vrem ton working
This commit is contained in:
@@ -8,6 +8,9 @@ __pycache__/
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# Custom
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# Custom
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data/model_small/
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data/model_small/
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data/model_large/
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data/model_large/
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data/v4_ru.pt
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wav_files/
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vocal.wav
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# C extensions
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# C extensions
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*.so
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*.so
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Generated
+50
-149
@@ -701,25 +701,6 @@ files = [
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{file = "kiwisolver-1.4.5.tar.gz", hash = "sha256:e57e563a57fb22a142da34f38acc2fc1a5c864bc29ca1517a88abc963e60d6ec"},
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{file = "kiwisolver-1.4.5.tar.gz", hash = "sha256:e57e563a57fb22a142da34f38acc2fc1a5c864bc29ca1517a88abc963e60d6ec"},
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]
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]
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[[package]]
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name = "lazy-loader"
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version = "0.4"
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description = "Makes it easy to load subpackages and functions on demand."
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optional = false
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python-versions = ">=3.7"
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files = [
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{file = "lazy_loader-0.4-py3-none-any.whl", hash = "sha256:342aa8e14d543a154047afb4ba8ef17f5563baad3fc610d7b15b213b0f119efc"},
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{file = "lazy_loader-0.4.tar.gz", hash = "sha256:47c75182589b91a4e1a85a136c074285a5ad4d9f39c63e0d7fb76391c4574cd1"},
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]
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[package.dependencies]
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packaging = "*"
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[package.extras]
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dev = ["changelist (==0.5)"]
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lint = ["pre-commit (==3.7.0)"]
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test = ["pytest (>=7.4)", "pytest-cov (>=4.1)"]
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[[package]]
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[[package]]
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name = "levenshtein"
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name = "levenshtein"
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version = "0.23.0"
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version = "0.23.0"
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@@ -842,34 +823,32 @@ rapidfuzz = ">=3.1.0,<4.0.0"
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[[package]]
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[[package]]
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name = "librosa"
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name = "librosa"
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version = "0.10.1"
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version = "0.8.1"
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description = "Python module for audio and music processing"
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description = "Python module for audio and music processing"
