm.scorer --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} 2>${TASKCLUSTER_TMP_DIR}/stderr)
status=$?
set -e
assert_correct_ldc93s1_prodmodel "${phrase_pbmodel_withlm}" "$status" "${_bitrate}"
set +e
phrase_pbmodel_withlm_stereo_44k=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --scorer ${TASKCLUSTER_TMP_DIR}/kenlm.scorer --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1_pcms16le_2_44100.wav 2>${TASKCLUSTER_TMP_DIR}/stderr)
status=$?
set -e
assert_correct_ldc93s1_prodmodel_stereo_44k "${phrase_pbmodel_withlm_stereo_44k}" "$status"
# Run down-sampling warning test only when we actually perform downsampling
if [ "${ldc93s1_sample_filename}" != "LDC93S1_pcms16le_1_8000.wav" ]; then
set +e
phrase_pbmodel_withlm_mono_8k=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --scorer ${TASKCLUSTER_TMP_DIR}/kenlm.scorer --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1_pcms16le_1_8000.wav 2>&1 1>/dev/null)
set -e
assert_correct_warning_upsampling "${phrase_pbmodel_withlm_mono_8k}"
fi;
}
run_prodtflite_inference_tests()
{
local _bitrate=$1
set +e
phrase_pbmodel_withlm=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name} --scorer ${TASKCLUSTER_TMP_DIR}/kenlm.scorer --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} 2>${TASKCLUSTER_TMP_DIR}/stderr)
status=$?
set -e
assert_correct_ldc93s1_prodtflitemodel "${phrase_pbmodel_withlm}" "$status" "${_bitrate}"
set +e
phrase_pbmodel_withlm=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --scorer ${TASKCLUSTER_TMP_DIR}/kenlm.scorer --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} 2>${TASKCLUSTER_TMP_DIR}/stderr)
status=$?
set -e
assert_correct_ldc93s1_prodtflitemodel "${phrase_pbmodel_withlm}" "$status" "${_bitrate}"
set +e
phrase_pbmodel_withlm_stereo_44k=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --scorer ${TASKCLUSTER_TMP_DIR}/kenlm.scorer --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1_pcms16le_2_44100.wav 2>${TASKCLUSTER_TMP_DIR}/stderr)
status=$?
set -e
assert_correct_ldc93s1_prodtflitemodel_stereo_44k "${phrase_pbmodel_withlm_stereo_44k}" "$status"
# Run down-sampling warning test only when we actually perform downsampling
if [ "${ldc93s1_sample_filename}" != "LDC93S1_pcms16le_1_8000.wav" ]; then
set +e
phrase_pbmodel_withlm_mono_8k=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --scorer ${TASKCLUSTER_TMP_DIR}/kenlm.scorer --audio ${TASKCLUSTER_TMP_DIR}/LDC93S1_pcms16le_1_8000.wav 2>&1 1>/dev/null)
set -e
assert_correct_warning_upsampling "${phrase_pbmodel_withlm_mono_8k}"
fi;
}
run_multi_inference_tests()
{
set +e -o pipefail
multi_phrase_pbmodel_nolm=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name} --audio ${TASKCLUSTER_TMP_DIR}/ 2>${TASKCLUSTER_TMP_DIR}/stderr | tr '\n' '%')
status=$?
set -e +o pipefail
assert_correct_multi_ldc93s1 "${multi_phrase_pbmodel_nolm}" "$status"
set +e -o pipefail
multi_phrase_pbmodel_withlm=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name} --scorer ${TASKCLUSTER_TMP_DIR}/kenlm.scorer --audio ${TASKCLUSTER_TMP_DIR}/ 2>${TASKCLUSTER_TMP_DIR}/stderr | tr '\n' '%')
status=$?
set -e +o pipefail
assert_correct_multi_ldc93s1 "${multi_phrase_pbmodel_withlm}" "$status"
}
run_hotword_tests()
{
DS_BINARY_FILE=${DS_BINARY_FILE:-"deepspeech"}
set +e
hotwords_decode=$(${DS_BINARY_PREFIX}${DS_BINARY_FILE} --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --scorer ${TASKCLUSTER_TMP_DIR}/kenlm.scorer --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} --hot_words "foo:0.0,bar:-0.1" 2>${TASKCLUSTER_TMP_DIR}/stderr)
status=$?
