mirror of
https://github.com/storytold/storyteller-ml.git
synced 2026-10-15 00:03:06 +00:00
Add back original so-vits-svc
This commit is contained in:
Brandon Thomas
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# Vim
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*~
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# Model files
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checkpoint_best_legacy_500.pt
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logs/44k/
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# Virtual environments
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python/
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##### BELOW WAS ORIGINAL GITIGNORE #####
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#
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# Created by https://www.toptal.com/developers/gitignore/api/python
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# Edit at https://www.toptal.com/developers/gitignore?templates=python
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|
||||
### Python ###
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# Byte-compiled / optimized / DLL files
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__pycache__/
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*.py[cod]
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*$py.class
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|
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# C extensions
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*.so
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|
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# Distribution / packaging
|
||||
.Python
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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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sdist/
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var/
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wheels/
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pip-wheel-metadata/
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share/python-wheels/
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*.egg-info/
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.installed.cfg
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*.egg
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MANIFEST
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# PyInstaller
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# Usually these files are written by a python script from a template
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# before PyInstaller builds the exe, so as to inject date/other infos into it.
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*.manifest
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*.spec
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# Installer logs
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pip-log.txt
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pip-delete-this-directory.txt
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|
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# Unit test / coverage reports
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htmlcov/
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.tox/
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.nox/
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.coverage
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.coverage.*
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.cache
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nosetests.xml
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coverage.xml
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*.cover
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*.py,cover
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.hypothesis/
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.pytest_cache/
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pytestdebug.log
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# Translations
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*.mo
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*.pot
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# Django stuff:
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*.log
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local_settings.py
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db.sqlite3
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db.sqlite3-journal
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# Flask stuff:
|
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instance/
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.webassets-cache
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||||
|
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# Scrapy stuff:
|
||||
.scrapy
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|
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# Sphinx documentation
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docs/_build/
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doc/_build/
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# PyBuilder
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target/
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# Jupyter Notebook
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.ipynb_checkpoints
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# IPython
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profile_default/
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ipython_config.py
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# pyenv
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.python-version
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# pipenv
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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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# PEP 582; used by e.g. github.com/David-OConnor/pyflow
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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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|
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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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.spyderproject
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.spyproject
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# Rope project settings
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.ropeproject
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# mkdocs documentation
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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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||||
.pyre/
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|
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# pytype static type analyzer
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.pytype/
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# End of https://www.toptal.com/developers/gitignore/api/python
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dataset
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dataset_raw
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raw
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results
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inference/chunks_temp.json
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logs
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hubert/checkpoint_best_legacy_500.pt
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configs/config.json
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filelists/test.txt
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filelists/train.txt
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filelists/val.txt
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@@ -0,0 +1,21 @@
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MIT License
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Copyright (c) 2021 Jingyi Li
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Permission is hereby granted, free of charge, to any person obtaining a copy
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of this software and associated documentation files (the "Software"), to deal
|
||||
in the Software without restriction, including without limitation the rights
|
||||
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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copies of the Software, and to permit persons to whom the Software is
|
||||
furnished to do so, subject to the following conditions:
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||||
|
||||
The above copyright notice and this permission notice shall be included in all
|
||||
copies or substantial portions of the Software.
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||||
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
|
||||
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
|
||||
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
|
||||
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
|
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LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
|
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SOFTWARE.
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@@ -0,0 +1,26 @@
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so-vits-svc
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===========
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Original docs are in [README\_ORIGINAL.md](./README_ORIGINAL.md).
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This is a snapshot of `f22819cb8718ead5e50dea4fdbd05195c72fb1a8` (branch `origin/4.0`). It was taken
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from justinjohn0306's fork at https://github.com/justinjohn0306/so-vits-svc
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Dev Installation
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----------------
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Use our `requirements.txt` instead of the original `upstream-requirements-but-broken.txt`
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Installing this may give you trouble. Brandon had trouble under Python3.10, so downgraded to Python 3.9
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using the deadsnakes PPA. Furthermore, the version of pyworld originally specified refused to build;
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upgrading to the latest point release fixed it.
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`pyworld==0.2.5` -> `pyworld==0.2.12`
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tl;dr:
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- Python3.9
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- pyworld 0.2.12
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@@ -0,0 +1,138 @@
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# SoftVC VITS Singing Voice Conversion
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## SoVITS has stopped updating and officially Archived
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Some other related warehouses for follow-up maintenance and other work:
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+ [so-vits-svc](https://github.com/svc-develop-team/so-vits-svc): The svc community is ready to take over and maintain sovits. If you have pr and issue, you can submit them here
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+ [SoftVitsResearch](https://github.com/NaruseMioShirakana/SoftVitsResearch): Used to make some fancy functions (mainly for Onnx-MoesS)
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## Terms of use
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1. Please solve the authorization problem of the data set by yourself. Any problems caused by using unauthorized data sets for training, you must bear full responsibility and all consequences, and have nothing to do with sovits!
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2. Any sovits-based video published to the video platform must clearly indicate in the introduction the input source singing and audio used for voice changer conversion, for example: use the video/audio released by others, and use the separated human voice as input If the source is converted, a clear link to the original video and music must be given; if the input source is converted using your own vocals or voice synthesized by other singing voice synthesis engines, you must also explain it in the introduction.
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3. Any copyright infringement caused by the input source shall bear full responsibility and all consequences. When using other commercial singing voice synthesis software as the input source, please make sure to abide by the terms of use of the software. Note that the terms of use of many singing voice synthesis engines clearly indicate that they cannot be used for input source conversion!
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## update
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> The 4.0-v2 model has been updated. The entire process is the same as 4.0. Compared with 4.0, there are some improvements in some scenarios, but there are also some cases where there are regressions. In the [4.0-v2 branch](https://github.com/innnky/so -vits-svc/tree/4.0-v2) This is the last update of sovits \
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## Model Introduction
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The singing voice conversion model extracts the voice features of the source audio through the SoftVC content encoder, and inputs VITS at the same time as F0 to replace the original text input to achieve the effect of singing voice conversion. At the same time, replace the vocoder with [NSF HiFiGAN](https://github.com/openvpi/DiffSinger/tree/refactor/modules/nsf_hifigan) to solve the staccato problem
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### 4.0 version update content
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+ Feature input replaced with [Content Vec](https://github.com/auspicious3000/contentvec)
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+ The sampling rate is uniformly used at 44100hz
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+ Due to the change of parameters such as hop size and the simplification of some model structures, the video memory usage required for inference has been greatly reduced**, and the 44khz video memory usage of version 4.0 is even smaller than the 32khz of version 3.0
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+ Adjusted part of the code structure
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+ Dataset production and training process are consistent with 3.0, but the model is not universal at all, and the dataset also needs to be re-preprocessed
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+ Added option 1: vc mode automatically predicts the pitch f0, that is, there is no need to manually input the transposition key when converting the voice, the male and female voices can be automatically converted, but only for voice conversion, the singing voice will be out of tune when converted in this mode
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+ Added option 2: reduce timbre leakage through the kmeans clustering scheme, that is, make the timbre more like the target timbre
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Online demo: [](https://huggingface.co/spaces/innnky/sovits4)
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## Pre-downloaded model files
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+ contentvec: [checkpoint_best_legacy_500.pt](https://ibm.box.com/s/z1wgl1stco8ffooyatzdwsqn2psd9lrr)
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+ placed in the `hubert` directory
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+ Pre-training base model file: [G_0.pth](https://huggingface.co/justinjohn-03/so-vits-svc-4.0/resolve/main/G_0.pth) and [D_0.pth](https://huggingface.co/justinjohn-03/so-vits-svc-4.0/resolve/main/D_0.pth)
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+ placed in the `logs/44k` directory
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+ The pre-trained base model training data set includes Yunhao, Jishuang, Huiyu·Xing AI Paimeng, Lingdi Ningning, covering the common vocal range of male and female girls, which can be considered as a relatively common base model
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```shell
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# One-click download
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# contentvec
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http://obs.cstcloud.cn/share/obs/sankagenkeshi/checkpoint_best_legacy_500.pt
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# It can also be manually downloaded and placed in the hubert directory
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# G and D pre-training model:
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wget -P logs/44k/ https://huggingface.co/justinjohn-03/so-vits-svc-4.0/resolve/main/G_0.pth
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wget -P logs/44k/ https://huggingface.co/justinjohn-03/so-vits-svc-4.0/resolve/main/D_0.pth
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```
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## Colab one-click data set production and training script
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[](https://colab.research.google.com/github/justinjohn0306/so-vits-svc/blob/4.0/Sovits4_0_training_inference.ipynb)
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## Dataset preparation
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Just put the dataset into the dataset_raw directory with the following file structure
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```shell
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dataset_raw
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├───speaker0
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│ ├───xxx1-xxx1.wav
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│ ├────...
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│ └───Lxx-0xx8.wav
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└───speaker1
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├───xx2-0xxx2.wav
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├────...