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optional = false
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optional = false
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python-versions = ">=3.7"
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python-versions = ">=3.6"
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files = [
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files = [
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{file = "librosa-0.10.1-py3-none-any.whl", hash = "sha256:7ab91d9f5fcb75ea14848a05d3b1f825cf8d0c42ca160d19ae6874f2de2d8223"},
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{file = "librosa-0.8.1-py3-none-any.whl", hash = "sha256:fd381e2d7067d4d4cf7691f2ef3620ef62a8aa6445dcf407e3328254692f742a"},
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{file = "librosa-0.10.1.tar.gz", hash = "sha256:832f7d150d6dd08ed2aa08c0567a4be58330635c32ddd2208de9bc91300802c7"},
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{file = "librosa-0.8.1.tar.gz", hash = "sha256:c53d05e768ae4a3e553ae21c2e5015293e5efbfd5c12d497f1104cb519cca6b3"},
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]
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]
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[package.dependencies]
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[package.dependencies]
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audioread = ">=2.1.9"
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audioread = ">=2.0.0"
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decorator = ">=4.3.0"
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decorator = ">=3.0.0"
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joblib = ">=0.14"
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joblib = ">=0.14"
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lazy-loader = ">=0.1"
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numba = ">=0.43.0"
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msgpack = ">=1.0"
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numpy = ">=1.15.0"
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numba = ">=0.51.0"
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packaging = ">=20.0"
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numpy = ">=1.20.3,<1.22.0 || >1.22.0,<1.22.1 || >1.22.1,<1.22.2 || >1.22.2"
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pooch = ">=1.0"
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pooch = ">=1.0"
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scikit-learn = ">=0.20.0"
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resampy = ">=0.2.2"
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scipy = ">=1.2.0"
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scikit-learn = ">=0.14.0,<0.19.0 || >0.19.0"
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soundfile = ">=0.12.1"
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scipy = ">=1.0.0"
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soxr = ">=0.3.2"
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soundfile = ">=0.10.2"
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typing-extensions = ">=4.1.1"
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[package.extras]
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[package.extras]
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display = ["matplotlib (>=3.3.0)"]
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display = ["matplotlib (>=1.5)"]
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docs = ["ipython (>=7.0)", "matplotlib (>=3.3.0)", "mir-eval (>=0.5)", "numba (>=0.51)", "numpydoc", "presets", "sphinx (!=1.3.1)", "sphinx-gallery (>=0.7)", "sphinx-multiversion (>=0.2.3)", "sphinx-rtd-theme (>=1.2.0)", "sphinxcontrib-svg2pdfconverter"]
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docs = ["matplotlib (>=2.0.0,<3.3)", "numba (<0.50)", "numpydoc", "presets", "sphinx (!=1.3.1)", "sphinx-gallery (>=0.7)", "sphinx-multiversion (>=0.2.3)", "sphinx-rtd-theme (==0.5.*)", "spinxcontrib-svg2pdfconverter"]
