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- .gitignore +185 -0
- .gitmodules +0 -0
- .pre-commit-config.yaml +25 -0
- Data/BangDream/config.json +187 -0
- Data/BangDream/configs/config.json +187 -0
- Data/BangDream/filelists/bushroid.list +0 -0
- Data/BangDream/filelists/bushroid.list.cleaned +0 -0
- Data/BangDream/filelists/train.list +0 -0
- Data/BangDream/filelists/val.list +8 -0
- Data/BangDream/models/G_10000.pth +3 -0
- Data/BangDream/models/G_12000.pth +3 -0
- app.py +90 -383
- attentions_onnx.py +378 -0
- bert/bert-large-japanese-v2/.gitattributes +34 -0
- bert/bert-large-japanese-v2/README.md +53 -0
- bert/bert-large-japanese-v2/config.json +19 -0
- bert/bert-large-japanese-v2/tokenizer_config.json +10 -0
- bert/bert-large-japanese-v2/vocab.txt +0 -0
- bert/bert_models.json +14 -0
- bert/deberta-v2-large-japanese/.gitattributes +34 -0
- bert/deberta-v2-large-japanese/README.md +111 -0
- bert/deberta-v2-large-japanese/config.json +38 -0
- bert/deberta-v2-large-japanese/pytorch_model.bin +3 -0
- bert/deberta-v2-large-japanese/special_tokens_map.json +9 -0
- bert/deberta-v2-large-japanese/tokenizer.json +0 -0
- bert/deberta-v2-large-japanese/tokenizer_config.json +15 -0
- bert/deberta-v3-large/.gitattributes +27 -0
- bert/deberta-v3-large/README.md +93 -0
- bert/deberta-v3-large/config.json +22 -0
- bert/deberta-v3-large/generator_config.json +22 -0
- bert/deberta-v3-large/pytorch_model.bin +3 -0
- bert/deberta-v3-large/spm.model +3 -0
- bert/deberta-v3-large/tokenizer_config.json +4 -0
- bert_gen.py +28 -14
- commons.py +7 -1
- config.py +237 -0
- config.yml +160 -0
- configs/config.json +9 -8
- configs/config_old.json +187 -0
- data_utils.py +26 -31
- default_config.yml +160 -0
- emo_gen.py +169 -0
- emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/.gitattributes +28 -0
- emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/LICENSE +437 -0
- emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/README.md +127 -0
- emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/config.json +122 -0
- emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/preprocessor_config.json +9 -0
- emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/vocab.json +1 -0
- export_onnx.py +56 -0
- infer.py +207 -0
.gitignore
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# Byte-compiled / optimized / DLL files
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| 2 |
+
__pycache__/
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| 3 |
+
*.py[cod]
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| 4 |
+
*$py.class
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| 5 |
+
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| 6 |
+
# C extensions
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| 7 |
+
*.so
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| 8 |
+
|
| 9 |
+
# Distribution / packaging
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| 10 |
+
.Python
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| 11 |
+
build/
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+
develop-eggs/
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+
dist/
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+
downloads/
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eggs/
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.eggs/
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+
lib/
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+
lib64/
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+
parts/
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| 20 |
+
sdist/
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| 21 |
+
var/
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| 22 |
+
wheels/
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| 23 |
+
share/python-wheels/
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| 24 |
+
*.egg-info/
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| 25 |
+
.installed.cfg
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| 26 |
+
*.egg
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| 27 |
+
MANIFEST
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| 28 |
+
|
| 29 |
+
# PyInstaller
|
| 30 |
+
# Usually these files are written by a python script from a template
|
| 31 |
+
# before PyInstaller builds the exe, so as to inject date/other infos into it.
|
| 32 |
+
*.manifest
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| 33 |
+
*.spec
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| 34 |
+
|
| 35 |
+
# Installer logs
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| 36 |
+
pip-log.txt
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| 37 |
+
pip-delete-this-directory.txt
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| 38 |
+
|
| 39 |
+
# Unit test / coverage reports
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| 40 |
+
htmlcov/
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| 41 |
+
.tox/
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| 42 |
+
.nox/
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| 43 |
+
.coverage
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+
.coverage.*
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| 45 |
+
.cache
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| 46 |
+
nosetests.xml
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| 47 |
+
coverage.xml
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| 48 |
+
*.cover
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+
*.py,cover
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.hypothesis/
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| 51 |
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.pytest_cache/
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| 52 |
+
cover/
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| 53 |
+
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# Translations
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| 55 |
+
*.mo
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| 56 |
+
*.pot
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| 57 |
+
|
| 58 |
+
# Django stuff:
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| 59 |
+
*.log
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| 60 |
+
local_settings.py
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| 61 |
+
db.sqlite3
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| 62 |
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db.sqlite3-journal
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| 63 |
+
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# Flask stuff:
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| 65 |
+
instance/
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| 66 |
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.webassets-cache
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| 67 |
+
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# Scrapy stuff:
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| 69 |
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.scrapy
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| 70 |
+
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# Sphinx documentation
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| 72 |
+
docs/_build/
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# PyBuilder
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| 75 |
+
.pybuilder/
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target/
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# Jupyter Notebook
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| 79 |
+
.ipynb_checkpoints
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| 80 |
+
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# IPython
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| 82 |
+
profile_default/
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| 83 |
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ipython_config.py
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# pyenv
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| 86 |
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# For a library or package, you might want to ignore these files since the code is
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# intended to run in multiple environments; otherwise, check them in:
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# .python-version
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| 89 |
+
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# pipenv
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| 91 |
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# According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
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# However, in case of collaboration, if having platform-specific dependencies or dependencies
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# having no cross-platform support, pipenv may install dependencies that don't work, or not
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# install all needed dependencies.
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#Pipfile.lock
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+
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# poetry
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# Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
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# This is especially recommended for binary packages to ensure reproducibility, and is more
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# commonly ignored for libraries.
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# https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
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#poetry.lock
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# pdm
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| 105 |
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# Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
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#pdm.lock
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# pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
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# in version control.
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# https://pdm.fming.dev/#use-with-ide
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.pdm.toml
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# PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
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__pypackages__/
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# Celery stuff
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celerybeat-schedule
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celerybeat.pid
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# SageMath parsed files
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*.sage.py
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# Environments
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.env
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.venv
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env/
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venv/
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ENV/
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env.bak/
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venv.bak/
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# Spyder project settings
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| 132 |
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.spyderproject
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| 133 |
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.spyproject
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| 134 |
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| 135 |
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# Rope project settings
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| 136 |
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.ropeproject
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+
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# mkdocs documentation
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| 139 |
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/site
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# mypy
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.mypy_cache/
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.dmypy.json
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dmypy.json
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# Pyre type checker
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| 147 |
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.pyre/
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# pytype static type analyzer
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.pytype/
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# Cython debug symbols
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cython_debug/
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# PyCharm
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# JetBrains specific template is maintained in a separate JetBrains.gitignore that can
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# be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
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# and can be added to the global gitignore or merged into this file. For a more nuclear
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# option (not recommended) you can uncomment the following to ignore the entire idea folder.
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#.idea/
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.DS_Store
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/models
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/logs
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filelists/*
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!/filelists/esd.list
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data/*
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/*.yml
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!/default_config.yml
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/Web/
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/emotional/*/*.bin
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/bert/*/*.bin
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/bert/*/*.h5
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/bert/*/*.model
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/bert/*/*.safetensors
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/bert/*/*.msgpack
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asr_transcript.py
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extract_list.py
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dataset
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/Data
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Model
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raw/
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logs/
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Data/*
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.gitmodules
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File without changes
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.pre-commit-config.yaml
ADDED
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repos:
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v4.5.0
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| 4 |
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hooks:
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| 5 |
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- id: check-yaml
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| 6 |
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- id: end-of-file-fixer
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| 7 |
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- id: trailing-whitespace
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| 8 |
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| 9 |
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- repo: https://github.com/astral-sh/ruff-pre-commit
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| 10 |
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rev: v0.1.4
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| 11 |
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hooks:
|
| 12 |
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- id: ruff
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| 13 |
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args: [ --fix ]
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| 14 |
+
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| 15 |
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- repo: https://github.com/psf/black
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| 16 |
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rev: 23.10.1
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| 17 |
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hooks:
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| 18 |
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- id: black
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| 19 |
+
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| 20 |
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- repo: https://github.com/codespell-project/codespell
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rev: v2.2.6
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| 22 |
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hooks:
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| 23 |
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- id: codespell
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| 24 |
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files: ^.*\.(py|md|rst|yml)$
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| 25 |
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args: [-L=fro]
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Data/BangDream/config.json
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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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|
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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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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"train": {
|
| 3 |
+
"log_interval": 200,
|
| 4 |
+
"eval_interval": 1000,
|
| 5 |
+
"seed": 42,
|
| 6 |
+
"epochs": 1000,
|
| 7 |
+
"learning_rate": 0.0002,
|
| 8 |
+
"betas": [
|
| 9 |
+
0.8,
|
| 10 |
+
0.99
|
| 11 |
+
],
|
| 12 |
+
"eps": 1e-09,
|
| 13 |
+
"batch_size": 16,
|
| 14 |
+
"fp16_run": false,
|
| 15 |
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"lr_decay": 0.99995,
|
| 16 |
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"segment_size": 16384,
|
| 17 |
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"init_lr_ratio": 1,
|
| 18 |
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"warmup_epochs": 0,
|
| 19 |
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"c_mel": 45,
|
| 20 |
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"c_kl": 1.0,
|
| 21 |
+
"skip_optimizer": true
|
| 22 |
+
},
|
| 23 |
+
"data": {
|
| 24 |
+
"training_files": "Data/BangDream/filelists/train.list",
|
| 25 |
+
"validation_files": "Data/BangDream/filelists/val.list",
|
| 26 |
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"max_wav_value": 32768.0,
|
| 27 |
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"sampling_rate": 44100,
|
| 28 |
+
"filter_length": 2048,
|
| 29 |
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"hop_length": 512,
|
| 30 |
+
"win_length": 2048,
|
| 31 |
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"n_mel_channels": 128,
|
| 32 |
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"mel_fmin": 0.0,
|
| 33 |
+
"mel_fmax": null,
|
| 34 |
+
"add_blank": true,
|
| 35 |
+
"n_speakers": 700,
|
| 36 |
+
"cleaned_text": true,
|
| 37 |
+
"spk2id": {
|
| 38 |
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"華戀": 0,
|
| 39 |
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"晶": 1,
|
| 40 |
+
"光": 2,
|
| 41 |
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|
| 42 |
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|
| 43 |
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"雙葉": 5,
|
| 44 |
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|
| 45 |
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"艾露": 7,
|
| 46 |
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"珠緒": 8,
|
| 47 |
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"艾露露": 9,
|
| 48 |
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"純那": 10,
|
| 49 |
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"克洛迪娜": 11,
|
| 50 |
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"真矢": 12,
|
| 51 |
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"奈奈": 13,
|
| 52 |
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"壘": 14,
|
| 53 |
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"文": 15,
|
| 54 |
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"一愛": 16,
|
| 55 |
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"菈樂菲": 17,
|
| 56 |
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"司": 18,
|
| 57 |
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"美空": 19,
|
| 58 |
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"靜羽": 20,
|
| 59 |
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"悠悠子": 21,
|
| 60 |
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"八千代": 22,
|
| 61 |
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"栞": 23,
|
| 62 |
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"美帆": 24,
|
| 63 |
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"芙蘿菈": 25,
|
| 64 |
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"克蕾兒": 26,
|
| 65 |
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"安德露": 27,
|
| 66 |
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"瑪莉亞貝菈": 28,
|
| 67 |
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"克拉迪亞": 29,
|
| 68 |
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"桃樂西": 30,
|
| 69 |
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"瑪麗安": 31,
|
| 70 |
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"三月七": 32,
|
| 71 |
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"香澄": 33,
|
| 72 |
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"有咲": 34,
|
| 73 |
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"沙綾": 35,
|
| 74 |
+
"りみ": 36,
|
| 75 |
+
"たえ": 37,
|
| 76 |
+
"沙綾、りみ、たえ": 38,
|
| 77 |
+
"巴": 39,
|
| 78 |
+
"一同": 40,
|
| 79 |
+
"まりな": 41,
|
| 80 |
+
"ゆり": 42,
|
| 81 |
+
"明日香": 43,
|
| 82 |
+
"？？？": 44,
|
| 83 |
+
"ひまり": 45,
|
| 84 |
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"モカ": 46,
|
| 85 |
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"つぐみ": 47,
|
| 86 |
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"蘭": 48,
|
| 87 |
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"リサ": 49,
|
| 88 |
+
"千聖": 50,
|
| 89 |
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"花音": 51,
|
| 90 |
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"イヴ": 52,
|
| 91 |
+
"日菜": 53,
|
| 92 |
+
"友希那": 54,
|
| 93 |
