feat: added whisper as a transcription model
This commit is contained in:
Thijs Houben
parent
fe72db9176
commit
cc78ef7c1f
15 changed files with 688 additions and 553 deletions
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@ -25,7 +25,7 @@ repos:
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hooks:
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- id: pyright
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types_or: [python, pyi, jupyter]
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additional_dependencies: [numpy, pytest, fastapi, praat-parselmouth, orjson, pydantic, scipy, psycopg, deepgram-sdk, pydub, ffmpeg-python, jiwer, beartype]
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additional_dependencies: [numpy, pytest, fastapi, praat-parselmouth, orjson, pydantic, scipy, psycopg, deepgram-sdk, pydub, ffmpeg-python, jiwer, beartype, openai]
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stages: [pre-commit]
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- repo: https://github.com/crate-ci/typos
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rev: v1.21.0
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@ -19,7 +19,7 @@
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let minZoom: number;
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let duration: number;
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let transcriptionType: { label?: string; value: string } = { value: 'empty' };
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const models: string[] = ['deepgram', 'allosaurus'];
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const models: string[] = ['whisper', 'deepgram', 'allosaurus'];
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function transcriptionTypeChanger(newSelection: { label?: string; value: string } | undefined) {
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if (!newSelection) return;
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@ -152,7 +152,7 @@
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...fileState.transcriptions,
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{
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id: generateIdFromEntropySize(10),
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name: 'new track',
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name: transcriptionType.value,
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captions: response
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}
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];
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458
kernel/poetry.lock
generated
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kernel/poetry.lock
generated
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@ -1,4 +1,4 @@
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[[package]]
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@ -209,13 +209,13 @@ test-tox-coverage = ["coverage (>=5.5)"]
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[package.dependencies]
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@ -342,6 +342,17 @@ typing-extensions = ">=4.9.0"
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verboselogs = ">=1.7"
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@ -1005,13 +1016,13 @@ files = [
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@ -1467,6 +1478,29 @@ files = [
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@ -1524,13 +1558,13 @@ files = [
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[package.dependencies]
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|
@ -1935,13 +1969,13 @@ windows-terminal = ["colorama (>=0.4.6)"]
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|||
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||||
[[package]]
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||||
name = "pytest"
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||||
version = "8.2.1"
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version = "8.2.2"
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description = "pytest: simple powerful testing with Python"
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optional = false
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[package.dependencies]
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@ -2124,104 +2158,104 @@ files = [
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{file = "rapidfuzz-3.9.3-cp312-cp312-musllinux_1_2_i686.whl", hash = "sha256:57e7c5bf7b61c7320cfa5dde1e60e678d954ede9bb7da8e763959b2138391401"},
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||||
{file = "rapidfuzz-3.9.3-cp312-cp312-musllinux_1_2_ppc64le.whl", hash = "sha256:51fa1ba84653ab480a2e2044e2277bd7f0123d6693051729755addc0d015c44f"},
|
||||
{file = "rapidfuzz-3.9.3-cp312-cp312-musllinux_1_2_s390x.whl", hash = "sha256:17ff7f7eecdb169f9236e3b872c96dbbaf116f7787f4d490abd34b0116e3e9c8"},
|
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{file = "rapidfuzz-3.9.3-cp312-cp312-musllinux_1_2_x86_64.whl", hash = "sha256:afe7c72d3f917b066257f7ff48562e5d462d865a25fbcabf40fca303a9fa8d35"},
|
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{file = "rapidfuzz-3.9.3-cp312-cp312-win32.whl", hash = "sha256:e53ed2e9b32674ce96eed80b3b572db9fd87aae6742941fb8e4705e541d861ce"},
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{file = "rapidfuzz-3.9.3-cp312-cp312-win_amd64.whl", hash = "sha256:35b7286f177e4d8ba1e48b03612f928a3c4bdac78e5651379cec59f95d8651e6"},
|
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{file = "rapidfuzz-3.9.3-cp312-cp312-win_arm64.whl", hash = "sha256:e6e4b9380ed4758d0cb578b0d1970c3f32dd9e87119378729a5340cb3169f879"},
|
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{file = "rapidfuzz-3.9.3-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:a39890013f6d5b056cc4bfdedc093e322462ece1027a57ef0c636537bdde7531"},
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{file = "rapidfuzz-3.9.3-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:b5bc0fdbf419493163c5c9cb147c5fbe95b8e25844a74a8807dcb1a125e630cf"},
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{file = "rapidfuzz-3.9.3-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:efe6e200a75a792d37b960457904c4fce7c928a96ae9e5d21d2bd382fe39066e"},