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tests = ["matplotlib (>=3.3.0)", "packaging (>=20.0)", "pytest", "pytest-cov", "pytest-mpl", "resampy (>=0.2.2)", "samplerate", "types-decorator"]
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tests = ["contextlib2", "matplotlib (>=3.0)", "pytest", "pytest-cov", "pytest-mpl", "samplerate", "soxr"]
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[[package]]
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[[package]]
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name = "llvmlite"
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name = "llvmlite"
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@@ -1107,71 +1086,6 @@ docs = ["sphinx"]
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gmpy = ["gmpy2 (>=2.1.0a4)"]
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gmpy = ["gmpy2 (>=2.1.0a4)"]
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tests = ["pytest (>=4.6)"]
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tests = ["pytest (>=4.6)"]
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[[package]]
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name = "msgpack"
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version = "1.0.8"
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description = "MessagePack serializer"
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optional = false
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python-versions = ">=3.8"
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files = [
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{file = "msgpack-1.0.8-cp310-cp310-macosx_10_9_universal2.whl", hash = "sha256:505fe3d03856ac7d215dbe005414bc28505d26f0c128906037e66d98c4e95868"},
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{file = "msgpack-1.0.8-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:e6b7842518a63a9f17107eb176320960ec095a8ee3b4420b5f688e24bf50c53c"},
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||||||
{file = "msgpack-1.0.8-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:376081f471a2ef24828b83a641a02c575d6103a3ad7fd7dade5486cad10ea659"},
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||||||
{file = "msgpack-1.0.8-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5e390971d082dba073c05dbd56322427d3280b7cc8b53484c9377adfbae67dc2"},
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||||||
{file = "msgpack-1.0.8-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:00e073efcba9ea99db5acef3959efa45b52bc67b61b00823d2a1a6944bf45982"},
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{file = "msgpack-1.0.8-cp310-cp310-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:82d92c773fbc6942a7a8b520d22c11cfc8fd83bba86116bfcf962c2f5c2ecdaa"},
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||||||
{file = "msgpack-1.0.8-cp310-cp310-musllinux_1_1_aarch64.whl", hash = "sha256:9ee32dcb8e531adae1f1ca568822e9b3a738369b3b686d1477cbc643c4a9c128"},
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||||||
{file = "msgpack-1.0.8-cp310-cp310-musllinux_1_1_i686.whl", hash = "sha256:e3aa7e51d738e0ec0afbed661261513b38b3014754c9459508399baf14ae0c9d"},
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||||||
{file = "msgpack-1.0.8-cp310-cp310-musllinux_1_1_x86_64.whl", hash = "sha256:69284049d07fce531c17404fcba2bb1df472bc2dcdac642ae71a2d079d950653"},
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||||||
{file = "msgpack-1.0.8-cp310-cp310-win32.whl", hash = "sha256:13577ec9e247f8741c84d06b9ece5f654920d8365a4b636ce0e44f15e07ec693"},
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||||||
{file = "msgpack-1.0.8-cp310-cp310-win_amd64.whl", hash = "sha256:e532dbd6ddfe13946de050d7474e3f5fb6ec774fbb1a188aaf469b08cf04189a"},
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||||||
{file = "msgpack-1.0.8-cp311-cp311-macosx_10_9_universal2.whl", hash = "sha256:9517004e21664f2b5a5fd6333b0731b9cf0817403a941b393d89a2f1dc2bd836"},