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"紗夜": 55,
|
| 94 |
+
"こころ": 56,
|
| 95 |
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"美咲": 57,
|
| 96 |
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"薫": 58,
|
| 97 |
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"はぐみ": 59,
|
| 98 |
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"ミッシェル": 60,
|
| 99 |
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"マリー": 61,
|
| 100 |
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"怪盗ハロハッピー": 62,
|
| 101 |
+
"ニコリーナ": 63,
|
| 102 |
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"彩": 64,
|
| 103 |
+
"麻弥": 65,
|
| 104 |
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"燐子": 66,
|
| 105 |
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"あこ": 67,
|
| 106 |
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"ゆきな": 68,
|
| 107 |
+
"ましろ": 69,
|
| 108 |
+
"つくし": 70,
|
| 109 |
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"透子": 71,
|
| 110 |
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"七深": 72,
|
| 111 |
+
"瑠唯": 73,
|
| 112 |
+
"六花": 74,
|
| 113 |
+
"パレオ": 75,
|
| 114 |
+
"レイヤ": 76,
|
| 115 |
+
"マスキング": 77,
|
| 116 |
+
"チュチュ": 78,
|
| 117 |
+
"ますき": 79,
|
| 118 |
+
"ロック": 80,
|
| 119 |
+
"令王那": 81,
|
| 120 |
+
"CHIYU": 82,
|
| 121 |
+
"レイ": 83,
|
| 122 |
+
"燈": 84,
|
| 123 |
+
"そよ": 85,
|
| 124 |
+
"祥子": 86,
|
| 125 |
+
"立希": 87,
|
| 126 |
+
"睦": 88,
|
| 127 |
+
"愛音": 89,
|
| 128 |
+
"楽奈": 90,
|
| 129 |
+
"海鈴": 91
|
| 130 |
+
}
|
| 131 |
+
},
|
| 132 |
+
"model": {
|
| 133 |
+
"use_spk_conditioned_encoder": true,
|
| 134 |
+
"use_noise_scaled_mas": true,
|
| 135 |
+
"use_mel_posterior_encoder": false,
|
| 136 |
+
"use_duration_discriminator": true,
|
| 137 |
+
"inter_channels": 192,
|
| 138 |
+
"hidden_channels": 192,
|
| 139 |
+
"filter_channels": 768,
|
| 140 |
+
"n_heads": 2,
|
| 141 |
+
"n_layers": 6,
|
| 142 |
+
"kernel_size": 3,
|
| 143 |
+
"p_dropout": 0.1,
|
| 144 |
+
"resblock": "1",
|
| 145 |
+
"resblock_kernel_sizes": [
|
| 146 |
+
3,
|
| 147 |
+
7,
|
| 148 |
+
11
|
| 149 |
+
],
|
| 150 |
+
"resblock_dilation_sizes": [
|
| 151 |
+
[
|
| 152 |
+
1,
|
| 153 |
+
3,
|
| 154 |
+
5
|
| 155 |
+
],
|
| 156 |
+
[
|
| 157 |
+
1,
|
| 158 |
+
3,
|
| 159 |
+
5
|
| 160 |
+
],
|
| 161 |
+
[
|
| 162 |
+
1,
|
| 163 |
+
3,
|
| 164 |
+
5
|
| 165 |
+
]
|
| 166 |
+
],
|
| 167 |
+
"upsample_rates": [
|
| 168 |
+
8,
|
| 169 |
+
8,
|
| 170 |
+
2,
|
| 171 |
+
2,
|
| 172 |
+
2
|
| 173 |
+
],
|
| 174 |
+
"upsample_initial_channel": 512,
|
| 175 |
+
"upsample_kernel_sizes": [
|
| 176 |
+
16,
|
| 177 |
+
16,
|
| 178 |
+
8,
|
| 179 |
+
2,
|
| 180 |
+
2
|
| 181 |
+
],
|
| 182 |
+
"n_layers_q": 3,
|
| 183 |
+
"use_spectral_norm": false,
|
| 184 |
+
"gin_channels": 256
|
| 185 |
+
},
|
| 186 |
+
"version": "2.0"
|
| 187 |
+
}
|
Data/BangDream/configs/config.json
ADDED
|
@@ -0,0 +1,187 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"train": {
|
| 3 |
+
"log_interval": 200,
|
| 4 |
+
"eval_interval": 1000,
|
| 5 |
+
"seed": 42,
|
| 6 |
+
"epochs": 1000,
|
| 7 |
+
"learning_rate": 0.0002,
|
| 8 |
+
"betas": [
|
| 9 |
+
0.8,
|
| 10 |
+
0.99
|
| 11 |
+
],
|
| 12 |
+
"eps": 1e-09,
|
| 13 |
+
"batch_size": 16,
|
| 14 |
+
"fp16_run": false,
|
| 15 |
+
"lr_decay": 0.99995,
|
| 16 |
+
"segment_size": 16384,
|
| 17 |
+
"init_lr_ratio": 1,
|
| 18 |
+
"warmup_epochs": 0,
|
| 19 |
+
"c_mel": 45,
|
| 20 |
+
"c_kl": 1.0,
|
| 21 |
+
"skip_optimizer": true
|
| 22 |
+
},
|
| 23 |
+
"data": {
|
| 24 |
+
"training_files": "Data/BangDream/filelists/train.list",
|
| 25 |
+
"validation_files": "Data/BangDream/filelists/val.list",
|
| 26 |
+
"max_wav_value": 32768.0,
|
| 27 |
+
"sampling_rate": 44100,
|
| 28 |
+
"filter_length": 2048,
|
| 29 |
+
"hop_length": 512,
|
| 30 |
+
"win_length": 2048,
|
| 31 |
+
"n_mel_channels": 128,
|
| 32 |
+
"mel_fmin": 0.0,
|
| 33 |
+
"mel_fmax": null,
|
| 34 |
+
"add_blank": true,
|
| 35 |
+
"n_speakers": 700,
|
| 36 |
+
"cleaned_text": true,
|
| 37 |
+
"spk2id": {
|
| 38 |
+
"華戀": 0,
|
| 39 |
+
"晶": 1,
|
| 40 |
+
"光": 2,
|
| 41 |
+
"未知留": 3,
|
| 42 |
+
"香子": 4,
|
| 43 |
+
"雙葉": 5,
|
| 44 |
+
"真晝": 6,
|
| 45 |
+
"艾露": 7,
|
| 46 |
+
"珠緒": 8,
|
| 47 |
+
"艾露露": 9,
|
| 48 |
+
"純那": 10,
|
| 49 |
+
"克洛迪娜": 11,
|
| 50 |
+
"真矢": 12,
|
| 51 |
+
"奈奈": 13,
|
| 52 |
+
"壘": 14,
|
| 53 |
+
"文": 15,
|
| 54 |
+
"一愛": 16,
|
| 55 |
+
"菈樂菲": 17,
|
| 56 |
+
"司": 18,
|
| 57 |
+
"美空": 19,
|
| 58 |
+
"靜羽": 20,
|
| 59 |
+
"悠悠子": 21,
|
| 60 |
+
"八千代": 22,
|
| 61 |
+
"栞": 23,
|
| 62 |
+
"美帆": 24,
|
| 63 |
+
"芙蘿菈": 25,
|
| 64 |
+
"克蕾兒": 26,
|
| 65 |
+
"安德露": 27,
|
| 66 |
+
"瑪莉亞貝菈": 28,
|
| 67 |
+
"克拉迪亞": 29,
|
| 68 |
+
"桃樂西": 30,
|
| 69 |
+
"瑪麗安": 31,
|
| 70 |
+
"三月七": 32,
|
| 71 |
+
"香澄": 33,
|
| 72 |
+
"有咲": 34,
|
| 73 |
+
"沙綾": 35,
|
| 74 |
+
"りみ": 36,
|
| 75 |
+
"たえ": 37,
|
| 76 |
+
"沙綾、りみ、たえ": 38,
|
| 77 |
+
"巴": 39,
|
| 78 |
+
"一同": 40,
|
| 79 |
+
"まりな": 41,
|
| 80 |
+
"ゆり": 42,
|
| 81 |
+
"明日香": 43,
|
| 82 |
+
"？？？": 44,
|
| 83 |
+
"ひまり": 45,
|
| 84 |
+
"モカ": 46,
|
| 85 |
+
"つぐみ": 47,
|
| 86 |
+
"蘭": 48,
|
| 87 |
+
"リサ": 49,
|
| 88 |
+
"千聖": 50,
|
| 89 |
+
"花音": 51,
|
| 90 |
+
"イヴ": 52,
|
| 91 |
+
"日菜": 53,
|
| 92 |
+
"友希那": 54,
|
| 93 |
+
"紗夜": 55,
|
| 94 |
+
"こころ": 56,
|
| 95 |
+
"美咲": 57,
|
| 96 |
+
"薫": 58,
|
| 97 |
+
"はぐみ": 59,
|
| 98 |
+
"ミッシェル": 60,
|
| 99 |
+
"マリー": 61,
|
| 100 |
+
"怪盗ハロハッピー": 62,
|
| 101 |
+
"ニコリーナ": 63,
|
| 102 |
+
"彩": 64,
|
| 103 |
+
"麻弥": 65,
|
| 104 |
+
"燐子": 66,
|
| 105 |
+
"あこ": 67,
|
| 106 |
+
"ゆきな": 68,
|
| 107 |
+
"ましろ": 69,
|
| 108 |
+
"つくし": 70,
|
| 109 |
+
"透子": 71,
|
| 110 |
+
"七深": 72,
|
| 111 |
+
"瑠唯": 73,
|
| 112 |
+
"六花": 74,
|
| 113 |
+
"パレオ": 75,
|
| 114 |
+
"レイヤ": 76,
|
| 115 |
+
"マスキング": 77,
|
| 116 |
+
"チュチュ": 78,
|
| 117 |
+
"ますき": 79,
|
| 118 |
+
"ロック": 80,
|
| 119 |
+
"令王那": 81,
|
| 120 |
+
"CHIYU": 82,
|
| 121 |
+
"レイ": 83,
|
| 122 |
+
"燈": 84,
|
| 123 |
+
"そよ": 85,
|
| 124 |
+
"祥子": 86,
|
| 125 |
+
"立希": 87,
|
| 126 |
+
"睦": 88,
|
| 127 |
+
"愛音": 89,
|
| 128 |
+
"楽奈": 90,
|
| 129 |
+
"海鈴": 91
|
| 130 |
+
}
|
| 131 |
+
},
|
| 132 |
+
"model": {
|
| 133 |
+
"use_spk_conditioned_encoder": true,
|
| 134 |
+
"use_noise_scaled_mas": true,
|
| 135 |
+
"use_mel_posterior_encoder": false,
|
| 136 |
+
"use_duration_discriminator": true,
|
| 137 |
+
"inter_channels": 192,
|
| 138 |
+
"hidden_channels": 192,
|
| 139 |
+
"filter_channels": 768,
|
| 140 |
+
"n_heads": 2,
|
| 141 |
+
"n_layers": 6,
|
| 142 |
+
"kernel_size": 3,
|
| 143 |
+
"p_dropout": 0.1,
|
| 144 |
+
"resblock": "1",
|
| 145 |
+
"resblock_kernel_sizes": [
|
| 146 |
+
3,
|
| 147 |
+
7,
|
| 148 |
+
11
|
| 149 |
+
],
|
| 150 |
+
"resblock_dilation_sizes": [
|
| 151 |
+
[
|
| 152 |
+
1,
|
| 153 |
+
3,
|
| 154 |
+
5
|
| 155 |
+
],
|
| 156 |
+
[
|
| 157 |
+
1,
|
| 158 |
+
3,
|
| 159 |
+
5
|
| 160 |
+
],
|
| 161 |
+
[
|
| 162 |
+
1,
|
| 163 |
+
3,
|
| 164 |
+
5
|
| 165 |
+
]
|
| 166 |
+
],
|
| 167 |
+
"upsample_rates": [
|
| 168 |
+
8,
|
| 169 |
+
8,
|
| 170 |
+
2,
|
| 171 |
+
2,
|
| 172 |
+
2
|
| 173 |
+
],
|
| 174 |
+
"upsample_initial_channel": 512,
|
| 175 |
+
"upsample_kernel_sizes": [
|
| 176 |
+
16,
|
| 177 |
+
16,
|
| 178 |
+
8,
|
| 179 |
+
2,
|
| 180 |
+
2
|
| 181 |
+
],
|
| 182 |
+
"n_layers_q": 3,
|
| 183 |
+
"use_spectral_norm": false,
|
| 184 |
+
"gin_channels": 256
|
| 185 |
+
},
|
| 186 |
+
"version": "2.0"
|
| 187 |
+
}
|
Data/BangDream/filelists/bushroid.list
ADDED
|
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See raw diff
|
|
|
Data/BangDream/filelists/bushroid.list.cleaned
ADDED
|
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See raw diff
|
|
|
Data/BangDream/filelists/train.list
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
Data/BangDream/filelists/val.list
ADDED
|
@@ -0,0 +1,8 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
/content/data/m1402_068.wav|華戀|JP|初稿の時はふわふわしてて,何をどう演じていいのかわからなかったんだけど…….|_ sh o k o o n o t o k i w a f u w a f u w a sh i t e t e , n a n i o d o o e n j i t e i i n o k a w a k a r a n a k a q t a n d a k e d o … … . _|0 0 0 1 1 1 0 0 0 0 1 1 0 0 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 1 1 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0|1 3 2 2 4 2 8 2 2 2 1 4 1 3 4 2 2 2 2 6 3 2 2 1 2 4 1 1 1 1
|
| 2 |
+
/content/data/m0401_041.wav|華戀|JP|はやく,取り戻さないと,本当に消えちゃうっ……!!|_ h a y a k u , t o r i m o d o s a n a i t o , h o n t o o n i k i e ch a u q … … ! ! _|0 0 0 1 1 0 0 0 0 0 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0 0 1 1 1 1 1 1 0 0 1 0 0 0 0 0 0 0 0 0|1 3 3 1 4 3 3 3 2 1 8 3 2 1 1 1 1 1 1 1
|
| 3 |
+
/content/data/m1403_003.wav|華戀|JP|今日はレッスン室の鍵開け当番だし,早起き早起き…….|_ ky o o w a r e q s u n sh i ts u n o k a g i a k e t o o b a n d a sh i , h a y a o k i h a y a o k i … … . _|0 1 1 0 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 1 1 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 0 0 0 0 0 0 0|1 3 2 6 4 2 4 3 6 2 2 1 7 7 1 1 1 1
|
| 4 |
+
/content/data/m0402_062.wav|華戀|JP|どんな舞台でも誰がいても,キラめいてみせる!スタァライトを守るために!|_ d o n n a b u t a i d e m o d a r e g a i t e m o , k i r a m e i t e m i s e r u ! s u t a a r a i t o o m a m o r u t a m e n i ! _|0 1 1 0 0 0 1 1 0 0 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 0 1 1 0 0 0 0 0 1 1 0 1 1 0 0 0 0 0 0 1 1 0 0 0 0 1 1 0 0 0 0|1 5 5 2 2 4 2 1 2 2 1 4 3 2 3 3 1 4 1 0 5 1 6 4 2 1 1
|
| 5 |
+
/content/data/m1708_030.wav|晶|JP|そうだ……私に憧れていただけの自分など超えてゆけ.フラウ,ルビン……リュウ,メイファン!!|_ s o o d a … … w a t a sh i n i a k o g a r e t e i t a d a k e n o j i b u n n a d o k o e t e y u k e . f u r a u , r u b i n … … ry u u , m e i f a n ! ! _|0 0 0 1 0 0 0 0 0 0 1 1 1 1 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 0 1 0 0 1 1 0 0 0 0 0 1 1 0 0 1 1 0 0 0 0 0 1 1 0 0 0 0 1 0 0 0 0 0 0|1 3 2 1 1 6 2 7 2 1 2 4 2 5 4 3 2 2 2 1 3 2 1 3 2 1 1 3 1 3 3 1 1 1
|
| 6 |
+
/content/data/m1101_137.wav|晶|JP|戯曲を生むは我らが舞台.それは我らが戯曲の登場人物ということ.|_ g i k i y o k u o u m u w a w a r e r a g a b u t a i . s o r e w a w a r e r a g a g i k i y o k u n o t o o j o o j i n b u ts u t o i u k o t o . _|0 0 0 1 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0|1 8 1 3 2 3 3 2 5 1 4 2 3 3 2 8 2 6 7 2 2 4 1 1
|
| 7 |
+
/content/data/m0806_031.wav|晶|JP|いざ,削劇を!シークフェルト音楽学院,エーデルよ,ここに!|_ i z a , s o g i g e k i o ! sh i i k u f e r u t o o n g a k u g a k u i n , e e d e r u y o , k o k o n i ! _|0 1 0 0 0 0 0 1 1 0 0 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0 0 1 0 0 0 0 0 0 0 0 0 0 1 1 0 0 0 0|1 3 1 4 4 1 1 3 2 6 6 6 1 3 3 2 1 4 2 1 1
|
| 8 |
+
/content/data/m1707_057.wav|晶|JP|まだこの程度の所で彷徨しているのか,お前たちは.苛烈な生存競争を勝ち抜いたお前たちが,この程度の人間だったとは…….|_ m a d a k o n o t e e d o n o t o k o r o d e h o o k o o sh i t e i r u n o k a , o m a e t a ch i w a . k a r e ts u n a s e e z o n ky o o s o o o k a ch i n u i t a o m a e t a ch i g a , k o n o t e e d o n o n i n g e n d a q t a t o w a … … . _|0 1 1 0 0 0 0 1 1 0 0 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 0 1 1 0 0 0 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 1 1 0 0 0 0 1 1 1 1 1 1 0 0 0 0 0 0 0 1 1 1 1 1 0 0 0 1 1 1 0 0 0 0 0 0 0 0 0 1 1 0 0 1 1 1 0 0 0 0 1 1 1 1 0 0 0 0 0 0 0 0 0 0 0 0 0|1 4 4 5 2 6 2 2 2 2 2 2 3 2 2 1 4 4 2 1 3 3 2 6 6 1 3 2 2 2 4 4 2 1 4 5 2 6 2 1 2 2 2 1 1 1 1
|
Data/BangDream/models/G_10000.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:4f8342e564362deed68b49a49ba86910bcdc6a57e201ab44effe42485834d596
|
| 3 |
+
size 705948086
|
Data/BangDream/models/G_12000.pth
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:90afbafe34786dd893d2d3a7088c241c401df6213d00b418e51874b8ffdcb37c
|
| 3 |
+
size 705948086
|
app.py
CHANGED
|
@@ -1,5 +1,9 @@
|
|
| 1 |
# flake8: noqa: E402
|
|
|
|
| 2 |
import logging
|
|
|
|
|
|
|
|
|
|
| 3 |
logging.getLogger("numba").setLevel(logging.WARNING)
|
| 4 |
logging.getLogger("markdown_it").setLevel(logging.WARNING)
|
| 5 |
logging.getLogger("urllib3").setLevel(logging.WARNING)
|
|
@@ -10,30 +14,25 @@ logging.basicConfig(
|
|
| 10 |
)
|
| 11 |
|
| 12 |
logger = logging.getLogger(__name__)
|
| 13 |
-
|
| 14 |
-
import
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 15 |
import torch
|
| 16 |
-
from ebooklib import epub
|
| 17 |
-
import PyPDF2
|
| 18 |
-
from PyPDF2 import PdfReader
|
| 19 |
-
import zipfile
|
| 20 |
-
import shutil
|
| 21 |
-
import sys, os
|
| 22 |
-
import json
|
| 23 |
-
from bs4 import BeautifulSoup
|
| 24 |
-
import argparse
|
| 25 |
-
import commons
|
| 26 |
import utils
|
| 27 |
-
from
|
| 28 |
-
from text.symbols import symbols
|
| 29 |
-
from text import cleaned_text_to_sequence, get_bert
|
| 30 |
-
from text.cleaner import clean_text
|
| 31 |
import gradio as gr
|
| 32 |
-
import
|
| 33 |
-
import
|
| 34 |
-
|
| 35 |
-
|
| 36 |
net_g = None
|
|
|
|
|
|
|
|
|
|
| 37 |
BandList = {
|
| 38 |
|
| 39 |
"PoppinParty":["香澄","有咲","たえ","りみ","沙綾"],
|
|
@@ -56,350 +55,104 @@ if sys.platform == "darwin" and torch.backends.mps.is_available():
|
|
| 56 |
else:
|
| 57 |
device = "cuda"
|
| 58 |
|
| 59 |
-
def
|
| 60 |
-
|
| 61 |
-
|
| 62 |
-
|
| 63 |
-
|
| 64 |
-
|
| 65 |
-
|
| 66 |
-
|
| 67 |
-
|
| 68 |
-
|
| 69 |
-
for i in result_list:
|
| 70 |
-
i = i.replace('\n','').replace(' ','')
|
| 71 |
-
#Current length of single sentence: 20
|
| 72 |
-
if len(i)>1:
|
| 73 |
-
if len(i) > 20:
|
| 74 |
-
try:
|
| 75 |
-
cur_list = re.split(r'。|！', i)
|
| 76 |
-
for i in cur_list:
|
| 77 |
-
if len(i)>1:
|
| 78 |
-
final_list.append(i+'。')
|
| 79 |
-
except:
|
| 80 |
-
pass
|
| 81 |
-
else:
|
| 82 |
-
final_list.append(i)
|
| 83 |
-
'''
|
| 84 |
-
final_list.append(i)
|
| 85 |
-
'''
|
| 86 |
-
final_list = [x for x in final_list if x != '']
|
| 87 |
-
return final_list
|
| 88 |
-
|
| 89 |
-
def get_text(text, language_str, hps):
|
| 90 |
-
norm_text, phone, tone, word2ph = clean_text(text, language_str)
|
| 91 |
-
phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
|
| 92 |
-
|
| 93 |
-
if hps.data.add_blank:
|
| 94 |
-
phone = commons.intersperse(phone, 0)
|
| 95 |
-
tone = commons.intersperse(tone, 0)
|
| 96 |
-
language = commons.intersperse(language, 0)
|
| 97 |
-
for i in range(len(word2ph)):
|
| 98 |
-
word2ph[i] = word2ph[i] * 2
|
| 99 |
-
word2ph[0] += 1
|
| 100 |
-
bert = get_bert(norm_text, word2ph, language_str, device)
|
| 101 |
-
del word2ph
|
| 102 |
-
assert bert.shape[-1] == len(phone), phone
|
| 103 |
-
|
| 104 |
-
if language_str == "ZH":
|
| 105 |
-
bert = bert
|
| 106 |
-
ja_bert = torch.zeros(768, len(phone))
|
| 107 |
-
elif language_str == "JA":
|
| 108 |
-
ja_bert = bert
|
| 109 |
-
bert = torch.zeros(1024, len(phone))
|
| 110 |
-
else:
|
| 111 |
-
bert = torch.zeros(1024, len(phone))
|
| 112 |
-
ja_bert = torch.zeros(768, len(phone))
|
| 113 |
-
|
| 114 |
-
assert bert.shape[-1] == len(
|
| 115 |
-
phone
|
| 116 |
-
), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
|
| 117 |
-
|
| 118 |
-
phone = torch.LongTensor(phone)
|
| 119 |
-
tone = torch.LongTensor(tone)
|
| 120 |
-
language = torch.LongTensor(language)
|
| 121 |
-
return bert, ja_bert, phone, tone, language
|
| 122 |
-
|
| 123 |
-
|
| 124 |
-
def infer(text, sdp_ratio, noise_scale, noise_scale_w, length_scale, sid, language):
|
| 125 |
-
global net_g
|
| 126 |
-
bert, ja_bert, phones, tones, lang_ids = get_text(text, language, hps)
|
| 127 |
with torch.no_grad():
|
| 128 |
-
|
| 129 |
-
|
| 130 |
-
|
| 131 |
-
|
| 132 |
-
|
| 133 |
-
|
| 134 |
-
|
| 135 |
-
|
| 136 |
-
|
| 137 |
-
|
| 138 |
-
|
| 139 |
-
|
| 140 |
-
|
| 141 |
-
|
| 142 |
-
|
| 143 |
-
|
| 144 |
-
|
| 145 |
-
|
| 146 |
-
noise_scale=noise_scale,
|
| 147 |
-
noise_scale_w=noise_scale_w,
|
| 148 |
-
length_scale=length_scale,
|
| 149 |
-
)[0][0, 0]
|
| 150 |
-
.data.cpu()
|
| 151 |
-
.float()
|
| 152 |
-
.numpy()
|
| 153 |
)
|
| 154 |
-
|
| 155 |
-
print(str(current_time)+':'+str(sid))
|
| 156 |
-
del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers
|
| 157 |
-
return audio
|
| 158 |
-
|
| 159 |
|
| 160 |
def tts_fn(
|
| 161 |
-
text
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 162 |
):
|
| 163 |
if not LongSentence:
|
| 164 |
with torch.no_grad():
|
| 165 |
-
audio =
|
| 166 |
text,
|
| 167 |
sdp_ratio=sdp_ratio,
|
| 168 |
noise_scale=noise_scale,
|
| 169 |
noise_scale_w=noise_scale_w,
|
| 170 |
length_scale=length_scale,
|
| 171 |
-
|
| 172 |
-
language=
|
| 173 |
)
|
| 174 |
torch.cuda.empty_cache()
|
| 175 |
return (hps.data.sampling_rate, audio)
|
| 176 |
else:
|
| 177 |
-
|
| 178 |
-
|
| 179 |
-
b = ['】',']',')','）']
|
| 180 |
-
for i in a:
|
| 181 |
-
text = text.replace(i,'<')
|
| 182 |
-
for i in b:
|
| 183 |
-
text = text.replace(i,'>')
|
| 184 |
-
final_list = extrac(text.replace('“','').replace('”',''))
|
| 185 |
audio_fin = []
|
| 186 |
for sentence in final_list:
|
| 187 |
-
|
| 188 |
-
|
| 189 |
-
|
| 190 |
-
|
| 191 |
-
|
| 192 |
-
|
| 193 |
-
|
| 194 |
-
|
| 195 |
-
|
| 196 |
-
|
| 197 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
| 198 |
return (hps.data.sampling_rate, np.concatenate(audio_fin))
|
| 199 |
|
| 200 |
-
def split_into_sentences(text):
|
| 201 |
-
"""将文本分割为句子，基于中文的标点符号"""
|
| 202 |
-
sentences = re.split(r'(?<=[。！？…\n])', text)
|
| 203 |
-
return [sentence.strip() for sentence in sentences if sentence]
|
| 204 |
-
|
| 205 |
-
|
| 206 |
-
def seconds_to_ass_time(seconds):
|
| 207 |
-
"""将秒数转换为ASS时间格式"""
|
| 208 |
-
hours = int(seconds / 3600)
|
| 209 |
-
minutes = int((seconds % 3600) / 60)
|
| 210 |
-
seconds = int(seconds) % 60
|
| 211 |
-
milliseconds = int((seconds - int(seconds)) * 1000)
|
| 212 |
-
return "{:01d}:{:02d}:{:02d}.{:02d}".format(hours, minutes, seconds, int(milliseconds / 10))
|
| 213 |
-
|
| 214 |
-
def generate_audio_and_srt_for_group(group, outputPath, group_index, sampling_rate, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale,spealerList,silenceTime):
|
| 215 |
-
audio_fin = []
|
| 216 |
-
ass_entries = []
|
| 217 |
-
start_time = 0
|
| 218 |
-
|
| 219 |
-
ass_header = """[Script Info]
|
| 220 |
-
; Script generated by OpenAI Assistant
|
| 221 |
-
Title: Audiobook
|
| 222 |
-
ScriptType: v4.00+
|
| 223 |
-
WrapStyle: 0
|
| 224 |
-
PlayResX: 640
|
| 225 |
-
PlayResY: 360
|
| 226 |
-
ScaledBorderAndShadow: yes
|
| 227 |
-
[V4+ Styles]
|
| 228 |
-
Format: Name, Fontname, Fontsize, PrimaryColour, SecondaryColour, OutlineColour, BackColour, Bold, Italic, Underline, StrikeOut, ScaleX, ScaleY, Spacing, Angle, BorderStyle, Outline, Shadow, Alignment, MarginL, MarginR, MarginV, Encoding
|
| 229 |
-
Style: Default,Arial,20,&H00FFFFFF,&H000000FF,&H00000000,&H00000000,0,0,0,0,100,100,0,0,1,1,1,2,10,10,10,1
|
| 230 |
-
[Events]
|
| 231 |
-
Format: Layer, Start, End, Style, Name, MarginL, MarginR, MarginV, Effect, Text
|
| 232 |
-
"""
|
| 233 |
-
|
| 234 |
-
for sentence in group:
|
| 235 |
-
try:
|
| 236 |
-
print(sentence)
|
| 237 |
-
FakeSpeaker = sentence.split("|")[0]
|
| 238 |
-
print(FakeSpeaker)
|
| 239 |
-
SpeakersList = re.split('\n', spealerList)
|
| 240 |
-
if FakeSpeaker in list(hps.data.spk2id.keys()):
|
| 241 |
-
speaker = FakeSpeaker
|
| 242 |
-
for i in SpeakersList:
|
| 243 |
-
if FakeSpeaker == i.split("|")[1]:
|
| 244 |
-
speaker = i.split("|")[0]
|
| 245 |
-
speaker_ids = hps.data.spk2id
|
| 246 |
-
|
| 247 |
-
_, audio = tts_fn(sentence.split("|")[-1], speaker=speaker, sdp_ratio=sdp_ratio, noise_scale=noise_scale, noise_scale_w=noise_scale_w, length_scale=length_scale, LongSentence=True)
|
| 248 |
-
silence_frames = int(silenceTime * 44010)
|
| 249 |
-
silence_data = np.zeros((silence_frames,), dtype=audio.dtype)
|
| 250 |
-
audio_fin.append(audio)
|
| 251 |
-
audio_fin.append(silence_data)
|
| 252 |
-
|
| 253 |
-
duration = len(audio) / sampling_rate
|
| 254 |
-
end_time = start_time + duration + silenceTime
|
| 255 |
-
ass_entries.append("Dialogue: 0,{},{},".format(seconds_to_ass_time(start_time), seconds_to_ass_time(end_time)) + "Default,,0,0,0,,{}".format(sentence.replace("|","：")))
|
| 256 |
-
start_time = end_time
|
| 257 |
-
except:
|
| 258 |
-
pass
|
| 259 |
-
wav_filename = os.path.join(outputPath, f'audiobook_part_{group_index}.wav')
|
| 260 |
-
ass_filename = os.path.join(outputPath, f'audiobook_part_{group_index}.ass')
|
| 261 |
-
|
| 262 |
-
write(wav_filename, sampling_rate, np.concatenate(audio_fin))
|
| 263 |
-
|
| 264 |
-
with open(ass_filename, 'w', encoding='utf-8') as f:
|
| 265 |
-
f.write(ass_header + '\n'.join(ass_entries))
|
| 266 |
-
return (hps.data.sampling_rate, np.concatenate(audio_fin))
|
| 267 |
-
def extract_text_from_epub(file_path):
|
| 268 |
-
book = epub.read_epub(file_path)
|
| 269 |
-
content = []
|
| 270 |
-
for item in book.items:
|
| 271 |
-
if isinstance(item, epub.EpubHtml):
|
| 272 |
-
soup = BeautifulSoup(item.content, 'html.parser')
|
| 273 |
-
content.append(soup.get_text())
|
| 274 |
-
return '\n'.join(content)
|
| 275 |
-
|
| 276 |
-
def extract_text_from_pdf(file_path):
|
| 277 |
-
with open(file_path, 'rb') as file:
|
| 278 |
-
reader = PdfReader(file)
|
| 279 |
-
content = [page.extract_text() for page in reader.pages]
|
| 280 |
-
return '\n'.join(content)
|
| 281 |
-
|
| 282 |
-
def extract_text_from_game2(data):
|
| 283 |
-
current_content = []
|
| 284 |
-
|
| 285 |
-
def _extract(data, current_data=None):
|
| 286 |
-
nonlocal current_content
|
| 287 |
-
|
| 288 |
-
if current_data is None:
|
| 289 |
-
current_data = {}
|
| 290 |
-
|
| 291 |
-
if isinstance(data, dict):
|
| 292 |
-
if 'name' in data and 'body' in data:
|
| 293 |
-
current_name = data['name']
|
| 294 |
-
current_body = data['body'].replace('\n', '')
|
| 295 |
-
current_content.append(f"{current_name}|{current_body}")
|
| 296 |
-
|
| 297 |
-
for key, value in data.items():
|
| 298 |
-
_extract(value, dict(current_data))
|
| 299 |
-
|
| 300 |
-
elif isinstance(data, list):
|
| 301 |
-
for item in data:
|
| 302 |
-
_extract(item, dict(current_data))
|
| 303 |
-
|
| 304 |
-
_extract(data)
|
| 305 |
-
return '\n'.join(current_content)
|
| 306 |
-
|
| 307 |
-
def extract_text_from_file(inputFile):
|
| 308 |
-
file_extension = os.path.splitext(inputFile)[1].lower()
|
| 309 |
-
|
| 310 |
-
if file_extension == ".epub":
|
| 311 |
-
return extract_text_from_epub(inputFile)
|
| 312 |
-
elif file_extension == ".pdf":
|
| 313 |
-
return extract_text_from_pdf(inputFile)
|
| 314 |
-
elif file_extension == ".txt":
|
| 315 |
-
with open(inputFile, 'r', encoding='utf-8') as f:
|
| 316 |
-
return f.read()
|
| 317 |
-
elif file_extension == ".asset":
|
| 318 |
-
with open(inputFile, 'r', encoding='utf-8') as f:
|
| 319 |
-
content = json.load(f)
|
| 320 |
-
return extract_text_from_game2(content) if extract_text_from_game2(content) != '' else extract_text_from_game2(content)
|
| 321 |
-
else:
|
| 322 |
-
raise ValueError(f"Unsupported file format: {file_extension}")
|
| 323 |
-
|
| 324 |
-
def audiobook(inputFile, groupsize, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale,spealerList,silenceTime):
|
| 325 |
-
directory_path = "books"
|
| 326 |
-
output_path = "books/audiobook_part_1.wav"
|
| 327 |
-
|
| 328 |
-
if os.path.exists(directory_path):
|
| 329 |
-
shutil.rmtree(directory_path)
|
| 330 |
-
|
| 331 |
-
os.makedirs(directory_path)
|
| 332 |
-
text = extract_text_from_file(inputFile.name)
|
| 333 |
-
sentences = split_into_sentences(text)
|
| 334 |
-
GROUP_SIZE = groupsize
|
| 335 |
-
for i in range(0, len(sentences), GROUP_SIZE):
|
| 336 |
-
group = sentences[i:i+GROUP_SIZE]
|
| 337 |
-
if spealerList == "":
|
| 338 |
-
spealerList = "无"
|
| 339 |
-
result = generate_audio_and_srt_for_group(group,directory_path, i//GROUP_SIZE + 1, 44100, speaker, sdp_ratio, noise_scale, noise_scale_w, length_scale,spealerList,silenceTime)
|
| 340 |
-
if not torch.cuda.is_available():
|
| 341 |
-
return result
|
| 342 |
-
return result
|
| 343 |