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||||
{file = "rapidfuzz-3.9.3-cp38-cp38-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:de077c468c225d4c18f7188c47d955a16d65f21aab121cbdd98e3e2011002c37"},
|
||||
{file = "rapidfuzz-3.9.3-cp38-cp38-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:8f917eaadf5388466a95f6a236f678a1588d231e52eda85374077101842e794e"},
|
||||
{file = "rapidfuzz-3.9.3-cp38-cp38-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:858ba57c05afd720db8088a8707079e8d024afe4644001fe0dbd26ef7ca74a65"},
|
||||
{file = "rapidfuzz-3.9.3-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:d36447d21b05f90282a6f98c5a33771805f9222e5d0441d03eb8824e33e5bbb4"},
|
||||
{file = "rapidfuzz-3.9.3-cp38-cp38-musllinux_1_2_aarch64.whl", hash = "sha256:acbe4b6f1ccd5b90c29d428e849aa4242e51bb6cab0448d5f3c022eb9a25f7b1"},
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||||
{file = "rapidfuzz-3.9.3-cp38-cp38-musllinux_1_2_i686.whl", hash = "sha256:53c7f27cdf899e94712972237bda48cfd427646aa6f5d939bf45d084780e4c16"},
|
||||
{file = "rapidfuzz-3.9.3-cp38-cp38-musllinux_1_2_ppc64le.whl", hash = "sha256:6175682a829c6dea4d35ed707f1dadc16513270ef64436568d03b81ccb6bdb74"},
|
||||
{file = "rapidfuzz-3.9.3-cp38-cp38-musllinux_1_2_s390x.whl", hash = "sha256:5276df395bd8497397197fca2b5c85f052d2e6a66ffc3eb0544dd9664d661f95"},
|
||||
{file = "rapidfuzz-3.9.3-cp38-cp38-musllinux_1_2_x86_64.whl", hash = "sha256:77b5c4f3e72924d7845f0e189c304270066d0f49635cf8a3938e122c437e58de"},
|
||||
{file = "rapidfuzz-3.9.3-cp38-cp38-win32.whl", hash = "sha256:8add34061e5cd561c72ed4febb5c15969e7b25bda2bb5102d02afc3abc1f52d0"},
|
||||
{file = "rapidfuzz-3.9.3-cp38-cp38-win_amd64.whl", hash = "sha256:604e0502a39cf8e67fa9ad239394dddad4cdef6d7008fdb037553817d420e108"},
|
||||
{file = "rapidfuzz-3.9.3-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:21047f55d674614eb4b0ab34e35c3dc66f36403b9fbfae645199c4a19d4ed447"},
|
||||
{file = "rapidfuzz-3.9.3-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:a56da3aff97cb56fe85d9ca957d1f55dbac7c27da927a86a2a86d8a7e17f80aa"},
|
||||
{file = "rapidfuzz-3.9.3-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:964c08481aec2fe574f0062e342924db2c6b321391aeb73d68853ed42420fd6d"},
|
||||
{file = "rapidfuzz-3.9.3-cp39-cp39-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:5e2b827258beefbe5d3f958243caa5a44cf46187eff0c20e0b2ab62d1550327a"},
|
||||
{file = "rapidfuzz-3.9.3-cp39-cp39-manylinux_2_17_ppc64le.manylinux2014_ppc64le.whl", hash = "sha256:c6e65a301fcd19fbfbee3a514cc0014ff3f3b254b9fd65886e8a9d6957fb7bca"},
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||||
{file = "rapidfuzz-3.9.3-cp39-cp39-manylinux_2_17_s390x.manylinux2014_s390x.whl", hash = "sha256:cbe93ba1725a8d47d2b9dca6c1f435174859427fbc054d83de52aea5adc65729"},
|
||||
{file = "rapidfuzz-3.9.3-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:aca21c0a34adee582775da997a600283e012a608a107398d80a42f9a57ad323d"},
|
||||
{file = "rapidfuzz-3.9.3-cp39-cp39-musllinux_1_2_aarch64.whl", hash = "sha256:256e07d3465173b2a91c35715a2277b1ee3ae0b9bbab4e519df6af78570741d0"},
|
||||
{file = "rapidfuzz-3.9.3-cp39-cp39-musllinux_1_2_i686.whl", hash = "sha256:802ca2cc8aa6b8b34c6fdafb9e32540c1ba05fca7ad60b3bbd7ec89ed1797a87"},
|
||||
{file = "rapidfuzz-3.9.3-cp39-cp39-musllinux_1_2_ppc64le.whl", hash = "sha256:dd789100fc852cffac1449f82af0da139d36d84fd9faa4f79fc4140a88778343"},
|
||||
{file = "rapidfuzz-3.9.3-cp39-cp39-musllinux_1_2_s390x.whl", hash = "sha256:5d0abbacdb06e27ff803d7ae0bd0624020096802758068ebdcab9bd49cf53115"},
|
||||
{file = "rapidfuzz-3.9.3-cp39-cp39-musllinux_1_2_x86_64.whl", hash = "sha256:378d1744828e27490a823fc6fe6ebfb98c15228d54826bf4e49e4b76eb5f5579"},
|
||||
{file = "rapidfuzz-3.9.3-cp39-cp39-win32.whl", hash = "sha256:5d0cb272d43e6d3c0dedefdcd9d00007471f77b52d2787a4695e9dd319bb39d2"},
|
||||
{file = "rapidfuzz-3.9.3-cp39-cp39-win_amd64.whl", hash = "sha256:15e4158ac4b3fb58108072ec35b8a69165f651ba1c8f43559a36d518dbf9fb3f"},
|
||||
{file = "rapidfuzz-3.9.3-cp39-cp39-win_arm64.whl", hash = "sha256:58c6a4936190c558d5626b79fc9e16497e5df7098589a7e80d8bff68148ff096"},
|
||||
{file = "rapidfuzz-3.9.3-pp310-pypy310_pp73-macosx_10_9_x86_64.whl", hash = "sha256:5410dc848c947a603792f4f51b904a3331cf1dc60621586bfbe7a6de72da1091"},
|
||||
{file = "rapidfuzz-3.9.3-pp310-pypy310_pp73-macosx_11_0_arm64.whl", hash = "sha256:282d55700a1a3d3a7980746eb2fcd48c9bbc1572ebe0840d0340d548a54d01fe"},
|
||||
{file = "rapidfuzz-3.9.3-pp310-pypy310_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:dc1037507810833646481f5729901a154523f98cbebb1157ba3a821012e16402"},
|
||||
{file = "rapidfuzz-3.9.3-pp310-pypy310_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:5e33f779391caedcba2ba3089fb6e8e557feab540e9149a5c3f7fea7a3a7df37"},
|
||||
{file = "rapidfuzz-3.9.3-pp310-pypy310_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:41a81a9f311dc83d22661f9b1a1de983b201322df0c4554042ffffd0f2040c37"},
|
||||
{file = "rapidfuzz-3.9.3-pp310-pypy310_pp73-win_amd64.whl", hash = "sha256:a93250bd8fae996350c251e1752f2c03335bb8a0a5b0c7e910a593849121a435"},
|
||||
{file = "rapidfuzz-3.9.3-pp38-pypy38_pp73-macosx_10_9_x86_64.whl", hash = "sha256:3617d1aa7716c57d120b6adc8f7c989f2d65bc2b0cbd5f9288f1fc7bf469da11"},
|
||||
{file = "rapidfuzz-3.9.3-pp38-pypy38_pp73-macosx_11_0_arm64.whl", hash = "sha256:ad04a3f5384b82933213bba2459f6424decc2823df40098920856bdee5fd6e88"},
|
||||
{file = "rapidfuzz-3.9.3-pp38-pypy38_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:8709918da8a88ad73c9d4dd0ecf24179a4f0ceba0bee21efc6ea21a8b5290349"},
|
||||
{file = "rapidfuzz-3.9.3-pp38-pypy38_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:b770f85eab24034e6ef7df04b2bfd9a45048e24f8a808e903441aa5abde8ecdd"},
|
||||
{file = "rapidfuzz-3.9.3-pp38-pypy38_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:930b4e6fdb4d914390141a2b99a6f77a52beacf1d06aa4e170cba3a98e24c1bc"},
|
||||
{file = "rapidfuzz-3.9.3-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:c8444e921bfc3757c475c4f4d7416a7aa69b2d992d5114fe55af21411187ab0d"},
|
||||
{file = "rapidfuzz-3.9.3-pp39-pypy39_pp73-macosx_10_9_x86_64.whl", hash = "sha256:2c1d3ef3878f871abe6826e386c3d61b5292ef5f7946fe646f4206b85836b5da"},