|
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||||||
{file = "msgpack-1.0.8-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:d16a786905034e7e34098634b184a7d81f91d4c3d246edc6bd7aefb2fd8ea6ad"},
|
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||||||
{file = "msgpack-1.0.8-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:e2872993e209f7ed04d963e4b4fbae72d034844ec66bc4ca403329db2074377b"},
|
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||||||
{file = "msgpack-1.0.8-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:5c330eace3dd100bdb54b5653b966de7f51c26ec4a7d4e87132d9b4f738220ba"},
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||||||
{file = "msgpack-1.0.8-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:83b5c044f3eff2a6534768ccfd50425939e7a8b5cf9a7261c385de1e20dcfc85"},
|
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||||||
{file = "msgpack-1.0.8-cp311-cp311-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:1876b0b653a808fcd50123b953af170c535027bf1d053b59790eebb0aeb38950"},
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||||||
{file = "msgpack-1.0.8-cp311-cp311-musllinux_1_1_aarch64.whl", hash = "sha256:dfe1f0f0ed5785c187144c46a292b8c34c1295c01da12e10ccddfc16def4448a"},
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||||||
{file = "msgpack-1.0.8-cp311-cp311-musllinux_1_1_i686.whl", hash = "sha256:3528807cbbb7f315bb81959d5961855e7ba52aa60a3097151cb21956fbc7502b"},
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||||||
{file = "msgpack-1.0.8-cp311-cp311-musllinux_1_1_x86_64.whl", hash = "sha256:e2f879ab92ce502a1e65fce390eab619774dda6a6ff719718069ac94084098ce"},
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|
||||||
{file = "msgpack-1.0.8-cp311-cp311-win32.whl", hash = "sha256:26ee97a8261e6e35885c2ecd2fd4a6d38252246f94a2aec23665a4e66d066305"},
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|
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{file = "msgpack-1.0.8-cp311-cp311-win_amd64.whl", hash = "sha256:eadb9f826c138e6cf3c49d6f8de88225a3c0ab181a9b4ba792e006e5292d150e"},
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{file = "msgpack-1.0.8-cp312-cp312-macosx_10_9_universal2.whl", hash = "sha256:114be227f5213ef8b215c22dde19532f5da9652e56e8ce969bf0a26d7c419fee"},
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{file = "msgpack-1.0.8-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:0726c282d188e204281ebd8de31724b7d749adebc086873a59efb8cf7ae27df3"},
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{file = "msgpack-1.0.8-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:8db8e423192303ed77cff4dce3a4b88dbfaf43979d280181558af5e2c3c71afc"},
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{file = "msgpack-1.0.8-cp312-cp312-manylinux_2_5_i686.manylinux1_i686.manylinux_2_17_i686.manylinux2014_i686.whl", hash = "sha256:99881222f4a8c2f641f25703963a5cefb076adffd959e0558dc9f803a52d6a58"},
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|
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{file = "msgpack-1.0.8-cp312-cp312-musllinux_1_1_aarch64.whl", hash = "sha256:b5505774ea2a73a86ea176e8a9a4a7c8bf5d521050f0f6f8426afe798689243f"},
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{file = "msgpack-1.0.8-cp312-cp312-musllinux_1_1_i686.whl", hash = "sha256:ef254a06bcea461e65ff0373d8a0dd1ed3aa004af48839f002a0c994a6f72d04"},
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{file = "msgpack-1.0.8-cp312-cp312-musllinux_1_1_x86_64.whl", hash = "sha256:e1dd7839443592d00e96db831eddb4111a2a81a46b028f0facd60a09ebbdd543"},
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{file = "msgpack-1.0.8-cp312-cp312-win32.whl", hash = "sha256:64d0fcd436c5683fdd7c907eeae5e2cbb5eb872fafbc03a43609d7941840995c"},
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{file = "msgpack-1.0.8-cp38-cp38-macosx_10_9_universal2.whl", hash = "sha256:0ceea77719d45c839fd73abcb190b8390412a890df2f83fb8cf49b2a4b5c2f40"},