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└───xxx7-xxx007.wav
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```
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## Data preprocessing
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1. Resample to 44100hz
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```shell
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python resample.py
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```
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2. Automatically divide training set, verification set, test set and automatically generate configuration files
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```shell
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python preprocess_flist_config.py
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```
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3. Generate hubert and f0
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```shell
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python preprocess_hubert_f0.py
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```
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After performing the above steps, the dataset directory is the preprocessed data, you can delete the dataset_raw folder
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## train
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```shell
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python train.py -c configs/config.json -m 44k
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```
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Note: The old model will be automatically cleared during training, and only the latest 3 models will be kept. If you want to prevent overfitting, you need to manually back up the model record points, or modify the configuration file keep_ckpts 0 to never clear
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## reasoning
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Use [inference_main.py](inference_main.py)
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As of now, the 4.0 usage method (training, reasoning) is exactly the same as 3.0, without any changes (command line support is added for reasoning)
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```shell
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# example
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python inference_main.py -m "logs/44k/G_30400.pth" -c "configs/config.json" -n "君の知らない物语-src.wav" -t 0 -s "nen"
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```
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required field
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+ -m, --model_path: model path.
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+ -c, --config_path: configuration file path.
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+ -n, --clean_names: list of wav file names, placed under the raw folder.
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+ -t, --trans: pitch adjustment, support positive and negative (semitones).
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+ -s, --spk_list: synthesize target speaker names.
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Optional section: see next section
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+ -a, --auto_predict_f0: Voice conversion automatically predicts the pitch, do not enable this when converting singing voices, it will seriously go out of tune.
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+ -cm, --cluster_model_path: The path of the cluster model, if there is no training cluster, just fill it in.
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+ -cr, --cluster_infer_ratio: Clustering scheme ratio, range 0-1, if no clustering model is trained, fill in 0.
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## Optional
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If the previous effect is satisfactory, or if you don’t understand what is going on below, then the following content can be ignored without affecting the use of the model. (These options have relatively little impact and may have some effect on some specific data, but most of the cases seem to be less obvious),
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### Automatic f0 prediction
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The 4.0 model training process will train an f0 predictor. For voice conversion, you can enable automatic pitch prediction. If the effect is not good, you can also use manual, but please do not enable this function when converting singing voices! ! ! It will be seriously out of tune! !
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+ Set auto_predict_f0 to true in inference_main
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### Cluster Timbre Leakage Control
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Introduction: The clustering scheme can reduce the timbre leakage, making the model trained more like the target timbre (but it is not particularly obvious), but the simple clustering scheme will reduce the model's articulation (it will be inarticulate) (this is obvious) , this model adopts the fusion method,
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You can linearly control the ratio of the clustering scheme and the non-clustering scheme, that is, you can manually adjust the ratio between "like the target tone" and "clear articulation" to find a suitable compromise point.
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Using the existing steps before clustering does not require any changes, only an additional clustering model needs to be trained, although the effect is relatively limited, but the training cost is relatively low
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+ Training process:
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+ Use a machine with better CPU performance for training. According to my experience, it takes about 4 minutes to train each speaker on Tencent Cloud 6-core CPU to complete the training
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+ Execute python cluster/train_cluster.py, the output of the model will be in logs/44k/kmeans_10000.pt
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+ Reasoning process:
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+ Specify cluster_model_path in inference_main
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+ Specify cluster_infer_ratio in inference_main, 0 means no clustering at all, 1 means only clustering, usually set to 0.5
|
||||
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## Onnx export
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Use [onnx_export.py](onnx_export.py)
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+ Create a new folder: `checkpoints` and open it
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||||
+ Create a new folder in the `checkpoints` folder as the project folder, and the folder name is your project name, such as `aziplayer`
|
||||
+ Rename your model to `model.pth`, rename the configuration file to `config.json`, and place it in the `aziplayer` folder you just created
|
||||
+ Change `"NyaruTaffy"` in `path = "NyaruTaffy"` in [onnx_export.py](onnx_export.py) to your project name, `path = "aziplayer"`
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||||
+ run [onnx_export.py](onnx_export.py)
|
||||
+ After the execution is completed, a `model.onnx` will be generated in your project folder, which is the exported model
|
||||
### Onnx model supported UI
|
||||
+ [MoeSS](https://github.com/NaruseMioShirakana/MoeSS)
|
||||
+ I removed all the training functions and all complicated transpositions, not a single line, because I think you only know that you are using Onnx if you remove these things
|
||||
+ Note: Please use the model provided by MoesS for the Hubert Onnx model, which cannot be exported at present (Hubert in fairseq has many operators that onnx does not support and things that involve constants, and an error will be reported when exporting or the exported model input and output shape and The results are problematic)
|
||||
[Hubert4.0](https://huggingface.co/NaruseMioShirakana/MoeSS-SUBModel)
|
||||
@@ -0,0 +1,366 @@
|
||||
{
|
||||
"nbformat": 4,
|
||||
"nbformat_minor": 0,
|
||||
"metadata": {
|
||||
"colab": {
|
||||
"provenance": []
|
||||
},
|
||||
"kernelspec": {
|
||||
"name": "python3",
|
||||
"display_name": "Python 3"
|
||||
},
|
||||
"language_info": {
|
||||
"name": "python"
|
||||
},
|
||||
"accelerator": "GPU",
|
||||
"gpuClass": "standard"
|
||||
},
|
||||
"cells": [
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"# **So-Vits-SVC 4.0 Training and Testing**\n",
|
||||
"___\n",
|
||||
"**This colab notebook is prepared and maintained by [justinjohn03](https://github.com/justinjohn0306)**"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "XiGNXkoTSUoT"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#@title Check the GPU\n",
|
||||
"!nvidia-smi"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "0gQcIZ8RsOkn",