-
|
| 344 |
def loadmodel(model):
|
| 345 |
_ = net_g.eval()
|
| 346 |
_ = utils.load_checkpoint(model, net_g, None, skip_optimizer=True)
|
| 347 |
return "success"
|
| 348 |
|
| 349 |
-
|
| 350 |
if __name__ == "__main__":
|
| 351 |
-
|
| 352 |
-
|
| 353 |
-
|
|
|
|
| 354 |
)
|
| 355 |
-
parser.add_argument(
|
| 356 |
-
"-c",
|
| 357 |
-
"--config",
|
| 358 |
-
default="configs/config.json",
|
| 359 |
-
help="path of your config file",
|
| 360 |
-
)
|
| 361 |
-
parser.add_argument(
|
| 362 |
-
"--share", default=True, help="make link public", action="store_true"
|
| 363 |
-
)
|
| 364 |
-
parser.add_argument(
|
| 365 |
-
"-d", "--debug", action="store_true", help="enable DEBUG-LEVEL log"
|
| 366 |
-
)
|
| 367 |
-
|
| 368 |
-
args = parser.parse_args()
|
| 369 |
-
if args.debug:
|
| 370 |
-
logger.info("Enable DEBUG-LEVEL log")
|
| 371 |
-
logging.basicConfig(level=logging.DEBUG)
|
| 372 |
-
device = (
|
| 373 |
-
"cuda:0"
|
| 374 |
-
if torch.cuda.is_available()
|
| 375 |
-
else (
|
| 376 |
-
"mps"
|
| 377 |
-
if sys.platform == "darwin" and torch.backends.mps.is_available()
|
| 378 |
-
else "cpu"
|
| 379 |
-
)
|
| 380 |
-
)
|
| 381 |
-
hps = utils.get_hparams_from_file(args.config)
|
| 382 |
-
net_g = SynthesizerTrn(
|
| 383 |
-
len(symbols),
|
| 384 |
-
hps.data.filter_length // 2 + 1,
|
| 385 |
-
hps.train.segment_size // hps.data.hop_length,
|
| 386 |
-
n_speakers=hps.data.n_speakers,
|
| 387 |
-
**hps.model,
|
| 388 |
-
).to(device)
|
| 389 |
-
loadmodel(args.model)
|
| 390 |
speaker_ids = hps.data.spk2id
|
| 391 |
speakers = list(speaker_ids.keys())
|
| 392 |
-
languages = ["ZH", "JP"]
|
| 393 |
-
examples = [
|
| 394 |
-
["filelist/Scenarioband6-018.asset", 500, "つくし", "ましろ|真白\n七深|七深\n透子|透子\nつくし|筑紫\n瑠唯|瑠唯\nそよ|素世\n祥子|祥子", "扩展功能"],
|
| 395 |
-
]
|
| 396 |
modelPaths = []
|
| 397 |
-
for dirpath, dirnames, filenames in os.walk("
|
| 398 |
for filename in filenames:
|
| 399 |
modelPaths.append(os.path.join(dirpath, filename))
|
| 400 |
with gr.Blocks() as app:
|
| 401 |
gr.Markdown(
|
| 402 |
-
f"少歌邦邦全员TTS,使用本模型请严格遵守法律法规!\n
|
| 403 |
)
|
| 404 |
for band in BandList:
|
| 405 |
with gr.TabItem(band):
|
|
@@ -416,7 +169,7 @@ if __name__ == "__main__":
|
|
| 416 |
length_scale = gr.Slider(
|
| 417 |
minimum=0.1, maximum=2, value=1, step=0.01, label="语速调节"
|
| 418 |
)
|
| 419 |
-
with gr.Accordion(label="切换模型
|
| 420 |
modelstrs = gr.Dropdown(label = "模型", choices = modelPaths, value = modelPaths[0], type = "value")
|
| 421 |
btnMod = gr.Button("载入模型")
|
| 422 |
statusa = gr.TextArea()
|
|
@@ -440,6 +193,9 @@ if __name__ == "__main__":
|
|
| 440 |
minimum=0.1, maximum=2, value=0.8, step=0.01, label="音素长度"
|
| 441 |
)
|
| 442 |
LongSentence = gr.Checkbox(value=True, label="Generate LongSentence")
|
|
|
|
|
|
|
|
|
|
| 443 |
speaker = gr.Dropdown(
|
| 444 |
choices=speakers, value=name, label="说话人"
|
| 445 |
)
|
|
@@ -452,60 +208,11 @@ if __name__ == "__main__":
|
|
| 452 |
noise_scale,
|
| 453 |
noise_scale_w,
|
| 454 |
length_scale,
|
|
|
|
| 455 |
LongSentence,
|
| 456 |
],
|
| 457 |
outputs=[audio_output],
|
| 458 |
)
|
| 459 |
-
|
| 460 |
-
|
| 461 |
-
with gr.Row():
|
| 462 |
-
with gr.Column():
|
| 463 |
-
gr.Markdown(
|
| 464 |
-
f"从 <a href='https://nijigaku.top/2023/10/03/BangDreamTTS/'>我的博客站点</a> 查看自制galgame使用说明\n</a>"
|
| 465 |
-
)
|
| 466 |
-
inputFile = gr.inputs.File(label="上传txt(可设置角色对应表)、epub或mobi文件")
|
| 467 |
-
groupSize = gr.Slider(
|
| 468 |
-
minimum=10, maximum=100,value = i[1], step=1, label="当个音频文件包含的最大字数"
|
| 469 |
-
)
|
| 470 |
-
silenceTime = gr.Slider(
|
| 471 |
-
minimum=0, maximum=1, value=0.5, step=0.1, label="句子的间隔"
|
| 472 |
-
)
|
| 473 |
-
spealerList = gr.TextArea(
|
| 474 |
-
label="角色对应表",
|
| 475 |
-
placeholder="左边是你想要在每一句话合成中用到的speaker(见角色清单)右边是你上传文本时分隔符左边设置的说话人:{ChoseSpeakerFromConfigList1}|{SeakerInUploadText1}\n{ChoseSpeakerFromConfigList2}|{SeakerInUploadText2}\n{ChoseSpeakerFromConfigList3}|{SeakerInUploadText3}\n",
|
| 476 |
-
value = i[3],
|
| 477 |
-
)
|
| 478 |
-
speaker = gr.Dropdown(
|
| 479 |
-
choices=speakers, value = i[2], label="选择默认说话人"
|
| 480 |
-
)
|
| 481 |
-
with gr.Column():
|
| 482 |
-
sdp_ratio = gr.Slider(
|
| 483 |
-
minimum=0, maximum=1, value=0.2, step=0.01, label="SDP/DP混合比"
|
| 484 |
-
)
|
| 485 |
-
noise_scale = gr.Slider(
|
| 486 |
-
minimum=0.1, maximum=2, value=0.6, step=0.01, label="感情调节"
|
| 487 |
-
)
|
| 488 |
-
noise_scale_w = gr.Slider(
|
| 489 |
-
minimum=0.1, maximum=2, value=0.8, step=0.01, label="音素长度"
|
| 490 |
-
)
|
| 491 |
-
length_scale = gr.Slider(
|
| 492 |
-
minimum=0.1, maximum=2, value=1, step=0.01, label="生成长度"
|
| 493 |
-
)
|
| 494 |
-
LastAudioOutput = gr.Audio(label="当用cuda在本地运行时才能在book文件夹下浏览全部合成内容")
|
| 495 |
-
btn2 = gr.Button("点击生成", variant="primary")
|
| 496 |
-
btn2.click(
|
| 497 |
-
audiobook,
|
| 498 |
-
inputs=[
|
| 499 |
-
inputFile,
|
| 500 |
-
groupSize,
|
| 501 |
-
speaker,
|
| 502 |
-
sdp_ratio,
|
| 503 |
-
noise_scale,
|
| 504 |
-
noise_scale_w,
|
| 505 |
-
length_scale,
|
| 506 |
-
spealerList,
|
| 507 |
-
silenceTime
|
| 508 |
-
],
|
| 509 |
-
outputs=[LastAudioOutput],
|
| 510 |
-
)
|
| 511 |
app.launch()
|
|
|
|
| 1 |
# flake8: noqa: E402
|
| 2 |
+
import os
|
| 3 |
import logging
|
| 4 |
+
|
| 5 |
+
import re_matching
|
| 6 |
+
|
| 7 |
logging.getLogger("numba").setLevel(logging.WARNING)
|
| 8 |
logging.getLogger("markdown_it").setLevel(logging.WARNING)
|
| 9 |
logging.getLogger("urllib3").setLevel(logging.WARNING)
|
|
|
|
| 14 |
)
|
| 15 |
|
| 16 |
logger = logging.getLogger(__name__)
|
| 17 |
+
|
| 18 |
+
import warnings
|
| 19 |
+
|
| 20 |
+
warnings.filterwarnings("ignore", category=UserWarning, module="gradio.blocks")
|
| 21 |
+
|
| 22 |
+
|
| 23 |
+
import re
|
| 24 |
import torch
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 25 |
import utils
|
| 26 |
+
from infer import infer, latest_version, get_net_g
|
|
|
|
|
|
|
|
|
|
| 27 |
import gradio as gr
|
| 28 |
+
import numpy as np
|
| 29 |
+
from tools.sentence import extrac, is_japanese, is_chinese
|
| 30 |
+
import sys, os
|
| 31 |
+
import math
|
| 32 |
net_g = None
|
| 33 |
+
|
| 34 |
+
cara_list = ["ひまり","たえ","彩","日菜","美咲","ましろ","燐子","香子","珠緒","たえ"]
|
| 35 |
+
|
| 36 |
BandList = {
|
| 37 |
|
| 38 |
"PoppinParty":["香澄","有咲","たえ","りみ","沙綾"],
|
|
|
|
| 55 |
else:
|
| 56 |
device = "cuda"
|
| 57 |
|
| 58 |
+
def generate_audio(
|
| 59 |
+
text,
|
| 60 |
+
sdp_ratio,
|
| 61 |
+
noise_scale,
|
| 62 |
+
noise_scale_w,
|
| 63 |
+
length_scale,
|
| 64 |
+
speaker,
|
| 65 |
+
language,
|
| 66 |
+
):
|
| 67 |
+
audio_list = []
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 68 |
with torch.no_grad():
|
| 69 |
+
if language == 'Auto':
|
| 70 |
+
language = "EN"
|
| 71 |
+
if is_japanese(text):
|
| 72 |
+
language = "JP"
|
| 73 |
+
elif is_chinese(text):
|
| 74 |
+
language = "ZH"
|
| 75 |
+
print(text+":"+language)
|
| 76 |
+
audio = infer(
|
| 77 |
+
text,
|
| 78 |
+
sdp_ratio=sdp_ratio,
|
| 79 |
+
noise_scale=noise_scale,
|
| 80 |
+
noise_scale_w=noise_scale_w,
|
| 81 |
+
length_scale=length_scale,
|
| 82 |
+
sid=speaker,
|
| 83 |
+
language=language,
|
| 84 |
+
hps=hps,
|
| 85 |
+
net_g=net_g,
|
| 86 |
+
device=device,
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 87 |
)
|
| 88 |
+
return audio
|
|
|
|
|
|
|
|
|
|
|
|
|
| 89 |
|
| 90 |
def tts_fn(
|
| 91 |
+
text: str,
|
| 92 |
+
speaker,
|
| 93 |
+
sdp_ratio,
|
| 94 |
+
noise_scale,
|
| 95 |
+
noise_scale_w,
|
| 96 |
+
length_scale,
|
| 97 |
+
language,
|
| 98 |
+
LongSentence,
|
| 99 |
):
|
| 100 |
if not LongSentence:
|
| 101 |
with torch.no_grad():
|
| 102 |
+
audio = generate_audio(
|
| 103 |
text,
|
| 104 |
sdp_ratio=sdp_ratio,
|
| 105 |
noise_scale=noise_scale,
|
| 106 |
noise_scale_w=noise_scale_w,
|
| 107 |
length_scale=length_scale,
|
| 108 |
+
speaker=speaker,
|
| 109 |
+
language= language,
|
| 110 |
)
|
| 111 |
torch.cuda.empty_cache()
|
| 112 |
return (hps.data.sampling_rate, audio)
|
| 113 |
else:
|
| 114 |
+
|
| 115 |
+
final_list = extrac(text)
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 116 |
audio_fin = []
|
| 117 |
for sentence in final_list:
|
| 118 |
+
if len(sentence) > 1:
|
| 119 |
+
with torch.no_grad():
|
| 120 |
+
audio = generate_audio(
|
| 121 |
+
sentence,
|
| 122 |
+
sdp_ratio=sdp_ratio,
|
| 123 |
+
noise_scale=noise_scale,
|
| 124 |
+
noise_scale_w=noise_scale_w,
|
| 125 |
+
length_scale=length_scale,
|
| 126 |
+
speaker=speaker,
|
| 127 |
+
language= language,
|
| 128 |
+
)
|
| 129 |
+
silence_frames = int(math.log(len(sentence)+1, 1000) * 44010) if is_chinese(sentence) else int(math.log(len(sentence)+1, 3000) * 44010)
|
| 130 |
+
silence_data = np.zeros((silence_frames,), dtype=audio.dtype)
|
| 131 |
+
audio_fin.append(audio)
|
| 132 |
+
audio_fin.append(silence_data)
|
| 133 |
return (hps.data.sampling_rate, np.concatenate(audio_fin))
|
| 134 |
|
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|
|
|
| 135 |
def loadmodel(model):
|
| 136 |
_ = net_g.eval()
|
| 137 |
_ = utils.load_checkpoint(model, net_g, None, skip_optimizer=True)
|
| 138 |
return "success"
|
| 139 |
|
|
|
|
| 140 |
if __name__ == "__main__":
|
| 141 |
+
hps = utils.get_hparams_from_file('Data/BangDream/config.json')
|
| 142 |
+
version = hps.version if hasattr(hps, "version") else latest_version
|
| 143 |
+
net_g = get_net_g(
|
| 144 |
+
model_path='Data/BangDream/models/G_10000.pth', version=version, device=device, hps=hps
|
| 145 |
)
|
|
|
|
|
|
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|
| 146 |
speaker_ids = hps.data.spk2id
|
| 147 |
speakers = list(speaker_ids.keys())
|
| 148 |
+
languages = [ "Auto", "ZH", "JP"]
|
|
|
|
|
|
|
|
|
|
| 149 |
modelPaths = []
|
| 150 |
+
for dirpath, dirnames, filenames in os.walk("Data/BangDream/models/"):
|
| 151 |
for filename in filenames:
|
| 152 |
modelPaths.append(os.path.join(dirpath, filename))
|
| 153 |
with gr.Blocks() as app:
|
| 154 |
gr.Markdown(
|
| 155 |
+
f"少歌邦邦全员TTS,使用本模型请严格遵守法律法规!\n 发布二创作品请注明项目和本模型作者<a href='https://space.bilibili.com/19874615/'>B站@Mahiroshi</a>及项目链接\n从 <a href='https://nijigaku.top/2023/10/03/BangDreamTTS/'>我的博客站点</a> 查看使用说明</a>"
|
| 156 |
)
|
| 157 |
for band in BandList:
|
| 158 |
with gr.TabItem(band):
|
|
|
|
| 169 |
length_scale = gr.Slider(
|
| 170 |
minimum=0.1, maximum=2, value=1, step=0.01, label="语速调节"
|
| 171 |
)
|
| 172 |
+
with gr.Accordion(label="切换模型", open=False):
|
| 173 |
modelstrs = gr.Dropdown(label = "模型", choices = modelPaths, value = modelPaths[0], type = "value")
|
| 174 |
btnMod = gr.Button("载入模型")
|
| 175 |
statusa = gr.TextArea()
|
|
|
|
| 193 |
minimum=0.1, maximum=2, value=0.8, step=0.01, label="音素长度"
|
| 194 |
)
|
| 195 |
LongSentence = gr.Checkbox(value=True, label="Generate LongSentence")
|
| 196 |
+
language = gr.Dropdown(
|
| 197 |
+
choices=languages, value=languages[0], label="选择语言(默认自动)"
|
| 198 |
+
)
|
| 199 |
speaker = gr.Dropdown(
|
| 200 |
choices=speakers, value=name, label="说话人"
|
| 201 |
)
|
|
|
|
| 208 |
noise_scale,
|
| 209 |
noise_scale_w,
|
| 210 |
length_scale,
|
| 211 |
+
language,
|
| 212 |
LongSentence,
|
| 213 |
],
|
| 214 |
outputs=[audio_output],
|
| 215 |
)
|
| 216 |
+
|
| 217 |
+
print("推理页面已开启!")
|
|
|
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|
|
|
|
| 218 |
app.launch()
|
attentions_onnx.py
ADDED
|
@@ -0,0 +1,378 @@
|
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|
|
|
| 1 |
+
import math
|
| 2 |
+
import torch
|
| 3 |
+
from torch import nn
|
| 4 |
+
from torch.nn import functional as F
|
| 5 |
+
|
| 6 |
+
import commons
|
| 7 |
+
import logging
|
| 8 |
+
|
| 9 |
+
logger = logging.getLogger(__name__)
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
class LayerNorm(nn.Module):
|
| 13 |
+
def __init__(self, channels, eps=1e-5):
|
| 14 |
+
super().__init__()
|
| 15 |
+
self.channels = channels
|
| 16 |
+
self.eps = eps
|
| 17 |
+
|
| 18 |
+
self.gamma = nn.Parameter(torch.ones(channels))
|
| 19 |
+
self.beta = nn.Parameter(torch.zeros(channels))
|
| 20 |
+
|
| 21 |
+
def forward(self, x):
|
| 22 |
+
x = x.transpose(1, -1)
|
| 23 |
+
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
|
| 24 |
+
return x.transpose(1, -1)
|
| 25 |
+
|
| 26 |
+
|
| 27 |
+
@torch.jit.script
|
| 28 |
+
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
|
| 29 |
+
n_channels_int = n_channels[0]
|
| 30 |
+
in_act = input_a + input_b
|
| 31 |
+
t_act = torch.tanh(in_act[:, :n_channels_int, :])
|
| 32 |
+
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
|
| 33 |
+
acts = t_act * s_act
|
| 34 |
+
return acts
|
| 35 |
+
|
| 36 |
+
|
| 37 |
+
class Encoder(nn.Module):
|
| 38 |
+
def __init__(
|
| 39 |
+
self,
|
| 40 |
+
hidden_channels,
|
| 41 |
+
filter_channels,
|
| 42 |
+
n_heads,
|
| 43 |
+
n_layers,
|
| 44 |
+
kernel_size=1,
|
| 45 |
+
p_dropout=0.0,
|
| 46 |
+
window_size=4,
|
| 47 |
+
isflow=True,
|
| 48 |
+
**kwargs
|
| 49 |
+
):
|
| 50 |
+
super().__init__()
|
| 51 |
+
self.hidden_channels = hidden_channels
|
| 52 |
+
self.filter_channels = filter_channels
|
| 53 |
+
self.n_heads = n_heads
|
| 54 |
+
self.n_layers = n_layers
|
| 55 |
+
self.kernel_size = kernel_size
|
| 56 |
+
self.p_dropout = p_dropout
|
| 57 |
+
self.window_size = window_size
|
| 58 |
+
# if isflow:
|
| 59 |
+
# cond_layer = torch.nn.Conv1d(256, 2*hidden_channels*n_layers, 1)
|
| 60 |
+
# self.cond_pre = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, 1)
|
| 61 |
+
# self.cond_layer = weight_norm(cond_layer, name='weight')
|
| 62 |
+
# self.gin_channels = 256
|
| 63 |
+
self.cond_layer_idx = self.n_layers
|
| 64 |
+
if "gin_channels" in kwargs:
|
| 65 |
+
self.gin_channels = kwargs["gin_channels"]
|
| 66 |
+
if self.gin_channels != 0:
|
| 67 |
+
self.spk_emb_linear = nn.Linear(self.gin_channels, self.hidden_channels)
|
| 68 |
+
# vits2 says 3rd block, so idx is 2 by default
|
| 69 |
+
self.cond_layer_idx = (
|
| 70 |
+
kwargs["cond_layer_idx"] if "cond_layer_idx" in kwargs else 2
|
| 71 |
+
)
|
| 72 |
+
logging.debug(self.gin_channels, self.cond_layer_idx)
|
| 73 |
+
assert (
|
| 74 |
+
self.cond_layer_idx < self.n_layers
|
| 75 |
+
), "cond_layer_idx should be less than n_layers"
|
| 76 |
+
self.drop = nn.Dropout(p_dropout)
|
| 77 |
+
self.attn_layers = nn.ModuleList()
|
| 78 |
+
self.norm_layers_1 = nn.ModuleList()
|
| 79 |
+
self.ffn_layers = nn.ModuleList()
|
| 80 |
+
self.norm_layers_2 = nn.ModuleList()
|
| 81 |
+
for i in range(self.n_layers):
|
| 82 |
+
self.attn_layers.append(
|
| 83 |
+
MultiHeadAttention(
|
| 84 |
+
hidden_channels,
|
| 85 |
+
hidden_channels,
|
| 86 |
+
n_heads,
|
| 87 |
+
p_dropout=p_dropout,
|
| 88 |
+
window_size=window_size,
|
| 89 |
+
)
|
| 90 |
+
)
|
| 91 |
+
self.norm_layers_1.append(LayerNorm(hidden_channels))
|
| 92 |
+
self.ffn_layers.append(
|
| 93 |
+
FFN(
|
| 94 |
+
hidden_channels,
|
| 95 |
+
hidden_channels,
|
| 96 |
+
filter_channels,
|
| 97 |
+
kernel_size,
|
| 98 |
+
p_dropout=p_dropout,
|
| 99 |
+
)
|
| 100 |
+
)
|
| 101 |
+
self.norm_layers_2.append(LayerNorm(hidden_channels))
|
| 102 |
+
|
| 103 |
+
def forward(self, x, x_mask, g=None):
|
| 104 |
+
attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
|
| 105 |
+
x = x * x_mask
|
| 106 |
+
for i in range(self.n_layers):
|
| 107 |
+
if i == self.cond_layer_idx and g is not None:
|
| 108 |
+
g = self.spk_emb_linear(g.transpose(1, 2))
|
| 109 |
+
g = g.transpose(1, 2)
|
| 110 |
+
x = x + g
|
| 111 |
+
x = x * x_mask
|
| 112 |
+
y = self.attn_layers[i](x, x, attn_mask)
|
| 113 |
+
y = self.drop(y)
|
| 114 |
+
x = self.norm_layers_1[i](x + y)
|
| 115 |
+
|
| 116 |
+
y = self.ffn_layers[i](x, x_mask)
|
| 117 |
+
y = self.drop(y)
|
| 118 |
+
x = self.norm_layers_2[i](x + y)
|
| 119 |
+
x = x * x_mask
|
| 120 |
+
return x
|
| 121 |
+
|
| 122 |
+
|
| 123 |
+
class MultiHeadAttention(nn.Module):
|
| 124 |
+
def __init__(
|
| 125 |
+
self,
|
| 126 |
+
channels,
|
| 127 |
+
out_channels,
|
| 128 |
+
n_heads,
|
| 129 |
+
p_dropout=0.0,
|
| 130 |
+
window_size=None,
|
| 131 |
+
heads_share=True,
|
| 132 |
+
block_length=None,
|
| 133 |
+
proximal_bias=False,
|
| 134 |
+
proximal_init=False,
|
| 135 |
+
):
|
| 136 |
+
super().__init__()
|
| 137 |
+
assert channels % n_heads == 0
|
| 138 |
+
|
| 139 |
+
self.channels = channels
|
| 140 |
+
self.out_channels = out_channels
|
| 141 |
+
self.n_heads = n_heads
|
| 142 |
+
self.p_dropout = p_dropout
|
| 143 |
+
self.window_size = window_size
|
| 144 |
+
self.heads_share = heads_share
|
| 145 |
+
self.block_length = block_length
|
| 146 |
+
self.proximal_bias = proximal_bias
|
| 147 |
+
self.proximal_init = proximal_init
|
| 148 |
+
self.attn = None
|
| 149 |
+
|
| 150 |
+
self.k_channels = channels // n_heads
|
| 151 |
+
self.conv_q = nn.Conv1d(channels, channels, 1)
|
| 152 |
+
self.conv_k = nn.Conv1d(channels, channels, 1)
|
| 153 |
+
self.conv_v = nn.Conv1d(channels, channels, 1)
|
| 154 |
+
self.conv_o = nn.Conv1d(channels, out_channels, 1)
|
| 155 |
+
self.drop = nn.Dropout(p_dropout)
|
| 156 |
+
|
| 157 |
+
if window_size is not None:
|
| 158 |
+
n_heads_rel = 1 if heads_share else n_heads
|
| 159 |
+
rel_stddev = self.k_channels**-0.5
|
| 160 |
+
self.emb_rel_k = nn.Parameter(
|
| 161 |
+
torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
|
| 162 |
+
* rel_stddev
|
| 163 |
+
)
|
| 164 |
+
self.emb_rel_v = nn.Parameter(
|
| 165 |
+
torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels)
|
| 166 |
+
* rel_stddev
|
| 167 |
+
)
|
| 168 |
+
|
| 169 |
+
nn.init.xavier_uniform_(self.conv_q.weight)
|
| 170 |
+
nn.init.xavier_uniform_(self.conv_k.weight)
|
| 171 |
+
nn.init.xavier_uniform_(self.conv_v.weight)
|
| 172 |
+
if proximal_init:
|
| 173 |
+
with torch.no_grad():
|
| 174 |
+
self.conv_k.weight.copy_(self.conv_q.weight)
|
| 175 |
+
self.conv_k.bias.copy_(self.conv_q.bias)
|
| 176 |
+
|
| 177 |
+
def forward(self, x, c, attn_mask=None):
|
| 178 |
+
q = self.conv_q(x)
|
| 179 |
+
k = self.conv_k(c)
|
| 180 |
+
v = self.conv_v(c)
|
| 181 |
+
|
| 182 |
+
x, self.attn = self.attention(q, k, v, mask=attn_mask)
|
| 183 |
+
|
| 184 |
+
x = self.conv_o(x)
|
| 185 |
+
return x
|
| 186 |
+
|
| 187 |
+
def attention(self, query, key, value, mask=None):
|
| 188 |
+
# reshape [b, d, t] -> [b, n_h, t, d_k]
|
| 189 |
+
b, d, t_s, t_t = (*key.size(), query.size(2))
|
| 190 |
+
query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
|
| 191 |
+
key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
| 192 |
+
value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
| 193 |
+
|
| 194 |
+
scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
|
| 195 |
+
if self.window_size is not None:
|
| 196 |
+
assert (
|
| 197 |
+
t_s == t_t
|
| 198 |
+
), "Relative attention is only available for self-attention."
|
| 199 |
+
key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
|
| 200 |
+
rel_logits = self._matmul_with_relative_keys(
|
| 201 |
+
query / math.sqrt(self.k_channels), key_relative_embeddings
|
| 202 |
+
)
|
| 203 |
+
scores_local = self._relative_position_to_absolute_position(rel_logits)
|
| 204 |
+
scores = scores + scores_local
|
| 205 |
+
if self.proximal_bias:
|
| 206 |
+
assert t_s == t_t, "Proximal bias is only available for self-attention."
|
| 207 |
+
scores = scores + self._attention_bias_proximal(t_s).to(
|
| 208 |
+
device=scores.device, dtype=scores.dtype
|
| 209 |
+
)
|
| 210 |
+
if mask is not None:
|
| 211 |
+
scores = scores.masked_fill(mask == 0, -1e4)
|
| 212 |
+
if self.block_length is not None:
|
| 213 |
+
assert (
|
| 214 |
+
t_s == t_t
|
| 215 |
+
), "Local attention is only available for self-attention."
|
| 216 |
+
block_mask = (
|
| 217 |
+
torch.ones_like(scores)
|
| 218 |
+
.triu(-self.block_length)
|
| 219 |
+
.tril(self.block_length)
|
| 220 |
+
)
|
| 221 |
+
scores = scores.masked_fill(block_mask == 0, -1e4)
|
| 222 |
+
p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
|
| 223 |
+
p_attn = self.drop(p_attn)
|
| 224 |
+
output = torch.matmul(p_attn, value)
|
| 225 |
+
if self.window_size is not None:
|
| 226 |
+
relative_weights = self._absolute_position_to_relative_position(p_attn)
|
| 227 |
+
value_relative_embeddings = self._get_relative_embeddings(
|
| 228 |
+
self.emb_rel_v, t_s
|
| 229 |
+
)
|
| 230 |
+
output = output + self._matmul_with_relative_values(
|
| 231 |
+
relative_weights, value_relative_embeddings
|
| 232 |
+
)
|
| 233 |
+
output = (
|
| 234 |
+
output.transpose(2, 3).contiguous().view(b, d, t_t)
|
| 235 |
+
) # [b, n_h, t_t, d_k] -> [b, d, t_t]
|
| 236 |
+
return output, p_attn
|
| 237 |
+
|
| 238 |
+
def _matmul_with_relative_values(self, x, y):
|
| 239 |
+
"""
|
| 240 |
+
x: [b, h, l, m]
|
| 241 |
+
y: [h or 1, m, d]
|
| 242 |
+
ret: [b, h, l, d]
|
| 243 |
+
"""
|
| 244 |
+
ret = torch.matmul(x, y.unsqueeze(0))
|
| 245 |
+
return ret
|
| 246 |
+
|
| 247 |
+
def _matmul_with_relative_keys(self, x, y):
|
| 248 |
+
"""
|
| 249 |
+
x: [b, h, l, d]
|
| 250 |
+
y: [h or 1, m, d]
|
| 251 |
+
ret: [b, h, l, m]
|
| 252 |
+
"""
|
| 253 |
+
ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
|
| 254 |
+
return ret
|
| 255 |
+
|
| 256 |
+
def _get_relative_embeddings(self, relative_embeddings, length):
|
| 257 |
+
max_relative_position = 2 * self.window_size + 1
|
| 258 |
+
# Pad first before slice to avoid using cond ops.
|
| 259 |
+
pad_length = max(length - (self.window_size + 1), 0)
|
| 260 |
+
slice_start_position = max((self.window_size + 1) - length, 0)
|
| 261 |
+
slice_end_position = slice_start_position + 2 * length - 1
|
| 262 |
+
if pad_length > 0:
|
| 263 |
+
padded_relative_embeddings = F.pad(
|
| 264 |
+
relative_embeddings,
|
| 265 |
+
commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]),
|
| 266 |
+
)
|
| 267 |
+
else:
|
| 268 |
+
padded_relative_embeddings = relative_embeddings
|
| 269 |
+
used_relative_embeddings = padded_relative_embeddings[
|
| 270 |
+
:, slice_start_position:slice_end_position
|
| 271 |
+
]
|
| 272 |
+
return used_relative_embeddings
|
| 273 |
+
|
| 274 |
+
def _relative_position_to_absolute_position(self, x):
|
| 275 |
+
"""
|
| 276 |
+
x: [b, h, l, 2*l-1]
|
| 277 |
+
ret: [b, h, l, l]
|
| 278 |
+
"""
|
| 279 |
+
batch, heads, length, _ = x.size()
|
| 280 |
+
# Concat columns of pad to shift from relative to absolute indexing.
|
| 281 |
+
x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, 1]]))
|
| 282 |
+
|
| 283 |
+
# Concat extra elements so to add up to shape (len+1, 2*len-1).
|
| 284 |
+
x_flat = x.view([batch, heads, length * 2 * length])
|
| 285 |
+
x_flat = F.pad(
|
| 286 |
+
x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [0, length - 1]])
|
| 287 |
+
)
|
| 288 |
+
|
| 289 |
+
# Reshape and slice out the padded elements.
|
| 290 |
+
x_final = x_flat.view([batch, heads, length + 1, 2 * length - 1])[
|
| 291 |
+
:, :, :length, length - 1 :
|
| 292 |
+
]
|
| 293 |
+
return x_final
|
| 294 |
+
|
| 295 |
+
def _absolute_position_to_relative_position(self, x):
|
| 296 |
+
"""
|
| 297 |
+
x: [b, h, l, l]
|
| 298 |
+
ret: [b, h, l, 2*l-1]
|
| 299 |
+
"""
|
| 300 |
+
batch, heads, length, _ = x.size()
|
| 301 |
+
# padd along column
|
| 302 |
+
x = F.pad(
|
| 303 |
+
x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length - 1]])
|
| 304 |
+
)
|
| 305 |
+
x_flat = x.view([batch, heads, length**2 + length * (length - 1)])
|
| 306 |
+
# add 0's in the beginning that will skew the elements after reshape
|
| 307 |
+
x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
|
| 308 |
+
x_final = x_flat.view([batch, heads, length, 2 * length])[:, :, :, 1:]
|
| 309 |
+
return x_final
|
| 310 |
+
|
| 311 |
+
def _attention_bias_proximal(self, length):
|
| 312 |
+
"""Bias for self-attention to encourage attention to close positions.