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||||||
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|
||||||
{file = "msgpack-1.0.8.tar.gz", hash = "sha256:95c02b0e27e706e48d0e5426d1710ca78e0f0628d6e89d5b5a5b91a5f12274f3"},
|
|
||||||
]
|
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "networkx"
|
name = "networkx"
|
||||||
version = "3.3"
|
version = "3.3"
|
||||||
@@ -1935,6 +1849,26 @@ urllib3 = ">=1.21.1,<3"
|
|||||||
socks = ["PySocks (>=1.5.6,!=1.5.7)"]
|
socks = ["PySocks (>=1.5.6,!=1.5.7)"]
|
||||||
use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
|
use-chardet-on-py3 = ["chardet (>=3.0.2,<6)"]
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "resampy"
|
||||||
|
version = "0.4.3"
|
||||||
|
description = "Efficient signal resampling"
|
||||||
|
optional = false
|
||||||
|
python-versions = "*"
|
||||||
|
files = [
|
||||||
|
{file = "resampy-0.4.3-py3-none-any.whl", hash = "sha256:ad2ed64516b140a122d96704e32bc0f92b23f45419e8b8f478e5a05f83edcebd"},
|
||||||
|
{file = "resampy-0.4.3.tar.gz", hash = "sha256:a0d1c28398f0e55994b739650afef4e3974115edbe96cd4bb81968425e916e47"},
|
||||||
|
]
|
||||||
|
|
||||||
|
[package.dependencies]
|
||||||
|
numba = ">=0.53"
|
||||||
|
numpy = ">=1.17"
|
||||||
|
|
||||||
|
[package.extras]
|
||||||
|
design = ["optuna (>=2.10.0)"]
|
||||||
|
docs = ["numpydoc", "sphinx (!=1.3.1)"]
|
||||||
|
tests = ["pytest (<8)", "pytest-cov", "scipy (>=1.1)"]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "rich"
|
name = "rich"
|
||||||
version = "13.7.1"
|
version = "13.7.1"
|
||||||
@@ -2144,52 +2078,6 @@ cffi = ">=1.0"
|
|||||||
[package.extras]
|
[package.extras]
|
||||||
numpy = ["numpy"]
|
numpy = ["numpy"]
|
||||||
|
|
||||||
[[package]]
|
|
||||||
name = "soxr"
|
|
||||||
version = "0.3.7"
|
|
||||||
description = "High quality, one-dimensional sample-rate conversion library"
|
|
||||||
optional = false
|
|
||||||
python-versions = ">=3.6"
|
|
||||||
files = [
|
|
||||||
{file = "soxr-0.3.7-cp310-cp310-macosx_10_9_x86_64.whl", hash = "sha256:ac81c4af6a993d5b7c0b466bbac4835bad2b14ec32f342b2c1f83e4cf825e301"},
|
|
||||||
{file = "soxr-0.3.7-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:8d8a2b3e7f8d0255e2484fb82cb66c86da6fb25b342ef793cceca9ce9a61aa16"},
|
|
||||||
{file = "soxr-0.3.7-cp310-cp310-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a0cd6eb6f6bbda2e8de36672cf2f0529ced6e638773150744ef075be0cc4f52c"},
|
|
||||||
{file = "soxr-0.3.7-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e47d86af35b942c92606fc2d5dfccf3f01309329475571ae2312bbf9edc3a790"},
|
|
||||||
{file = "soxr-0.3.7-cp310-cp310-win_amd64.whl", hash = "sha256:0e291adfaf9f2a7c4dd180a1b8c280f9beb1c84cb381853e4f4b3434d002ed7f"},
|
|
||||||
{file = "soxr-0.3.7-cp311-cp311-macosx_10_9_x86_64.whl", hash = "sha256:9e811450f0e91972932bd37ac58e32e44002c2c99db2aa926a9e7ba164545034"},
|
|
||||||
{file = "soxr-0.3.7-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:9cea63014ce91035074e1228c9340e2b8609faf964e268705fcac5135d05060c"},
|
|
||||||
{file = "soxr-0.3.7-cp311-cp311-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:bfab27830f6217a15b83445988225c3aeea3bbccfa9399ced291e53e1b05925d"},
|
|
||||||
{file = "soxr-0.3.7-cp311-cp311-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:286858e3078d76c11b6d490b66fed3c9bb2a4229759f6be03ceef5c02189bf2c"},
|
|
||||||
{file = "soxr-0.3.7-cp311-cp311-win_amd64.whl", hash = "sha256:54985ff33292192d2937be80df3e5f3a44d6d53e6835f727d6b99b7cdd3f1611"},
|
|
||||||
{file = "soxr-0.3.7-cp312-cp312-macosx_10_9_x86_64.whl", hash = "sha256:83c74ef6d61d7dcd81be26f91bee0a420f792f5c1982266f2a80e655f0650a98"},
|
|
||||||
{file = "soxr-0.3.7-cp312-cp312-macosx_11_0_arm64.whl", hash = "sha256:cb1e14663a43fe88b8fbc287822a159028366a820abe1a0a9670fb53618cb47b"},
|
|
||||||
{file = "soxr-0.3.7-cp312-cp312-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:48acdfbcf870ab54f645b1cfd641bce92c1e3a67346c3bf0f6c0ad2873c1dd35"},
|
|
||||||