|
||||
"cellView": "form"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#@title Installs mamba and updates base\n",
|
||||
"\n",
|
||||
"%%shell\n",
|
||||
"curl -L -O https://github.com/conda-forge/miniforge/releases/latest/download/Mambaforge-Linux-x86_64.sh\n",
|
||||
"chmod +x Mambaforge-Linux-x86_64.sh\n",
|
||||
"bash Mambaforge-Linux-x86_64.sh -b -f -p /usr/local\n",
|
||||
"mamba config --env --set always_yes true\n",
|
||||
"rm Mambaforge-Linux-x86_64.sh\n",
|
||||
"mamba update -n base -c defaults conda -y"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "DLF3hGn_Nmkc",
|
||||
"cellView": "form"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"execution_count": null,
|
||||
"metadata": {
|
||||
"id": "LS0OPRkL4Pme",
|
||||
"cellView": "form"
|
||||
},
|
||||
"outputs": [],
|
||||
"source": [
|
||||
"#@title Clone github repository\n",
|
||||
"!git clone https://github.com/justinjohn0306/so-vits-svc -b 4.0\n",
|
||||
"%cd /content/so-vits-svc"
|
||||
]
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#@title Creates mamba environment and activates it from environment.yaml. Takes ~8 minutes.\n",
|
||||
"\n",
|
||||
"%%shell\n",
|
||||
"mamba env create -f environment.yaml\n",
|
||||
"mamba init bash\n",
|
||||
"source /root/.bashrc\n",
|
||||
"source activate sovits\n",
|
||||
"pip install -r requirements_colab.txt"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "zXBLkXxL4T1O",
|
||||
"cellView": "form"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#@title Download necessary model files\n",
|
||||
"# 源仓库地址：[contentvec](https://github.com/auspicious3000/contentvec)\n",
|
||||
"# 模型原下载链接：[checkpoint_best_legacy_500.pt](https://ibm.box.com/s/z1wgl1stco8ffooyatzdwsqn2psd9lrr)\n",
|
||||
"# 由于源网盘无法提供http直链，根据mit协议，对模型进行二次分发，提供下载直链\n",
|
||||
"!wget -P hubert/ https://huggingface.co/justinjohn-03/so-vits-svc-4.0/resolve/main/checkpoint_best_legacy_500.pt"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "pCqf3W0d6ify",
|
||||
"cellView": "form"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"# Dataset preprocessing"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "k1qadJBFehMo"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#@title Loading Google Drive\n",
|
||||
"#@markdown load Google Drive\n",
|
||||
"from google.colab import drive\n",
|
||||
"drive.mount('/content/drive')"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "wmUkpUmfn_Hs",
|
||||
"cellView": "form"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"This processing script can preprocess multiple speakers at one time, and generate multi-speaker filelists and corresponding configuration files\n",
|
||||
"\n",
|
||||
"Just put your dataset into the dataset_raw directory according to the following file structure\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"dataset_raw\n",
|
||||
"├───speaker0\n",
|
||||
"│ ├───xxx1-xxx1.wav\n",
|
||||
"│ ├───...\n",
|
||||
"│ └───Lxx-0xx8.wav\n",
|
||||
"└───speaker1\n",
|
||||
" ├───xx2-0xxx2.wav\n",
|
||||
" ├───...\n",
|
||||
" └───xxx7-xxx007.wav\n",
|
||||
"\n",
|
||||
"```\n",
|
||||
"\n",
|
||||
"\n"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "kBlju6Q3lSM6"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#@title Load the packaged dataset from Google Cloud Disk for preprocessing\n",
|
||||
"#@markdown **The data set of sovits3.0 no longer needs a specific file structure. Put all the wav files of the data set in the same folder, compress them into zip and upload them to Google Cloud Disk. This processing script can be used once When processing multiple datasets, please decompress each dataset in turn**\n",
|
||||
"\n",
|
||||
"#@markdown dataset name (**character’s English/Pinyin name**, which is the same as when creating the data folder; without zip.)\n",
|
||||
"DATASETNAME = \"kiritan\" #@param {type:\"string\"}\n",
|
||||
"#@markdown Compressed package path (Google disk path, don’t change this if you pass it to dataset, create a new one if there is no dataset folder)\n",
|
||||
"ZIP_PATH = \"/content/drive/MyDrive/dataset/\" #@param {type:\"string\"}\n",
|
||||
"ZIP_NAME = ZIP_PATH + DATASETNAME\n",
|
||||
"\n",
|
||||
"!unzip -d /content/so-vits-svc/dataset_raw {ZIP_NAME}.zip"
|
||||
],
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "U05CXlAipvJR"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#@title Resample audio to 44100hz\n",
|
||||
"!python resample.py"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "_ThKTzYs5CfL",
|
||||
"cellView": "form"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#@title Divide training set and Generate configuration file\n",
|
||||
"!python preprocess_flist_config.py"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "svITReeL5N8K",
|
||||
"cellView": "form"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#@title Generate hubert and f0\n",
|
||||
"!python preprocess_hubert_f0.py"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "xHUXMi836DMe",
|
||||
"cellView": "form"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#@title At this point, the preprocessing of the data set is completed, and the data set and related files are saved to the dataset folder of Google Cloud Disk, which is convenient for the next training\n",
|
||||
"#Compress the dataset folder\n",
|
||||
"!zip -r dataset.zip /content/so-vits-svc/dataset\n",
|
||||
"#@markdown Customize the name of the dataset folder under the dataset folder of Google Cloud Disk to avoid confusion\n",
|
||||
"dataset_name_drive = \"kiritan_preprocessed\" #@param {type:\"string\"}\n",
|
||||
"DATASET_PATH_DRIVE = \"/content/drive/MyDrive/dataset/\" + dataset_name_drive\n",
|
||||
"!mkdir -p {DATASET_PATH_DRIVE}\n",
|
||||
"\n",
|
||||
"!cp /content/so-vits-svc/dataset.zip \"{DATASET_PATH_DRIVE}\"\n",
|
||||
"!cp configs/config.json \"{DATASET_PATH_DRIVE}\"\n",
|
||||
"!cp filelists/train.txt \"{DATASET_PATH_DRIVE}\"\n",
|
||||
"!cp filelists/val.txt \"{DATASET_PATH_DRIVE}\""
|
||||
],
|
||||
"metadata": {
|
||||
"id": "Wo4OTmTAUXgj",
|
||||
"cellView": "form"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#@title If the data set has been preprocessed, you can skip the preprocessing part and decompress the processed data and configuration files directly from the cloud disk\n",
|
||||
"#@markdown Load the preprocessed data set from Google Cloud Disk, the folder name is the same as what you entered when you backed up\n",
|
||||
"back_up_name = \"kiritan_preprocessed\" #@param {type:\"string\"}\n",
|
||||
"BACK_UP_DATASET_PATH = \"/content/drive/MyDrive/dataset/\" + back_up_name\n",
|
||||
"!unzip {BACK_UP_DATASET_PATH}/dataset.zip -d /\n",
|
||||
"!cp {BACK_UP_DATASET_PATH}/config.json /content/so-vits-svc/configs/config.json \n",
|
||||
"!cp {BACK_UP_DATASET_PATH}/val.txt filelists/val.txt\n",
|
||||
"!cp {BACK_UP_DATASET_PATH}/train.txt filelists/train.txt\n",
|
||||
"\n",
|
||||
"\n",
|
||||
"# 拷贝云盘上保存的记录点\n",
|
||||
"# !cp /content/drive/MyDrive/G_800.pth logs/48k/\n",
|
||||
"# !cp /content/drive/MyDrive/D_800.pth logs/48k/"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "P2G6v_6zblWK",
|
||||
"cellView": "form"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"# Train"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "ENoH-pShel7w"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#@title Choose whether to save the trained model to Google Cloud Disk and whether to use the pre-model\n",
|
||||
"\n",
|
||||
"#@markdown **Save the trained model file to Google Cloud Disk. After checking, you need to check and execute it when resuming training**\n",
|
||||
"Save_to_drive = True #@param {type:\"boolean\"}\n",
|
||||
"if Save_to_drive:\n",
|
||||
" !rm -rf /content/so-vits-svc/logs/44k\n",
|
||||
" !mkdir -p /content/drive/MyDrive/44k\n",
|
||||
" !ln -s /content/drive/MyDrive/44k /content/so-vits-svc/logs/44k\n",
|
||||
"\n",
|
||||
"#@markdown **Download the pre-model for the first training and continue training after using the record points saved by yourself, no need to download again**\n",
|
||||
"\n",
|
||||
"#@markdown **Use the pre-model, check the box below to automatically download and enable**\n",
|
||||
"pre_pth = True #@param {type:\"boolean\"}\n",
|
||||
"if pre_pth:\n",
|
||||
" !wget -P logs/44k/ https://huggingface.co/justinjohn-03/so-vits-svc-4.0/resolve/main/G_0.pth\n",
|
||||
" !wget -P logs/44k/ https://huggingface.co/justinjohn-03/so-vits-svc-4.0/resolve/main/D_0.pth\n"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "l8J2ubh9KV5J",
|
||||
"cellView": "form"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#@title Start Training\n",
|
||||
"\n",
|
||||
"#@markdown **Enable tensorboard to visualize data**\n",
|
||||
"tensorboard_on = True #@param {type:\"boolean\"}\n",
|
||||
"if tensorboard_on:\n",
|
||||
" %load_ext tensorboard\n",
|
||||
" %tensorboard --logdir logs/44k\n",
|
||||
"\n",
|
||||
"!python train.py -c configs/config.json -m 44k\n"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "-hEFFTCfZf57",
|
||||