|
| 313 |
+
Args:
|
| 314 |
+
length: an integer scalar.
|
| 315 |
+
Returns:
|
| 316 |
+
a Tensor with shape [1, 1, length, length]
|
| 317 |
+
"""
|
| 318 |
+
r = torch.arange(length, dtype=torch.float32)
|
| 319 |
+
diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
|
| 320 |
+
return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
|
| 321 |
+
|
| 322 |
+
|
| 323 |
+
class FFN(nn.Module):
|
| 324 |
+
def __init__(
|
| 325 |
+
self,
|
| 326 |
+
in_channels,
|
| 327 |
+
out_channels,
|
| 328 |
+
filter_channels,
|
| 329 |
+
kernel_size,
|
| 330 |
+
p_dropout=0.0,
|
| 331 |
+
activation=None,
|
| 332 |
+
causal=False,
|
| 333 |
+
):
|
| 334 |
+
super().__init__()
|
| 335 |
+
self.in_channels = in_channels
|
| 336 |
+
self.out_channels = out_channels
|
| 337 |
+
self.filter_channels = filter_channels
|
| 338 |
+
self.kernel_size = kernel_size
|
| 339 |
+
self.p_dropout = p_dropout
|
| 340 |
+
self.activation = activation
|
| 341 |
+
self.causal = causal
|
| 342 |
+
|
| 343 |
+
if causal:
|
| 344 |
+
self.padding = self._causal_padding
|
| 345 |
+
else:
|
| 346 |
+
self.padding = self._same_padding
|
| 347 |
+
|
| 348 |
+
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
|
| 349 |
+
self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
|
| 350 |
+
self.drop = nn.Dropout(p_dropout)
|
| 351 |
+
|
| 352 |
+
def forward(self, x, x_mask):
|
| 353 |
+
x = self.conv_1(self.padding(x * x_mask))
|
| 354 |
+
if self.activation == "gelu":
|
| 355 |
+
x = x * torch.sigmoid(1.702 * x)
|
| 356 |
+
else:
|
| 357 |
+
x = torch.relu(x)
|
| 358 |
+
x = self.drop(x)
|
| 359 |
+
x = self.conv_2(self.padding(x * x_mask))
|
| 360 |
+
return x * x_mask
|
| 361 |
+
|
| 362 |
+
def _causal_padding(self, x):
|
| 363 |
+
if self.kernel_size == 1:
|
| 364 |
+
return x
|
| 365 |
+
pad_l = self.kernel_size - 1
|
| 366 |
+
pad_r = 0
|
| 367 |
+
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
| 368 |
+
x = F.pad(x, commons.convert_pad_shape(padding))
|
| 369 |
+
return x
|
| 370 |
+
|
| 371 |
+
def _same_padding(self, x):
|
| 372 |
+
if self.kernel_size == 1:
|
| 373 |
+
return x
|
| 374 |
+
pad_l = (self.kernel_size - 1) // 2
|
| 375 |
+
pad_r = self.kernel_size // 2
|
| 376 |
+
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
| 377 |
+
x = F.pad(x, commons.convert_pad_shape(padding))
|
| 378 |
+
return x
|
bert/bert-large-japanese-v2/.gitattributes
ADDED
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*.bin filter=lfs diff=lfs merge=lfs -text
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*.joblib filter=lfs diff=lfs merge=lfs -text
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*.lfs.* filter=lfs diff=lfs merge=lfs -text
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*.msgpack filter=lfs diff=lfs merge=lfs -text
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*.npy filter=lfs diff=lfs merge=lfs -text
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*.npz filter=lfs diff=lfs merge=lfs -text
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*.onnx filter=lfs diff=lfs merge=lfs -text
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*.ot filter=lfs diff=lfs merge=lfs -text
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*.parquet filter=lfs diff=lfs merge=lfs -text
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*.pb filter=lfs diff=lfs merge=lfs -text
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| 20 |
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*.pickle filter=lfs diff=lfs merge=lfs -text
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*.pkl filter=lfs diff=lfs merge=lfs -text
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*.pt filter=lfs diff=lfs merge=lfs -text
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*.pth filter=lfs diff=lfs merge=lfs -text
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*.rar filter=lfs diff=lfs merge=lfs -text
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| 25 |
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*.safetensors filter=lfs diff=lfs merge=lfs -text
|
| 26 |
+
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
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| 27 |
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*.tar.* filter=lfs diff=lfs merge=lfs -text
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*.tflite filter=lfs diff=lfs merge=lfs -text
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| 29 |
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*.tgz filter=lfs diff=lfs merge=lfs -text
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| 30 |
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*.wasm filter=lfs diff=lfs merge=lfs -text
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| 31 |
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*.xz filter=lfs diff=lfs merge=lfs -text
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| 32 |
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*.zip filter=lfs diff=lfs merge=lfs -text
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| 33 |
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*.zst filter=lfs diff=lfs merge=lfs -text
|
| 34 |
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*tfevents* filter=lfs diff=lfs merge=lfs -text
|
bert/bert-large-japanese-v2/README.md
ADDED
|
@@ -0,0 +1,53 @@
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|
| 1 |
+
---
|
| 2 |
+
license: apache-2.0
|
| 3 |
+
datasets:
|
| 4 |
+
- cc100
|
| 5 |
+
- wikipedia
|
| 6 |
+
language:
|
| 7 |
+
- ja
|
| 8 |
+
widget:
|
| 9 |
+
- text: 東北大学で[MASK]の研究をしています。
|
| 10 |
+
---
|
| 11 |
+
|
| 12 |
+
# BERT large Japanese (unidic-lite with whole word masking, CC-100 and jawiki-20230102)
|
| 13 |
+
|
| 14 |
+
This is a [BERT](https://github.com/google-research/bert) model pretrained on texts in the Japanese language.
|
| 15 |
+
|
| 16 |
+
This version of the model processes input texts with word-level tokenization based on the Unidic 2.1.2 dictionary (available in [unidic-lite](https://pypi.org/project/unidic-lite/) package), followed by the WordPiece subword tokenization.
|
| 17 |
+
Additionally, the model is trained with the whole word masking enabled for the masked language modeling (MLM) objective.
|
| 18 |
+
|
| 19 |
+
The codes for the pretraining are available at [cl-tohoku/bert-japanese](https://github.com/cl-tohoku/bert-japanese/).
|
| 20 |
+
|
| 21 |
+
## Model architecture
|
| 22 |
+
|
| 23 |
+
The model architecture is the same as the original BERT large model; 24 layers, 1024 dimensions of hidden states, and 16 attention heads.
|
| 24 |
+
|
| 25 |
+
## Training Data
|
| 26 |
+
|
| 27 |
+
The model is trained on the Japanese portion of [CC-100 dataset](https://data.statmt.org/cc-100/) and the Japanese version of Wikipedia.
|
| 28 |
+
For Wikipedia, we generated a text corpus from the [Wikipedia Cirrussearch dump file](https://dumps.wikimedia.org/other/cirrussearch/) as of January 2, 2023.
|
| 29 |
+
The corpus files generated from CC-100 and Wikipedia are 74.3GB and 4.9GB in size and consist of approximately 392M and 34M sentences, respectively.
|
| 30 |
+
|
| 31 |
+
For the purpose of splitting texts into sentences, we used [fugashi](https://github.com/polm/fugashi) with [mecab-ipadic-NEologd](https://github.com/neologd/mecab-ipadic-neologd) dictionary (v0.0.7).
|
| 32 |
+
|
| 33 |
+
## Tokenization
|
| 34 |
+
|
| 35 |
+
The texts are first tokenized by MeCab with the Unidic 2.1.2 dictionary and then split into subwords by the WordPiece algorithm.
|
| 36 |
+
The vocabulary size is 32768.
|
| 37 |
+
|
| 38 |
+
We used [fugashi](https://github.com/polm/fugashi) and [unidic-lite](https://github.com/polm/unidic-lite) packages for the tokenization.
|
| 39 |
+
|
| 40 |
+
## Training
|
| 41 |
+
|
| 42 |
+
We trained the model first on the CC-100 corpus for 1M steps and then on the Wikipedia corpus for another 1M steps.
|
| 43 |
+
For training of the MLM (masked language modeling) objective, we introduced whole word masking in which all of the subword tokens corresponding to a single word (tokenized by MeCab) are masked at once.
|
| 44 |
+
|
| 45 |
+
For training of each model, we used a v3-8 instance of Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/).
|
| 46 |
+
|
| 47 |
+
## Licenses
|
| 48 |
+
|
| 49 |
+
The pretrained models are distributed under the Apache License 2.0.
|
| 50 |
+
|
| 51 |
+
## Acknowledgments
|
| 52 |
+
|
| 53 |
+
This model is trained with Cloud TPUs provided by [TPU Research Cloud](https://sites.research.google/trc/about/) program.
|
bert/bert-large-japanese-v2/config.json
ADDED
|
@@ -0,0 +1,19 @@
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|
| 1 |
+
{
|
| 2 |
+
"architectures": [
|
| 3 |
+
"BertForPreTraining"
|
| 4 |
+
],
|
| 5 |
+
"attention_probs_dropout_prob": 0.1,
|
| 6 |
+
"hidden_act": "gelu",
|
| 7 |
+
"hidden_dropout_prob": 0.1,
|
| 8 |
+
"hidden_size": 1024,
|
| 9 |
+
"initializer_range": 0.02,
|
| 10 |
+
"intermediate_size": 4096,
|
| 11 |
+
"layer_norm_eps": 1e-12,
|
| 12 |
+
"max_position_embeddings": 512,
|
| 13 |
+
"model_type": "bert",
|
| 14 |
+
"num_attention_heads": 16,
|
| 15 |
+
"num_hidden_layers": 24,
|
| 16 |
+
"pad_token_id": 0,
|
| 17 |
+
"type_vocab_size": 2,
|
| 18 |
+
"vocab_size": 32768
|
| 19 |
+
}
|
bert/bert-large-japanese-v2/tokenizer_config.json
ADDED
|
@@ -0,0 +1,10 @@
|
|
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|
| 1 |
+
{
|
| 2 |
+
"tokenizer_class": "BertJapaneseTokenizer",
|
| 3 |
+
"model_max_length": 512,
|
| 4 |
+
"do_lower_case": false,
|
| 5 |
+
"word_tokenizer_type": "mecab",
|
| 6 |
+
"subword_tokenizer_type": "wordpiece",
|
| 7 |
+
"mecab_kwargs": {
|
| 8 |
+
"mecab_dic": "unidic_lite"
|
| 9 |
+
}
|
| 10 |
+
}
|
bert/bert-large-japanese-v2/vocab.txt
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
bert/bert_models.json
ADDED
|
@@ -0,0 +1,14 @@
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|
| 1 |
+
{
|
| 2 |
+
"deberta-v2-large-japanese": {
|
| 3 |
+
"repo_id": "ku-nlp/deberta-v2-large-japanese",
|
| 4 |
+
"files": ["pytorch_model.bin"]
|
| 5 |
+
},
|
| 6 |
+
"chinese-roberta-wwm-ext-large": {
|
| 7 |
+
"repo_id": "hfl/chinese-roberta-wwm-ext-large",
|
| 8 |
+
"files": ["pytorch_model.bin"]
|
| 9 |
+
},
|
| 10 |
+
"deberta-v3-large": {
|
| 11 |
+
"repo_id": "microsoft/deberta-v3-large",
|
| 12 |
+
"files": ["spm.model", "pytorch_model.bin"]
|
| 13 |
+
}
|
| 14 |
+
}
|
bert/deberta-v2-large-japanese/.gitattributes
ADDED
|
@@ -0,0 +1,34 @@
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*.7z filter=lfs diff=lfs merge=lfs -text
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*.arrow filter=lfs diff=lfs merge=lfs -text
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*.bin filter=lfs diff=lfs merge=lfs -text
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| 4 |
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*.bz2 filter=lfs diff=lfs merge=lfs -text
|
| 5 |
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*.ckpt filter=lfs diff=lfs merge=lfs -text
|
| 6 |
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*.ftz filter=lfs diff=lfs merge=lfs -text
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| 7 |
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*.gz filter=lfs diff=lfs merge=lfs -text
|
| 8 |
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*.h5 filter=lfs diff=lfs merge=lfs -text
|
| 9 |
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*.joblib filter=lfs diff=lfs merge=lfs -text
|
| 10 |
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*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
| 11 |
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*.mlmodel filter=lfs diff=lfs merge=lfs -text
|
| 12 |
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*.model filter=lfs diff=lfs merge=lfs -text
|
| 13 |
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*.msgpack filter=lfs diff=lfs merge=lfs -text
|
| 14 |
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*.npy filter=lfs diff=lfs merge=lfs -text
|
| 15 |
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*.npz filter=lfs diff=lfs merge=lfs -text
|
| 16 |
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*.onnx filter=lfs diff=lfs merge=lfs -text
|
| 17 |
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*.ot filter=lfs diff=lfs merge=lfs -text
|
| 18 |
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*.parquet filter=lfs diff=lfs merge=lfs -text
|
| 19 |
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*.pb filter=lfs diff=lfs merge=lfs -text
|
| 20 |
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*.pickle filter=lfs diff=lfs merge=lfs -text
|
| 21 |
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*.pkl filter=lfs diff=lfs merge=lfs -text
|
| 22 |
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*.pt filter=lfs diff=lfs merge=lfs -text
|
| 23 |
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*.pth filter=lfs diff=lfs merge=lfs -text
|
| 24 |
+
*.rar filter=lfs diff=lfs merge=lfs -text
|
| 25 |
+
*.safetensors filter=lfs diff=lfs merge=lfs -text
|
| 26 |
+
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
| 27 |
+
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
| 28 |
+
*.tflite filter=lfs diff=lfs merge=lfs -text
|
| 29 |
+
*.tgz filter=lfs diff=lfs merge=lfs -text
|
| 30 |
+
*.wasm filter=lfs diff=lfs merge=lfs -text
|
| 31 |
+
*.xz filter=lfs diff=lfs merge=lfs -text
|
| 32 |
+
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 33 |
+
*.zst filter=lfs diff=lfs merge=lfs -text
|
| 34 |
+
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
bert/deberta-v2-large-japanese/README.md
ADDED
|
@@ -0,0 +1,111 @@
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|
|
|
| 1 |
+
---
|
| 2 |
+
language: ja
|
| 3 |
+
license: cc-by-sa-4.0
|
| 4 |
+
library_name: transformers
|
| 5 |
+
tags:
|
| 6 |
+
- deberta
|
| 7 |
+
- deberta-v2
|
| 8 |
+
- fill-mask
|
| 9 |
+
datasets:
|
| 10 |
+
- wikipedia
|
| 11 |
+
- cc100
|
| 12 |
+
- oscar
|
| 13 |
+
metrics:
|
| 14 |
+
- accuracy
|
| 15 |
+
mask_token: "[MASK]"
|
| 16 |
+
widget:
|
| 17 |
+
- text: "京都 大学 で 自然 言語 処理 を [MASK] する 。"
|
| 18 |
+
---
|
| 19 |
+
|
| 20 |
+
# Model Card for Japanese DeBERTa V2 large
|
| 21 |
+
|
| 22 |
+
## Model description
|
| 23 |
+
|
| 24 |
+
This is a Japanese DeBERTa V2 large model pre-trained on Japanese Wikipedia, the Japanese portion of CC-100, and the
|
| 25 |
+
Japanese portion of OSCAR.
|
| 26 |
+
|
| 27 |
+
## How to use
|
| 28 |
+
|
| 29 |
+
You can use this model for masked language modeling as follows:
|
| 30 |
+
|
| 31 |
+
```python
|
| 32 |
+
from transformers import AutoTokenizer, AutoModelForMaskedLM
|
| 33 |
+
|
| 34 |
+
tokenizer = AutoTokenizer.from_pretrained('ku-nlp/deberta-v2-large-japanese')
|
| 35 |
+
model = AutoModelForMaskedLM.from_pretrained('ku-nlp/deberta-v2-large-japanese')
|
| 36 |
+
|
| 37 |
+
sentence = '京都 大学 で 自然 言語 処理 を [MASK] する 。' # input should be segmented into words by Juman++ in advance
|
| 38 |
+
encoding = tokenizer(sentence, return_tensors='pt')
|
| 39 |
+
...
|
| 40 |
+
```
|
| 41 |
+
|
| 42 |
+
You can also fine-tune this model on downstream tasks.
|
| 43 |
+
|
| 44 |
+
## Tokenization
|
| 45 |
+
|
| 46 |
+
The input text should be segmented into words by [Juman++](https://github.com/ku-nlp/jumanpp) in
|
| 47 |
+
advance. [Juman++ 2.0.0-rc3](https://github.com/ku-nlp/jumanpp/releases/tag/v2.0.0-rc3) was used for pre-training. Each
|
| 48 |
+
word is tokenized into subwords by [sentencepiece](https://github.com/google/sentencepiece).
|
| 49 |
+
|
| 50 |
+
## Training data
|
| 51 |
+
|
| 52 |
+
We used the following corpora for pre-training:
|
| 53 |
+
|
| 54 |
+
- Japanese Wikipedia (as of 20221020, 3.2GB, 27M sentences, 1.3M documents)
|
| 55 |
+
- Japanese portion of CC-100 (85GB, 619M sentences, 66M documents)
|
| 56 |
+
- Japanese portion of OSCAR (54GB, 326M sentences, 25M documents)
|
| 57 |
+
|
| 58 |
+
Note that we filtered out documents annotated with "header", "footer", or "noisy" tags in OSCAR.
|
| 59 |
+
Also note that Japanese Wikipedia was duplicated 10 times to make the total size of the corpus comparable to that of
|
| 60 |
+
CC-100 and OSCAR. As a result, the total size of the training data is 171GB.
|
| 61 |
+
|
| 62 |
+
## Training procedure
|
| 63 |
+
|
| 64 |
+
We first segmented texts in the corpora into words using [Juman++](https://github.com/ku-nlp/jumanpp).
|
| 65 |
+
Then, we built a sentencepiece model with 32000 tokens including words ([JumanDIC](https://github.com/ku-nlp/JumanDIC))
|
| 66 |
+
and subwords induced by the unigram language model of [sentencepiece](https://github.com/google/sentencepiece).
|
| 67 |
+
|
| 68 |
+
We tokenized the segmented corpora into subwords using the sentencepiece model and trained the Japanese DeBERTa model
|
| 69 |
+
using [transformers](https://github.com/huggingface/transformers) library.
|
| 70 |
+
The training took 36 days using 8 NVIDIA A100-SXM4-40GB GPUs.
|
| 71 |
+
|
| 72 |
+
The following hyperparameters were used during pre-training:
|
| 73 |
+
|
| 74 |
+
- learning_rate: 1e-4
|
| 75 |
+
- per_device_train_batch_size: 18
|
| 76 |
+
- distributed_type: multi-GPU
|
| 77 |
+
- num_devices: 8
|
| 78 |
+
- gradient_accumulation_steps: 16
|
| 79 |
+
- total_train_batch_size: 2,304
|
| 80 |
+
- max_seq_length: 512
|
| 81 |
+
- optimizer: Adam with betas=(0.9,0.999) and epsilon=1e-06
|
| 82 |
+
- lr_scheduler_type: linear schedule with warmup
|
| 83 |
+
- training_steps: 300,000
|
| 84 |
+
- warmup_steps: 10,000
|
| 85 |
+
|
| 86 |
+
The accuracy of the trained model on the masked language modeling task was 0.799.
|
| 87 |
+
The evaluation set consists of 5,000 randomly sampled documents from each of the training corpora.
|
| 88 |
+
|
| 89 |
+
## Fine-tuning on NLU tasks
|
| 90 |
+
|
| 91 |
+
We fine-tuned the following models and evaluated them on the dev set of JGLUE.
|
| 92 |
+
We tuned learning rate and training epochs for each model and task
|
| 93 |
+
following [the JGLUE paper](https://www.jstage.jst.go.jp/article/jnlp/30/1/30_63/_pdf/-char/ja).
|
| 94 |
+
|
| 95 |
+
| Model | MARC-ja/acc | JSTS/pearson | JSTS/spearman | JNLI/acc | JSQuAD/EM | JSQuAD/F1 | JComQA/acc |
|
| 96 |
+
|-------------------------------|-------------|--------------|---------------|----------|-----------|-----------|------------|
|
| 97 |
+
| Waseda RoBERTa base | 0.965 | 0.913 | 0.876 | 0.905 | 0.853 | 0.916 | 0.853 |
|
| 98 |
+
| Waseda RoBERTa large (seq512) | 0.969 | 0.925 | 0.890 | 0.928 | 0.910 | 0.955 | 0.900 |
|
| 99 |
+
| LUKE Japanese base* | 0.965 | 0.916 | 0.877 | 0.912 | - | - | 0.842 |
|
| 100 |
+
| LUKE Japanese large* | 0.965 | 0.932 | 0.902 | 0.927 | - | - | 0.893 |
|
| 101 |
+
| DeBERTaV2 base | 0.970 | 0.922 | 0.886 | 0.922 | 0.899 | 0.951 | 0.873 |
|
| 102 |
+
| DeBERTaV2 large | 0.968 | 0.925 | 0.892 | 0.924 | 0.912 | 0.959 | 0.890 |
|
| 103 |
+
|
| 104 |
+
*The scores of LUKE are from [the official repository](https://github.com/studio-ousia/luke).
|
| 105 |
+
|
| 106 |
+
## Acknowledgments
|
| 107 |
+
|
| 108 |
+
This work was supported by Joint Usage/Research Center for Interdisciplinary Large-scale Information Infrastructures (
|
| 109 |
+
JHPCN) through General Collaboration Project no. jh221004, "Developing a Platform for Constructing and Sharing of
|
| 110 |
+
Large-Scale Japanese Language Models".
|
| 111 |
+
For training models, we used the mdx: a platform for the data-driven future.
|
bert/deberta-v2-large-japanese/config.json
ADDED
|
@@ -0,0 +1,38 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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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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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "configs/deberta_v2_large.json",
|
| 3 |
+
"architectures": [
|
| 4 |
+
"DebertaV2ForMaskedLM"
|
| 5 |
+
],
|
| 6 |
+
"attention_head_size": 64,
|
| 7 |
+
"attention_probs_dropout_prob": 0.1,
|
| 8 |
+
"conv_act": "gelu",
|
| 9 |
+
"conv_kernel_size": 3,
|
| 10 |
+
"hidden_act": "gelu",
|
| 11 |
+
"hidden_dropout_prob": 0.1,
|
| 12 |
+
"hidden_size": 1024,
|
| 13 |
+
"initializer_range": 0.02,
|
| 14 |
+
"intermediate_size": 4096,
|
| 15 |
+
"layer_norm_eps": 1e-07,
|
| 16 |
+
"max_position_embeddings": 512,
|
| 17 |
+
"max_relative_positions": -1,
|
| 18 |
+
"model_type": "deberta-v2",
|
| 19 |
+
"norm_rel_ebd": "layer_norm",
|
| 20 |
+
"num_attention_heads": 16,
|
| 21 |
+
"num_hidden_layers": 24,
|
| 22 |
+
"pad_token_id": 0,
|
| 23 |
+
"pooler_dropout": 0,
|
| 24 |
+
"pooler_hidden_act": "gelu",
|
| 25 |
+
"pooler_hidden_size": 1024,
|
| 26 |
+
"pos_att_type": [
|
| 27 |
+
"p2c",
|
| 28 |
+
"c2p"
|
| 29 |
+
],
|
| 30 |
+
"position_biased_input": false,
|
| 31 |
+
"position_buckets": 256,
|
| 32 |
+
"relative_attention": true,
|
| 33 |
+
"share_att_key": true,
|
| 34 |
+
"torch_dtype": "float32",
|
| 35 |
+
"transformers_version": "4.23.1",
|
| 36 |
+
"type_vocab_size": 0,
|
| 37 |
+
"vocab_size": 32000
|
| 38 |
+
}
|
bert/deberta-v2-large-japanese/pytorch_model.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:a6c15feac0dea77ab8835c70e1befa4cf4c2137862c6fb2443b1553f70840047
|
| 3 |
+
size 1490693213
|
bert/deberta-v2-large-japanese/special_tokens_map.json
ADDED
|
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token": "[CLS]",
|
| 3 |
+
"cls_token": "[CLS]",
|
| 4 |
+
"eos_token": "[SEP]",
|
| 5 |
+
"mask_token": "[MASK]",
|
| 6 |
+
"pad_token": "[PAD]",
|
| 7 |
+
"sep_token": "[SEP]",
|
| 8 |
+
"unk_token": "[UNK]"
|
| 9 |
+
}
|
bert/deberta-v2-large-japanese/tokenizer.json
ADDED
|
The diff for this file is too large to render.
