vrem ton working
This commit is contained in:
@@ -8,6 +8,9 @@ __pycache__/
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# Custom
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data/model_small/
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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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*.so
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Generated
+50
-149
@@ -701,25 +701,6 @@ files = [
|
||||
{file = "kiwisolver-1.4.5.tar.gz", hash = "sha256:e57e563a57fb22a142da34f38acc2fc1a5c864bc29ca1517a88abc963e60d6ec"},
|
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]
|
||||
|
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[[package]]
|
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name = "lazy-loader"
|
||||
version = "0.4"
|
||||
description = "Makes it easy to load subpackages and functions on demand."
|
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optional = false
|
||||
python-versions = ">=3.7"
|
||||
files = [
|
||||
{file = "lazy_loader-0.4-py3-none-any.whl", hash = "sha256:342aa8e14d543a154047afb4ba8ef17f5563baad3fc610d7b15b213b0f119efc"},
|
||||
{file = "lazy_loader-0.4.tar.gz", hash = "sha256:47c75182589b91a4e1a85a136c074285a5ad4d9f39c63e0d7fb76391c4574cd1"},
|
||||
]
|
||||
|
||||
[package.dependencies]
|
||||
packaging = "*"
|
||||
|
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[package.extras]
|
||||
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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name = "levenshtein"
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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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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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optional = false
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python-versions = ">=3.7"
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python-versions = ">=3.6"
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files = [
|
||||
{file = "librosa-0.10.1-py3-none-any.whl", hash = "sha256:7ab91d9f5fcb75ea14848a05d3b1f825cf8d0c42ca160d19ae6874f2de2d8223"},
|
||||
{file = "librosa-0.10.1.tar.gz", hash = "sha256:832f7d150d6dd08ed2aa08c0567a4be58330635c32ddd2208de9bc91300802c7"},
|
||||
{file = "librosa-0.8.1-py3-none-any.whl", hash = "sha256:fd381e2d7067d4d4cf7691f2ef3620ef62a8aa6445dcf407e3328254692f742a"},
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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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audioread = ">=2.1.9"
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decorator = ">=4.3.0"
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audioread = ">=2.0.0"
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decorator = ">=3.0.0"
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joblib = ">=0.14"
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lazy-loader = ">=0.1"
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msgpack = ">=1.0"
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numba = ">=0.51.0"
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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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numba = ">=0.43.0"
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numpy = ">=1.15.0"
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packaging = ">=20.0"
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pooch = ">=1.0"
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scikit-learn = ">=0.20.0"
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scipy = ">=1.2.0"
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||||
soundfile = ">=0.12.1"
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||||
soxr = ">=0.3.2"
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typing-extensions = ">=4.1.1"
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resampy = ">=0.2.2"
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scikit-learn = ">=0.14.0,<0.19.0 || >0.19.0"
|
||||
scipy = ">=1.0.0"
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soundfile = ">=0.10.2"
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||||
|
||||
[package.extras]
|
||||
display = ["matplotlib (>=3.3.0)"]
|
||||
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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tests = ["matplotlib (>=3.3.0)", "packaging (>=20.0)", "pytest", "pytest-cov", "pytest-mpl", "resampy (>=0.2.2)", "samplerate", "types-decorator"]
|
||||
display = ["matplotlib (>=1.5)"]
|
||||
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"]
|
||||
tests = ["contextlib2", "matplotlib (>=3.0)", "pytest", "pytest-cov", "pytest-mpl", "samplerate", "soxr"]
|
||||
|
||||
[[package]]
|
||||
name = "llvmlite"
|
||||
@@ -1107,71 +1086,6 @@ docs = ["sphinx"]
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gmpy = ["gmpy2 (>=2.1.0a4)"]
|
||||
tests = ["pytest (>=4.6)"]
|
||||
|
||||
[[package]]
|
||||
name = "msgpack"
|
||||
version = "1.0.8"
|
||||
description = "MessagePack serializer"
|
||||
optional = false
|
||||
python-versions = ">=3.8"
|
||||
files = [
|
||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
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||||
]
|
||||
|
||||
[[package]]
|
||||
name = "networkx"
|
||||
version = "3.3"
|
||||
@@ -1935,6 +1849,26 @@ urllib3 = ">=1.21.1,<3"
|
||||
socks = ["PySocks (>=1.5.6,!=1.5.7)"]
|
||||
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 = [
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||||
{file = "resampy-0.4.3-py3-none-any.whl", hash = "sha256:ad2ed64516b140a122d96704e32bc0f92b23f45419e8b8f478e5a05f83edcebd"},
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||||
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||||
]
|
||||
|
||||
[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]]
|
||||
name = "rich"
|
||||
version = "13.7.1"
|
||||
@@ -2144,52 +2078,6 @@ cffi = ">=1.0"
|
||||
[package.extras]
|
||||
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]]
|
||||
name = "srt"
|
||||
version = "3.5.3"
|
||||
@@ -2415,6 +2303,19 @@ srt = "*"
|
||||
tqdm = "*"
|
||||
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]]
|
||||
name = "websockets"
|
||||
version = "12.0"
|
||||
@@ -2499,4 +2400,4 @@ files = [
|
||||
[metadata]
|
||||
lock-version = "2.0"
|
||||
python-versions = "^3.11"
|
||||
content-hash = "f3829914195ce01bfac29dea5117fb3f28f095ecc6081a77eddb6bacc895718b"
|
||||
content-hash = "c86def154e0a021a940b80bedcb08103ec9c0762a80432a58bf81d31c6a9244f"
|
||||
|
||||
@@ -28,6 +28,7 @@ ollama = "^0.1.6"
|
||||
ruff = "^0.4.2"
|
||||
noisereduce = "^3.0.2"
|
||||
environs = "^11.0.0"
|
||||
webrtcvad = "^2.0.10"
|
||||
|
||||
|
||||
[[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