|
||||
{file = "rapidfuzz-3.9.3-pp39-pypy39_pp73-macosx_11_0_arm64.whl", hash = "sha256:d861bf326ee7dabc35c532a40384541578cd1ec1e1b7db9f9ecbba56eb76ca22"},
|
||||
{file = "rapidfuzz-3.9.3-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:cde6b9d9ba5007077ee321ec722fa714ebc0cbd9a32ccf0f4dd3cc3f20952d71"},
|
||||
{file = "rapidfuzz-3.9.3-pp39-pypy39_pp73-manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:3bb6546e7b6bed1aefbe24f68a5fb9b891cc5aef61bca6c1a7b1054b7f0359bb"},
|
||||
{file = "rapidfuzz-3.9.3-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:3d8a57261ef7996d5ced7c8cba9189ada3fbeffd1815f70f635e4558d93766cb"},
|
||||
{file = "rapidfuzz-3.9.3-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:67201c02efc596923ad950519e0b75ceb78d524177ea557134d6567b9ac2c283"},
|
||||
{file = "rapidfuzz-3.9.3.tar.gz", hash = "sha256:b398ea66e8ed50451bce5997c430197d5e4b06ac4aa74602717f792d8d8d06e2"},
|
||||
]
|
||||
|
||||
[package.extras]
|
||||
|
|
@ -2552,6 +2586,26 @@ typing-extensions = ">=4.8.0"
|
|||
opt-einsum = ["opt-einsum (>=3.3)"]
|
||||
optree = ["optree (>=0.9.1)"]
|
||||
|
||||
[[package]]
|
||||
name = "tqdm"
|
||||
version = "4.66.4"
|
||||
description = "Fast, Extensible Progress Meter"
|
||||
optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "tqdm-4.66.4-py3-none-any.whl", hash = "sha256:b75ca56b413b030bc3f00af51fd2c1a1a5eac6a0c1cca83cbb37a5c52abce644"},
|
||||
{file = "tqdm-4.66.4.tar.gz", hash = "sha256:e4d936c9de8727928f3be6079590e97d9abfe8d39a590be678eb5919ffc186bb"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
colorama = {version = "*", markers = "platform_system == \"Windows\""}
|
||||
|
||||
[package.extras]
|
||||
dev = ["pytest (>=6)", "pytest-cov", "pytest-timeout", "pytest-xdist"]
|
||||
notebook = ["ipywidgets (>=6)"]
|
||||
slack = ["slack-sdk"]
|
||||
telegram = ["requests"]
|
||||
|
||||
[[package]]
|
||||
name = "triton"
|
||||
version = "2.3.1"
|
||||
|
|
@ -2594,13 +2648,13 @@ typing-extensions = ">=3.7.4.3"
|
|||
|
||||
[[package]]
|
||||
name = "typing-extensions"
|
||||
version = "4.12.0"
|
||||
version = "4.12.2"
|
||||
description = "Backported and Experimental Type Hints for Python 3.8+"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "typing_extensions-4.12.0-py3-none-any.whl", hash = "sha256:b349c66bea9016ac22978d800cfff206d5f9816951f12a7d0ec5578b0a819594"},
|
||||
{file = "typing_extensions-4.12.0.tar.gz", hash = "sha256:8cbcdc8606ebcb0d95453ad7dc5065e6237b6aa230a31e81d0f440c30fed5fd8"},
|
||||
{file = "typing_extensions-4.12.2-py3-none-any.whl", hash = "sha256:04e5ca0351e0f3f85c6853954072df659d0d13fac324d0072316b67d7794700d"},
|
||||
{file = "typing_extensions-4.12.2.tar.gz", hash = "sha256:1a7ead55c7e559dd4dee8856e3a88b41225abfe1ce8df57b7c13915fe121ffb8"},
|
||||
]
|
||||
|
||||
[[package]]
|
||||
|
|
@ -2728,13 +2782,13 @@ files = [
|
|||
|
||||
[[package]]
|
||||
name = "uvicorn"
|
||||
version = "0.30.0"
|
||||
version = "0.30.1"
|
||||
description = "The lightning-fast ASGI server."
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
{file = "uvicorn-0.30.0-py3-none-any.whl", hash = "sha256:78fa0b5f56abb8562024a59041caeb555c86e48d0efdd23c3fe7de7a4075bdab"},
|
||||
{file = "uvicorn-0.30.0.tar.gz", hash = "sha256:f678dec4fa3a39706bbf49b9ec5fc40049d42418716cea52b53f07828a60aa37"},
|
||||
{file = "uvicorn-0.30.1-py3-none-any.whl", hash = "sha256:cd17daa7f3b9d7a24de3617820e634d0933b69eed8e33a516071174427238c81"},
|
||||
{file = "uvicorn-0.30.1.tar.gz", hash = "sha256:d46cd8e0fd80240baffbcd9ec1012a712938754afcf81bce56c024c1656aece8"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
|
|
@ -3080,4 +3134,4 @@ multidict = ">=4.0"
|
|||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.11"
|
||||
content-hash = "f2ccf666bf19504a96354945c1adbd3beeed52c8c0800a86ead0a384610317b3"
|
||||
content-hash = "1d80abdd00b535835cd97ff65cebb5669c48b51dcf271f3802cf3db07ed3e7d8"
|
||||
|
|
|
|||
|
|
@ -40,6 +40,7 @@ ffmpeg-python = "^0.2.0"
|
|||
jiwer = "^3.0.4"
|
||||
allosaurus = "^1.0.2"
|
||||
python-magic = "^0.4.27"
|
||||
openai = "^1.33.0"
|
||||
|
||||
[tool.poetry.group.dev.dependencies]
|
||||
pytest = "^8.2.0"
|
||||
|
|
|
|||
|
|
@ -51,17 +51,16 @@ class Database:
|
|||
)
|
||||
self.cursor = self.conn.cursor()
|
||||
|
||||
def fetch_file(self, id: int) -> dict:
|
||||
def fetch_file(self, id: str) -> dict:
|
||||
"""
|
||||
Fetches a file record from the database by its ID.
|
||||
|
||||
Args:
|
||||
id (int): The ID of the file to fetch.
|
||||
id (str): The ID of the file to fetch.
|
||||
|
||||
Returns:
|
||||
dict: A dictionary containing the file record's details.
|
||||
"""
|
||||
|
||||
self.cursor.execute("""
|
||||
SELECT column_name, ordinal_position
|
||||
FROM information_schema.columns
|
||||
|
|
@ -97,12 +96,12 @@ class Database:
|
|||
components = snake_case_str.split("_")
|
||||
return components[0] + "".join(x.title() for x in components[1:])
|
||||
|
||||
def get_transcriptions(self, file_id: int) -> list[list]:
|
||||
def get_transcriptions(self, file_id: str) -> list[list]:
|
||||
"""
|
||||
Fetches transcriptions associated with a file from the database.
|
||||
|
||||
Args:
|
||||
file_id (int): The ID of the file to fetch transcriptions for.
|
||||
file_id (str): The ID of the file to fetch transcriptions for.
|
||||
|
||||
Returns:
|
||||
list: A list of lists containing transcription entries, where each inner list represents a file transcription and contains dictionaries with "start", "end", and "value" keys.
|
||||
|
|
|
|||
148
kernel/spectral/error_rates.py
Normal file
148
kernel/spectral/error_rates.py
Normal file
|
|
@ -0,0 +1,148 @@
|
|||
from jiwer import process_words, process_characters
|
||||
import jiwer
|
||||
from typing import Any
|
||||
|
||||
|
||||
def calculate_error_rates(
|
||||
reference_annotations: list[dict], hypothesis_annotations: list[dict]
|
||||
) -> dict | None:
|
||||
"""
|
||||
Calculate error rates between the reference transcription and annotations.
|
||||
|
||||
This function calculates both word-level and character-level error rates
|
||||
based on the provided reference transcription and annotations.
|
||||
|
||||
Parameters:
|
||||
- reference (str): The reference transcription.