See raw diff
|
|
|
bert/deberta-v2-large-japanese/tokenizer_config.json
ADDED
|
@@ -0,0 +1,15 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"bos_token": "[CLS]",
|
| 3 |
+
"cls_token": "[CLS]",
|
| 4 |
+
"do_lower_case": false,
|
| 5 |
+
"eos_token": "[SEP]",
|
| 6 |
+
"keep_accents": true,
|
| 7 |
+
"mask_token": "[MASK]",
|
| 8 |
+
"pad_token": "[PAD]",
|
| 9 |
+
"sep_token": "[SEP]",
|
| 10 |
+
"sp_model_kwargs": {},
|
| 11 |
+
"special_tokens_map_file": null,
|
| 12 |
+
"split_by_punct": false,
|
| 13 |
+
"tokenizer_class": "DebertaV2Tokenizer",
|
| 14 |
+
"unk_token": "[UNK]"
|
| 15 |
+
}
|
bert/deberta-v3-large/.gitattributes
ADDED
|
@@ -0,0 +1,27 @@
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
*.7z filter=lfs diff=lfs merge=lfs -text
|
| 2 |
+
*.arrow filter=lfs diff=lfs merge=lfs -text
|
| 3 |
+
*.bin filter=lfs diff=lfs merge=lfs -text
|
| 4 |
+
*.bin.* filter=lfs diff=lfs merge=lfs -text
|
| 5 |
+
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
| 6 |
+
*.ftz filter=lfs diff=lfs merge=lfs -text
|
| 7 |
+
*.gz filter=lfs diff=lfs merge=lfs -text
|
| 8 |
+
*.h5 filter=lfs diff=lfs merge=lfs -text
|
| 9 |
+
*.joblib filter=lfs diff=lfs merge=lfs -text
|
| 10 |
+
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
| 11 |
+
*.model filter=lfs diff=lfs merge=lfs -text
|
| 12 |
+
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
| 13 |
+
*.onnx filter=lfs diff=lfs merge=lfs -text
|
| 14 |
+
*.ot filter=lfs diff=lfs merge=lfs -text
|
| 15 |
+
*.parquet filter=lfs diff=lfs merge=lfs -text
|
| 16 |
+
*.pb filter=lfs diff=lfs merge=lfs -text
|
| 17 |
+
*.pt filter=lfs diff=lfs merge=lfs -text
|
| 18 |
+
*.pth filter=lfs diff=lfs merge=lfs -text
|
| 19 |
+
*.rar filter=lfs diff=lfs merge=lfs -text
|
| 20 |
+
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
| 21 |
+
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
| 22 |
+
*.tflite filter=lfs diff=lfs merge=lfs -text
|
| 23 |
+
*.tgz filter=lfs diff=lfs merge=lfs -text
|
| 24 |
+
*.xz filter=lfs diff=lfs merge=lfs -text
|
| 25 |
+
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 26 |
+
*.zstandard filter=lfs diff=lfs merge=lfs -text
|
| 27 |
+
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
bert/deberta-v3-large/README.md
ADDED
|
@@ -0,0 +1,93 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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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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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
---
|
| 2 |
+
language: en
|
| 3 |
+
tags:
|
| 4 |
+
- deberta
|
| 5 |
+
- deberta-v3
|
| 6 |
+
- fill-mask
|
| 7 |
+
thumbnail: https://huggingface.co/front/thumbnails/microsoft.png
|
| 8 |
+
license: mit
|
| 9 |
+
---
|
| 10 |
+
|
| 11 |
+
## DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing
|
| 12 |
+
|
| 13 |
+
[DeBERTa](https://arxiv.org/abs/2006.03654) improves the BERT and RoBERTa models using disentangled attention and enhanced mask decoder. With those two improvements, DeBERTa out perform RoBERTa on a majority of NLU tasks with 80GB training data.
|
| 14 |
+
|
| 15 |
+
In [DeBERTa V3](https://arxiv.org/abs/2111.09543), we further improved the efficiency of DeBERTa using ELECTRA-Style pre-training with Gradient Disentangled Embedding Sharing. Compared to DeBERTa, our V3 version significantly improves the model performance on downstream tasks. You can find more technique details about the new model from our [paper](https://arxiv.org/abs/2111.09543).
|
| 16 |
+
|
| 17 |
+
Please check the [official repository](https://github.com/microsoft/DeBERTa) for more implementation details and updates.
|
| 18 |
+
|
| 19 |
+
The DeBERTa V3 large model comes with 24 layers and a hidden size of 1024. It has 304M backbone parameters with a vocabulary containing 128K tokens which introduces 131M parameters in the Embedding layer. This model was trained using the 160GB data as DeBERTa V2.
|
| 20 |
+
|
| 21 |
+
|
| 22 |
+
#### Fine-tuning on NLU tasks
|
| 23 |
+
|
| 24 |
+
We present the dev results on SQuAD 2.0 and MNLI tasks.
|
| 25 |
+
|
| 26 |
+
| Model |Vocabulary(K)|Backbone #Params(M)| SQuAD 2.0(F1/EM) | MNLI-m/mm(ACC)|
|
| 27 |
+
|-------------------|----------|-------------------|-----------|----------|
|
| 28 |
+
| RoBERTa-large |50 |304 | 89.4/86.5 | 90.2 |
|
| 29 |
+
| XLNet-large |32 |- | 90.6/87.9 | 90.8 |
|
| 30 |
+
| DeBERTa-large |50 |- | 90.7/88.0 | 91.3 |
|
| 31 |
+
| **DeBERTa-v3-large**|128|304 | **91.5/89.0**| **91.8/91.9**|
|
| 32 |
+
|
| 33 |
+
|
| 34 |
+
#### Fine-tuning with HF transformers
|
| 35 |
+
|
| 36 |
+
```bash
|
| 37 |
+
#!/bin/bash
|
| 38 |
+
|
| 39 |
+
cd transformers/examples/pytorch/text-classification/
|
| 40 |
+
|
| 41 |
+
pip install datasets
|
| 42 |
+
export TASK_NAME=mnli
|
| 43 |
+
|
| 44 |
+
output_dir="ds_results"
|
| 45 |
+
|
| 46 |
+
num_gpus=8
|
| 47 |
+
|
| 48 |
+
batch_size=8
|
| 49 |
+
|
| 50 |
+
python -m torch.distributed.launch --nproc_per_node=${num_gpus} \
|
| 51 |
+
run_glue.py \
|
| 52 |
+
--model_name_or_path microsoft/deberta-v3-large \
|
| 53 |
+
--task_name $TASK_NAME \
|
| 54 |
+
--do_train \
|
| 55 |
+
--do_eval \
|
| 56 |
+
--evaluation_strategy steps \
|
| 57 |
+
--max_seq_length 256 \
|
| 58 |
+
--warmup_steps 50 \
|
| 59 |
+
--per_device_train_batch_size ${batch_size} \
|
| 60 |
+
--learning_rate 6e-6 \
|
| 61 |
+
--num_train_epochs 2 \
|
| 62 |
+
--output_dir $output_dir \
|
| 63 |
+
--overwrite_output_dir \
|
| 64 |
+
--logging_steps 1000 \
|
| 65 |
+
--logging_dir $output_dir
|
| 66 |
+
|
| 67 |
+
```
|
| 68 |
+
|
| 69 |
+
### Citation
|
| 70 |
+
|
| 71 |
+
If you find DeBERTa useful for your work, please cite the following papers:
|
| 72 |
+
|
| 73 |
+
``` latex
|
| 74 |
+
@misc{he2021debertav3,
|
| 75 |
+
title={DeBERTaV3: Improving DeBERTa using ELECTRA-Style Pre-Training with Gradient-Disentangled Embedding Sharing},
|
| 76 |
+
author={Pengcheng He and Jianfeng Gao and Weizhu Chen},
|
| 77 |
+
year={2021},
|
| 78 |
+
eprint={2111.09543},
|
| 79 |
+
archivePrefix={arXiv},
|
| 80 |
+
primaryClass={cs.CL}
|
| 81 |
+
}
|
| 82 |
+
```
|
| 83 |
+
|
| 84 |
+
``` latex
|
| 85 |
+
@inproceedings{
|
| 86 |
+
he2021deberta,
|
| 87 |
+
title={DEBERTA: DECODING-ENHANCED BERT WITH DISENTANGLED ATTENTION},
|
| 88 |
+
author={Pengcheng He and Xiaodong Liu and Jianfeng Gao and Weizhu Chen},
|
| 89 |
+
booktitle={International Conference on Learning Representations},
|
| 90 |
+
year={2021},
|
| 91 |
+
url={https://openreview.net/forum?id=XPZIaotutsD}
|
| 92 |
+
}
|
| 93 |
+
```
|
bert/deberta-v3-large/config.json
ADDED
|
@@ -0,0 +1,22 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"model_type": "deberta-v2",
|
| 3 |
+
"attention_probs_dropout_prob": 0.1,
|
| 4 |
+
"hidden_act": "gelu",
|
| 5 |
+
"hidden_dropout_prob": 0.1,
|
| 6 |
+
"hidden_size": 1024,
|
| 7 |
+
"initializer_range": 0.02,
|
| 8 |
+
"intermediate_size": 4096,
|
| 9 |
+
"max_position_embeddings": 512,
|
| 10 |
+
"relative_attention": true,
|
| 11 |
+
"position_buckets": 256,
|
| 12 |
+
"norm_rel_ebd": "layer_norm",
|
| 13 |
+
"share_att_key": true,
|
| 14 |
+
"pos_att_type": "p2c|c2p",
|
| 15 |
+
"layer_norm_eps": 1e-7,
|
| 16 |
+
"max_relative_positions": -1,
|
| 17 |
+
"position_biased_input": false,
|
| 18 |
+
"num_attention_heads": 16,
|
| 19 |
+
"num_hidden_layers": 24,
|
| 20 |
+
"type_vocab_size": 0,
|
| 21 |
+
"vocab_size": 128100
|
| 22 |
+
}
|
bert/deberta-v3-large/generator_config.json
ADDED
|
@@ -0,0 +1,22 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"model_type": "deberta-v2",
|
| 3 |
+
"attention_probs_dropout_prob": 0.1,
|
| 4 |
+
"hidden_act": "gelu",
|
| 5 |
+
"hidden_dropout_prob": 0.1,
|
| 6 |
+
"hidden_size": 1024,
|
| 7 |
+
"initializer_range": 0.02,
|
| 8 |
+
"intermediate_size": 4096,
|
| 9 |
+
"max_position_embeddings": 512,
|
| 10 |
+
"relative_attention": true,
|
| 11 |
+
"position_buckets": 256,
|
| 12 |
+
"norm_rel_ebd": "layer_norm",
|
| 13 |
+
"share_att_key": true,
|
| 14 |
+
"pos_att_type": "p2c|c2p",
|
| 15 |
+
"layer_norm_eps": 1e-7,
|
| 16 |
+
"max_relative_positions": -1,
|
| 17 |
+
"position_biased_input": false,
|
| 18 |
+
"num_attention_heads": 16,
|
| 19 |
+
"num_hidden_layers": 12,
|
| 20 |
+
"type_vocab_size": 0,
|
| 21 |
+
"vocab_size": 128100
|
| 22 |
+
}
|
bert/deberta-v3-large/pytorch_model.bin
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:dd5b5d93e2db101aaf281df0ea1216c07ad73620ff59c5b42dccac4bf2eef5b5
|
| 3 |
+
size 873673253
|
bert/deberta-v3-large/spm.model
ADDED
|
@@ -0,0 +1,3 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
version https://git-lfs.github.com/spec/v1
|
| 2 |
+
oid sha256:c679fbf93643d19aab7ee10c0b99e460bdbc02fedf34b92b05af343b4af586fd
|
| 3 |
+
size 2464616
|
bert/deberta-v3-large/tokenizer_config.json
ADDED
|
@@ -0,0 +1,4 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"do_lower_case": false,
|
| 3 |
+
"vocab_type": "spm"
|
| 4 |
+
}
|
bert_gen.py
CHANGED
|
@@ -3,17 +3,22 @@ from multiprocessing import Pool
|
|
| 3 |
import commons
|
| 4 |
import utils
|
| 5 |
from tqdm import tqdm
|
| 6 |
-
from text import cleaned_text_to_sequence, get_bert
|
| 7 |
import argparse
|
| 8 |
import torch.multiprocessing as mp
|
|
|
|
| 9 |
|
| 10 |
|
| 11 |
def process_line(line):
|
| 12 |
-
|
| 13 |
-
|
| 14 |
-
|
| 15 |
-
|
| 16 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
| 17 |
wav_path, _, language_str, text, phones, tone, word2ph = line.strip().split("|")
|
| 18 |
phone = phones.split(" ")
|
| 19 |
tone = [int(i) for i in tone.split(" ")]
|
|
@@ -28,7 +33,7 @@ def process_line(line):
|
|
| 28 |
word2ph[i] = word2ph[i] * 2
|
| 29 |
word2ph[0] += 1
|
| 30 |
|
| 31 |
-
bert_path = wav_path.replace(".wav", ".bert.pt")
|
| 32 |
|
| 33 |
try:
|
| 34 |
bert = torch.load(bert_path)
|
|
@@ -39,21 +44,30 @@ def process_line(line):
|
|
| 39 |
torch.save(bert, bert_path)
|
| 40 |
|
| 41 |
|
|
|
|
|
|
|
| 42 |
if __name__ == "__main__":
|
| 43 |
parser = argparse.ArgumentParser()
|
| 44 |
-
parser.add_argument(
|
| 45 |
-
|
| 46 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
| 47 |
config_path = args.config
|
| 48 |
hps = utils.get_hparams_from_file(config_path)
|
|
|
|
| 49 |
lines = []
|
| 50 |
with open(hps.data.training_files, encoding="utf-8") as f:
|
| 51 |
lines.extend(f.readlines())
|
| 52 |
|
| 53 |
with open(hps.data.validation_files, encoding="utf-8") as f:
|
| 54 |
lines.extend(f.readlines())
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 55 |
|
| 56 |
-
|
| 57 |
-
with Pool(processes=num_processes) as pool:
|
| 58 |
-
for _ in tqdm(pool.imap_unordered(process_line, lines), total=len(lines)):
|
| 59 |
-
pass
|
|
|
|
| 3 |
import commons
|
| 4 |
import utils
|
| 5 |
from tqdm import tqdm
|
| 6 |
+
from text import check_bert_models, cleaned_text_to_sequence, get_bert
|
| 7 |
import argparse
|
| 8 |
import torch.multiprocessing as mp
|
| 9 |
+
from config import config
|
| 10 |
|
| 11 |
|
| 12 |
def process_line(line):
|
| 13 |
+
device = config.bert_gen_config.device
|
| 14 |
+
if config.bert_gen_config.use_multi_device:
|
| 15 |
+
rank = mp.current_process()._identity
|
| 16 |
+
rank = rank[0] if len(rank) > 0 else 0
|
| 17 |
+
if torch.cuda.is_available():
|
| 18 |
+
gpu_id = rank % torch.cuda.device_count()
|
| 19 |
+
device = torch.device(f"cuda:{gpu_id}")
|
| 20 |
+
else:
|
| 21 |
+
device = torch.device("cpu")
|
| 22 |
wav_path, _, language_str, text, phones, tone, word2ph = line.strip().split("|")
|
| 23 |
phone = phones.split(" ")
|
| 24 |
tone = [int(i) for i in tone.split(" ")]
|
|
|
|
| 33 |
word2ph[i] = word2ph[i] * 2
|
| 34 |
word2ph[0] += 1
|
| 35 |
|
| 36 |
+
bert_path = wav_path.replace(".WAV", ".wav").replace(".wav", ".bert.pt")
|
| 37 |
|
| 38 |
try:
|
| 39 |
bert = torch.load(bert_path)
|
|
|
|
| 44 |
torch.save(bert, bert_path)
|
| 45 |
|
| 46 |
|
| 47 |
+
preprocess_text_config = config.preprocess_text_config
|
| 48 |
+
|
| 49 |
if __name__ == "__main__":
|
| 50 |
parser = argparse.ArgumentParser()
|
| 51 |
+
parser.add_argument(
|
| 52 |
+
"-c", "--config", type=str, default=config.bert_gen_config.config_path
|
| 53 |
+
)
|
| 54 |
+
parser.add_argument(
|
| 55 |
+
"--num_processes", type=int, default=config.bert_gen_config.num_processes
|
| 56 |
+
)
|
| 57 |
+
args, _ = parser.parse_known_args()
|
| 58 |
config_path = args.config
|
| 59 |
hps = utils.get_hparams_from_file(config_path)
|
| 60 |
+
check_bert_models()
|
| 61 |
lines = []
|
| 62 |
with open(hps.data.training_files, encoding="utf-8") as f:
|
| 63 |
lines.extend(f.readlines())
|
| 64 |
|
| 65 |
with open(hps.data.validation_files, encoding="utf-8") as f:
|
| 66 |
lines.extend(f.readlines())
|
| 67 |
+
if len(lines) != 0:
|
| 68 |
+
num_processes = args.num_processes
|
| 69 |
+
with Pool(processes=num_processes) as pool:
|
| 70 |
+
for _ in tqdm(pool.imap_unordered(process_line, lines), total=len(lines)):
|
| 71 |
+
pass
|
| 72 |
|
| 73 |
+
print(f"bert生成完毕!, 共有{len(lines)}个bert.pt生成!")
|
|
|
|
|
|
|
|
|
commons.py
CHANGED
|
@@ -50,7 +50,13 @@ def slice_segments(x, ids_str, segment_size=4):
|
|
| 50 |
for i in range(x.size(0)):
|
| 51 |
idx_str = ids_str[i]
|
| 52 |
idx_end = idx_str + segment_size
|
| 53 |
-
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 54 |
return ret
|
| 55 |
|
| 56 |
|
|
|
|
| 50 |
for i in range(x.size(0)):
|
| 51 |
idx_str = ids_str[i]
|
| 52 |
idx_end = idx_str + segment_size
|
| 53 |
+
if idx_str < 0:
|
| 54 |
+
i1 = x.size(2) + idx_str
|
| 55 |
+
r1 = x[i, :, i1:]
|
| 56 |
+
r2 = x[i, :, :idx_end]
|
| 57 |
+
ret[i] = torch.cat([r1, r2], dim=1)
|
| 58 |
+
else:
|
| 59 |
+
ret[i] = x[i, :, idx_str:idx_end]
|
| 60 |
return ret
|
| 61 |
|
| 62 |
|
config.py
ADDED
|
@@ -0,0 +1,237 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""
|
| 2 |
+
@Desc: 全局配置文件读取
|
| 3 |
+
"""
|
| 4 |
+
import argparse
|
| 5 |
+
import yaml
|
| 6 |
+
from typing import Dict, List
|
| 7 |
+
import os
|
| 8 |
+
import shutil
|
| 9 |
+
import sys
|
| 10 |
+
|
| 11 |
+
|
| 12 |
+
class Resample_config:
|
| 13 |
+
"""重采样配置"""
|
| 14 |
+
|
| 15 |
+
def __init__(self, in_dir: str, out_dir: str, sampling_rate: int = 44100):
|
| 16 |
+
self.sampling_rate: int = sampling_rate # 目标采样率
|
| 17 |
+
self.in_dir: str = in_dir # 待处理音频目录路径
|
| 18 |
+
self.out_dir: str = out_dir # 重采样输出路径
|
| 19 |
+
|
| 20 |
+
@classmethod
|
| 21 |
+
def from_dict(cls, dataset_path: str, data: Dict[str, any]):
|
| 22 |
+
"""从字典中生成实例"""
|
| 23 |
+
|
| 24 |
+
# 不检查路径是否有效，此逻辑在resample.py中处理
|
| 25 |
+
data["in_dir"] = os.path.join(dataset_path, data["in_dir"])
|
| 26 |
+
data["out_dir"] = os.path.join(dataset_path, data["out_dir"])
|
| 27 |
+
|
| 28 |
+
return cls(**data)
|
| 29 |
+
|
| 30 |
+
|
| 31 |
+
class Preprocess_text_config:
|
| 32 |
+
"""数据预处理配置"""
|
| 33 |
+
|
| 34 |
+
def __init__(
|
| 35 |
+
self,
|
| 36 |
+
transcription_path: str,
|
| 37 |
+
cleaned_path: str,
|
| 38 |
+
train_path: str,
|
| 39 |
+
val_path: str,
|
| 40 |
+
config_path: str,
|
| 41 |
+
val_per_spk: int = 5,
|
| 42 |
+
max_val_total: int = 10000,
|
| 43 |
+
clean: bool = True,
|
| 44 |
+
):
|
| 45 |
+
self.transcription_path: str = transcription_path # 原始文本文件路径，文本格式应为{wav_path}|{speaker_name}|{language}|{text}。
|
| 46 |
+
self.cleaned_path: str = cleaned_path # 数据清洗后文本路径，可以不填。不填则将在原始文本目录生成
|
| 47 |
+
self.train_path: str = train_path # 训练集路径，可以不填。不填则将在原始文本目录生成
|
| 48 |
+
self.val_path: str = val_path # 验证集路径，可以不填。不填则将在原始文本目录生成
|
| 49 |
+
self.config_path: str = config_path # 配置文件路径
|
| 50 |
+
self.val_per_spk: int = val_per_spk # 每个speaker的验证集条数
|
| 51 |
+
self.max_val_total: int = max_val_total # 验证集最大条数，多于的会被截断并放到训练集中
|
| 52 |
+
self.clean: bool = clean # 是否进行数据清洗
|
| 53 |
+
|
| 54 |
+
@classmethod
|
| 55 |
+
def from_dict(cls, dataset_path: str, data: Dict[str, any]):
|
| 56 |
+
"""从字典中生成实例"""
|
| 57 |
+
|
| 58 |
+
data["transcription_path"] = os.path.join(
|
| 59 |
+
dataset_path, data["transcription_path"]
|
| 60 |
+
)
|
| 61 |
+
if data["cleaned_path"] == "" or data["cleaned_path"] is None:
|
| 62 |
+
data["cleaned_path"] = None
|
| 63 |
+
else:
|
| 64 |
+
data["cleaned_path"] = os.path.join(dataset_path, data["cleaned_path"])
|
| 65 |
+
data["train_path"] = os.path.join(dataset_path, data["train_path"])
|
| 66 |
+
data["val_path"] = os.path.join(dataset_path, data["val_path"])
|
| 67 |
+
data["config_path"] = os.path.join(dataset_path, data["config_path"])
|
| 68 |
+
|
| 69 |
+
return cls(**data)
|
| 70 |
+
|
| 71 |
+
|
| 72 |
+
class Bert_gen_config:
|
| 73 |
+
"""bert_gen 配置"""
|
| 74 |
+
|
| 75 |
+
def __init__(
|
| 76 |
+
self,
|
| 77 |
+
config_path: str,
|
| 78 |
+
num_processes: int = 2,
|
| 79 |
+
device: str = "cuda",
|
| 80 |
+
use_multi_device: bool = False,
|
| 81 |
+
):
|
| 82 |
+
self.config_path = config_path
|
| 83 |
+
self.num_processes = num_processes
|
| 84 |
+
self.device = device
|
| 85 |
+
self.use_multi_device = use_multi_device
|
| 86 |
+
|
| 87 |
+
@classmethod
|
| 88 |
+
def from_dict(cls, dataset_path: str, data: Dict[str, any]):
|
| 89 |
+
data["config_path"] = os.path.join(dataset_path, data["config_path"])
|
| 90 |
+
|
| 91 |
+
return cls(**data)
|
| 92 |
+
|
| 93 |
+
|
| 94 |
+
class Emo_gen_config:
|
| 95 |
+
"""emo_gen 配置"""
|
| 96 |
+
|
| 97 |
+
def __init__(
|
| 98 |
+
self,
|
| 99 |
+
config_path: str,
|
| 100 |
+
num_processes: int = 2,
|
| 101 |
+
device: str = "cuda",
|
| 102 |
+
):
|
| 103 |
+
self.config_path = config_path
|
| 104 |
+
self.num_processes = num_processes
|
| 105 |
+
self.device = device
|
| 106 |
+
|
| 107 |
+
@classmethod
|
| 108 |
+
def from_dict(cls, dataset_path: str, data: Dict[str, any]):
|
| 109 |
+
data["config_path"] = os.path.join(dataset_path, data["config_path"])
|
| 110 |
+
|
| 111 |
+
return cls(**data)
|
| 112 |
+
|
| 113 |
+
|
| 114 |
+
class Train_ms_config:
|
| 115 |
+
"""训练配置"""
|
| 116 |
+
|
| 117 |
+
def __init__(
|
| 118 |
+
self,
|
| 119 |
+
config_path: str,
|
| 120 |
+
env: Dict[str, any],
|
| 121 |
+
base: Dict[str, any],
|
| 122 |
+
model: str,
|
| 123 |
+
):
|
| 124 |
+
self.env = env # 需要加载的环境变量
|
| 125 |
+
self.base = base # 底模配置
|
| 126 |
+
self.model = model # 训练模型存储目录，该路径为相对于dataset_path的路径，而非项目根目录