set -e
assert_working_ldc93s1_lm "${hotwords_decode}" "$status"
}
run_android_hotword_tests()
{
set +e
hotwords_decode=$(${DS_BINARY_PREFIX}deepspeech --model ${DATA_TMP_DIR}/${model_name} --scorer ${DATA_TMP_DIR}/kenlm.scorer --audio ${DATA_TMP_DIR}/${ldc93s1_sample_filename} --hot_words "foo:0.0,bar:-0.1" 2>${TASKCLUSTER_TMP_DIR}/stderr)
status=$?
set -e
assert_correct_ldc93s1_lm "${hotwords_decode}" "$status"
}
run_cpp_only_inference_tests()
{
set +e
phrase_pbmodel_withlm_intermediate_decode=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --scorer ${TASKCLUSTER_TMP_DIR}/kenlm.scorer --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} --stream 1280 2>${TASKCLUSTER_TMP_DIR}/stderr | tail -n 1)
status=$?
set -e
assert_correct_ldc93s1_lm "${phrase_pbmodel_withlm_intermediate_decode}" "$status"
}
run_js_streaming_inference_tests()
{
set +e
phrase_pbmodel_withlm=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --scorer ${TASKCLUSTER_TMP_DIR}/kenlm.scorer --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} --stream 2>${TASKCLUSTER_TMP_DIR}/stderr | tail -n 1)
status=$?
set -e
assert_correct_ldc93s1_lm "${phrase_pbmodel_withlm}" "$status"
set +e
phrase_pbmodel_withlm=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --scorer ${TASKCLUSTER_TMP_DIR}/kenlm.scorer --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} --stream --extended 2>${TASKCLUSTER_TMP_DIR}/stderr | tail -n 1)
status=$?
set -e
assert_correct_ldc93s1_lm "${phrase_pbmodel_withlm}" "$status"
}
run_js_streaming_prod_inference_tests()
{
local _bitrate=$1
set +e
phrase_pbmodel_withlm=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --scorer ${TASKCLUSTER_TMP_DIR}/kenlm.scorer --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} --stream 2>${TASKCLUSTER_TMP_DIR}/stderr | tail -n 1)
status=$?
set -e
assert_correct_ldc93s1_prodmodel "${phrase_pbmodel_withlm}" "$status" "${_bitrate}"
local _bitrate=$1
set +e
phrase_pbmodel_withlm=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --scorer ${TASKCLUSTER_TMP_DIR}/kenlm.scorer --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} --stream --extended 2>${TASKCLUSTER_TMP_DIR}/stderr | tail -n 1)
status=$?
set -e
assert_correct_ldc93s1_prodmodel "${phrase_pbmodel_withlm}" "$status" "${_bitrate}"
}
run_js_streaming_prodtflite_inference_tests()
{
local _bitrate=$1
set +e
phrase_pbmodel_withlm=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --scorer ${TASKCLUSTER_TMP_DIR}/kenlm.scorer --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} --stream 2>${TASKCLUSTER_TMP_DIR}/stderr | tail -n 1)
status=$?
set -e
assert_correct_ldc93s1_prodtflitemodel "${phrase_pbmodel_withlm}" "$status" "${_bitrate}"
local _bitrate=$1
set +e
phrase_pbmodel_withlm=$(deepspeech --model ${TASKCLUSTER_TMP_DIR}/${model_name_mmap} --scorer ${TASKCLUSTER_TMP_DIR}/kenlm.scorer --audio ${TASKCLUSTER_TMP_DIR}/${ldc93s1_sample_filename} --stream --extended 2>${TASKCLUSTER_TMP_DIR}/stderr | tail -n 1)
status=$?
set -e
assert_correct_ldc93s1_prodtflitemodel "${phrase_pbmodel_withlm}" "$status" "${_bitrate}"
}
Reference in New Issue View Git Blame Copy Permalink