|
||||
- annotations (list of dict): The list of annotations where each annotation is a dictionary with a "value" key.
|
||||
|
||||
Returns:
|
||||
- dict: A dictionary containing word-level and character-level error rates.
|
||||
|
||||
"""
|
||||
reference = annotation_to_sentence(reference_annotations)
|
||||
if reference == "":
|
||||
return None
|
||||
hypothesis = annotation_to_sentence(hypothesis_annotations)
|
||||
word_level = word_level_processing(reference, hypothesis)
|
||||
character_level = character_level_processing(reference, hypothesis)
|
||||
|
||||
return {"wordLevel": word_level, "characterLevel": character_level}
|
||||
|
||||
|
||||
def word_level_processing(reference: str, hypothesis: str) -> dict[str, Any]:
|
||||
"""
|
||||
Process word-level error metrics between the reference and hypothesis.
|
||||
|
||||
This function processes word-level metrics.
|
||||
|
||||
Parameters:
|
||||
- reference (str): The reference transcription.
|
||||
- hypothesis (str): The hypothesis transcription.
|
||||
|
||||
Returns:
|
||||
- dict: A dictionary containing word-level error metrics and alignments.
|
||||
|
||||
"""
|
||||
processed_data = process_words(reference=reference, hypothesis=hypothesis)
|
||||
result = {
|
||||
"wer": processed_data.wer,
|
||||
"mer": processed_data.mer,
|
||||
"wil": processed_data.wil,
|
||||
"wip": processed_data.wip,
|
||||
"hits": processed_data.hits,
|
||||
"substitutions": processed_data.substitutions,
|
||||
"insertions": processed_data.insertions,
|
||||
"deletions": processed_data.deletions,
|
||||
"reference": processed_data.references[0],
|
||||
"hypothesis": processed_data.hypotheses[0],
|
||||
"alignments": get_alignments(processed_data.alignments[0]),
|
||||
}
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def character_level_processing(reference: str, hypothesis: str) -> dict[str, Any]:
|
||||
"""
|
||||
Process character-level error metrics between the reference and hypothesis.
|
||||
|
||||
This function processes character-level metrics.
|
||||
|
||||
Parameters:
|
||||
- reference (str): The reference transcription.
|
||||
- hypothesis (str): The hypothesis transcription.
|
||||
|
||||
Returns:
|
||||
- dict: A dictionary containing character-level error metrics and alignments.
|
||||
|
||||
"""
|
||||
processed_data = process_characters(reference=reference, hypothesis=hypothesis)
|
||||
|
||||
result = {
|
||||
"cer": processed_data.cer,
|
||||
"hits": processed_data.hits,
|
||||
"substitutions": processed_data.substitutions,
|
||||
"insertions": processed_data.insertions,
|
||||
"deletions": processed_data.deletions,
|
||||
"reference": processed_data.references[0],
|
||||
"hypothesis": processed_data.hypotheses[0],
|
||||
"alignments": get_alignments(processed_data.alignments[0]),
|
||||
}
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def annotation_to_sentence(annotations: list) -> str:
|
||||
"""
|
||||
Convert annotations to a single hypothesis string.
|
||||
|
||||
This function concatenates the values from the annotations list to form a hypothesis string.
|
||||
|
||||
Parameters:
|
||||
- annotations (list of dict): The list of annotations where each annotation is a dictionary with a "value" key.
|
||||
|
||||
Returns:
|
||||
- str: A single concatenated hypothesis string.
|
||||
|
||||
"""
|
||||
res = ""
|
||||
if len(annotations) == 0:
|
||||
return res
|
||||
|
||||
for annotation in annotations:
|
||||
if annotation["value"] == "":
|
||||
continue
|
||||
res += annotation["value"] + " "
|
||||
|
||||
return res[: len(res) - 1]
|
||||
|
||||
|
||||
def get_alignments(
|
||||
unparsed_alignments: list[jiwer.process.AlignmentChunk],
|
||||
) -> list[dict]:
|
||||
"""
|
||||
Convert unparsed alignments into a structured format.
|
||||
|
||||
This function processes unparsed alignment data and converts it into a list of dictionaries
|
||||
with detailed alignment information.
|
||||
|
||||
Parameters:
|
||||
- unparsed_alignments (list): A list of unparsed alignment objects.
|
||||
|
||||
Returns:
|
||||
- list of dict: A list of dictionaries where each dictionary contains alignment information.
|
||||
|
||||
"""
|
||||
alignments = []
|
||||
|
||||
for alignment in unparsed_alignments:
|
||||
alignment_dict = {
|
||||
"type": alignment.type,
|
||||
"referenceStartIndex": alignment.ref_start_idx,
|
||||
"referenceEndIndex": alignment.ref_end_idx,
|
||||
"hypothesisStartIndex": alignment.hyp_start_idx,
|
||||
"hypothesisEndIndex": alignment.hyp_end_idx,
|
||||
}
|
||||
alignments.append(alignment_dict)
|
||||
|
||||
return alignments
|
||||
|
|
@ -14,7 +14,7 @@ from .response_examples import (
|
|||
signal_modes_response_examples,
|
||||
transcription_response_examples,
|
||||
)
|
||||
from .transcription import get_transcription
|
||||
from .transcription.transcription import get_transcription
|
||||
from .data_objects import (
|
||||
SimpleInfoResponse,
|
||||
VowelSpaceResponse,
|
||||
|
|
@ -101,19 +101,20 @@ async def analyze_signal_mode(
|
|||
Raises:
|
||||
- HTTPException: If the mode is not found or input data is invalid.
|
||||
"""
|
||||
db_session = next(database)
|
||||
fileState = json.loads(fileState)
|
||||
if mode == "simple-info":
|
||||
return simple_info_mode(database, fileState)
|
||||
return simple_info_mode(db_session, fileState)
|
||||
if mode == "spectrogram":
|
||||
return spectrogram_mode(database, fileState)
|
||||
return spectrogram_mode(db_session, fileState)
|
||||
if mode == "waveform":
|
||||
return waveform_mode(database, fileState)
|
||||
return waveform_mode(db_session, fileState)
|
||||
if mode == "vowel-space":
|
||||
return vowel_space_mode(database, fileState)
|
||||
return vowel_space_mode(db_session, fileState)
|
||||
if mode == "transcription":
|
||||
return transcription_mode(database, fileState)
|
||||
return transcription_mode(db_session, fileState)
|
||||
if mode == "error-rate":
|
||||
return error_rate_mode(database, fileState)
|
||||
return error_rate_mode(db_session, fileState)
|
||||
|
||||
|
||||
@app.get(
|
||||
|
|
@ -141,8 +142,9 @@ async def transcribe_file(
|
|||
Raises:
|
||||
- HTTPException: If the file is not found or an error occurs during transcription or storing the transcription.