"cellView": "form"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#@title Manually back up the trained model files to Google Cloud Disk\n",
|
||||
"#@markdown You need to check the file name of the model under the /content/so-vits-svc/logs/44k/ folder by yourself, and manually modify the file name at the end of the command below\n",
|
||||
"!mv /content/so-vits-svc/logs/44k/G_1000.pth /content/drive/MyDrive\n",
|
||||
"!mv /content/so-vits-svc/logs/44k/D_1000.pth /content/drive/MyDrive"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "KiNCWprSPlKH",
|
||||
"cellView": "form"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
},
|
||||
{
|
||||
"cell_type": "markdown",
|
||||
"source": [
|
||||
"# Reasoning"
|
||||
],
|
||||
"metadata": {
|
||||
"id": "oCnbX-OT897k"
|
||||
}
|
||||
},
|
||||
{
|
||||
"cell_type": "code",
|
||||
"source": [
|
||||
"#@title Synthesize Audio (Inference)\n",
|
||||
"#@markdown Upload the audio to the ``so-vits-svc/raw`` folder, and then set the model path, configuration file path, and synthesized audio name\n",
|
||||
"\n",
|
||||
"!python inference_main.py -m \"logs/44k/G_1600.pth\" -c \"configs/config.json\" -n \"君の知らない物語-src.wav\" -s kiritan\n"
|
||||
],
|
||||
"metadata": {
|
||||
"cellView": "form",
|
||||
"id": "dYnKuKTIj3z1"
|
||||
},
|
||||
"execution_count": null,
|
||||
"outputs": []
|
||||
}
|
||||
]
|
||||
}
|
||||
@@ -0,0 +1,69 @@
|
||||
import io
|
||||
import os
|
||||
|
||||
# os.system("wget -P cvec/ https://huggingface.co/spaces/innnky/nanami/resolve/main/checkpoint_best_legacy_500.pt")
|
||||
import gradio as gr
|
||||
import librosa
|
||||
import numpy as np
|
||||
import soundfile
|
||||
from inference.infer_tool import Svc
|
||||
import logging
|
||||
|
||||
logging.getLogger('numba').setLevel(logging.WARNING)
|
||||
logging.getLogger('markdown_it').setLevel(logging.WARNING)
|
||||
logging.getLogger('urllib3').setLevel(logging.WARNING)
|
||||
logging.getLogger('matplotlib').setLevel(logging.WARNING)
|
||||
|
||||
config_path = "configs/config.json"
|
||||
|
||||
model = Svc("logs/44k/G_114400.pth", "configs/config.json", cluster_model_path="logs/44k/kmeans_10000.pt")
|
||||
|
||||
|
||||
|
||||
def vc_fn(sid, input_audio, vc_transform, auto_f0,cluster_ratio, slice_db, noise_scale):
|
||||
if input_audio is None:
|
||||
return "You need to upload an audio", None
|
||||
sampling_rate, audio = input_audio
|
||||
# print(audio.shape,sampling_rate)
|
||||
duration = audio.shape[0] / sampling_rate
|
||||
if duration > 90:
|
||||
return "请上传小于90s的音频，需要转换长音频请本地进行转换", None
|
||||
audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
|
||||
if len(audio.shape) > 1:
|
||||
audio = librosa.to_mono(audio.transpose(1, 0))
|
||||
if sampling_rate != 16000:
|
||||
audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
|
||||
print(audio.shape)
|
||||
out_wav_path = "temp.wav"
|
||||
soundfile.write(out_wav_path, audio, 16000, format="wav")
|
||||
print( cluster_ratio, auto_f0, noise_scale)
|
||||
_audio = model.slice_inference(out_wav_path, sid, vc_transform, slice_db, cluster_ratio, auto_f0, noise_scale)
|
||||
return "Success", (44100, _audio)
|
||||
|
||||
|
||||
app = gr.Blocks()
|
||||
with app:
|
||||
with gr.Tabs():
|
||||
with gr.TabItem("Basic"):
|
||||
gr.Markdown(value="""
|
||||
sovits4.0 在线demo
|
||||
|
||||
此demo为预训练底模在线demo，使用数据：云灏 即霜 辉宇·星AI 派蒙 绫地宁宁
|
||||
""")
|
||||
spks = list(model.spk2id.keys())
|
||||
sid = gr.Dropdown(label="音色", choices=spks, value=spks[0])
|
||||
vc_input3 = gr.Audio(label="上传音频（长度小于90秒）")
|
||||
vc_transform = gr.Number(label="变调（整数，可以正负，半音数量，升高八度就是12）", value=0)
|
||||
cluster_ratio = gr.Number(label="聚类模型混合比例，0-1之间，默认为0不启用聚类，能提升音色相似度，但会导致咬字下降（如果使用建议0.5左右）", value=0)
|
||||
auto_f0 = gr.Checkbox(label="自动f0预测，配合聚类模型f0预测效果更好,会导致变调功能失效（仅限转换语音，歌声不要勾选此项会究极跑调）", value=False)
|
||||
slice_db = gr.Number(label="切片阈值", value=-40)
|
||||
noise_scale = gr.Number(label="noise_scale 建议不要动，会影响音质，玄学参数", value=0.4)
|
||||
vc_submit = gr.Button("转换", variant="primary")
|
||||
vc_output1 = gr.Textbox(label="Output Message")
|
||||
vc_output2 = gr.Audio(label="Output Audio")
|
||||
vc_submit.click(vc_fn, [sid, vc_input3, vc_transform,auto_f0,cluster_ratio, slice_db, noise_scale], [vc_output1, vc_output2])
|
||||
|
||||
app.launch()
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,29 @@
|
||||
import numpy as np
|
||||
import torch
|
||||
from sklearn.cluster import KMeans
|
||||
|
||||
def get_cluster_model(ckpt_path):
|
||||
checkpoint = torch.load(ckpt_path)
|
||||
kmeans_dict = {}
|
||||
for spk, ckpt in checkpoint.items():
|
||||
km = KMeans(ckpt["n_features_in_"])
|
||||
km.__dict__["n_features_in_"] = ckpt["n_features_in_"]
|
||||
km.__dict__["_n_threads"] = ckpt["_n_threads"]
|
||||
km.__dict__["cluster_centers_"] = ckpt["cluster_centers_"]
|
||||
kmeans_dict[spk] = km
|
||||
return kmeans_dict
|
||||
|
||||
def get_cluster_result(model, x, speaker):
|
||||
"""
|
||||
x: np.array [t, 256]
|
||||
return cluster class result
|
||||
"""
|
||||
return model[speaker].predict(x)
|
||||
|
||||
def get_cluster_center_result(model, x,speaker):
|
||||
"""x: np.array [t, 256]"""
|
||||
predict = model[speaker].predict(x)
|
||||
return model[speaker].cluster_centers_[predict]
|
||||
|
||||
def get_center(model, x,speaker):
|
||||
return model[speaker].cluster_centers_[x]
|
||||
@@ -0,0 +1,89 @@
|
||||
import os
|
||||
from glob import glob
|
||||
from pathlib import Path
|
||||
import torch
|
||||
import logging
|
||||
import argparse
|
||||
import torch
|
||||
import numpy as np
|
||||
from sklearn.cluster import KMeans, MiniBatchKMeans
|
||||
import tqdm
|
||||
logging.basicConfig(level=logging.INFO)
|
||||
logger = logging.getLogger(__name__)
|
||||
import time
|
||||
import random
|
||||
|
||||
def train_cluster(in_dir, n_clusters, use_minibatch=True, verbose=False):
|
||||
|
||||
logger.info(f"Loading features from {in_dir}")
|
||||
features = []
|
||||
nums = 0
|
||||
for path in tqdm.tqdm(in_dir.glob("*.soft.pt")):
|
||||
features.append(torch.load(path).squeeze(0).numpy().T)
|
||||
# print(features[-1].shape)
|
||||
features = np.concatenate(features, axis=0)
|
||||
print(nums, features.nbytes/ 1024**2, "MB , shape:",features.shape, features.dtype)
|
||||
features = features.astype(np.float32)
|
||||
logger.info(f"Clustering features of shape: {features.shape}")
|
||||
t = time.time()
|
||||
if use_minibatch:
|
||||
kmeans = MiniBatchKMeans(n_clusters=n_clusters,verbose=verbose, batch_size=4096, max_iter=80).fit(features)
|
||||
else:
|
||||
kmeans = KMeans(n_clusters=n_clusters,verbose=verbose).fit(features)
|
||||
print(time.time()-t, "s")
|
||||
|
||||
x = {
|
||||
"n_features_in_": kmeans.n_features_in_,
|
||||
"_n_threads": kmeans._n_threads,
|
||||
"cluster_centers_": kmeans.cluster_centers_,
|
||||
}
|
||||
print("end")
|
||||
|
||||
return x
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('--dataset', type=Path, default="./dataset/44k",
|
||||
help='path of training data directory')
|
||||
parser.add_argument('--output', type=Path, default="logs/44k",
|
||||
help='path of model output directory')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
checkpoint_dir = args.output
|
||||
dataset = args.dataset
|
||||
n_clusters = 10000
|
||||
|
||||
ckpt = {}
|
||||
for spk in os.listdir(dataset):
|
||||
if os.path.isdir(dataset/spk):
|
||||
print(f"train kmeans for {spk}...")
|
||||
in_dir = dataset/spk
|
||||
x = train_cluster(in_dir, n_clusters, verbose=False)
|
||||
ckpt[spk] = x
|
||||
|
||||
checkpoint_path = checkpoint_dir / f"kmeans_{n_clusters}.pt"
|
||||
checkpoint_path.parent.mkdir(exist_ok=True, parents=True)
|
||||
torch.save(
|
||||
ckpt,
|
||||
checkpoint_path,
|
||||
)
|
||||
|
||||
|
||||
# import cluster
|
||||
# for spk in tqdm.tqdm(os.listdir("dataset")):
|
||||
# if os.path.isdir(f"dataset/{spk}"):
|
||||
# print(f"start kmeans inference for {spk}...")
|
||||
# for feature_path in tqdm.tqdm(glob(f"dataset/{spk}/*.discrete.npy", recursive=True)):
|
||||
# mel_path = feature_path.replace(".discrete.npy",".mel.npy")
|
||||
# mel_spectrogram = np.load(mel_path)
|
||||
# feature_len = mel_spectrogram.shape[-1]
|
||||
# c = np.load(feature_path)
|
||||
# c = utils.tools.repeat_expand_2d(torch.FloatTensor(c), feature_len).numpy()
|
||||
# feature = c.T
|
||||
# feature_class = cluster.get_cluster_result(feature, spk)
|
||||
# np.save(feature_path.replace(".discrete.npy", ".discrete_class.npy"), feature_class)
|
||||
|
||||
|
||||
@@ -0,0 +1,142 @@
|
||||
import time
|
||||
import os
|
||||
import random
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.utils.data
|
||||
|
||||
import modules.commons as commons
|
||||
import utils
|
||||
from modules.mel_processing import spectrogram_torch, spec_to_mel_torch
|
||||
from utils import load_wav_to_torch, load_filepaths_and_text
|
||||
|
||||
# import h5py
|
||||
|
||||
|
||||
"""Multi speaker version"""
|
||||
|
||||
|
||||
class TextAudioSpeakerLoader(torch.utils.data.Dataset):
|
||||
"""
|
||||
1) loads audio, speaker_id, text pairs
|
||||
2) normalizes text and converts them to sequences of integers
|
||||
3) computes spectrograms from audio files.