|
| 127 |
+
self.config_path = config_path # 配置文件路径
|
| 128 |
+
|
| 129 |
+
@classmethod
|
| 130 |
+
def from_dict(cls, dataset_path: str, data: Dict[str, any]):
|
| 131 |
+
# data["model"] = os.path.join(dataset_path, data["model"])
|
| 132 |
+
data["config_path"] = os.path.join(dataset_path, data["config_path"])
|
| 133 |
+
|
| 134 |
+
return cls(**data)
|
| 135 |
+
|
| 136 |
+
|
| 137 |
+
class Webui_config:
|
| 138 |
+
"""webui 配置"""
|
| 139 |
+
|
| 140 |
+
def __init__(
|
| 141 |
+
self,
|
| 142 |
+
device: str,
|
| 143 |
+
model: str,
|
| 144 |
+
config_path: str,
|
| 145 |
+
language_identification_library: str,
|
| 146 |
+
port: int = 7860,
|
| 147 |
+
share: bool = False,
|
| 148 |
+
debug: bool = False,
|
| 149 |
+
):
|
| 150 |
+
self.device: str = device
|
| 151 |
+
self.model: str = model # 端口号
|
| 152 |
+
self.config_path: str = config_path # 是否公开部署，对外网开放
|
| 153 |
+
self.port: int = port # ��否开启debug模式
|
| 154 |
+
self.share: bool = share # 模型路径
|
| 155 |
+
self.debug: bool = debug # 配置文件路径
|
| 156 |
+
self.language_identification_library: str = (
|
| 157 |
+
language_identification_library # 语种识别库
|
| 158 |
+
)
|
| 159 |
+
|
| 160 |
+
@classmethod
|
| 161 |
+
def from_dict(cls, dataset_path: str, data: Dict[str, any]):
|
| 162 |
+
data["config_path"] = os.path.join(dataset_path, data["config_path"])
|
| 163 |
+
data["model"] = os.path.join(dataset_path, data["model"])
|
| 164 |
+
return cls(**data)
|
| 165 |
+
|
| 166 |
+
|
| 167 |
+
class Server_config:
|
| 168 |
+
def __init__(
|
| 169 |
+
self, models: List[Dict[str, any]], port: int = 5000, device: str = "cuda"
|
| 170 |
+
):
|
| 171 |
+
self.models: List[Dict[str, any]] = models # 需要加载的所有模型的配置
|
| 172 |
+
self.port: int = port # 端口号
|
| 173 |
+
self.device: str = device # 模型默认使用设备
|
| 174 |
+
|
| 175 |
+
@classmethod
|
| 176 |
+
def from_dict(cls, data: Dict[str, any]):
|
| 177 |
+
return cls(**data)
|
| 178 |
+
|
| 179 |
+
|
| 180 |
+
class Translate_config:
|
| 181 |
+
"""翻译api配置"""
|
| 182 |
+
|
| 183 |
+
def __init__(self, app_key: str, secret_key: str):
|
| 184 |
+
self.app_key = app_key
|
| 185 |
+
self.secret_key = secret_key
|
| 186 |
+
|
| 187 |
+
@classmethod
|
| 188 |
+
def from_dict(cls, data: Dict[str, any]):
|
| 189 |
+
return cls(**data)
|
| 190 |
+
|
| 191 |
+
|
| 192 |
+
class Config:
|
| 193 |
+
def __init__(self, config_path: str):
|
| 194 |
+
if not os.path.isfile(config_path) and os.path.isfile("default_config.yml"):
|
| 195 |
+
shutil.copy(src="default_config.yml", dst=config_path)
|
| 196 |
+
print(
|
| 197 |
+
f"已根据默认配置文件default_config.yml生成配置文件{config_path}。请按该配置文件的说明进行配置后重新运行。"
|
| 198 |
+
)
|
| 199 |
+
print("如无特殊需求，请勿修改default_config.yml或备份该文件。")
|
| 200 |
+
sys.exit(0)
|
| 201 |
+
with open(file=config_path, mode="r", encoding="utf-8") as file:
|
| 202 |
+
yaml_config: Dict[str, any] = yaml.safe_load(file.read())
|
| 203 |
+
dataset_path: str = yaml_config["dataset_path"]
|
| 204 |
+
openi_token: str = yaml_config["openi_token"]
|
| 205 |
+
self.dataset_path: str = dataset_path
|
| 206 |
+
self.mirror: str = yaml_config["mirror"]
|
| 207 |
+
self.openi_token: str = openi_token
|
| 208 |
+
self.resample_config: Resample_config = Resample_config.from_dict(
|
| 209 |
+
dataset_path, yaml_config["resample"]
|
| 210 |
+
)
|
| 211 |
+
self.preprocess_text_config: Preprocess_text_config = (
|
| 212 |
+
Preprocess_text_config.from_dict(
|
| 213 |
+
dataset_path, yaml_config["preprocess_text"]
|
| 214 |
+
)
|
| 215 |
+
)
|
| 216 |
+
self.bert_gen_config: Bert_gen_config = Bert_gen_config.from_dict(
|
| 217 |
+
dataset_path, yaml_config["bert_gen"]
|
| 218 |
+
)
|
| 219 |
+
self.train_ms_config: Train_ms_config = Train_ms_config.from_dict(
|
| 220 |
+
dataset_path, yaml_config["train_ms"]
|
| 221 |
+
)
|
| 222 |
+
self.webui_config: Webui_config = Webui_config.from_dict(
|
| 223 |
+
dataset_path, yaml_config["webui"]
|
| 224 |
+
)
|
| 225 |
+
self.server_config: Server_config = Server_config.from_dict(
|
| 226 |
+
yaml_config["server"]
|
| 227 |
+
)
|
| 228 |
+
self.translate_config: Translate_config = Translate_config.from_dict(
|
| 229 |
+
yaml_config["translate"]
|
| 230 |
+
)
|
| 231 |
+
|
| 232 |
+
|
| 233 |
+
parser = argparse.ArgumentParser()
|
| 234 |
+
# 为避免与以前的config.json起冲突，将其更名如下
|
| 235 |
+
parser.add_argument("-y", "--yml_config", type=str, default="config.yml")
|
| 236 |
+
args, _ = parser.parse_known_args()
|
| 237 |
+
config = Config(args.yml_config)
|
config.yml
ADDED
|
@@ -0,0 +1,160 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
|
|
|
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|
|
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# 全局配置
|
| 2 |
+
# 对于希望在同一时间使用多个配置文件的情况，例如两个GPU同时跑两个训练集：通过环境变量指定配置文件，不指定则默认为./config.yml
|
| 3 |
+
|
| 4 |
+
# 拟提供通用路径配置，统一存放数据，避免数据放得很乱
|
| 5 |
+
# 每个数据集与其对应的模型存放至统一路径下，后续所有的路径配置均为相对于datasetPath的路径
|
| 6 |
+
# 不填或者填空则路径为相对于项目根目录的路径
|
| 7 |
+
dataset_path: ""
|
| 8 |
+
|
| 9 |
+
# 模型镜像源，默认huggingface，使用openi镜像源需指定openi_token
|
| 10 |
+
mirror: ""
|
| 11 |
+
openi_token: "" # openi token
|
| 12 |
+
|
| 13 |
+
# resample 音频重采样配置
|
| 14 |
+
# 注意， “:” 后需要加空格
|
| 15 |
+
resample:
|
| 16 |
+
# 目标重采样率
|
| 17 |
+
sampling_rate: 44100
|
| 18 |
+
# 音频文件输入路径，重采样会将该路径下所有.wav音频文件重采样
|
| 19 |
+
# 请填入相对于datasetPath的相对路径
|
| 20 |
+
in_dir: "" # 相对于根目录的路径为 /datasetPath/in_dir
|
| 21 |
+
# 音频文件重采样后输出路径
|
| 22 |
+
out_dir: ""
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
# preprocess_text 数据集预处理相关配置
|
| 26 |
+
# 注意， “:” 后需要加空格
|
| 27 |
+
preprocess_text:
|
| 28 |
+
# 原始文本文件路径，文本格式应为{wav_path}|{speaker_name}|{language}|{text}。
|
| 29 |
+
transcription_path: "filelists/bushroid.list"
|
| 30 |
+
# 数据清洗后文本路径，可以不填。不填则将在原始文本目录生成
|
| 31 |
+
cleaned_path: ""
|
| 32 |
+
# 训练集路径
|
| 33 |
+
train_path: "filelists/train.list"
|
| 34 |
+
# 验证集路径
|
| 35 |
+
val_path: "filelists/val.list"
|
| 36 |
+
# 配置文件路径
|
| 37 |
+
config_path: "config.json"
|
| 38 |
+
# 每个speaker的验证集条数
|
| 39 |
+
val_per_spk: 4
|
| 40 |
+
# 验证集最大条数，多于的会被截断并放到训练集中
|
| 41 |
+
max_val_total: 8
|
| 42 |
+
# 是否进行数据清洗
|
| 43 |
+
clean: true
|
| 44 |
+
|
| 45 |
+
|
| 46 |
+
# bert_gen 相关配置
|
| 47 |
+
# 注意， “:” 后需要加空格
|
| 48 |
+
bert_gen:
|
| 49 |
+
# 训练数据集配置文件路径
|
| 50 |
+
config_path: "config.json"
|
| 51 |
+
# 并行数
|
| 52 |
+
num_processes: 2
|
| 53 |
+
# 使用设备：可选项 "cuda" 显卡推理，"cpu" cpu推理
|
| 54 |
+
# 该选项同时决定了get_bert_feature的默认设备
|
| 55 |
+
device: "cuda"
|
| 56 |
+
# 使用多卡推理
|
| 57 |
+
use_multi_device: false
|
| 58 |
+
|
| 59 |
+
|
| 60 |
+
# train 训练配置
|
| 61 |
+
# 注意， “:” 后需要加空格
|
| 62 |
+
train_ms:
|
| 63 |
+
# 需要加载的环境变量，多显卡训练时RANK请手动在环境变量填写
|
| 64 |
+
# 环境变量对应名称环境变量不存在时加载，也就是说手动添加的环境变量优先级更高，会覆盖本配置文件
|
| 65 |
+
env:
|
| 66 |
+
MASTER_ADDR: "localhost"
|
| 67 |
+
MASTER_PORT: 10086
|
| 68 |
+
WORLD_SIZE: 1
|
| 69 |
+
RANK: 0
|
| 70 |
+
# 可以填写任意名的环境变量
|
| 71 |
+
# THE_ENV_VAR_YOU_NEED_TO_USE: "1234567"
|
| 72 |
+
# 底模设置
|
| 73 |
+
base:
|
| 74 |
+
use_base_model: True
|
| 75 |
+
repo_id: "Stardust_minus/Bert-VITS2"
|
| 76 |
+
model_image: "Bert-VITS2中日英底模-fix" # openi网页的模型名
|
| 77 |
+
# 训练模型存储目录：与旧版本的区别，原先数据集是存放在logs/model_name下的，现在改为统一存放在Data/你的数据集/models下
|
| 78 |
+
model: "models"
|
| 79 |
+
# 配置文件路径
|
| 80 |
+
config_path: "configs/config.json"
|
| 81 |
+
|
| 82 |
+
|
| 83 |
+
# webui webui配置
|
| 84 |
+
# 注意， “:” 后需要加空格
|
| 85 |
+
webui:
|
| 86 |
+
# 推理设备
|
| 87 |
+
device: "cuda"
|
| 88 |
+
# 模型路径
|
| 89 |
+
model: "genshin/models/G_8000.pth"
|
| 90 |
+
# 配置文件路径
|
| 91 |
+
config_path: "configs/config.json"
|
| 92 |
+
# 端口号
|
| 93 |
+
port: 7860
|
| 94 |
+
# 是否公开部署，对外网开放
|
| 95 |
+
share: false
|
| 96 |
+
# 是否开启debug模式
|
| 97 |
+
debug: false
|
| 98 |
+
# 语种识别库，可选langid, fastlid
|
| 99 |
+
language_identification_library: "langid"
|
| 100 |
+
|
| 101 |
+
|
| 102 |
+
# server api配置
|
| 103 |
+
# 注意， “:” 后需要加空格
|
| 104 |
+
# 注意，本配置下的所有配置均为相对于根目录的路径
|
| 105 |
+
server:
|
| 106 |
+
# 端口号
|
| 107 |
+
port: 5000
|
| 108 |
+
# 模型默认使用设备：但是当前并没有实现这个配置。
|
| 109 |
+
device: "cuda"
|
| 110 |
+
# 需要加载的所有模型的配置
|
| 111 |
+
# 注意，所有模型都必须正确配置model与config的路径，空路径会导致加载错误。
|
| 112 |
+
models:
|
| 113 |
+
- # 模型的路径
|
| 114 |
+
model: ""
|
| 115 |
+
# 模型config.json的路径
|
| 116 |
+
config: ""
|
| 117 |
+
# 模型使用设备，若填写则会覆盖默认配置
|
| 118 |
+
device: "cuda"
|
| 119 |
+
# 模型默认使用的语言
|
| 120 |
+
language: "ZH"
|
| 121 |
+
# 模型人物默认参数
|
| 122 |
+
# 不必填写所有人物，不填的使用默认值
|
| 123 |
+
# 暂时不用填写，当前尚未实现按人区分配置
|
| 124 |
+
speakers:
|
| 125 |
+
- speaker: "科比"
|
| 126 |
+
sdp_ratio: 0.2
|
| 127 |
+
noise_scale: 0.6
|
| 128 |
+
noise_scale_w: 0.8
|
| 129 |
+
length_scale: 1
|
| 130 |
+
- speaker: "五条悟"
|
| 131 |
+
sdp_ratio: 0.3
|
| 132 |
+
noise_scale: 0.7
|
| 133 |
+
noise_scale_w: 0.8
|
| 134 |
+
length_scale: 0.5
|
| 135 |
+
- speaker: "安倍晋三"
|
| 136 |
+
sdp_ratio: 0.2
|
| 137 |
+
noise_scale: 0.6
|
| 138 |
+
noise_scale_w: 0.8
|
| 139 |
+
length_scale: 1.2
|
| 140 |
+
- # 模型的路径
|
| 141 |
+
model: ""
|
| 142 |
+
# 模型config.json的路径
|
| 143 |
+
config: ""
|
| 144 |
+
# 模型使用设备，若填写则会覆盖默认配置
|
| 145 |
+
device: "cpu"
|
| 146 |
+
# 模型默认使用的语言
|
| 147 |
+
language: "JP"
|
| 148 |
+
# 模型人物默认参数
|
| 149 |
+
# 不必填写所有人物，不填的使用默认���
|
| 150 |
+
speakers: [ ] # 也可以不填
|
| 151 |
+
|
| 152 |
+
|
| 153 |
+
# 百度翻译开放平台 api配置
|
| 154 |
+
# api接入文档 https://api.fanyi.baidu.com/doc/21
|
| 155 |
+
# 请不要在github等网站公开分享你的app id 与 key
|
| 156 |
+
translate:
|
| 157 |
+
# 你的APPID
|
| 158 |
+
"app_key": ""
|
| 159 |
+
# 你的密钥
|
| 160 |
+
"secret_key": ""
|
configs/config.json
CHANGED
|
@@ -2,9 +2,9 @@
|
|
| 2 |
"train": {
|
| 3 |
"log_interval": 200,
|
| 4 |
"eval_interval": 1000,
|
| 5 |
-
"seed":
|
| 6 |
-
"epochs":
|
| 7 |
-
"learning_rate": 0.
|
| 8 |
"betas": [
|
| 9 |
0.8,
|
| 10 |
0.99
|
|
@@ -12,7 +12,7 @@
|
|
| 12 |
"eps": 1e-09,
|
| 13 |
"batch_size": 16,
|
| 14 |
"fp16_run": false,
|
| 15 |
-
"lr_decay": 0.
|
| 16 |
"segment_size": 16384,
|
| 17 |
"init_lr_ratio": 1,
|
| 18 |
"warmup_epochs": 0,
|
|
@@ -21,8 +21,8 @@
|
|
| 21 |
"skip_optimizer": true
|
| 22 |
},
|
| 23 |
"data": {
|
| 24 |
-
"training_files": "filelists/train.list",
|
| 25 |
-
"validation_files": "filelists/val.list",
|
| 26 |
"max_wav_value": 32768.0,
|
| 27 |
"sampling_rate": 44100,
|
| 28 |
"filter_length": 2048,
|
|
@@ -32,7 +32,7 @@
|
|
| 32 |
"mel_fmin": 0.0,
|
| 33 |
"mel_fmax": null,
|
| 34 |
"add_blank": true,
|
| 35 |
-
"n_speakers":
|
| 36 |
"cleaned_text": true,
|
| 37 |
"spk2id": {
|
| 38 |
"華戀": 0,
|
|
@@ -182,5 +182,6 @@
|
|
| 182 |
"n_layers_q": 3,
|
| 183 |
"use_spectral_norm": false,
|
| 184 |
"gin_channels": 256
|
| 185 |
-
}
|
|
|
|
| 186 |
}
|
|
|
|
| 2 |
"train": {
|
| 3 |
"log_interval": 200,
|
| 4 |
"eval_interval": 1000,
|
| 5 |
+
"seed": 42,
|
| 6 |
+
"epochs": 1000,
|
| 7 |
+
"learning_rate": 0.0002,
|
| 8 |
"betas": [
|
| 9 |
0.8,
|
| 10 |
0.99
|
|
|
|
| 12 |
"eps": 1e-09,
|
| 13 |
"batch_size": 16,
|
| 14 |
"fp16_run": false,
|
| 15 |
+
"lr_decay": 0.99995,
|
| 16 |
"segment_size": 16384,
|
| 17 |
"init_lr_ratio": 1,
|
| 18 |
"warmup_epochs": 0,
|
|
|
|
| 21 |
"skip_optimizer": true
|
| 22 |
},
|
| 23 |
"data": {
|
| 24 |
+
"training_files": "Data/BangDream/filelists/train.list",
|
| 25 |
+
"validation_files": "Data/BangDream/filelists/val.list",
|
| 26 |
"max_wav_value": 32768.0,
|
| 27 |
"sampling_rate": 44100,
|
| 28 |
"filter_length": 2048,
|
|
|
|
| 32 |
"mel_fmin": 0.0,
|
| 33 |
"mel_fmax": null,
|
| 34 |
"add_blank": true,
|
| 35 |
+
"n_speakers": 700,
|
| 36 |
"cleaned_text": true,
|
| 37 |
"spk2id": {
|
| 38 |
"華戀": 0,
|
|
|
|
| 182 |
"n_layers_q": 3,
|
| 183 |
"use_spectral_norm": false,
|
| 184 |
"gin_channels": 256
|
| 185 |
+
},
|
| 186 |
+
"version": "2.0"
|
| 187 |
}
|
configs/config_old.json
ADDED
|
@@ -0,0 +1,187 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"train": {
|
| 3 |
+
"log_interval": 200,
|
| 4 |
+
"eval_interval": 1000,
|
| 5 |
+
"seed": 52,
|
| 6 |
+
"epochs": 10000,
|
| 7 |
+
"learning_rate": 0.0003,
|
| 8 |
+
"betas": [
|
| 9 |
+
0.8,
|
| 10 |
+
0.99
|
| 11 |
+
],
|
| 12 |
+
"eps": 1e-09,
|
| 13 |
+
"batch_size": 16,
|
| 14 |
+
"fp16_run": false,
|
| 15 |
+
"lr_decay": 0.999875,
|
| 16 |
+
"segment_size": 16384,
|
| 17 |
+
"init_lr_ratio": 1,
|
| 18 |
+
"warmup_epochs": 0,
|
| 19 |
+
"c_mel": 45,
|
| 20 |
+
"c_kl": 1.0,
|
| 21 |
+
"skip_optimizer": true
|
| 22 |
+
},
|
| 23 |
+
"data": {
|
| 24 |
+
"training_files": "filelists/train.list",
|
| 25 |
+
"validation_files": "filelists/val.list",
|
| 26 |
+
"max_wav_value": 32768.0,
|
| 27 |
+
"sampling_rate": 44100,
|
| 28 |
+
"filter_length": 2048,
|
| 29 |
+
"hop_length": 512,
|
| 30 |
+
"win_length": 2048,
|
| 31 |
+
"n_mel_channels": 128,
|
| 32 |
+
"mel_fmin": 0.0,
|
| 33 |
+
"mel_fmax": null,
|
| 34 |
+
"add_blank": true,
|
| 35 |
+
"n_speakers": 256,
|
| 36 |
+
"cleaned_text": true,
|
| 37 |
+
"spk2id": {
|
| 38 |
+
"華戀": 0,
|
| 39 |
+
"晶": 1,
|
| 40 |
+
"光": 2,
|
| 41 |
+
"未知留": 3,
|
| 42 |
+
"香子": 4,
|
| 43 |
+
"雙葉": 5,
|
| 44 |
+
"真晝": 6,
|
| 45 |
+
"艾露": 7,
|
| 46 |
+
"珠緒": 8,
|
| 47 |
+
"艾露露": 9,
|
| 48 |
+
"純那": 10,
|
| 49 |
+
"克洛迪娜": 11,
|
| 50 |
+
"真矢": 12,
|
| 51 |
+
"奈奈": 13,
|
| 52 |
+
"壘": 14,
|
| 53 |
+
"文": 15,
|
| 54 |
+
"一愛": 16,
|
| 55 |
+
"菈樂菲": 17,
|
| 56 |
+
"司": 18,
|
| 57 |
+
"美空": 19,
|
| 58 |
+
"靜羽": 20,
|
| 59 |
+
"悠悠子": 21,
|
| 60 |
+
"八千代": 22,
|
| 61 |
+
"栞": 23,
|
| 62 |
+
"美帆": 24,
|
| 63 |
+
"芙蘿菈": 25,
|
| 64 |
+
"克蕾兒": 26,
|
| 65 |
+
"安德露": 27,
|
| 66 |
+
"瑪莉亞貝菈": 28,
|
| 67 |
+
"克拉迪亞": 29,
|
| 68 |
+
"桃樂西": 30,
|
| 69 |
+
"瑪麗安": 31,
|
| 70 |
+
"三月七": 32,
|
| 71 |
+
"香澄": 33,
|
| 72 |
+
"有咲": 34,
|
| 73 |
+
"沙綾": 35,
|
| 74 |
+
"りみ": 36,
|
| 75 |
+
"たえ": 37,
|
| 76 |
+
"沙綾、りみ、たえ": 38,
|
| 77 |
+
"巴": 39,
|
| 78 |
+
"一同": 40,
|
| 79 |
+
"まりな": 41,
|
| 80 |
+
"ゆり": 42,
|
| 81 |
+
"明日香": 43,
|
| 82 |
+
"？？？": 44,
|
| 83 |
+
"ひまり": 45,
|
| 84 |
+
"モカ": 46,
|
| 85 |
+
"つぐみ": 47,
|
| 86 |
+
"蘭": 48,
|
| 87 |
+
"リサ": 49,
|
| 88 |
+
"千聖": 50,
|
| 89 |
+
"花音": 51,
|
| 90 |
+
"イヴ": 52,
|
| 91 |
+
"日菜": 53,
|
| 92 |
+
"友希那": 54,
|
| 93 |
+
"紗夜": 55,
|
| 94 |
+
"こころ": 56,
|
| 95 |
+
"美咲": 57,
|
| 96 |
+
"薫": 58,
|
| 97 |
+
"はぐみ": 59,
|
| 98 |
+
"ミッシェル": 60,
|
| 99 |
+
"マリー": 61,
|
| 100 |
+
"怪盗ハロハッピー": 62,
|
| 101 |
+
"ニコリーナ": 63,
|
| 102 |
+
"彩": 64,
|
| 103 |
+
"麻弥": 65,
|
| 104 |
+
"燐子": 66,
|
| 105 |
+
"あこ": 67,
|
| 106 |
+
"ゆきな": 68,
|
| 107 |
+
"ましろ": 69,
|
| 108 |
+
"つくし": 70,
|
| 109 |
+
"透子": 71,
|
| 110 |
+
"七深": 72,
|
| 111 |
+
"瑠唯": 73,
|
| 112 |
+
"六花": 74,
|
| 113 |
+
"パレオ": 75,
|
| 114 |
+
"レイヤ": 76,
|
| 115 |
+
"マスキング": 77,
|
| 116 |
+
"チュチュ": 78,
|
| 117 |
+
"ますき": 79,
|
| 118 |
+
"ロック": 80,
|
| 119 |
+
"令王那": 81,
|
| 120 |
+
"CHIYU": 82,
|
| 121 |
+
"レイ": 83,
|
| 122 |
+
"燈": 84,
|
| 123 |
+
"そよ": 85,
|
| 124 |
+
"祥子": 86,
|
| 125 |
+
"立希": 87,
|
| 126 |
+
"睦": 88,
|
| 127 |
+
"愛音": 89,
|
| 128 |
+
"楽奈": 90,
|
| 129 |
+
"海鈴": 91
|
| 130 |
+
}
|
| 131 |
+
},
|
| 132 |
+
"model": {
|
| 133 |
+
"use_spk_conditioned_encoder": true,
|
| 134 |
+
"use_noise_scaled_mas": true,
|
| 135 |
+
"use_mel_posterior_encoder": false,
|
| 136 |
+
"use_duration_discriminator": true,
|
| 137 |
+
"inter_channels": 192,
|
| 138 |
+
"hidden_channels": 192,
|
| 139 |
+
"filter_channels": 768,
|
| 140 |
+
"n_heads": 2,
|
| 141 |
+
"n_layers": 6,
|
| 142 |
+
"kernel_size": 3,
|
| 143 |
+
"p_dropout": 0.1,
|
| 144 |
+
"resblock": "1",
|
| 145 |
+
"resblock_kernel_sizes": [
|
| 146 |
+
3,
|
| 147 |
+
7,
|
| 148 |
+
11
|
| 149 |
+
],
|
| 150 |
+
"resblock_dilation_sizes": [
|
| 151 |
+
[
|
| 152 |
+
1,
|
| 153 |
+
3,
|
| 154 |
+
5
|
| 155 |
+
],
|
| 156 |
+
[
|
| 157 |
+
1,
|
| 158 |
+
3,
|
| 159 |
+
5
|
| 160 |
+
],
|
| 161 |
+
[
|
| 162 |
+
1,
|
| 163 |
+
3,
|
| 164 |
+
5
|
| 165 |
+
]
|
| 166 |
+
],
|
| 167 |
+
"upsample_rates": [
|
| 168 |
+
8,
|
| 169 |
+
8,
|
| 170 |
+
2,
|
| 171 |
+
2,
|
| 172 |
+
2
|
| 173 |
+
],
|
| 174 |
+
"upsample_initial_channel": 512,
|
| 175 |
+
"upsample_kernel_sizes": [
|
| 176 |
+
16,
|
| 177 |
+
16,
|
| 178 |
+
8,
|
| 179 |
+
2,
|
| 180 |
+
2
|
| 181 |
+
],
|
| 182 |
+
"n_layers_q": 3,
|
| 183 |
+
"use_spectral_norm": false,
|
| 184 |
+
"gin_channels": 256
|
| 185 |
+
},
|
| 186 |
+
"version": "2.0"
|
| 187 |
+
}
|
data_utils.py
CHANGED
|
@@ -3,11 +3,11 @@ import random
|
|
| 3 |
import torch
|
| 4 |
import torch.utils.data
|
| 5 |
from tqdm import tqdm
|
| 6 |
-
from
|
| 7 |
import commons
|
| 8 |
from mel_processing import spectrogram_torch, mel_spectrogram_torch
|
| 9 |
from utils import load_wav_to_torch, load_filepaths_and_text
|
| 10 |
-
from text import cleaned_text_to_sequence
|
| 11 |
|
| 12 |
"""Multi speaker version"""
|
| 13 |
|
|
@@ -85,13 +85,13 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
|
|
| 85 |
# separate filename, speaker_id and text
|
| 86 |
audiopath, sid, language, text, phones, tone, word2ph = audiopath_sid_text
|
| 87 |
|
| 88 |
-
bert, ja_bert, phones, tone, language = self.get_text(
|
| 89 |
text, word2ph, phones, tone, language, audiopath
|
| 90 |
)
|
| 91 |
|
| 92 |
spec, wav = self.get_audio(audiopath)
|
| 93 |
sid = torch.LongTensor([int(self.spk_map[sid])])
|
| 94 |
-
return (phones, spec, wav, sid, tone, language, bert, ja_bert)
|
| 95 |
|
| 96 |
def get_audio(self, filename):
|
| 97 |
audio, sampling_rate = load_wav_to_torch(filename)
|
|
@@ -145,40 +145,28 @@ class TextAudioSpeakerLoader(torch.utils.data.Dataset):
|
|
| 145 |
word2ph[0] += 1
|
| 146 |
bert_path = wav_path.replace(".wav", ".bert.pt")
|
| 147 |
try:
|
| 148 |
-
|
| 149 |
-
assert
|
| 150 |
-
except:
|
| 151 |
-
|
| 152 |
-
|
| 153 |
-
assert bert.shape[-1] == len(phone), phone
|
| 154 |
|
| 155 |
if language_str == "ZH":
|
| 156 |
-
bert =
|
| 157 |
-
ja_bert = torch.zeros(
|
|
|
|
| 158 |
elif language_str == "JP":
|
| 159 |
-
ja_bert = bert
|
| 160 |
bert = torch.zeros(1024, len(phone))
|
| 161 |
-
|
|
|
|
|
|
|
| 162 |
bert = torch.zeros(1024, len(phone))
|
| 163 |
-
ja_bert = torch.zeros(
|
| 164 |
-
|
| 165 |
-
bert.shape,
|
| 166 |
-
len(phone),
|
| 167 |
-
sum(word2ph),
|
| 168 |
-
p1,
|
| 169 |
-
p2,
|
| 170 |
-
t1,
|
| 171 |
-
t2,
|
| 172 |
-
pold,
|
| 173 |
-
pold2,
|
| 174 |
-
word2ph,
|
| 175 |
-
text,
|
| 176 |
-
w2pho,
|
| 177 |
-
)
|
| 178 |
phone = torch.LongTensor(phone)
|
| 179 |
tone = torch.LongTensor(tone)
|
| 180 |