|
||||
"""
|
||||
db_session = next(database)
|
||||
try:
|
||||
file = database.fetch_file(file_id)
|
||||
file = db_session.fetch_file(file_id)
|
||||
except Exception as _:
|
||||
raise HTTPException(status_code=404, detail="File not found")
|
||||
|
||||
|
|
|
|||
|
|
@ -7,7 +7,7 @@ from .frame_analysis import (
|
|||
calculate_frame_f1_f2,
|
||||
validate_frame_index,
|
||||
)
|
||||
from .transcription import calculate_error_rates
|
||||
from .error_rates import calculate_error_rates
|
||||
from .types import FileStateType
|
||||
import tempfile
|
||||
import subprocess
|
||||
|
|
@ -46,7 +46,9 @@ def simple_info_mode(database: Database, file_state: FileStateType) -> dict[str,
|
|||
|
||||
frame_index = validate_frame_index(audio.get_array_of_samples(), file_state)
|
||||
|
||||
result["frame"] = simple_frame_info(audio.get_array_of_samples(), audio.frame_rate, frame_index)
|
||||
result["frame"] = simple_frame_info(
|
||||
audio.get_array_of_samples(), audio.frame_rate, frame_index
|
||||
)
|
||||
|
||||
return result
|
||||
|
||||
|
|
@ -65,7 +67,9 @@ def waveform_mode(database: Database, file_state: FileStateType) -> Any:
|
|||
return None
|
||||
|
||||
|
||||
def vowel_space_mode(database: Database, file_state: FileStateType) -> dict[str, float] | None:
|
||||
def vowel_space_mode(
|
||||
database: Database, file_state: FileStateType
|
||||
) -> dict[str, float] | None:
|
||||
"""
|
||||
Extracts and returns the first and second formants of a specified frame.
|
||||
|
||||
|
|
@ -105,7 +109,9 @@ def transcription_mode(database: Database, file_state: FileStateType) -> Any:
|
|||
return None
|
||||
|
||||
|
||||
def error_rate_mode(database: Database, file_state: FileStateType) -> dict[str, Any] | None:
|
||||
def error_rate_mode(
|
||||
database: Database, file_state: FileStateType
|
||||
) -> dict[str, Any] | None:
|
||||
"""
|
||||
Calculate the error rates of transcriptions against the ground truth.
|
||||
|
||||
|
|
@ -163,6 +169,8 @@ def get_file(database: Database, file_state: FileStateType) -> FileStateType:
|
|||
if "id" not in file_state:
|
||||
raise HTTPException(status_code=404, detail="file_state did not include id")
|
||||
try:
|
||||
print(file_state["id"])
|
||||
print(database)
|
||||
file = database.fetch_file(file_state["id"])
|
||||
except Exception as _:
|
||||
raise HTTPException(status_code=404, detail="File not found")
|
||||
|
|
|
|||
|
|
@ -1,327 +0,0 @@
|
|||
from deepgram import DeepgramClient, PrerecordedOptions, FileSource
|
||||
from fastapi import HTTPException
|
||||
from jiwer import process_words, process_characters
|
||||
import jiwer
|
||||
from .signal_analysis import get_audio, calculate_signal_duration
|
||||
from .types import FileStateType
|
||||
from allosaurus.app import read_recognizer # type: ignore
|
||||
import tempfile
|
||||
import os
|
||||
from typing import Any
|
||||
|
||||
|
||||
def calculate_error_rates(
|
||||
reference_annotations: list[dict], hypothesis_annotations: list[dict]
|
||||
) -> dict | None:
|
||||
"""
|
||||
Calculate error rates between the reference transcription and annotations.
|
||||
|
||||
This function calculates both word-level and character-level error rates
|
||||
based on the provided reference transcription and annotations.
|
||||
|
||||
Parameters:
|
||||
- reference (str): The reference transcription.
|
||||
- annotations (list of dict): The list of annotations where each annotation is a dictionary with a "value" key.
|
||||
|
||||
Returns:
|
||||
- dict: A dictionary containing word-level and character-level error rates.
|
||||
|
||||
"""
|
||||
reference = annotation_to_sentence(reference_annotations)
|
||||
if reference == "":
|
||||
return None
|
||||
hypothesis = annotation_to_sentence(hypothesis_annotations)
|
||||
word_level = word_level_processing(reference, hypothesis)
|
||||
character_level = character_level_processing(reference, hypothesis)
|
||||
|
||||
return {"wordLevel": word_level, "characterLevel": character_level}
|
||||
|
||||
|
||||
def annotation_to_sentence(annotations: list) -> str:
|
||||
"""
|
||||
Convert annotations to a single hypothesis string.
|
||||
|
||||
This function concatenates the values from the annotations list to form a hypothesis string.
|
||||
|
||||
Parameters:
|
||||
- annotations (list of dict): The list of annotations where each annotation is a dictionary with a "value" key.
|
||||
|
||||
Returns:
|
||||
- str: A single concatenated hypothesis string.
|
||||
|
||||
"""
|
||||
res = ""
|
||||
if len(annotations) == 0:
|
||||
return res
|
||||
|
||||
for annotation in annotations:
|
||||
if annotation["value"] == "":
|
||||
continue
|
||||
res += annotation["value"] + " "
|
||||
|
||||
return res[: len(res) - 1]
|
||||
|
||||
|
||||
def word_level_processing(reference: str, hypothesis: str) -> dict[str, Any]:
|
||||
"""
|
||||
Process word-level error metrics between the reference and hypothesis.
|
||||
|
||||
This function processes word-level metrics.
|
||||
|
||||
Parameters:
|
||||
- reference (str): The reference transcription.
|
||||
- hypothesis (str): The hypothesis transcription.
|
||||
|
||||
Returns:
|
||||
- dict: A dictionary containing word-level error metrics and alignments.
|
||||
|
||||
"""
|
||||
processed_data = process_words(reference=reference, hypothesis=hypothesis)
|
||||
result = {
|
||||
"wer": processed_data.wer,
|
||||
"mer": processed_data.mer,
|
||||
"wil": processed_data.wil,
|
||||
"wip": processed_data.wip,
|
||||
"hits": processed_data.hits,
|
||||
"substitutions": processed_data.substitutions,
|
||||
"insertions": processed_data.insertions,
|
||||
"deletions": processed_data.deletions,
|
||||
"reference": processed_data.references[0],
|
||||
"hypothesis": processed_data.hypotheses[0],
|
||||
"alignments": get_alignments(processed_data.alignments[0]),
|
||||
}
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def character_level_processing(reference: str, hypothesis: str) -> dict[str, Any]:
|
||||
"""
|
||||
Process character-level error metrics between the reference and hypothesis.
|
||||
|
||||
This function processes character-level metrics.
|
||||
|
||||
Parameters:
|
||||
- reference (str): The reference transcription.
|
||||
- hypothesis (str): The hypothesis transcription.
|
||||
|
||||
Returns:
|
||||
- dict: A dictionary containing character-level error metrics and alignments.