|
||||
"""
|
||||
|
||||
def __init__(self, audiopaths, hparams):
|
||||
self.audiopaths = load_filepaths_and_text(audiopaths)
|
||||
self.max_wav_value = hparams.data.max_wav_value
|
||||
self.sampling_rate = hparams.data.sampling_rate
|
||||
self.filter_length = hparams.data.filter_length
|
||||
self.hop_length = hparams.data.hop_length
|
||||
self.win_length = hparams.data.win_length
|
||||
self.sampling_rate = hparams.data.sampling_rate
|
||||
self.use_sr = hparams.train.use_sr
|
||||
self.spec_len = hparams.train.max_speclen
|
||||
self.spk_map = hparams.spk
|
||||
|
||||
random.seed(1234)
|
||||
random.shuffle(self.audiopaths)
|
||||
|
||||
def get_audio(self, filename):
|
||||
filename = filename.replace("\\", "/")
|
||||
audio, sampling_rate = load_wav_to_torch(filename)
|
||||
if sampling_rate != self.sampling_rate:
|
||||
raise ValueError("{} SR doesn't match target {} SR".format(
|
||||
sampling_rate, self.sampling_rate))
|
||||
audio_norm = audio / self.max_wav_value
|
||||
audio_norm = audio_norm.unsqueeze(0)
|
||||
spec_filename = filename.replace(".wav", ".spec.pt")
|
||||
if os.path.exists(spec_filename):
|
||||
spec = torch.load(spec_filename)
|
||||
else:
|
||||
spec = spectrogram_torch(audio_norm, self.filter_length,
|
||||
self.sampling_rate, self.hop_length, self.win_length,
|
||||
center=False)
|
||||
spec = torch.squeeze(spec, 0)
|
||||
torch.save(spec, spec_filename)
|
||||
|
||||
spk = filename.split("/")[-2]
|
||||
spk = torch.LongTensor([self.spk_map[spk]])
|
||||
|
||||
f0 = np.load(filename + ".f0.npy")
|
||||
f0, uv = utils.interpolate_f0(f0)
|
||||
f0 = torch.FloatTensor(f0)
|
||||
uv = torch.FloatTensor(uv)
|
||||
|
||||
c = torch.load(filename+ ".soft.pt")
|
||||
c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[0])
|
||||
|
||||
|
||||
lmin = min(c.size(-1), spec.size(-1))
|
||||
assert abs(c.size(-1) - spec.size(-1)) < 3, (c.size(-1), spec.size(-1), f0.shape, filename)
|
||||
assert abs(audio_norm.shape[1]-lmin * self.hop_length) < 3 * self.hop_length
|
||||
spec, c, f0, uv = spec[:, :lmin], c[:, :lmin], f0[:lmin], uv[:lmin]
|
||||
audio_norm = audio_norm[:, :lmin * self.hop_length]
|
||||
# if spec.shape[1] < 30:
|
||||
# print("skip too short audio:", filename)
|
||||
# return None
|
||||
if spec.shape[1] > 800:
|
||||
start = random.randint(0, spec.shape[1]-800)
|
||||
end = start + 790
|
||||
spec, c, f0, uv = spec[:, start:end], c[:, start:end], f0[start:end], uv[start:end]
|
||||
audio_norm = audio_norm[:, start * self.hop_length : end * self.hop_length]
|
||||
|
||||
return c, f0, spec, audio_norm, spk, uv
|
||||
|
||||
def __getitem__(self, index):
|
||||
return self.get_audio(self.audiopaths[index][0])
|
||||
|
||||
def __len__(self):
|
||||
return len(self.audiopaths)
|
||||
|
||||
|
||||
class TextAudioCollate:
|
||||
|
||||
def __call__(self, batch):
|
||||
batch = [b for b in batch if b is not None]
|
||||
|
||||
input_lengths, ids_sorted_decreasing = torch.sort(
|
||||
torch.LongTensor([x[0].shape[1] for x in batch]),
|
||||
dim=0, descending=True)
|
||||
|
||||
max_c_len = max([x[0].size(1) for x in batch])
|
||||
max_wav_len = max([x[3].size(1) for x in batch])
|
||||
|
||||
lengths = torch.LongTensor(len(batch))
|
||||
|
||||
c_padded = torch.FloatTensor(len(batch), batch[0][0].shape[0], max_c_len)
|
||||
f0_padded = torch.FloatTensor(len(batch), max_c_len)
|
||||
spec_padded = torch.FloatTensor(len(batch), batch[0][2].shape[0], max_c_len)
|
||||
wav_padded = torch.FloatTensor(len(batch), 1, max_wav_len)
|
||||
spkids = torch.LongTensor(len(batch), 1)
|
||||
uv_padded = torch.FloatTensor(len(batch), max_c_len)
|
||||
|
||||
c_padded.zero_()
|
||||
spec_padded.zero_()
|
||||
f0_padded.zero_()
|
||||
wav_padded.zero_()
|
||||
uv_padded.zero_()
|
||||
|
||||
for i in range(len(ids_sorted_decreasing)):
|
||||
row = batch[ids_sorted_decreasing[i]]
|
||||
|
||||
c = row[0]
|
||||
c_padded[i, :, :c.size(1)] = c
|
||||
lengths[i] = c.size(1)
|
||||
|
||||
f0 = row[1]
|
||||
f0_padded[i, :f0.size(0)] = f0
|
||||
|
||||
spec = row[2]
|
||||
spec_padded[i, :, :spec.size(1)] = spec
|
||||
|
||||
wav = row[3]
|
||||
wav_padded[i, :, :wav.size(1)] = wav
|
||||
|
||||
spkids[i, 0] = row[4]
|
||||
|
||||
uv = row[5]
|
||||
uv_padded[i, :uv.size(0)] = uv
|
||||
|
||||
return c_padded, f0_padded, spec_padded, wav_padded, spkids, lengths, uv_padded
|
||||
@@ -0,0 +1,20 @@
|
||||
name: sovits
|
||||
channels:
|
||||
- pytorch
|
||||
- nvidia
|
||||
- conda-forge
|
||||
- fvcore
|
||||
- iopath
|
||||
- bottler
|
||||
- defaults
|
||||
dependencies:
|
||||
- python=3.8
|
||||
- pytorch
|
||||
- torchvision
|
||||
- fvcore
|
||||
- iopath
|
||||
- nvidiacub
|
||||
- pyembree
|
||||
- cupy
|
||||
- cython
|
||||
- pip
|
||||
@@ -0,0 +1,56 @@
|
||||
import io
|
||||
import logging
|
||||
|
||||
import soundfile
|
||||
import torch
|
||||
import torchaudio
|
||||
from flask import Flask, request, send_file
|
||||
from flask_cors import CORS
|
||||
|
||||
from inference.infer_tool import Svc, RealTimeVC
|
||||
|
||||
app = Flask(__name__)
|
||||
|
||||
CORS(app)
|
||||
|
||||
logging.getLogger('numba').setLevel(logging.WARNING)
|
||||
|
||||
|
||||
@app.route("/voiceChangeModel", methods=["POST"])
|
||||
def voice_change_model():
|
||||
request_form = request.form
|
||||
wave_file = request.files.get("sample", None)
|
||||
# 变调信息
|
||||
f_pitch_change = float(request_form.get("fPitchChange", 0))
|
||||
# DAW所需的采样率
|
||||
daw_sample = int(float(request_form.get("sampleRate", 0)))
|
||||
speaker_id = int(float(request_form.get("sSpeakId", 0)))
|
||||
# http获得wav文件并转换
|
||||
input_wav_path = io.BytesIO(wave_file.read())
|
||||
|
||||
# 模型推理
|
||||
if raw_infer:
|
||||
out_audio, out_sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path)
|
||||
tar_audio = torchaudio.functional.resample(out_audio, svc_model.target_sample, daw_sample)
|
||||
else:
|
||||
out_audio = svc.process(svc_model, speaker_id, f_pitch_change, input_wav_path)
|
||||
tar_audio = torchaudio.functional.resample(torch.from_numpy(out_audio), svc_model.target_sample, daw_sample)
|
||||
# 返回音频
|
||||
out_wav_path = io.BytesIO()
|
||||
soundfile.write(out_wav_path, tar_audio.cpu().numpy(), daw_sample, format="wav")
|
||||
out_wav_path.seek(0)
|
||||
return send_file(out_wav_path, download_name="temp.wav", as_attachment=True)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
# 启用则为直接切片合成，False为交叉淡化方式
|
||||
# vst插件调整0.3-0.5s切片时间可以降低延迟，直接切片方法会有连接处爆音、交叉淡化会有轻微重叠声音
|
||||
# 自行选择能接受的方法，或将vst最大切片时间调整为1s，此处设为Ture，延迟大音质稳定一些
|
||||
raw_infer = True
|
||||
# 每个模型和config是唯一对应的
|
||||
model_name = "logs/32k/G_174000-Copy1.pth"
|
||||
config_name = "configs/config.json"
|
||||
svc_model = Svc(model_name, config_name)
|
||||
svc = RealTimeVC()
|
||||
# 此处与vst插件对应，不建议更改
|
||||
app.run(port=6842, host="0.0.0.0", debug=False, threaded=False)
|
||||
@@ -0,0 +1,222 @@
|
||||
import copy