{file = "soxr-0.3.7-cp312-cp312-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:ea663b76f2b0ec1576b8a43aef317aec080abc0a67a4015fcd9f3407039f260a"},
|
|
||||||
{file = "soxr-0.3.7-cp312-cp312-win_amd64.whl", hash = "sha256:42da0d9eb79c70e5a41917f1b48a032e241a48eb4a1bcea7c80577302ff26974"},
|
|
||||||
{file = "soxr-0.3.7-cp37-cp37m-macosx_10_9_x86_64.whl", hash = "sha256:511c6b2279c8ddd83459d129d69f628f7aae4616ae0a1912963985bd89e35df7"},
|
|
||||||
{file = "soxr-0.3.7-cp37-cp37m-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:a37c518c0b5d70162956d808d6c2e249bae0672e414e0dcfc101e200d8c31f3c"},
|
|
||||||
{file = "soxr-0.3.7-cp37-cp37m-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:27f2890528d2b2e358938ab660a6b8346802863f5b6b646204d7ff8ab0ca2c66"},
|
|
||||||
{file = "soxr-0.3.7-cp37-cp37m-win_amd64.whl", hash = "sha256:52467c8c012495544a6dcfcce6b5bcbbc653d24fe9bb33c0b6191acecdb5e297"},
|
|
||||||
{file = "soxr-0.3.7-cp38-cp38-macosx_10_9_x86_64.whl", hash = "sha256:ce12b93747958f2769d6b297e6e27c73d9ad635fe8104ef052bece9c8a322824"},
|
|
||||||
{file = "soxr-0.3.7-cp38-cp38-macosx_11_0_arm64.whl", hash = "sha256:1cd65dc7b96ea3cb6c8c48e6020e859680556cc42dd3d4de44779530cce21037"},
|
|
||||||
{file = "soxr-0.3.7-cp38-cp38-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:d994f1a7690b1b13ab639ea33e0c1d78415b64d88d6df4af705a9443f97b9687"},
|
|
||||||
{file = "soxr-0.3.7-cp38-cp38-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:e87b58bc9e8c2caa16f07726f666bd043f0a49ca937baa803ce7708003b27833"},
|
|
||||||
{file = "soxr-0.3.7-cp38-cp38-win_amd64.whl", hash = "sha256:07f4c0c6125ea1482fa187ad5f007216712ee0a93586a9b2f80e79c0bf944cf7"},
|
|
||||||
{file = "soxr-0.3.7-cp39-cp39-macosx_10_9_x86_64.whl", hash = "sha256:e5267c3ba34d4b873d9bbe3a9e58418b01ae4fd04349a4f944d9943b9ddac0f7"},
|
|
||||||
{file = "soxr-0.3.7-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:6e39668c250e221db888cf3b290a16fbe10a702d9a4eb604a127f720040de583"},
|
|
||||||
{file = "soxr-0.3.7-cp39-cp39-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:f8ceeb74e5a55d903cc286d3bd12c2d8f8c85d02894071e9ec92ab405430907c"},
|
|
||||||
{file = "soxr-0.3.7-cp39-cp39-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0eed6bf58192dd1bb93becd2444de4d712689713d727b32fd55623ae9aae7df7"},
|
|
||||||
{file = "soxr-0.3.7-cp39-cp39-win_amd64.whl", hash = "sha256:7221302b4547d02a3f38dd3cd15317ab2b78873c75921db5f4a070848f0c71be"},
|
|
||||||
{file = "soxr-0.3.7.tar.gz", hash = "sha256:436ddff00c6eb2c75b79c19cfdca7527b1e31b5fad738652f044045ba6258593"},
|
|
||||||
]
|
|
||||||
|
|
||||||
[package.dependencies]
|
|
||||||
numpy = "*"
|
|
||||||
|
|
||||||
[package.extras]
|
|
||||||
docs = ["linkify-it-py", "myst-parser", "sphinx", "sphinx-book-theme"]
|
|
||||||
test = ["pytest"]
|
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "srt"
|
name = "srt"
|
||||||
version = "3.5.3"
|
version = "3.5.3"
|
||||||
@@ -2415,6 +2303,19 @@ srt = "*"
|
|||||||
tqdm = "*"
|
tqdm = "*"
|
||||||
websockets = "*"
|
websockets = "*"
|
||||||
|
|
||||||
|
[[package]]
|
||||||
|
name = "webrtcvad"
|
||||||
|
version = "2.0.10"
|
||||||
|
description = "Python interface to the Google WebRTC Voice Activity Detector (VAD)"
|
||||||
|
optional = false
|
||||||
|
python-versions = "*"
|
||||||
|
files = [
|
||||||
|
{file = "webrtcvad-2.0.10.tar.gz", hash = "sha256:f1bed2fb25b63fb7b1a55d64090c993c9c9167b28485ae0bcdd81cf6ede96aea"},
|
||||||
|
]
|
||||||
|
|
||||||
|
[package.extras]
|
||||||
|
dev = ["check-manifest", "memory_profiler", "nose", "psutil", "unittest2", "zest.releaser"]
|
||||||
|
|
||||||
[[package]]
|
[[package]]
|
||||||
name = "websockets"
|
name = "websockets"
|
||||||
version = "12.0"
|
version = "12.0"
|
||||||
@@ -2499,4 +2400,4 @@ files = [
|
|||||||
[metadata]
|
[metadata]
|
||||||
lock-version = "2.0"
|
lock-version = "2.0"
|
||||||
python-versions = "^3.11"
|
python-versions = "^3.11"
|
||||||
content-hash = "f3829914195ce01bfac29dea5117fb3f28f095ecc6081a77eddb6bacc895718b"
|
content-hash = "c86def154e0a021a940b80bedcb08103ec9c0762a80432a58bf81d31c6a9244f"
|
||||||