|
||||
|
||||
"""
|
||||
processed_data = process_characters(reference=reference, hypothesis=hypothesis)
|
||||
|
||||
result = {
|
||||
"cer": processed_data.cer,
|
||||
"hits": processed_data.hits,
|
||||
"substitutions": processed_data.substitutions,
|
||||
"insertions": processed_data.insertions,
|
||||
"deletions": processed_data.deletions,
|
||||
"reference": processed_data.references[0],
|
||||
"hypothesis": processed_data.hypotheses[0],
|
||||
"alignments": get_alignments(processed_data.alignments[0]),
|
||||
}
|
||||
|
||||
return result
|
||||
|
||||
|
||||
def get_alignments(unparsed_alignments: list[jiwer.process.AlignmentChunk]) -> list[dict]:
|
||||
"""
|
||||
Convert unparsed alignments into a structured format.
|
||||
|
||||
This function processes unparsed alignment data and converts it into a list of dictionaries
|
||||
with detailed alignment information.
|
||||
|
||||
Parameters:
|
||||
- unparsed_alignments (list): A list of unparsed alignment objects.
|
||||
|
||||
Returns:
|
||||
- list of dict: A list of dictionaries where each dictionary contains alignment information.
|
||||
|
||||
"""
|
||||
alignments = []
|
||||
|
||||
for alignment in unparsed_alignments:
|
||||
alignment_dict = {
|
||||
"type": alignment.type,
|
||||
"referenceStartIndex": alignment.ref_start_idx,
|
||||
"referenceEndIndex": alignment.ref_end_idx,
|
||||
"hypothesisStartIndex": alignment.hyp_start_idx,
|
||||
"hypothesisEndIndex": alignment.hyp_end_idx,
|
||||
}
|
||||
alignments.append(alignment_dict)
|
||||
|
||||
return alignments
|
||||
|
||||
|
||||
def get_transcription(model: str, file: FileStateType):
|
||||
"""
|
||||
Get transcription of an audio file using the specified model.
|
||||
|
||||
This function gets the transcription of an audio file using the specified model.
|
||||
|
||||
Parameters:
|
||||
- model (str): The transcription model to use.
|
||||
- file (dict): The file object containing the audio data.
|
||||
|
||||
Returns:
|
||||
- list: A list of transcriptions containing words with their start and end times.
|
||||
|
||||
Raises:
|
||||
- HTTPException: If the specified model is not found.
|
||||
"""
|
||||
if model == "deepgram":
|
||||
return fill_gaps(deepgram_transcription(file["data"]), file)
|
||||
if model == "allosaurus":
|
||||
return fill_gaps(allosaurs_transcription(file), file)
|
||||
raise HTTPException(status_code=404, detail="Model was not found")
|
||||
|
||||
|
||||
def fill_gaps(transcriptions: list[dict], file: FileStateType) -> list[dict]:
|
||||
res = []
|
||||
|
||||
audio = get_audio(file)
|
||||
duration = calculate_signal_duration(audio)
|
||||
|
||||
if len(transcriptions) == 0:
|
||||
return [{"value": "", "start": 0, "end": duration}]
|
||||
|
||||
time = 0
|
||||
|
||||
for transcription in transcriptions:
|
||||
if time != transcription["start"]:
|
||||
res.append({"value": "", "start": time, "end": transcription["start"]})
|
||||
time = transcription["end"]
|
||||
res.append(transcription)
|
||||
|
||||
if time != duration:
|
||||
res.append({"value": "", "start": time, "end": duration})
|
||||
|
||||
return res
|
||||
|
||||
|
||||
def deepgram_transcription(data: bytes) -> list[dict]:
|
||||
"""
|
||||
Transcribe audio data using Deepgram API.
|
||||
|
||||
This function transcribes audio data using the Deepgram API.
|
||||
|
||||
Parameters:
|
||||
- data (bytes): The audio data to transcribe.
|
||||
|
||||
Returns:
|
||||
- list: A list of transcriptions containing words with their start and end times.
|
||||
|
||||
Raises:
|
||||
- Exception: If an error occurs during the transcription process.
|
||||
"""
|
||||
try:
|
||||
# STEP 1: Create a Deepgram client using the API key
|
||||
key = os.getenv("DG_KEY")
|
||||
deepgram = None
|
||||
if key is None:
|
||||
raise Exception("No API key for Deepgram is found")
|
||||
else:
|
||||
deepgram = DeepgramClient(key)
|
||||
|
||||
payload: FileSource = {
|
||||
"buffer": data,
|
||||
}
|
||||
|
||||
# STEP 2: Configure Deepgram options for audio analysis
|
||||
options = PrerecordedOptions(
|
||||
model="nova-2",
|
||||
smart_format=True,
|
||||
profanity_filter=False,
|
||||
)
|
||||
|
||||
# STEP 3: Call the transcribe_file method with the text payload and options
|
||||
response = deepgram.listen.prerecorded.v("1").transcribe_file(payload, options)
|
||||
|
||||
res = []
|
||||
for word in response["results"]["channels"][0]["alternatives"][0]["words"]:
|
||||
res.append({"value": word["word"], "start": word["start"], "end": word["end"]})
|
||||
return res
|
||||
|
||||
except Exception as e:
|
||||
print(f"Exception: {e}")
|
||||
return []
|
||||
|
||||
|
||||
def allosaurs_transcription(file: FileStateType) -> Any:
|
||||
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_wav:
|
||||
temp_wav.write(file["data"])
|
||||
temp_wav_filename = temp_wav.name
|
||||
|
||||
word_level_transcription = fill_gaps(deepgram_transcription(file["data"]), file)
|
||||
|
||||
model = read_recognizer()
|
||||
phoneme_level_transcription = model.recognize(temp_wav_filename, timestamp=True, emit=1.2)
|
||||
|
||||
phoneme_level_parsed = []
|
||||
|
||||
for phoneme_string in phoneme_level_transcription.splitlines():
|
||||
phoneme_level_parsed.append(
|
||||
[float(phoneme_string.split(" ")[0]), phoneme_string.split(" ")[2]]
|
||||
)
|
||||
|
||||
phoneme_word_splits = get_phoneme_word_splits(word_level_transcription, phoneme_level_parsed)
|
||||
return get_phoneme_transcriptions(phoneme_word_splits)
|
||||
|
||||
|
||||
def get_phoneme_word_splits(
|
||||
word_level_transcription: list[dict], phoneme_level_parsed: list[list]
|
||||
) -> list[dict]:
|
||||
if len(word_level_transcription) == 0:
|
||||
return []
|
||||
|
||||
word_pointer = 0
|
||||
phoneme_pointer = 0
|
||||
|
||||
phoneme_word_splits = []
|
||||
|
||||
current_split = {"phonemes": [], "word_transcription": None}
|
||||
|
||||