|
||||
import random
|
||||
from typing import Optional, Tuple
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as t_func
|
||||
from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
|
||||
|
||||
|
||||
class Hubert(nn.Module):
|
||||
def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
|
||||
super().__init__()
|
||||
self._mask = mask
|
||||
self.feature_extractor = FeatureExtractor()
|
||||
self.feature_projection = FeatureProjection()
|
||||
self.positional_embedding = PositionalConvEmbedding()
|
||||
self.norm = nn.LayerNorm(768)
|
||||
self.dropout = nn.Dropout(0.1)
|
||||
self.encoder = TransformerEncoder(
|
||||
nn.TransformerEncoderLayer(
|
||||
768, 12, 3072, activation="gelu", batch_first=True
|
||||
),
|
||||
12,
|
||||
)
|
||||
self.proj = nn.Linear(768, 256)
|
||||
|
||||
self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
|
||||
self.label_embedding = nn.Embedding(num_label_embeddings, 256)
|
||||
|
||||
def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
mask = None
|
||||
if self.training and self._mask:
|
||||
mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
|
||||
x[mask] = self.masked_spec_embed.to(x.dtype)
|
||||
return x, mask
|
||||
|
||||
def encode(
|
||||
self, x: torch.Tensor, layer: Optional[int] = None
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
x = self.feature_extractor(x)
|
||||
x = self.feature_projection(x.transpose(1, 2))
|
||||
x, mask = self.mask(x)
|
||||
x = x + self.positional_embedding(x)
|
||||
x = self.dropout(self.norm(x))
|
||||
x = self.encoder(x, output_layer=layer)
|
||||
return x, mask
|
||||
|
||||
def logits(self, x: torch.Tensor) -> torch.Tensor:
|
||||
logits = torch.cosine_similarity(
|
||||
x.unsqueeze(2),
|
||||
self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
|
||||
dim=-1,
|
||||
)
|
||||
return logits / 0.1
|
||||
|
||||
def forward(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
x, mask = self.encode(x)
|
||||
x = self.proj(x)
|
||||
logits = self.logits(x)
|
||||
return logits, mask
|
||||
|
||||
|
||||
class HubertSoft(Hubert):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
@torch.inference_mode()
|
||||
def units(self, wav: torch.Tensor) -> torch.Tensor:
|
||||
wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
|
||||
x, _ = self.encode(wav)
|
||||
return self.proj(x)
|
||||
|
||||
|
||||
class FeatureExtractor(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
|
||||
self.norm0 = nn.GroupNorm(512, 512)
|
||||
self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
|
||||
self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
|
||||
self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
|
||||
self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
|
||||
self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
|
||||
self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
x = t_func.gelu(self.norm0(self.conv0(x)))
|
||||
x = t_func.gelu(self.conv1(x))
|
||||
x = t_func.gelu(self.conv2(x))
|
||||
x = t_func.gelu(self.conv3(x))
|
||||
x = t_func.gelu(self.conv4(x))
|
||||
x = t_func.gelu(self.conv5(x))
|
||||
x = t_func.gelu(self.conv6(x))
|
||||
return x
|
||||
|
||||
|
||||
class FeatureProjection(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.norm = nn.LayerNorm(512)
|
||||
self.projection = nn.Linear(512, 768)
|
||||
self.dropout = nn.Dropout(0.1)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
x = self.norm(x)
|
||||
x = self.projection(x)
|
||||
x = self.dropout(x)
|
||||
return x
|
||||
|
||||
|
||||
class PositionalConvEmbedding(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.conv = nn.Conv1d(
|
||||
768,
|
||||
768,
|
||||
kernel_size=128,
|
||||
padding=128 // 2,
|
||||
groups=16,
|
||||
)
|
||||
self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
x = self.conv(x.transpose(1, 2))
|
||||
x = t_func.gelu(x[:, :, :-1])
|
||||
return x.transpose(1, 2)
|
||||
|
||||
|
||||
class TransformerEncoder(nn.Module):
|
||||
def __init__(
|
||||
self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
|
||||
) -> None:
|
||||
super(TransformerEncoder, self).__init__()
|
||||
self.layers = nn.ModuleList(
|
||||
[copy.deepcopy(encoder_layer) for _ in range(num_layers)]
|
||||
)
|
||||
self.num_layers = num_layers
|
||||
|
||||
def forward(
|
||||
self,
|
||||
src: torch.Tensor,
|
||||
mask: torch.Tensor = None,
|
||||
src_key_padding_mask: torch.Tensor = None,
|
||||
output_layer: Optional[int] = None,
|
||||
) -> torch.Tensor:
|
||||
output = src
|
||||
for layer in self.layers[:output_layer]:
|
||||
output = layer(
|
||||
output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
|
||||
)
|
||||
return output
|
||||
|
||||
|
||||
def _compute_mask(
|
||||
shape: Tuple[int, int],
|
||||
mask_prob: float,
|
||||
mask_length: int,
|
||||
device: torch.device,
|
||||
min_masks: int = 0,
|
||||
) -> torch.Tensor:
|
||||
batch_size, sequence_length = shape
|
||||
|
||||
if mask_length < 1:
|
||||
raise ValueError("`mask_length` has to be bigger than 0.")
|
||||
|
||||
if mask_length > sequence_length:
|
||||
raise ValueError(
|
||||
f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
|
||||
)
|
||||
|
||||
# compute number of masked spans in batch
|
||||
num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
|
||||
num_masked_spans = max(num_masked_spans, min_masks)
|
||||
|
||||
# make sure num masked indices <= sequence_length
|
||||
if num_masked_spans * mask_length > sequence_length:
|
||||
num_masked_spans = sequence_length // mask_length
|
||||
|
||||
# SpecAugment mask to fill
|
||||
mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
|
||||
|
||||
# uniform distribution to sample from, make sure that offset samples are < sequence_length
|
||||
uniform_dist = torch.ones(
|
||||
(batch_size, sequence_length - (mask_length - 1)), device=device
|
||||
)
|
||||
|
||||
# get random indices to mask
|
||||
mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
|
||||
|
||||
# expand masked indices to masked spans
|
||||
mask_indices = (
|
||||
mask_indices.unsqueeze(dim=-1)
|
||||
.expand((batch_size, num_masked_spans, mask_length))
|
||||
.reshape(batch_size, num_masked_spans * mask_length)
|
||||
)
|
||||
offsets = (
|
||||
torch.arange(mask_length, device=device)[None, None, :]
|
||||
.expand((batch_size, num_masked_spans, mask_length))
|
||||
.reshape(batch_size, num_masked_spans * mask_length)
|
||||
)
|
||||
mask_idxs = mask_indices + offsets
|
||||
|
||||
# scatter indices to mask
|
||||
mask = mask.scatter(1, mask_idxs, True)
|
||||
|
||||
return mask
|
||||
|
||||
|
||||
def hubert_soft(
|
||||
path: str,
|
||||
) -> HubertSoft:
|
||||
r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
|
||||
Args:
|
||||
path (str): path of a pretrained model
|
||||
"""
|
||||
hubert = HubertSoft()
|
||||
checkpoint = torch.load(path)
|
||||
consume_prefix_in_state_dict_if_present(checkpoint, "module.")