language = torch.LongTensor(language)
|
| 181 |
-
return bert, ja_bert, phone, tone, language
|
| 182 |
|
| 183 |
def get_sid(self, sid):
|
| 184 |
sid = torch.LongTensor([int(sid)])
|
|
@@ -221,7 +209,8 @@ class TextAudioSpeakerCollate:
|
|
| 221 |
tone_padded = torch.LongTensor(len(batch), max_text_len)
|
| 222 |
language_padded = torch.LongTensor(len(batch), max_text_len)
|
| 223 |
bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)
|
| 224 |
-
ja_bert_padded = torch.FloatTensor(len(batch),
|
|
|
|
| 225 |
|
| 226 |
spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
|
| 227 |
wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
|
|
@@ -232,6 +221,8 @@ class TextAudioSpeakerCollate:
|
|
| 232 |
wav_padded.zero_()
|
| 233 |
bert_padded.zero_()
|
| 234 |
ja_bert_padded.zero_()
|
|
|
|
|
|
|
| 235 |
for i in range(len(ids_sorted_decreasing)):
|
| 236 |
row = batch[ids_sorted_decreasing[i]]
|
| 237 |
|
|
@@ -261,6 +252,9 @@ class TextAudioSpeakerCollate:
|
|
| 261 |
ja_bert = row[7]
|
| 262 |
ja_bert_padded[i, :, : ja_bert.size(1)] = ja_bert
|
| 263 |
|
|
|
|
|
|
|
|
|
|
| 264 |
return (
|
| 265 |
text_padded,
|
| 266 |
text_lengths,
|
|
@@ -273,6 +267,7 @@ class TextAudioSpeakerCollate:
|
|
| 273 |
language_padded,
|
| 274 |
bert_padded,
|
| 275 |
ja_bert_padded,
|
|
|
|
| 276 |
)
|
| 277 |
|
| 278 |
|
|
|
|
| 3 |
import torch
|
| 4 |
import torch.utils.data
|
| 5 |
from tqdm import tqdm
|
| 6 |
+
from tools.log import logger
|
| 7 |
import commons
|
| 8 |
from mel_processing import spectrogram_torch, mel_spectrogram_torch
|
| 9 |
from utils import load_wav_to_torch, load_filepaths_and_text
|
| 10 |
+
from text import cleaned_text_to_sequence
|
| 11 |
|
| 12 |
"""Multi speaker version"""
|
| 13 |
|
|
|
|
| 85 |
# separate filename, speaker_id and text
|
| 86 |
audiopath, sid, language, text, phones, tone, word2ph = audiopath_sid_text
|
| 87 |
|
| 88 |
+
bert, ja_bert, en_bert, phones, tone, language = self.get_text(
|
| 89 |
text, word2ph, phones, tone, language, audiopath
|
| 90 |
)
|
| 91 |
|
| 92 |
spec, wav = self.get_audio(audiopath)
|
| 93 |
sid = torch.LongTensor([int(self.spk_map[sid])])
|
| 94 |
+
return (phones, spec, wav, sid, tone, language, bert, ja_bert, en_bert)
|
| 95 |
|
| 96 |
def get_audio(self, filename):
|
| 97 |
audio, sampling_rate = load_wav_to_torch(filename)
|
|
|
|
| 145 |
word2ph[0] += 1
|
| 146 |
bert_path = wav_path.replace(".wav", ".bert.pt")
|
| 147 |
try:
|
| 148 |
+
bert_ori = torch.load(bert_path)
|
| 149 |
+
assert bert_ori.shape[-1] == len(phone)
|
| 150 |
+
except Exception as e:
|
| 151 |
+
logger.warning("Bert load Failed")
|
| 152 |
+
logger.warning(e)
|
|
|
|
| 153 |
|
| 154 |
if language_str == "ZH":
|
| 155 |
+
bert = bert_ori
|
| 156 |
+
ja_bert = torch.zeros(1024, len(phone))
|
| 157 |
+
en_bert = torch.zeros(1024, len(phone))
|
| 158 |
elif language_str == "JP":
|
|
|
|
| 159 |
bert = torch.zeros(1024, len(phone))
|
| 160 |
+
ja_bert = bert_ori
|
| 161 |
+
en_bert = torch.zeros(1024, len(phone))
|
| 162 |
+
elif language_str == "EN":
|
| 163 |
bert = torch.zeros(1024, len(phone))
|
| 164 |
+
ja_bert = torch.zeros(1024, len(phone))
|
| 165 |
+
en_bert = bert_ori
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 166 |
phone = torch.LongTensor(phone)
|
| 167 |
tone = torch.LongTensor(tone)
|
| 168 |
language = torch.LongTensor(language)
|
| 169 |
+
return bert, ja_bert, en_bert, phone, tone, language
|
| 170 |
|
| 171 |
def get_sid(self, sid):
|
| 172 |
sid = torch.LongTensor([int(sid)])
|
|
|
|
| 209 |
tone_padded = torch.LongTensor(len(batch), max_text_len)
|
| 210 |
language_padded = torch.LongTensor(len(batch), max_text_len)
|
| 211 |
bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)
|
| 212 |
+
ja_bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)
|
| 213 |
+
en_bert_padded = torch.FloatTensor(len(batch), 1024, max_text_len)
|
| 214 |
|
| 215 |
spec_padded = torch.FloatTensor(len(batch), batch[0][1].size(0), max_spec_len)
|
| 216 |
wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
|
|
|
|
| 221 |
wav_padded.zero_()
|
| 222 |
bert_padded.zero_()
|
| 223 |
ja_bert_padded.zero_()
|
| 224 |
+
en_bert_padded.zero_()
|
| 225 |
+
|
| 226 |
for i in range(len(ids_sorted_decreasing)):
|
| 227 |
row = batch[ids_sorted_decreasing[i]]
|
| 228 |
|
|
|
|
| 252 |
ja_bert = row[7]
|
| 253 |
ja_bert_padded[i, :, : ja_bert.size(1)] = ja_bert
|
| 254 |
|
| 255 |
+
en_bert = row[8]
|
| 256 |
+
en_bert_padded[i, :, : en_bert.size(1)] = en_bert
|
| 257 |
+
|
| 258 |
return (
|
| 259 |
text_padded,
|
| 260 |
text_lengths,
|
|
|
|
| 267 |
language_padded,
|
| 268 |
bert_padded,
|
| 269 |
ja_bert_padded,
|
| 270 |
+
en_bert_padded,
|
| 271 |
)
|
| 272 |
|
| 273 |
|
default_config.yml
ADDED
|
@@ -0,0 +1,160 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
# 全局配置
|
| 2 |
+
# 对于希望在同一时间使用多个配置文件的情况，例如两个GPU同时跑两个训练集：通过环境变量指定配置文件，不指定则默认为./config.yml
|
| 3 |
+
|
| 4 |
+
# 拟提供通用路径配置，统一存放数据，避免数据放得很乱
|
| 5 |
+
# 每个数据集与其对应的模型存放至统一路径下，后续所有的路径配置均为相对于datasetPath的路径
|
| 6 |
+
# 不填或者填空则路径为相对于项目根目录的路径
|
| 7 |
+
dataset_path: ""
|
| 8 |
+
|
| 9 |
+
# 模型镜像源，默认huggingface，使用openi镜像源需指定openi_token
|
| 10 |
+
mirror: ""
|
| 11 |
+
openi_token: "" # openi token
|
| 12 |
+
|
| 13 |
+
# resample 音频重采样配置
|
| 14 |
+
# 注意， “:” 后需要加空格
|
| 15 |
+
resample:
|
| 16 |
+
# 目标重采样率
|
| 17 |
+
sampling_rate: 44100
|
| 18 |
+
# 音频文件输入路径，重采样会将该路径下所有.wav音频文件重采样
|
| 19 |
+
# 请填入相对于datasetPath的相对路径
|
| 20 |
+
in_dir: "" # 相对于根目录的路径为 /datasetPath/in_dir
|
| 21 |
+
# 音频文件重采样后输出路径
|
| 22 |
+
out_dir: ""
|
| 23 |
+
|
| 24 |
+
|
| 25 |
+
# preprocess_text 数据集预处理相关配置
|
| 26 |
+
# 注意， “:” 后需要加空格
|
| 27 |
+
preprocess_text:
|
| 28 |
+
# 原始文本文件路径，文本格式应为{wav_path}|{speaker_name}|{language}|{text}。
|
| 29 |
+
transcription_path: "filelists/bushroid.list"
|
| 30 |
+
# 数据清洗后文本路径，可以不填。不填则将在原始文本目录生成
|
| 31 |
+
cleaned_path: ""
|
| 32 |
+
# 训练集路径
|
| 33 |
+
train_path: "filelists/train.list"
|
| 34 |
+
# 验证集路径
|
| 35 |
+
val_path: "filelists/val.list"
|
| 36 |
+
# 配置文件路径
|
| 37 |
+
config_path: "config.json"
|
| 38 |
+
# 每个speaker的验证集条数
|
| 39 |
+
val_per_spk: 4
|
| 40 |
+
# 验证集最大条数，多于的会被截断并放到训练集中
|
| 41 |
+
max_val_total: 8
|
| 42 |
+
# 是否进行数据清洗
|
| 43 |
+
clean: true
|
| 44 |
+
|
| 45 |
+
|
| 46 |
+
# bert_gen 相关配置
|
| 47 |
+
# 注意， “:” 后需要加空格
|
| 48 |
+
bert_gen:
|
| 49 |
+
# 训练数据集配置文件路径
|
| 50 |
+
config_path: "config.json"
|
| 51 |
+
# 并行数
|
| 52 |
+
num_processes: 2
|
| 53 |
+
# 使用设备：可选项 "cuda" 显卡推理，"cpu" cpu推理
|
| 54 |
+
# 该选项同时决定了get_bert_feature的默认设备
|
| 55 |
+
device: "cuda"
|
| 56 |
+
# 使用多卡推理
|
| 57 |
+
use_multi_device: false
|
| 58 |
+
|
| 59 |
+
|
| 60 |
+
# train 训练配置
|
| 61 |
+
# 注意， “:” 后需要加空格
|
| 62 |
+
train_ms:
|
| 63 |
+
# 需要加载的环境变量，多显卡训练时RANK请手动在环境变量填写
|
| 64 |
+
# 环境变量对应名称环境变量不存在时加载，也就是说手动添加的环境变量优先级更高，会覆盖本配置文件
|
| 65 |
+
env:
|
| 66 |
+
MASTER_ADDR: "localhost"
|
| 67 |
+
MASTER_PORT: 10086
|
| 68 |
+
WORLD_SIZE: 1
|
| 69 |
+
RANK: 0
|
| 70 |
+
# 可以填写任意名的环境变量
|
| 71 |
+
# THE_ENV_VAR_YOU_NEED_TO_USE: "1234567"
|
| 72 |
+
# 底模设置
|
| 73 |
+
base:
|
| 74 |
+
use_base_model: True
|
| 75 |
+
repo_id: "Stardust_minus/Bert-VITS2"
|
| 76 |
+
model_image: "Bert-VITS2中日英底模-fix" # openi网页的模型名
|
| 77 |
+
# 训练模型存储目录：与旧版本的区别，原先数据集是存放在logs/model_name下的，现在改为统一存放在Data/你的数据集/models下
|
| 78 |
+
model: "models"
|
| 79 |
+
# 配置文件路径
|
| 80 |
+
config_path: "configs/config.json"
|
| 81 |
+
|
| 82 |
+
|
| 83 |
+
# webui webui配置
|
| 84 |
+
# 注意， “:” 后需要加空格
|
| 85 |
+
webui:
|
| 86 |
+
# 推理设备
|
| 87 |
+
device: "cuda"
|
| 88 |
+
# 模型路径
|
| 89 |
+
model: "genshin/models/G_8000.pth"
|
| 90 |
+
# 配置文件路径
|
| 91 |
+
config_path: "configs/config.json"
|
| 92 |
+
# 端口号
|
| 93 |
+
port: 7860
|
| 94 |
+
# 是否公开部署，对外网开放
|
| 95 |
+
share: false
|
| 96 |
+
# 是否开启debug模式
|
| 97 |
+
debug: false
|
| 98 |
+
# 语种识别库，可选langid, fastlid
|
| 99 |
+
language_identification_library: "langid"
|
| 100 |
+
|
| 101 |
+
|
| 102 |
+
# server api配置
|
| 103 |
+
# 注意， “:” 后需要加空格
|
| 104 |
+
# 注意，本配置下的所有配置均为相对于根目录的路径
|
| 105 |
+
server:
|
| 106 |
+
# 端口号
|
| 107 |
+
port: 5000
|
| 108 |
+
# 模型默认使用设备：但是当前并没有实现这个配置。
|
| 109 |
+
device: "cuda"
|
| 110 |
+
# 需要加载的所有模型的配置
|
| 111 |
+
# 注意，所有模型都必须正确配置model与config的路径，空路径会导致加载错误。
|
| 112 |
+
models:
|
| 113 |
+
- # 模型的路径
|
| 114 |
+
model: ""
|
| 115 |
+
# 模型config.json的路径
|
| 116 |
+
config: ""
|
| 117 |
+
# 模型使用设备，若填写则会覆盖默认配置
|
| 118 |
+
device: "cuda"
|
| 119 |
+
# 模型默认使用的语言
|
| 120 |
+
language: "ZH"
|
| 121 |
+
# 模型人物默认参数
|
| 122 |
+
# 不必填写所有人物，不填的使用默认值
|
| 123 |
+
# 暂时不用填写，当前尚未实现按人区分配置
|
| 124 |
+
speakers:
|
| 125 |
+
- speaker: "科比"
|
| 126 |
+
sdp_ratio: 0.2
|
| 127 |
+
noise_scale: 0.6
|
| 128 |
+
noise_scale_w: 0.8
|
| 129 |
+
length_scale: 1
|
| 130 |
+
- speaker: "五条悟"
|
| 131 |
+
sdp_ratio: 0.3
|
| 132 |
+
noise_scale: 0.7
|
| 133 |
+
noise_scale_w: 0.8
|
| 134 |
+
length_scale: 0.5
|
| 135 |
+
- speaker: "安倍晋三"
|
| 136 |
+
sdp_ratio: 0.2
|
| 137 |
+
noise_scale: 0.6
|
| 138 |
+
noise_scale_w: 0.8
|
| 139 |
+
length_scale: 1.2
|
| 140 |
+
- # 模型的路径
|
| 141 |
+
model: ""
|
| 142 |
+
# 模型config.json的路径
|
| 143 |
+
config: ""
|
| 144 |
+
# 模型使用设备，若填写则会覆盖默认配置
|
| 145 |
+
device: "cpu"
|
| 146 |
+
# 模型默认使用的语言
|
| 147 |
+
language: "JP"
|
| 148 |
+
# 模型人物默认参数
|
| 149 |
+
# 不必填写所有人物，不填的使用默认���
|
| 150 |
+
speakers: [ ] # 也可以不填
|
| 151 |
+
|
| 152 |
+
|
| 153 |
+
# 百度翻译开放平台 api配置
|
| 154 |
+
# api接入文档 https://api.fanyi.baidu.com/doc/21
|
| 155 |
+
# 请不要在github等网站公开分享你的app id 与 key
|
| 156 |
+
translate:
|
| 157 |
+
# 你的APPID
|
| 158 |
+
"app_key": ""
|
| 159 |
+
# 你的密钥
|
| 160 |
+
"secret_key": ""
|
emo_gen.py
ADDED
|
@@ -0,0 +1,169 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
import torch
|
| 2 |
+
import torch.nn as nn
|
| 3 |
+
from torch.utils.data import Dataset
|
| 4 |
+
from torch.utils.data import DataLoader
|
| 5 |
+
from transformers import Wav2Vec2Processor
|
| 6 |
+
from transformers.models.wav2vec2.modeling_wav2vec2 import (
|
| 7 |
+
Wav2Vec2Model,
|
| 8 |
+
Wav2Vec2PreTrainedModel,
|
| 9 |
+
)
|
| 10 |
+
import librosa
|
| 11 |
+
import numpy as np
|
| 12 |
+
import argparse
|
| 13 |
+
from config import config
|
| 14 |
+
import utils
|
| 15 |
+
import os
|
| 16 |
+
from tqdm import tqdm
|
| 17 |
+
|
| 18 |
+
|
| 19 |
+
class RegressionHead(nn.Module):
|
| 20 |
+
r"""Classification head."""
|
| 21 |
+
|
| 22 |
+
def __init__(self, config):
|
| 23 |
+
super().__init__()
|
| 24 |
+
|
| 25 |
+
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
| 26 |
+
self.dropout = nn.Dropout(config.final_dropout)
|
| 27 |
+
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
|
| 28 |
+
|
| 29 |
+
def forward(self, features, **kwargs):
|
| 30 |
+
x = features
|
| 31 |
+
x = self.dropout(x)
|
| 32 |
+
x = self.dense(x)
|
| 33 |
+
x = torch.tanh(x)
|
| 34 |
+
x = self.dropout(x)
|
| 35 |
+
x = self.out_proj(x)
|
| 36 |
+
|
| 37 |
+
return x
|
| 38 |
+
|
| 39 |
+
|
| 40 |
+
class EmotionModel(Wav2Vec2PreTrainedModel):
|
| 41 |
+
r"""Speech emotion classifier."""
|
| 42 |
+
|
| 43 |
+
def __init__(self, config):
|
| 44 |
+
super().__init__(config)
|
| 45 |
+
|
| 46 |
+
self.config = config
|
| 47 |
+
self.wav2vec2 = Wav2Vec2Model(config)
|
| 48 |
+
self.classifier = RegressionHead(config)
|
| 49 |
+
self.init_weights()
|
| 50 |
+
|
| 51 |
+
def forward(
|
| 52 |
+
self,
|
| 53 |
+
input_values,
|
| 54 |
+
):
|
| 55 |
+
outputs = self.wav2vec2(input_values)
|
| 56 |
+
hidden_states = outputs[0]
|
| 57 |
+
hidden_states = torch.mean(hidden_states, dim=1)
|
| 58 |
+
logits = self.classifier(hidden_states)
|
| 59 |
+
|
| 60 |
+
return hidden_states, logits
|
| 61 |
+
|
| 62 |
+
|
| 63 |
+
class AudioDataset(Dataset):
|
| 64 |
+
def __init__(self, list_of_wav_files, sr, processor):
|
| 65 |
+
self.list_of_wav_files = list_of_wav_files
|
| 66 |
+
self.processor = processor
|
| 67 |
+
self.sr = sr
|
| 68 |
+
|
| 69 |
+
def __len__(self):
|
| 70 |
+
return len(self.list_of_wav_files)
|
| 71 |
+
|
| 72 |
+
def __getitem__(self, idx):
|
| 73 |
+
wav_file = self.list_of_wav_files[idx]
|
| 74 |
+
audio_data, _ = librosa.load(wav_file, sr=self.sr)
|
| 75 |
+
processed_data = self.processor(audio_data, sampling_rate=self.sr)[
|
| 76 |
+
"input_values"
|
| 77 |
+
][0]
|
| 78 |
+
return torch.from_numpy(processed_data)
|
| 79 |
+
|
| 80 |
+
|
| 81 |
+
model_name = "./emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim"
|
| 82 |
+
processor = Wav2Vec2Processor.from_pretrained(model_name)
|
| 83 |
+
model = EmotionModel.from_pretrained(model_name)
|
| 84 |
+
|
| 85 |
+
|
| 86 |
+
def process_func(
|
| 87 |
+
x: np.ndarray,
|
| 88 |
+
sampling_rate: int,
|
| 89 |
+
model: EmotionModel,
|
| 90 |
+
processor: Wav2Vec2Processor,
|
| 91 |
+
device: str,
|
| 92 |
+
embeddings: bool = False,
|
| 93 |
+
) -> np.ndarray:
|
| 94 |
+
r"""Predict emotions or extract embeddings from raw audio signal."""
|
| 95 |
+
model = model.to(device)
|
| 96 |
+
y = processor(x, sampling_rate=sampling_rate)
|
| 97 |
+
y = y["input_values"][0]
|
| 98 |
+
y = torch.from_numpy(y).unsqueeze(0).to(device)
|
| 99 |
+
|
| 100 |
+
# run through model
|
| 101 |
+
with torch.no_grad():
|
| 102 |
+
y = model(y)[0 if embeddings else 1]
|
| 103 |
+
|
| 104 |
+
# convert to numpy
|
| 105 |
+
y = y.detach().cpu().numpy()
|
| 106 |
+
|
| 107 |
+
return y
|
| 108 |
+
|
| 109 |
+
|
| 110 |
+
def get_emo(path):
|
| 111 |
+
wav, sr = librosa.load(path, 16000)
|
| 112 |
+
device = config.bert_gen_config.device
|
| 113 |
+
return process_func(
|
| 114 |
+
np.expand_dims(wav, 0).astype(np.float),
|
| 115 |
+
sr,
|
| 116 |
+
model,
|
| 117 |
+
processor,
|
| 118 |
+
device,
|
| 119 |
+
embeddings=True,
|
| 120 |
+
).squeeze(0)
|
| 121 |
+
|
| 122 |
+
|
| 123 |
+
if __name__ == "__main__":
|
| 124 |
+
parser = argparse.ArgumentParser()
|
| 125 |
+
parser.add_argument(
|
| 126 |
+
"-c", "--config", type=str, default=config.bert_gen_config.config_path
|
| 127 |
+
)
|
| 128 |
+
parser.add_argument(
|
| 129 |
+
"--num_processes", type=int, default=config.bert_gen_config.num_processes
|
| 130 |
+
)
|
| 131 |
+
args, _ = parser.parse_known_args()
|
| 132 |
+
config_path = args.config
|
| 133 |
+
hps = utils.get_hparams_from_file(config_path)
|
| 134 |
+
|
| 135 |
+
device = config.bert_gen_config.device
|
| 136 |
+
|
| 137 |
+
model_name = "./emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim"
|
| 138 |
+
processor = (
|
| 139 |
+
Wav2Vec2Processor.from_pretrained(model_name)
|
| 140 |
+
if processor is None
|
| 141 |
+
else processor
|
| 142 |
+
)
|
| 143 |
+
model = (
|
| 144 |
+
EmotionModel.from_pretrained(model_name).to(device)
|
| 145 |
+
if model is None
|
| 146 |
+
else model.to(device)
|
| 147 |
+
)
|
| 148 |
+
|
| 149 |
+
lines = []
|
| 150 |
+
with open(hps.data.training_files, encoding="utf-8") as f:
|
| 151 |
+
lines.extend(f.readlines())
|
| 152 |
+
|
| 153 |
+
with open(hps.data.validation_files, encoding="utf-8") as f:
|
| 154 |
+
lines.extend(f.readlines())
|
| 155 |
+
|
| 156 |
+
wavnames = [line.split("|")[0] for line in lines]
|
| 157 |
+
dataset = AudioDataset(wavnames, 16000, processor)
|
| 158 |
+
data_loader = DataLoader(dataset, batch_size=1, shuffle=False, num_workers=16)
|
| 159 |
+
|
| 160 |
+
with torch.no_grad():
|
| 161 |
+
for i, data in tqdm(enumerate(data_loader), total=len(data_loader)):
|
| 162 |
+
wavname = wavnames[i]
|
| 163 |
+
emo_path = wavname.replace(".wav", ".emo.npy")
|
| 164 |
+
if os.path.exists(emo_path):
|
| 165 |
+
continue
|
| 166 |
+
emb = model(data.to(device))[0].detach().cpu().numpy()
|
| 167 |
+
np.save(emo_path, emb)
|
| 168 |
+
|
| 169 |
+
print("Emo vec 生成完毕!")
|
emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/.gitattributes
ADDED
|
@@ -0,0 +1,28 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
*.7z filter=lfs diff=lfs merge=lfs -text
|
| 2 |
+
*.arrow filter=lfs diff=lfs merge=lfs -text
|
| 3 |
+
*.bin filter=lfs diff=lfs merge=lfs -text
|
| 4 |
+
*.bin.* filter=lfs diff=lfs merge=lfs -text
|
| 5 |
+
*.bz2 filter=lfs diff=lfs merge=lfs -text
|
| 6 |
+
*.ftz filter=lfs diff=lfs merge=lfs -text
|
| 7 |
+
*.gz filter=lfs diff=lfs merge=lfs -text
|
| 8 |
+
*.h5 filter=lfs diff=lfs merge=lfs -text
|
| 9 |
+
*.joblib filter=lfs diff=lfs merge=lfs -text
|
| 10 |
+
*.lfs.* filter=lfs diff=lfs merge=lfs -text
|
| 11 |
+
*.model filter=lfs diff=lfs merge=lfs -text
|
| 12 |
+
*.msgpack filter=lfs diff=lfs merge=lfs -text
|
| 13 |
+
*.onnx filter=lfs diff=lfs merge=lfs -text
|
| 14 |
+
*.ot filter=lfs diff=lfs merge=lfs -text
|
| 15 |
+
*.parquet filter=lfs diff=lfs merge=lfs -text
|
| 16 |
+
*.pb filter=lfs diff=lfs merge=lfs -text
|
| 17 |
+
*.pt filter=lfs diff=lfs merge=lfs -text
|
| 18 |
+
*.pth filter=lfs diff=lfs merge=lfs -text
|
| 19 |
+
*.rar filter=lfs diff=lfs merge=lfs -text
|
| 20 |
+
saved_model/**/* filter=lfs diff=lfs merge=lfs -text
|
| 21 |
+
*.tar.* filter=lfs diff=lfs merge=lfs -text
|
| 22 |
+
*.tflite filter=lfs diff=lfs merge=lfs -text
|
| 23 |
+
*.tgz filter=lfs diff=lfs merge=lfs -text
|
| 24 |
+
*.wasm filter=lfs diff=lfs merge=lfs -text
|
| 25 |
+
*.xz filter=lfs diff=lfs merge=lfs -text
|
| 26 |
+
*.zip filter=lfs diff=lfs merge=lfs -text
|
| 27 |
+
*.zstandard filter=lfs diff=lfs merge=lfs -text
|
| 28 |
+
*tfevents* filter=lfs diff=lfs merge=lfs -text
|
emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/LICENSE
ADDED
|
@@ -0,0 +1,437 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
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|
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|
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|
|
|
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|
|
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|
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reserves any right to collect such royalties, including when
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Section 3 -- License Conditions.
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| 238 |
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|
| 239 |
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Your exercise of the Licensed Rights is expressly made subject to the
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following conditions.