while word_pointer < len(word_level_transcription) and phoneme_pointer < len(
|
||||
phoneme_level_parsed
|
||||
):
|
||||
if phoneme_level_parsed[phoneme_pointer][0] > word_level_transcription[word_pointer]["end"]:
|
||||
current_split["word_transcription"] = word_level_transcription[word_pointer]
|
||||
phoneme_word_splits.append(current_split)
|
||||
current_split = {"phonemes": [], "word_transcription": None}
|
||||
word_pointer += 1
|
||||
continue
|
||||
|
||||
current_split["phonemes"].append(phoneme_level_parsed[phoneme_pointer])
|
||||
phoneme_pointer += 1
|
||||
|
||||
if phoneme_pointer == len(phoneme_level_parsed):
|
||||
current_split["word_transcription"] = word_level_transcription[word_pointer]
|
||||
phoneme_word_splits.append(current_split)
|
||||
|
||||
return phoneme_word_splits
|
||||
|
||||
|
||||
def get_phoneme_transcriptions(phoneme_word_splits: list[Any]) -> list[dict]:
|
||||
res = []
|
||||
|
||||
for phoneme_split in phoneme_word_splits:
|
||||
if len(phoneme_split) == 0:
|
||||
continue
|
||||
|
||||
for i in range(len(phoneme_split["phonemes"])):
|
||||
start = 0
|
||||
if i == 0:
|
||||
start = phoneme_split["word_transcription"]["start"]
|
||||
else:
|
||||
# this is an (educated) guess, it could be way off :D
|
||||
start = (phoneme_split["phonemes"][i - 1][0] + phoneme_split["phonemes"][i][0]) / 2
|
||||
|
||||
end = 0
|
||||
if i + 1 == len(phoneme_split["phonemes"]):
|
||||
end = phoneme_split["word_transcription"]["end"]
|
||||
else:
|
||||
end = (phoneme_split["phonemes"][i + 1][0] + phoneme_split["phonemes"][i][0]) / 2
|
||||
|
||||
res.append({"value": phoneme_split["phonemes"][i][1], "start": start, "end": end})
|
||||
|
||||
return res
|
||||
100
kernel/spectral/transcription/models/allosaurus.py
Normal file
100
kernel/spectral/transcription/models/allosaurus.py
Normal file
|
|
@ -0,0 +1,100 @@
|
|||
import tempfile
|
||||
from ...types import FileStateType
|
||||
from ..transcription_utils import fill_gaps
|
||||
from .deepgram import deepgram_transcription
|
||||
from allosaurus.app import read_recognizer # type: ignore
|
||||
|
||||
|
||||
def allosaurus_transcription(file: FileStateType) -> list[dict]:
|
||||
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_wav:
|
||||
temp_wav.write(file["data"])
|
||||
temp_wav_filename = temp_wav.name
|
||||
|
||||
word_level_transcription = fill_gaps(deepgram_transcription(file["data"]), file)
|
||||
|
||||
model = read_recognizer()
|
||||
phoneme_level_transcription = model.recognize(
|
||||
temp_wav_filename, timestamp=True, emit=1.2
|
||||
)
|
||||
|
||||
phoneme_level_parsed = []
|
||||
|
||||
for phoneme_string in phoneme_level_transcription.splitlines():
|
||||
phoneme_level_parsed.append(
|
||||
[float(phoneme_string.split(" ")[0]), phoneme_string.split(" ")[2]]
|
||||
)
|
||||
|
||||
phoneme_word_splits = get_phoneme_word_splits(
|
||||
word_level_transcription, phoneme_level_parsed
|
||||
)
|
||||
return get_phoneme_transcriptions(phoneme_word_splits)
|
||||
|
||||
|
||||
def get_phoneme_word_splits(
|
||||
word_level_transcription: list[dict], phoneme_level_parsed: list[list]
|
||||
) -> list[dict]:
|
||||
if len(word_level_transcription) == 0:
|
||||
return []
|
||||
|
||||
word_pointer = 0
|
||||
phoneme_pointer = 0
|
||||
|
||||
phoneme_word_splits = []
|
||||
|
||||
current_split = {"phonemes": [], "word_transcription": None}
|
||||
|
||||
while word_pointer < len(word_level_transcription) and phoneme_pointer < len(
|
||||
phoneme_level_parsed
|
||||
):
|
||||
if (
|
||||
phoneme_level_parsed[phoneme_pointer][0]
|
||||
> word_level_transcription[word_pointer]["end"]
|
||||
):
|
||||
current_split["word_transcription"] = word_level_transcription[word_pointer]
|
||||
phoneme_word_splits.append(current_split)
|
||||
current_split = {"phonemes": [], "word_transcription": None}
|
||||
word_pointer += 1
|
||||
continue
|
||||
|
||||
current_split["phonemes"].append(phoneme_level_parsed[phoneme_pointer])
|
||||
phoneme_pointer += 1
|
||||
|
||||
if phoneme_pointer == len(phoneme_level_parsed):
|
||||
current_split["word_transcription"] = word_level_transcription[word_pointer]
|
||||
phoneme_word_splits.append(current_split)
|
||||
|
||||
return phoneme_word_splits
|
||||
|
||||
|
||||
def get_phoneme_transcriptions(phoneme_word_splits: list[dict]) -> list[dict]:
|
||||
res = []
|
||||
|
||||
for phoneme_split in phoneme_word_splits:
|
||||
if len(phoneme_split) == 0:
|
||||
continue
|
||||
|
||||
for i in range(len(phoneme_split["phonemes"])):
|
||||
start = 0
|
||||
if i == 0:
|
||||
start = phoneme_split["word_transcription"]["start"]
|
||||
else:
|
||||
# this is an (educated) guess, it could be way off :D
|
||||
start = (
|
||||
phoneme_split["phonemes"][i - 1][0]
|
||||
+ phoneme_split["phonemes"][i][0]
|
||||
) / 2
|
||||
|
||||
end = 0
|
||||
if i + 1 == len(phoneme_split["phonemes"]):
|
||||
end = phoneme_split["word_transcription"]["end"]
|
||||
else:
|
||||
end = (
|
||||
phoneme_split["phonemes"][i + 1][0]
|
||||
+ phoneme_split["phonemes"][i][0]
|
||||
) / 2
|
||||
|
||||
res.append(
|
||||
{"value": phoneme_split["phonemes"][i][1], "start": start, "end": end}
|
||||
)
|
||||
|
||||
return res
|
||||
52
kernel/spectral/transcription/models/deepgram.py
Normal file
52
kernel/spectral/transcription/models/deepgram.py
Normal file
|
|
@ -0,0 +1,52 @@
|
|||
from deepgram import DeepgramClient, PrerecordedOptions, FileSource
|
||||
import os
|
||||
|
||||
|
||||
def deepgram_transcription(data: bytes) -> list[dict]:
|
||||
"""
|
||||
Transcribe audio data using Deepgram API.
|
||||
|
||||
This function transcribes audio data using the Deepgram API.
|
||||
|
||||
Parameters:
|
||||
- data (bytes): The audio data to transcribe.
|
||||
|
||||
Returns:
|
||||
- list: A list of transcriptions containing words with their start and end times.
|
||||
|
||||
Raises:
|
||||
- Exception: If an error occurs during the transcription process.