|
||||
hubert.load_state_dict(checkpoint)
|
||||
hubert.eval()
|
||||
return hubert
|
||||
@@ -0,0 +1,217 @@
|
||||
import copy
|
||||
import random
|
||||
from typing import Optional, Tuple
|
||||
|
||||
import torch
|
||||
import torch.nn as nn
|
||||
import torch.nn.functional as t_func
|
||||
from torch.nn.modules.utils import consume_prefix_in_state_dict_if_present
|
||||
|
||||
|
||||
class Hubert(nn.Module):
|
||||
def __init__(self, num_label_embeddings: int = 100, mask: bool = True):
|
||||
super().__init__()
|
||||
self._mask = mask
|
||||
self.feature_extractor = FeatureExtractor()
|
||||
self.feature_projection = FeatureProjection()
|
||||
self.positional_embedding = PositionalConvEmbedding()
|
||||
self.norm = nn.LayerNorm(768)
|
||||
self.dropout = nn.Dropout(0.1)
|
||||
self.encoder = TransformerEncoder(
|
||||
nn.TransformerEncoderLayer(
|
||||
768, 12, 3072, activation="gelu", batch_first=True
|
||||
),
|
||||
12,
|
||||
)
|
||||
self.proj = nn.Linear(768, 256)
|
||||
|
||||
self.masked_spec_embed = nn.Parameter(torch.FloatTensor(768).uniform_())
|
||||
self.label_embedding = nn.Embedding(num_label_embeddings, 256)
|
||||
|
||||
def mask(self, x: torch.Tensor) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
mask = None
|
||||
if self.training and self._mask:
|
||||
mask = _compute_mask((x.size(0), x.size(1)), 0.8, 10, x.device, 2)
|
||||
x[mask] = self.masked_spec_embed.to(x.dtype)
|
||||
return x, mask
|
||||
|
||||
def encode(
|
||||
self, x: torch.Tensor, layer: Optional[int] = None
|
||||
) -> Tuple[torch.Tensor, torch.Tensor]:
|
||||
x = self.feature_extractor(x)
|
||||
x = self.feature_projection(x.transpose(1, 2))
|
||||
x, mask = self.mask(x)
|
||||
x = x + self.positional_embedding(x)
|
||||
x = self.dropout(self.norm(x))
|
||||
x = self.encoder(x, output_layer=layer)
|
||||
return x, mask
|
||||
|
||||
def logits(self, x: torch.Tensor) -> torch.Tensor:
|
||||
logits = torch.cosine_similarity(
|
||||
x.unsqueeze(2),
|
||||
self.label_embedding.weight.unsqueeze(0).unsqueeze(0),
|
||||
dim=-1,
|
||||
)
|
||||
return logits / 0.1
|
||||
|
||||
|
||||
class HubertSoft(Hubert):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
|
||||
def units(self, wav: torch.Tensor) -> torch.Tensor:
|
||||
wav = t_func.pad(wav, ((400 - 320) // 2, (400 - 320) // 2))
|
||||
x, _ = self.encode(wav)
|
||||
return self.proj(x)
|
||||
|
||||
def forward(self, x):
|
||||
return self.units(x)
|
||||
|
||||
class FeatureExtractor(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.conv0 = nn.Conv1d(1, 512, 10, 5, bias=False)
|
||||
self.norm0 = nn.GroupNorm(512, 512)
|
||||
self.conv1 = nn.Conv1d(512, 512, 3, 2, bias=False)
|
||||
self.conv2 = nn.Conv1d(512, 512, 3, 2, bias=False)
|
||||
self.conv3 = nn.Conv1d(512, 512, 3, 2, bias=False)
|
||||
self.conv4 = nn.Conv1d(512, 512, 3, 2, bias=False)
|
||||
self.conv5 = nn.Conv1d(512, 512, 2, 2, bias=False)
|
||||
self.conv6 = nn.Conv1d(512, 512, 2, 2, bias=False)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
x = t_func.gelu(self.norm0(self.conv0(x)))
|
||||
x = t_func.gelu(self.conv1(x))
|
||||
x = t_func.gelu(self.conv2(x))
|
||||
x = t_func.gelu(self.conv3(x))
|
||||
x = t_func.gelu(self.conv4(x))
|
||||
x = t_func.gelu(self.conv5(x))
|
||||
x = t_func.gelu(self.conv6(x))
|
||||
return x
|
||||
|
||||
|
||||
class FeatureProjection(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.norm = nn.LayerNorm(512)
|
||||
self.projection = nn.Linear(512, 768)
|
||||
self.dropout = nn.Dropout(0.1)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
x = self.norm(x)
|
||||
x = self.projection(x)
|
||||
x = self.dropout(x)
|
||||
return x
|
||||
|
||||
|
||||
class PositionalConvEmbedding(nn.Module):
|
||||
def __init__(self):
|
||||
super().__init__()
|
||||
self.conv = nn.Conv1d(
|
||||
768,
|
||||
768,
|
||||
kernel_size=128,
|
||||
padding=128 // 2,
|
||||
groups=16,
|
||||
)
|
||||
self.conv = nn.utils.weight_norm(self.conv, name="weight", dim=2)
|
||||
|
||||
def forward(self, x: torch.Tensor) -> torch.Tensor:
|
||||
x = self.conv(x.transpose(1, 2))
|
||||
x = t_func.gelu(x[:, :, :-1])
|
||||
return x.transpose(1, 2)
|
||||
|
||||
|
||||
class TransformerEncoder(nn.Module):
|
||||
def __init__(
|
||||
self, encoder_layer: nn.TransformerEncoderLayer, num_layers: int
|
||||
) -> None:
|
||||
super(TransformerEncoder, self).__init__()
|
||||
self.layers = nn.ModuleList(
|
||||
[copy.deepcopy(encoder_layer) for _ in range(num_layers)]
|
||||
)
|
||||
self.num_layers = num_layers
|
||||
|
||||
def forward(
|
||||
self,
|
||||
src: torch.Tensor,
|
||||
mask: torch.Tensor = None,
|
||||
src_key_padding_mask: torch.Tensor = None,
|
||||
output_layer: Optional[int] = None,
|
||||
) -> torch.Tensor:
|
||||
output = src
|
||||
for layer in self.layers[:output_layer]:
|
||||
output = layer(
|
||||
output, src_mask=mask, src_key_padding_mask=src_key_padding_mask
|
||||
)
|
||||
return output
|
||||
|
||||
|
||||
def _compute_mask(
|
||||
shape: Tuple[int, int],
|
||||
mask_prob: float,
|
||||
mask_length: int,
|
||||
device: torch.device,
|
||||
min_masks: int = 0,
|
||||
) -> torch.Tensor:
|
||||
batch_size, sequence_length = shape
|
||||
|
||||
if mask_length < 1:
|
||||
raise ValueError("`mask_length` has to be bigger than 0.")
|
||||
|
||||
if mask_length > sequence_length:
|
||||
raise ValueError(
|
||||
f"`mask_length` has to be smaller than `sequence_length`, but got `mask_length`: {mask_length} and `sequence_length`: {sequence_length}`"
|
||||
)
|
||||
|
||||
# compute number of masked spans in batch
|
||||
num_masked_spans = int(mask_prob * sequence_length / mask_length + random.random())
|
||||
num_masked_spans = max(num_masked_spans, min_masks)
|
||||
|
||||
# make sure num masked indices <= sequence_length
|
||||
if num_masked_spans * mask_length > sequence_length:
|
||||
num_masked_spans = sequence_length // mask_length
|
||||
|
||||
# SpecAugment mask to fill
|
||||
mask = torch.zeros((batch_size, sequence_length), device=device, dtype=torch.bool)
|
||||
|
||||
# uniform distribution to sample from, make sure that offset samples are < sequence_length
|
||||
uniform_dist = torch.ones(
|
||||
(batch_size, sequence_length - (mask_length - 1)), device=device
|
||||
)
|
||||
|
||||
# get random indices to mask
|
||||
mask_indices = torch.multinomial(uniform_dist, num_masked_spans)
|
||||
|
||||
# expand masked indices to masked spans
|
||||
mask_indices = (
|
||||
mask_indices.unsqueeze(dim=-1)
|
||||
.expand((batch_size, num_masked_spans, mask_length))
|
||||
.reshape(batch_size, num_masked_spans * mask_length)
|
||||
)
|
||||
offsets = (
|
||||
torch.arange(mask_length, device=device)[None, None, :]
|
||||
.expand((batch_size, num_masked_spans, mask_length))
|
||||
.reshape(batch_size, num_masked_spans * mask_length)
|
||||
)
|
||||
mask_idxs = mask_indices + offsets
|
||||
|
||||
# scatter indices to mask
|
||||
mask = mask.scatter(1, mask_idxs, True)
|
||||
|
||||
return mask
|
||||
|
||||
|
||||
def hubert_soft(
|
||||
path: str,
|
||||
) -> HubertSoft:
|
||||
r"""HuBERT-Soft from `"A Comparison of Discrete and Soft Speech Units for Improved Voice Conversion"`.
|
||||
Args:
|
||||
path (str): path of a pretrained model
|
||||
"""
|
||||
hubert = HubertSoft()
|
||||
checkpoint = torch.load(path)
|
||||
consume_prefix_in_state_dict_if_present(checkpoint, "module.")