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|
emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/README.md
ADDED
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|
| 1 |
+
---
|
| 2 |
+
language: en
|
| 3 |
+
datasets:
|
| 4 |
+
- msp-podcast
|
| 5 |
+
inference: true
|
| 6 |
+
tags:
|
| 7 |
+
- speech
|
| 8 |
+
- audio
|
| 9 |
+
- wav2vec2
|
| 10 |
+
- audio-classification
|
| 11 |
+
- emotion-recognition
|
| 12 |
+
license: cc-by-nc-sa-4.0
|
| 13 |
+
pipeline_tag: audio-classification
|
| 14 |
+
---
|
| 15 |
+
|
| 16 |
+
# Model for Dimensional Speech Emotion Recognition based on Wav2vec 2.0
|
| 17 |
+
|
| 18 |
+
The model expects a raw audio signal as input and outputs predictions for arousal, dominance and valence in a range of approximately 0...1. In addition, it also provides the pooled states of the last transformer layer. The model was created by fine-tuning [
|
| 19 |
+
Wav2Vec2-Large-Robust](https://huggingface.co/facebook/wav2vec2-large-robust) on [MSP-Podcast](https://ecs.utdallas.edu/research/researchlabs/msp-lab/MSP-Podcast.html) (v1.7). The model was pruned from 24 to 12 transformer layers before fine-tuning. An [ONNX](https://onnx.ai/") export of the model is available from [doi:10.5281/zenodo.6221127](https://zenodo.org/record/6221127). Further details are given in the associated [paper](https://arxiv.org/abs/2203.07378) and [tutorial](https://github.com/audeering/w2v2-how-to).
|
| 20 |
+
|
| 21 |
+
# Usage
|
| 22 |
+
|
| 23 |
+
```python
|
| 24 |
+
import numpy as np
|
| 25 |
+
import torch
|
| 26 |
+
import torch.nn as nn
|
| 27 |
+
from transformers import Wav2Vec2Processor
|
| 28 |
+
from transformers.models.wav2vec2.modeling_wav2vec2 import (
|
| 29 |
+
Wav2Vec2Model,
|
| 30 |
+
Wav2Vec2PreTrainedModel,
|
| 31 |
+
)
|
| 32 |
+
|
| 33 |
+
|
| 34 |
+
class RegressionHead(nn.Module):
|
| 35 |
+
r"""Classification head."""
|
| 36 |
+
|
| 37 |
+
def __init__(self, config):
|
| 38 |
+
|
| 39 |
+
super().__init__()
|
| 40 |
+
|
| 41 |
+
self.dense = nn.Linear(config.hidden_size, config.hidden_size)
|
| 42 |
+
self.dropout = nn.Dropout(config.final_dropout)
|
| 43 |
+
self.out_proj = nn.Linear(config.hidden_size, config.num_labels)
|
| 44 |
+
|
| 45 |
+
def forward(self, features, **kwargs):
|
| 46 |
+
|
| 47 |
+
x = features
|
| 48 |
+
x = self.dropout(x)
|
| 49 |
+
x = self.dense(x)
|
| 50 |
+
x = torch.tanh(x)
|
| 51 |
+
x = self.dropout(x)
|
| 52 |
+
x = self.out_proj(x)
|
| 53 |
+
|
| 54 |
+
return x
|
| 55 |
+
|
| 56 |
+
|
| 57 |
+
class EmotionModel(Wav2Vec2PreTrainedModel):
|
| 58 |
+
r"""Speech emotion classifier."""
|
| 59 |
+
|
| 60 |
+
def __init__(self, config):
|
| 61 |
+
|
| 62 |
+
super().__init__(config)
|
| 63 |
+
|
| 64 |
+
self.config = config
|
| 65 |
+
self.wav2vec2 = Wav2Vec2Model(config)
|
| 66 |
+
self.classifier = RegressionHead(config)
|
| 67 |
+
self.init_weights()
|
| 68 |
+
|
| 69 |
+
def forward(
|
| 70 |
+
self,
|
| 71 |
+
input_values,
|
| 72 |
+
):
|
| 73 |
+
|
| 74 |
+
outputs = self.wav2vec2(input_values)
|
| 75 |
+
hidden_states = outputs[0]
|
| 76 |
+
hidden_states = torch.mean(hidden_states, dim=1)
|
| 77 |
+
logits = self.classifier(hidden_states)
|
| 78 |
+
|
| 79 |
+
return hidden_states, logits
|
| 80 |
+
|
| 81 |
+
|
| 82 |
+
|
| 83 |
+
# load model from hub
|
| 84 |
+
device = 'cpu'
|
| 85 |
+
model_name = 'audeering/wav2vec2-large-robust-12-ft-emotion-msp-dim'
|
| 86 |
+
processor = Wav2Vec2Processor.from_pretrained(model_name)
|
| 87 |
+
model = EmotionModel.from_pretrained(model_name)
|
| 88 |
+
|
| 89 |
+
# dummy signal
|
| 90 |
+
sampling_rate = 16000
|
| 91 |
+
signal = np.zeros((1, sampling_rate), dtype=np.float32)
|
| 92 |
+
|
| 93 |
+
|
| 94 |
+
def process_func(
|
| 95 |
+
x: np.ndarray,
|
| 96 |
+
sampling_rate: int,
|
| 97 |
+
embeddings: bool = False,
|
| 98 |
+
) -> np.ndarray:
|
| 99 |
+
r"""Predict emotions or extract embeddings from raw audio signal."""
|
| 100 |
+
|
| 101 |
+
# run through processor to normalize signal
|
| 102 |
+
# always returns a batch, so we just get the first entry
|
| 103 |
+
# then we put it on the device
|
| 104 |
+
y = processor(x, sampling_rate=sampling_rate)
|
| 105 |
+
y = y['input_values'][0]
|
| 106 |
+
y = y.reshape(1, -1)
|
| 107 |
+
y = torch.from_numpy(y).to(device)
|
| 108 |
+
|
| 109 |
+
# run through model
|
| 110 |
+
with torch.no_grad():
|
| 111 |
+
y = model(y)[0 if embeddings else 1]
|
| 112 |
+
|
| 113 |
+
# convert to numpy
|
| 114 |
+
y = y.detach().cpu().numpy()
|
| 115 |
+
|
| 116 |
+
return y
|
| 117 |
+
|
| 118 |
+
|
| 119 |
+
print(process_func(signal, sampling_rate))
|
| 120 |
+
# Arousal dominance valence
|
| 121 |
+
# [[0.5460754 0.6062266 0.40431657]]
|
| 122 |
+
|
| 123 |
+
print(process_func(signal, sampling_rate, embeddings=True))
|
| 124 |
+
# Pooled hidden states of last transformer layer
|
| 125 |
+
# [[-0.00752167 0.0065819 -0.00746342 ... 0.00663632 0.00848748
|
| 126 |
+
# 0.00599211]]
|
| 127 |
+
```
|
emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/config.json
ADDED
|
@@ -0,0 +1,122 @@
|
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|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"_name_or_path": "torch",
|
| 3 |
+
"activation_dropout": 0.1,
|
| 4 |
+
"adapter_kernel_size": 3,
|
| 5 |
+
"adapter_stride": 2,
|
| 6 |
+
"add_adapter": false,
|
| 7 |
+
"apply_spec_augment": true,
|
| 8 |
+
"architectures": [
|
| 9 |
+
"Wav2Vec2ForSpeechClassification"
|
| 10 |
+
],
|
| 11 |
+
"attention_dropout": 0.1,
|
| 12 |
+
"bos_token_id": 1,
|
| 13 |
+
"classifier_proj_size": 256,
|
| 14 |
+
"codevector_dim": 768,
|
| 15 |
+
"contrastive_logits_temperature": 0.1,
|
| 16 |
+
"conv_bias": true,
|
| 17 |
+
"conv_dim": [
|
| 18 |
+
512,
|
| 19 |
+
512,
|
| 20 |
+
512,
|
| 21 |
+
512,
|
| 22 |
+
512,
|
| 23 |
+
512,
|
| 24 |
+
512
|
| 25 |
+
],
|
| 26 |
+
"conv_kernel": [
|
| 27 |
+
10,
|
| 28 |
+
3,
|
| 29 |
+
3,
|
| 30 |
+
3,
|
| 31 |
+
3,
|
| 32 |
+
2,
|
| 33 |
+
2
|
| 34 |
+
],
|
| 35 |
+
"conv_stride": [
|
| 36 |
+
5,
|
| 37 |
+
2,
|
| 38 |
+
2,
|
| 39 |
+
2,
|
| 40 |
+
2,
|
| 41 |
+
2,
|
| 42 |
+
2
|
| 43 |
+
],
|
| 44 |
+
"ctc_loss_reduction": "sum",
|
| 45 |
+
"ctc_zero_infinity": false,
|
| 46 |
+
"diversity_loss_weight": 0.1,
|
| 47 |
+
"do_stable_layer_norm": true,
|
| 48 |
+
"eos_token_id": 2,
|
| 49 |
+
"feat_extract_activation": "gelu",
|
| 50 |
+
"feat_extract_dropout": 0.0,
|
| 51 |
+
"feat_extract_norm": "layer",
|
| 52 |
+
"feat_proj_dropout": 0.1,
|
| 53 |
+
"feat_quantizer_dropout": 0.0,
|
| 54 |
+
"final_dropout": 0.1,
|
| 55 |
+
"finetuning_task": "wav2vec2_reg",
|
| 56 |
+
"gradient_checkpointing": false,
|
| 57 |
+
"hidden_act": "gelu",
|
| 58 |
+
"hidden_dropout": 0.1,
|
| 59 |
+
"hidden_dropout_prob": 0.1,
|
| 60 |
+
"hidden_size": 1024,
|
| 61 |
+
"id2label": {
|
| 62 |
+
"0": "arousal",
|
| 63 |
+
"1": "dominance",
|
| 64 |
+
"2": "valence"
|
| 65 |
+
},
|
| 66 |
+
"initializer_range": 0.02,
|
| 67 |
+
"intermediate_size": 4096,
|
| 68 |
+
"label2id": {
|
| 69 |
+
"arousal": 0,
|
| 70 |
+
"dominance": 1,
|
| 71 |
+
"valence": 2
|
| 72 |
+
},
|
| 73 |
+
"layer_norm_eps": 1e-05,
|
| 74 |
+
"layerdrop": 0.1,
|
| 75 |
+
"mask_feature_length": 10,
|
| 76 |
+
"mask_feature_min_masks": 0,
|
| 77 |
+
"mask_feature_prob": 0.0,
|
| 78 |
+
"mask_time_length": 10,
|
| 79 |
+
"mask_time_min_masks": 2,
|
| 80 |
+
"mask_time_prob": 0.05,
|
| 81 |
+
"model_type": "wav2vec2",
|
| 82 |
+
"num_adapter_layers": 3,
|
| 83 |
+
"num_attention_heads": 16,
|
| 84 |
+
"num_codevector_groups": 2,
|
| 85 |
+
"num_codevectors_per_group": 320,
|
| 86 |
+
"num_conv_pos_embedding_groups": 16,
|
| 87 |
+
"num_conv_pos_embeddings": 128,
|
| 88 |
+
"num_feat_extract_layers": 7,
|
| 89 |
+
"num_hidden_layers": 12,
|
| 90 |
+
"num_negatives": 100,
|
| 91 |
+
"output_hidden_size": 1024,
|
| 92 |
+
"pad_token_id": 0,
|
| 93 |
+
"pooling_mode": "mean",
|
| 94 |
+
"problem_type": "regression",
|
| 95 |
+
"proj_codevector_dim": 768,
|
| 96 |
+
"tdnn_dilation": [
|
| 97 |
+
1,
|
| 98 |
+
2,
|
| 99 |
+
3,
|
| 100 |
+
1,
|
| 101 |
+
1
|
| 102 |
+
],
|
| 103 |
+
"tdnn_dim": [
|
| 104 |
+
512,
|
| 105 |
+
512,
|
| 106 |
+
512,
|
| 107 |
+
512,
|
| 108 |
+
1500
|
| 109 |
+
],
|
| 110 |
+
"tdnn_kernel": [
|
| 111 |
+
5,
|
| 112 |
+
3,
|
| 113 |
+
3,
|
| 114 |
+
1,
|
| 115 |
+
1
|
| 116 |
+
],
|
| 117 |
+
"torch_dtype": "float32",
|
| 118 |
+
"transformers_version": "4.17.0.dev0",
|
| 119 |
+
"use_weighted_layer_sum": false,
|
| 120 |
+
"vocab_size": null,
|
| 121 |
+
"xvector_output_dim": 512
|
| 122 |
+
}
|
emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/preprocessor_config.json
ADDED
|
@@ -0,0 +1,9 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
{
|
| 2 |
+
"do_normalize": true,
|
| 3 |
+
"feature_extractor_type": "Wav2Vec2FeatureExtractor",
|
| 4 |
+
"feature_size": 1,
|
| 5 |
+
"padding_side": "right",
|
| 6 |
+
"padding_value": 0.0,
|
| 7 |
+
"return_attention_mask": true,
|
| 8 |
+
"sampling_rate": 16000
|
| 9 |
+
}
|
emotional/wav2vec2-large-robust-12-ft-emotion-msp-dim/vocab.json
ADDED
|
@@ -0,0 +1 @@
|
|
|
|
|
|
|
| 1 |
+
{}
|
export_onnx.py
ADDED
|
@@ -0,0 +1,56 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
from models_onnx import SynthesizerTrn
|
| 2 |
+
import utils
|
| 3 |
+
from text.symbols import symbols
|
| 4 |
+
import os
|
| 5 |
+
import json
|
| 6 |
+
|
| 7 |
+
|
| 8 |
+
def export_onnx(export_path, model_path, config_path):
|
| 9 |
+
hps = utils.get_hparams_from_file(config_path)
|
| 10 |
+
net_g = SynthesizerTrn(
|
| 11 |
+
len(symbols),
|
| 12 |
+
hps.data.filter_length // 2 + 1,
|
| 13 |
+
hps.train.segment_size // hps.data.hop_length,
|
| 14 |
+
n_speakers=hps.data.n_speakers,
|
| 15 |
+
**hps.model,
|
| 16 |
+
)
|
| 17 |
+
_ = net_g.eval()
|
| 18 |
+
_ = utils.load_checkpoint(model_path, net_g, None, skip_optimizer=True)
|
| 19 |
+
net_g.export_onnx(export_path)
|
| 20 |
+
|
| 21 |
+
spklist = []
|
| 22 |
+
for key in hps.data.spk2id.keys():
|
| 23 |
+
spklist.append(key)
|
| 24 |
+
|
| 25 |
+
MoeVSConf = {
|
| 26 |
+
"Folder": f"{export_path}",
|
| 27 |
+
"Name": f"{export_path}",
|
| 28 |
+
"Type": "BertVits",
|
| 29 |
+
"Symbol": symbols,
|
| 30 |
+
"Cleaner": "",
|
| 31 |
+
"Rate": hps.data.sampling_rate,
|
| 32 |
+
"CharaMix": True,
|
| 33 |
+
"Characters": spklist,
|
| 34 |
+
"LanguageMap": {"ZH": [0, 0], "JP": [1, 6], "EN": [2, 8]},
|
| 35 |
+
"Dict": "BasicDict",
|
| 36 |
+
"BertPath": [
|
| 37 |
+
"chinese-roberta-wwm-ext-large",
|
| 38 |
+
"deberta-v2-large-japanese",
|
| 39 |
+
"bert-base-japanese-v3",
|
| 40 |
+
],
|
| 41 |
+
}
|
| 42 |
+
|
| 43 |
+
with open(f"onnx/{export_path}.json", "w") as MoeVsConfFile:
|
| 44 |
+
json.dump(MoeVSConf, MoeVsConfFile, indent=4)
|
| 45 |
+
|
| 46 |
+
|
| 47 |
+
if __name__ == "__main__":
|
| 48 |
+
print(symbols)
|
| 49 |
+
export_path = "HimenoSena"
|
| 50 |
+
model_path = "G_53000.pth"
|
| 51 |
+
config_path = "config.json"
|
| 52 |
+
if not os.path.exists("onnx"):
|
| 53 |
+
os.makedirs("onnx")
|
| 54 |
+
if not os.path.exists(f"onnx/{export_path}"):
|
| 55 |
+
os.makedirs(f"onnx/{export_path}")
|
| 56 |
+
export_onnx(export_path, model_path, config_path)
|
infer.py
ADDED
|
@@ -0,0 +1,207 @@
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
| 1 |
+
"""
|
| 2 |
+
版本管理、兼容推理及模型加载实现。
|
| 3 |
+
版本说明：
|
| 4 |
+
1. 版本号与github的release版本号对应，使用哪个release版本训练的模型即对应其版本号
|
| 5 |
+
2. 请在模型的config.json中显示声明版本号，添加一个字段"version" : "你的版本号"
|
| 6 |
+
特殊版本说明：
|
| 7 |
+
1.1.1-fix： 1.1.1版本训练的模型，但是在推理时使用dev的日语修复
|
| 8 |
+
1.1.1-dev： dev开发
|
| 9 |
+
2.0：当前版本
|
| 10 |
+
"""
|
| 11 |
+
import torch
|
| 12 |
+
import commons
|
| 13 |
+
from text import cleaned_text_to_sequence, get_bert
|
| 14 |
+
from text.cleaner import clean_text
|
| 15 |
+
import utils
|
| 16 |
+
|
| 17 |
+
from models import SynthesizerTrn
|
| 18 |
+
from text.symbols import symbols
|
| 19 |
+
from oldVersion.V111.models import SynthesizerTrn as V111SynthesizerTrn
|
| 20 |
+
from oldVersion.V111.text import symbols as V111symbols
|
| 21 |
+
from oldVersion.V110.models import SynthesizerTrn as V110SynthesizerTrn
|
| 22 |
+
from oldVersion.V110.text import symbols as V110symbols
|
| 23 |
+
from oldVersion.V101.models import SynthesizerTrn as V101SynthesizerTrn
|
| 24 |
+
from oldVersion.V101.text import symbols as V101symbols
|
| 25 |
+
|
| 26 |
+
from oldVersion import V111, V110, V101
|
| 27 |
+
|
| 28 |
+
# 当前版本信息
|
| 29 |
+
latest_version = "2.0"
|
| 30 |
+
|
| 31 |
+
# 版本兼容
|
| 32 |
+
SynthesizerTrnMap = {
|
| 33 |
+
"1.1.1-fix": V111SynthesizerTrn,
|
| 34 |
+
"1.1.1": V111SynthesizerTrn,
|
| 35 |
+
"1.1": V110SynthesizerTrn,
|
| 36 |
+
"1.1.0": V110SynthesizerTrn,
|
| 37 |
+
"1.0.1": V101SynthesizerTrn,
|
| 38 |
+
"1.0": V101SynthesizerTrn,
|
| 39 |
+
"1.0.0": V101SynthesizerTrn,
|
| 40 |
+
}
|
| 41 |
+
|
| 42 |
+
symbolsMap = {
|
| 43 |
+
"1.1.1-fix": V111symbols,
|
| 44 |
+
"1.1.1": V111symbols,
|
| 45 |
+
"1.1": V110symbols,
|
| 46 |
+
"1.1.0": V110symbols,
|
| 47 |
+
"1.0.1": V101symbols,
|
| 48 |
+
"1.0": V101symbols,
|
| 49 |
+
"1.0.0": V101symbols,
|
| 50 |
+
}
|
| 51 |
+
|
| 52 |
+
|
| 53 |
+
def get_net_g(model_path: str, version: str, device: str, hps):
|
| 54 |
+
if version != latest_version:
|
| 55 |
+
net_g = SynthesizerTrnMap[version](
|
| 56 |
+
len(symbolsMap[version]),
|
| 57 |
+
hps.data.filter_length // 2 + 1,
|
| 58 |
+
hps.train.segment_size // hps.data.hop_length,
|
| 59 |
+
n_speakers=hps.data.n_speakers,
|
| 60 |
+
**hps.model,
|
| 61 |
+
).to(device)
|
| 62 |
+
else:
|
| 63 |
+
# 当前版本模型 net_g
|
| 64 |
+
net_g = SynthesizerTrn(
|
| 65 |
+
len(symbols),
|
| 66 |
+
hps.data.filter_length // 2 + 1,
|
| 67 |
+
hps.train.segment_size // hps.data.hop_length,
|
| 68 |
+
n_speakers=hps.data.n_speakers,
|
| 69 |
+
**hps.model,
|
| 70 |
+
).to(device)
|
| 71 |
+
_ = net_g.eval()
|
| 72 |
+
_ = utils.load_checkpoint(model_path, net_g, None, skip_optimizer=True)
|
| 73 |
+
return net_g
|
| 74 |
+
|
| 75 |
+
|
| 76 |
+
def get_text(text, language_str, hps, device):
|
| 77 |
+
# 在此处实现当前版本的get_text
|
| 78 |
+
norm_text, phone, tone, word2ph = clean_text(text, language_str)
|
| 79 |
+
phone, tone, language = cleaned_text_to_sequence(phone, tone, language_str)
|
| 80 |
+
|
| 81 |
+
if hps.data.add_blank:
|
| 82 |
+
phone = commons.intersperse(phone, 0)
|
| 83 |
+
tone = commons.intersperse(tone, 0)
|
| 84 |
+
language = commons.intersperse(language, 0)
|
| 85 |
+
for i in range(len(word2ph)):
|
| 86 |
+
word2ph[i] = word2ph[i] * 2
|
| 87 |
+
word2ph[0] += 1
|
| 88 |
+
bert_ori = get_bert(norm_text, word2ph, language_str, device)
|
| 89 |
+
del word2ph
|
| 90 |
+
assert bert_ori.shape[-1] == len(phone), phone
|
| 91 |
+
|
| 92 |
+
if language_str == "ZH":
|
| 93 |
+
bert = bert_ori
|
| 94 |
+
ja_bert = torch.zeros(1024, len(phone))
|
| 95 |
+
en_bert = torch.zeros(1024, len(phone))
|
| 96 |
+
elif language_str == "JP":
|
| 97 |
+
bert = torch.zeros(1024, len(phone))
|
| 98 |
+
ja_bert = bert_ori
|
| 99 |
+
en_bert = torch.zeros(1024, len(phone))
|
| 100 |
+
elif language_str == "EN":
|
| 101 |
+
bert = torch.zeros(1024, len(phone))
|
| 102 |
+
ja_bert = torch.zeros(1024, len(phone))
|
| 103 |
+
en_bert = bert_ori
|
| 104 |
+
else:
|
| 105 |
+
raise ValueError("language_str should be ZH, JP or EN")
|
| 106 |
+
|
| 107 |
+
assert bert.shape[-1] == len(
|
| 108 |
+
phone
|
| 109 |
+
), f"Bert seq len {bert.shape[-1]} != {len(phone)}"
|
| 110 |
+
|
| 111 |
+
phone = torch.LongTensor(phone)
|
| 112 |
+
tone = torch.LongTensor(tone)
|
| 113 |
+
language = torch.LongTensor(language)
|
| 114 |
+
return bert, ja_bert, en_bert, phone, tone, language
|
| 115 |
+
|
| 116 |
+
|
| 117 |
+
def infer(
|
| 118 |
+
text,
|
| 119 |
+
sdp_ratio,
|
| 120 |
+
noise_scale,
|
| 121 |
+
noise_scale_w,
|
| 122 |
+
length_scale,
|
| 123 |
+
sid,
|
| 124 |
+
language,
|
| 125 |
+
hps,
|
| 126 |
+
net_g,
|
| 127 |
+
device,
|
| 128 |
+
):
|
| 129 |
+
# 支持中日双语版本
|
| 130 |
+
inferMap_V2 = {
|
| 131 |
+
"1.1.1-fix": V111.infer_fix,
|
| 132 |
+
"1.1.1": V111.infer,
|
| 133 |
+
"1.1": V110.infer,
|
| 134 |
+
"1.1.0": V110.infer,
|
| 135 |
+
}
|
| 136 |
+
# 仅支持中文版本
|
| 137 |
+
# 在测试中，并未发现两个版本的模型不能互相通用
|
| 138 |
+
inferMap_V1 = {
|
| 139 |
+
"1.0.1": V101.infer,
|
| 140 |
+
"1.0": V101.infer,
|
| 141 |
+
"1.0.0": V101.infer,
|
| 142 |
+
}
|
| 143 |
+
version = hps.version if hasattr(hps, "version") else latest_version
|
| 144 |
+
# 非当前版本，根据版本号选择合适的infer
|
| 145 |
+
if version != latest_version:
|
| 146 |
+
if version in inferMap_V2.keys():
|
| 147 |
+
return inferMap_V2[version](
|
| 148 |
+
text,
|
| 149 |
+
sdp_ratio,
|
| 150 |
+
noise_scale,
|
| 151 |
+
noise_scale_w,
|
| 152 |
+
length_scale,
|
| 153 |
+
sid,
|
| 154 |
+
language,
|
| 155 |
+
hps,
|
| 156 |
+
net_g,
|
| 157 |
+
device,
|
| 158 |
+
)
|
| 159 |
+
if version in inferMap_V1.keys():
|
| 160 |
+
return inferMap_V1[version](
|
| 161 |
+
text,
|
| 162 |
+
sdp_ratio,
|
| 163 |
+
noise_scale,
|
| 164 |
+
noise_scale_w,
|
| 165 |
+
length_scale,
|
| 166 |
+
sid,
|
| 167 |
+
hps,
|
| 168 |
+
net_g,
|
| 169 |
+
device,
|
| 170 |
+
)
|
| 171 |
+
# 在此处实现当前版本的推理
|
| 172 |
+
bert, ja_bert, en_bert, phones, tones, lang_ids = get_text(
|
| 173 |
+
text, language, hps, device
|
| 174 |
+
)
|
| 175 |
+
with torch.no_grad():
|
| 176 |
+
x_tst = phones.to(device).unsqueeze(0)
|
| 177 |
+
tones = tones.to(device).unsqueeze(0)
|
| 178 |
+
lang_ids = lang_ids.to(device).unsqueeze(0)
|
| 179 |
+
bert = bert.to(device).unsqueeze(0)
|
| 180 |
+
ja_bert = ja_bert.to(device).unsqueeze(0)
|
| 181 |
+
en_bert = en_bert.to(device).unsqueeze(0)
|
| 182 |
+
x_tst_lengths = torch.LongTensor([phones.size(0)]).to(device)
|
| 183 |
+
del phones
|
| 184 |
+
speakers = torch.LongTensor([hps.data.spk2id[sid]]).to(device)
|
| 185 |
+
audio = (
|
| 186 |
+
net_g.infer(
|
| 187 |
+
x_tst,
|
| 188 |
+
x_tst_lengths,
|
| 189 |
+
speakers,
|
| 190 |
+
tones,
|
| 191 |
+
lang_ids,
|
| 192 |
+
bert,
|
| 193 |
+
ja_bert,
|
| 194 |
+
en_bert,
|
| 195 |
+
sdp_ratio=sdp_ratio,
|
| 196 |
+
noise_scale=noise_scale,
|
| 197 |
+
noise_scale_w=noise_scale_w,
|
| 198 |
+
length_scale=length_scale,
|
| 199 |
+
)[0][0, 0]
|
| 200 |
+
.data.cpu()
|
| 201 |
+
.float()
|
| 202 |
+
.numpy()
|
| 203 |
+
)
|
| 204 |
+
del x_tst, tones, lang_ids, bert, x_tst_lengths, speakers, ja_bert, en_bert
|
| 205 |
+
if torch.cuda.is_available():
|
| 206 |
+
torch.cuda.empty_cache()
|
| 207 |
+
return audio
|