|
||||
"""
|
||||
try:
|
||||
# STEP 1: Create a Deepgram client using the API key
|
||||
key = os.getenv("DG_KEY")
|
||||
deepgram = None
|
||||
if key is None:
|
||||
raise Exception("No API key for Deepgram is found")
|
||||
else:
|
||||
deepgram = DeepgramClient(key)
|
||||
|
||||
payload: FileSource = {
|
||||
"buffer": data,
|
||||
}
|
||||
|
||||
# STEP 2: Configure Deepgram options for audio analysis
|
||||
options = PrerecordedOptions(
|
||||
model="nova-2",
|
||||
smart_format=True,
|
||||
profanity_filter=False,
|
||||
)
|
||||
|
||||
# STEP 3: Call the transcribe_file method with the text payload and options
|
||||
response = deepgram.listen.prerecorded.v("1").transcribe_file(payload, options)
|
||||
|
||||
res = []
|
||||
for word in response["results"]["channels"][0]["alternatives"][0]["words"]:
|
||||
res.append(
|
||||
{"value": word["word"], "start": word["start"], "end": word["end"]}
|
||||
)
|
||||
return res
|
||||
|
||||
except Exception as e:
|
||||
print(f"Exception: {e}")
|
||||
return []
|
||||
34
kernel/spectral/transcription/models/whisper.py
Normal file
34
kernel/spectral/transcription/models/whisper.py
Normal file
|
|
@ -0,0 +1,34 @@
|
|||
from openai import OpenAI
|
||||
import os
|
||||
import tempfile
|
||||
|
||||
|
||||
def whisper_transcription(data: bytes) -> list[dict]:
|
||||
try:
|
||||
client = OpenAI(api_key=os.getenv("WHISPER_KEY"))
|
||||
with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as temp_wav:
|
||||
temp_wav.write(data)
|
||||
temp_wav_filename = temp_wav.name
|
||||
|
||||
transcription = client.audio.transcriptions.create(
|
||||
model="whisper-1",
|
||||
file=open(temp_wav_filename, "rb"),
|
||||
response_format="verbose_json",
|
||||
timestamp_granularities=["word"],
|
||||
)
|
||||
|
||||
res = []
|
||||
|
||||
if hasattr(transcription, "words"):
|
||||
words = transcription.words # pyright: ignore[reportAttributeAccessIssue]
|
||||
|
||||
for word in words:
|
||||
res.append(
|
||||
{"value": word["word"], "start": word["start"], "end": word["end"]}
|
||||
)
|
||||
|
||||
return res
|
||||
|
||||
except Exception as e:
|
||||
print(f"Exception: {e}")
|
||||
return []
|
||||
31
kernel/spectral/transcription/transcription.py
Normal file
31
kernel/spectral/transcription/transcription.py
Normal file
|
|
@ -0,0 +1,31 @@
|
|||
from fastapi import HTTPException
|
||||
from ..types import FileStateType
|
||||
from .transcription_utils import fill_gaps
|
||||
from .models.allosaurus import allosaurus_transcription
|
||||
from .models.deepgram import deepgram_transcription
|
||||
from .models.whisper import whisper_transcription
|
||||
|
||||
|
||||
def get_transcription(model: str, file: FileStateType):
|
||||
"""
|
||||
Get transcription of an audio file using the specified model.
|
||||
|
||||
This function gets the transcription of an audio file using the specified model.
|
||||
|
||||
Parameters:
|
||||
- model (str): The transcription model to use.
|
||||
- file (dict): The file object containing the audio data.
|
||||
|
||||
Returns:
|
||||
- list: A list of transcriptions containing words with their start and end times.
|
||||
|
||||
Raises:
|
||||
- HTTPException: If the specified model is not found.
|
||||
"""
|
||||
if model == "deepgram":
|
||||
return fill_gaps(deepgram_transcription(file["data"]), file)
|
||||
if model == "whisper":
|
||||
return fill_gaps(whisper_transcription(file["data"]), file)
|
||||
if model == "allosaurus":
|
||||
return fill_gaps(allosaurus_transcription(file), file)
|
||||
raise HTTPException(status_code=404, detail="Model was not found")
|
||||
25
kernel/spectral/transcription/transcription_utils.py
Normal file
25
kernel/spectral/transcription/transcription_utils.py
Normal file
|
|
@ -0,0 +1,25 @@
|
|||
from ..signal_analysis import get_audio, calculate_signal_duration
|
||||
from ..types import FileStateType
|
||||
|
||||
|
||||
def fill_gaps(transcriptions: list[dict], file: FileStateType) -> list[dict]:
|
||||
res = []
|
||||
|
||||
audio = get_audio(file)
|
||||
duration = calculate_signal_duration(audio)
|
||||
|
||||
if len(transcriptions) == 0:
|
||||
return [{"value": "", "start": 0, "end": duration}]
|
||||
|
||||
time = 0
|
||||
|
||||
for transcription in transcriptions:
|
||||
if time != transcription["start"]:
|
||||
res.append({"value": "", "start": time, "end": transcription["start"]})
|
||||
time = transcription["end"]
|
||||
res.append(transcription)
|
||||
|
||||
if time != duration:
|
||||
res.append({"value": "", "start": time, "end": duration})
|
||||
|
||||
return res
|
||||
|
|
@ -1,9 +1,11 @@
|
|||
import pytest
|
||||
from unittest.mock import Mock, patch
|
||||
from fastapi import HTTPException
|
||||
from spectral.transcription import (
|
||||
from kernel.spectral.transcription.transcription import (
|
||||
get_transcription,
|
||||
deepgram_transcription,
|
||||
)
|
||||
from kernel.spectral.transcription.models.allosaurus import (
|
||||
get_phoneme_transcriptions,
|
||||
get_phoneme_word_splits,
|
||||
)
|
||||
|
|
@ -68,7 +70,9 @@ def test_deepgram_transcription(mock_deepgram_client):
|
|||
]
|
||||
}
|
||||
}
|
||||
mock_client_instance.listen.prerecorded.v("1").transcribe_file.return_value = mock_response
|
||||
mock_client_instance.listen.prerecorded.v(
|
||||
"1"
|
||||
).transcribe_file.return_value = mock_response
|
||||
|
||||
data = b"audio data"
|
||||
result = deepgram_transcription(data)
|
||||
|
|
@ -80,7 +84,11 @@ def test_deepgram_transcription(mock_deepgram_client):
|
|||
|
||||
assert result == expected_result, f"Expected {expected_result}, but got {result}"
|
||||
(mock_deepgram_client.assert_called_once_with("test_key"))
|
||||
(mock_client_instance.listen.prerecorded.v("1").transcribe_file.assert_called_once())
|
||||
(
|
||||
mock_client_instance.listen.prerecorded.v(
|
||||
"1"
|
||||
).transcribe_file.assert_called_once()
|
||||
)
|
||||
|
||||
|
||||
@patch.dict(os.environ, {}, clear=True)
|
||||
|
|
@ -95,7 +103,7 @@ def test_deepgram_transcription_no_api_key(capfd):
|
|||
|
||||
|
||||
def test_get_phoneme_transcription_empty_transcription():
|
||||
result = get_phoneme_transcriptions([[]])
|
||||
result = get_phoneme_transcriptions([{}])
|
||||
expected_result = []
|
||||
|
||||
assert result == expected_result, f"Expected an empty list, but got {result}"
|
||||
|
|
|
|||
Loading…
Add table
Add a link
Reference in a new issue