|
|||||||
@@ -28,6 +28,7 @@ ollama = "^0.1.6"
|
|||||||
ruff = "^0.4.2"
|
ruff = "^0.4.2"
|
||||||
noisereduce = "^3.0.2"
|
noisereduce = "^3.0.2"
|
||||||
environs = "^11.0.0"
|
environs = "^11.0.0"
|
||||||
|
webrtcvad = "^2.0.10"
|
||||||
|
|
||||||
|
|
||||||
[[tool.poetry.source]]
|
[[tool.poetry.source]]
|
||||||
|
|||||||
@@ -0,0 +1,41 @@
|
|||||||
|
import os
|
||||||
|
|
||||||
|
import requests
|
||||||
|
from bs4 import BeautifulSoup
|
||||||
|
|
||||||
|
# URL веб-страницы, которую нужно спарсить
|
||||||
|
url = 'https://theportalwiki.com/wiki/GLaDOS_voice_lines/ru'
|
||||||
|
|
||||||
|
# Получаем содержимое страницы
|
||||||
|
response = requests.get(url)
|
||||||
|
response.raise_for_status() # Проверка на успешный запрос
|
||||||
|
|
||||||
|
# Парсим HTML
|
||||||
|
soup = BeautifulSoup(response.text, 'html.parser')
|
||||||
|
|
||||||
|
# Находим все теги <a>
|
||||||
|
links = soup.find_all('a')
|
||||||
|
|
||||||
|
# Фильтруем ссылки, которые заканчиваются на .wav
|
||||||
|
wav_links = [
|
||||||
|
link.get('href') for link in links if link.get('href') and link.get('href').endswith('.wav')
|
||||||
|
]
|
||||||
|
|
||||||
|
# Создаем директорию для сохранения файлов, если её нет
|
||||||
|
os.makedirs('wav_files', exist_ok=True)
|
||||||
|
|
||||||
|
# Скачиваем каждый wav-файл
|
||||||
|
for wav_link in wav_links:
|
||||||
|
# Получаем имя файла из URL
|
||||||
|
filename = wav_link.split('/')[-1]
|
||||||
|
file_path = os.path.join('wav_files', filename)
|
||||||
|
|
||||||
|
# Скачиваем файл
|
||||||
|
response = requests.get(wav_link)
|
||||||
|
response.raise_for_status()
|
||||||
|
|
||||||
|
# Сохраняем файл
|
||||||
|
with open(file_path, 'wb') as file:
|
||||||
|
file.write(response.content)
|
||||||
|
|
||||||
|
print(f"Downloaded {filename}")
|
||||||
|
|||||||
@@ -0,0 +1,57 @@
|
|||||||
|
import os
|
||||||
|
|
||||||
|
import torch
|
||||||
|
import torchaudio
|
||||||
|
|
||||||
|
|
||||||
|
def load_data(audio_folder):
|
||||||
|
audios = []
|
||||||
|
texts = []
|
||||||
|
for audio_file in os.listdir(audio_folder):
|
||||||
|
if audio_file.endswith('.wav'):
|
||||||
|
audio_path = os.path.join(audio_folder, audio_file)
|
||||||
|
waveform, sample_rate = torchaudio.load(audio_path)
|
||||||
|
text_path = audio_path.replace('.wav', '.txt')
|
||||||
|
with open(text_path) as f:
|
||||||
|
text = f.read().strip()
|
||||||
|
audios.append((waveform, sample_rate))
|
||||||
|
texts.append(text)
|
||||||
|
return audios, texts
|
||||||
|
|
||||||
|
|
||||||
|
def train(model, audios, texts, epochs=3, learning_rate=1e-4):
|
||||||
|
optimizer = torch.optim.Adam(model.parameters(), lr=learning_rate)
|
||||||
|
criterion = torch.nn.MSELoss() # Вам нужно будет настроить эту функцию под вашу задачу
|
||||||
|
|
||||||
|
model.train()
|
||||||
|
for epoch in range(epochs):
|
||||||
|
total_loss = 0
|
||||||
|
for waveform, text in zip(audios, texts):
|
||||||
|
optimizer.zero_grad()
|
||||||
|
# Предполагается, что модель принимает текст и возвращает аудио
|
||||||
|
predicted_waveform = model(text)
|
||||||
|
loss = criterion(predicted_waveform, waveform)
|
||||||
|
loss.backward()
|
||||||
|
optimizer.step()
|
||||||
|
total_loss += loss.item()
|
||||||
|
average_loss = total_loss / len(audios)
|
||||||
|
print(f'Epoch {epoch + 1}: Average Loss = {average_loss}')
|
||||||
|
|
||||||
|
|
||||||
|
def main():
|
||||||
|
model_path = 'data/v4_ru.pt'
|
||||||
|
model = torch.load(model_path)
|
||||||
|
model.eval()
|
||||||
|
audio_folder = 'wav_files'
|
||||||
|
audios, texts = load_data(audio_folder)
|
||||||
|
train(model, audios, texts)
|
||||||
|
torch.save(model.state_dict(), 'fine_tuned_model.pth')
|
||||||
|
model.eval()
|
||||||
|
sample_text = "Пример текста для синтеза."
|
||||||
|
with torch.no_grad():
|
||||||
|
generated_waveform = model(sample_text)
|
||||||
|
torchaudio.save('output_audio.wav', generated_waveform, 16000)
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
main()
|
||||||
Reference in New Issue
Block a user