|
||||
hubert.load_state_dict(checkpoint)
|
||||
hubert.eval()
|
||||
return hubert
|
||||
@@ -0,0 +1,244 @@
|
||||
import hashlib
|
||||
import io
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import time
|
||||
from pathlib import Path
|
||||
from inference import slicer
|
||||
|
||||
import librosa
|
||||
import numpy as np
|
||||
# import onnxruntime
|
||||
import parselmouth
|
||||
import soundfile
|
||||
import torch
|
||||
import torchaudio
|
||||
|
||||
import cluster
|
||||
from hubert import hubert_model
|
||||
import utils
|
||||
from models import SynthesizerTrn
|
||||
|
||||
logging.getLogger('matplotlib').setLevel(logging.WARNING)
|
||||
|
||||
|
||||
def read_temp(file_name):
|
||||
if not os.path.exists(file_name):
|
||||
with open(file_name, "w") as f:
|
||||
f.write(json.dumps({"info": "temp_dict"}))
|
||||
return {}
|
||||
else:
|
||||
try:
|
||||
with open(file_name, "r") as f:
|
||||
data = f.read()
|
||||
data_dict = json.loads(data)
|
||||
if os.path.getsize(file_name) > 50 * 1024 * 1024:
|
||||
f_name = file_name.replace("\\", "/").split("/")[-1]
|
||||
print(f"clean {f_name}")
|
||||
for wav_hash in list(data_dict.keys()):
|
||||
if int(time.time()) - int(data_dict[wav_hash]["time"]) > 14 * 24 * 3600:
|
||||
del data_dict[wav_hash]
|
||||
except Exception as e:
|
||||
print(e)
|
||||
print(f"{file_name} error,auto rebuild file")
|
||||
data_dict = {"info": "temp_dict"}
|
||||
return data_dict
|
||||
|
||||
|
||||
def write_temp(file_name, data):
|
||||
with open(file_name, "w") as f:
|
||||
f.write(json.dumps(data))
|
||||
|
||||
|
||||
def timeit(func):
|
||||
def run(*args, **kwargs):
|
||||
t = time.time()
|
||||
res = func(*args, **kwargs)
|
||||
print('executing \'%s\' costed %.3fs' % (func.__name__, time.time() - t))
|
||||
return res
|
||||
|
||||
return run
|
||||
|
||||
|
||||
def format_wav(audio_path):
|
||||
if Path(audio_path).suffix == '.wav':
|
||||
return
|
||||
raw_audio, raw_sample_rate = librosa.load(audio_path, mono=True, sr=None)
|
||||
soundfile.write(Path(audio_path).with_suffix(".wav"), raw_audio, raw_sample_rate)
|
||||
|
||||
|
||||
def get_end_file(dir_path, end):
|
||||
file_lists = []
|
||||
for root, dirs, files in os.walk(dir_path):
|
||||
files = [f for f in files if f[0] != '.']
|
||||
dirs[:] = [d for d in dirs if d[0] != '.']
|
||||
for f_file in files:
|
||||
if f_file.endswith(end):
|
||||
file_lists.append(os.path.join(root, f_file).replace("\\", "/"))
|
||||
return file_lists
|
||||
|
||||
|
||||
def get_md5(content):
|
||||
return hashlib.new("md5", content).hexdigest()
|
||||
|
||||
def fill_a_to_b(a, b):
|
||||
if len(a) < len(b):
|
||||
for _ in range(0, len(b) - len(a)):
|
||||
a.append(a[0])
|
||||
|
||||
def mkdir(paths: list):
|
||||
for path in paths:
|
||||
if not os.path.exists(path):
|
||||
os.mkdir(path)
|
||||
|
||||
def pad_array(arr, target_length):
|
||||
current_length = arr.shape[0]
|
||||
if current_length >= target_length:
|
||||
return arr
|
||||
else:
|
||||
pad_width = target_length - current_length
|
||||
pad_left = pad_width // 2
|
||||
pad_right = pad_width - pad_left
|
||||
padded_arr = np.pad(arr, (pad_left, pad_right), 'constant', constant_values=(0, 0))
|
||||
return padded_arr
|
||||
|
||||
|
||||
class Svc(object):
|
||||
def __init__(self, net_g_path, config_path,
|
||||
device=None,
|
||||
cluster_model_path="logs/44k/kmeans_10000.pt"):
|
||||
self.net_g_path = net_g_path
|
||||
if device is None:
|
||||
self.dev = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
else:
|
||||
self.dev = torch.device(device)
|
||||
self.net_g_ms = None
|
||||
self.hps_ms = utils.get_hparams_from_file(config_path)
|
||||
self.target_sample = self.hps_ms.data.sampling_rate
|
||||
self.hop_size = self.hps_ms.data.hop_length
|
||||
self.spk2id = self.hps_ms.spk
|
||||
# 加载hubert
|
||||
self.hubert_model = utils.get_hubert_model().to(self.dev)
|
||||
self.load_model()
|
||||
if os.path.exists(cluster_model_path):
|
||||
self.cluster_model = cluster.get_cluster_model(cluster_model_path)
|
||||
|
||||
def load_model(self):
|
||||
# 获取模型配置
|
||||
self.net_g_ms = SynthesizerTrn(
|
||||
self.hps_ms.data.filter_length // 2 + 1,
|
||||
self.hps_ms.train.segment_size // self.hps_ms.data.hop_length,
|
||||
**self.hps_ms.model)
|
||||
_ = utils.load_checkpoint(self.net_g_path, self.net_g_ms, None)
|
||||
if "half" in self.net_g_path and torch.cuda.is_available():
|
||||
_ = self.net_g_ms.half().eval().to(self.dev)
|
||||
else:
|
||||
_ = self.net_g_ms.eval().to(self.dev)
|
||||
|
||||
|
||||
|
||||
def get_unit_f0(self, in_path, tran, cluster_infer_ratio, speaker):
|
||||
|
||||
wav, sr = librosa.load(in_path, sr=self.target_sample)
|
||||
|
||||
f0 = utils.compute_f0_parselmouth(wav, sampling_rate=self.target_sample, hop_length=self.hop_size)
|
||||
f0, uv = utils.interpolate_f0(f0)
|
||||
f0 = torch.FloatTensor(f0)
|
||||
uv = torch.FloatTensor(uv)
|
||||
f0 = f0 * 2 ** (tran / 12)
|
||||
f0 = f0.unsqueeze(0).to(self.dev)
|
||||
uv = uv.unsqueeze(0).to(self.dev)
|
||||
|
||||
wav16k = librosa.resample(wav, orig_sr=self.target_sample, target_sr=16000)
|
||||
wav16k = torch.from_numpy(wav16k).to(self.dev)
|
||||
c = utils.get_hubert_content(self.hubert_model, wav_16k_tensor=wav16k)
|
||||
c = utils.repeat_expand_2d(c.squeeze(0), f0.shape[1])
|
||||
|
||||
if cluster_infer_ratio !=0:
|
||||
cluster_c = cluster.get_cluster_center_result(self.cluster_model, c.cpu().numpy().T, speaker).T
|
||||
cluster_c = torch.FloatTensor(cluster_c).to(self.dev)
|
||||
c = cluster_infer_ratio * cluster_c + (1 - cluster_infer_ratio) * c
|
||||
|
||||
c = c.unsqueeze(0)
|
||||
return c, f0, uv
|
||||
|
||||
def infer(self, speaker, tran, raw_path,
|
||||
cluster_infer_ratio=0,
|
||||
auto_predict_f0=False,
|
||||
noice_scale=0.4):
|
||||
speaker_id = self.spk2id[speaker]
|
||||
sid = torch.LongTensor([int(speaker_id)]).to(self.dev).unsqueeze(0)
|
||||
c, f0, uv = self.get_unit_f0(raw_path, tran, cluster_infer_ratio, speaker)
|
||||
if "half" in self.net_g_path and torch.cuda.is_available():
|
||||
c = c.half()
|
||||
with torch.no_grad():
|
||||
start = time.time()
|
||||
audio = self.net_g_ms.infer(c, f0=f0, g=sid, uv=uv, predict_f0=auto_predict_f0, noice_scale=noice_scale)[0,0].data.float()
|
||||
use_time = time.time() - start
|
||||
print("vits use time:{}".format(use_time))
|
||||
return audio, audio.shape[-1]
|
||||
|
||||
def slice_inference(self,raw_audio_path, spk, tran, slice_db,cluster_infer_ratio, auto_predict_f0,noice_scale, pad_seconds=0.5):
|
||||
wav_path = raw_audio_path
|
||||
chunks = slicer.cut(wav_path, db_thresh=slice_db)
|
||||
audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
|
||||
|
||||
audio = []
|
||||
for (slice_tag, data) in audio_data:
|
||||
print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
|
||||
# padd
|
||||
pad_len = int(audio_sr * pad_seconds)
|
||||
data = np.concatenate([np.zeros([pad_len]), data, np.zeros([pad_len])])
|
||||
length = int(np.ceil(len(data) / audio_sr * self.target_sample))
|
||||
raw_path = io.BytesIO()
|
||||
soundfile.write(raw_path, data, audio_sr, format="wav")
|
||||
raw_path.seek(0)
|
||||
if slice_tag:
|
||||
print('jump empty segment')
|
||||
_audio = np.zeros(length)
|
||||
else:
|
||||
out_audio, out_sr = self.infer(spk, tran, raw_path,
|
||||
cluster_infer_ratio=cluster_infer_ratio,
|
||||
auto_predict_f0=auto_predict_f0,
|
||||
noice_scale=noice_scale
|
||||
)
|
||||
_audio = out_audio.cpu().numpy()
|
||||
|
||||
pad_len = int(self.target_sample * pad_seconds)
|
||||
_audio = _audio[pad_len:-pad_len]
|
||||
audio.extend(list(_audio))
|
||||
return np.array(audio)
|
||||
|
||||
|
||||
class RealTimeVC:
|
||||
def __init__(self):
|
||||
self.last_chunk = None
|
||||
self.last_o = None
|
||||
self.chunk_len = 16000 # 区块长度
|
||||
self.pre_len = 3840 # 交叉淡化长度，640的倍数
|
||||
|
||||
"""输入输出都是1维numpy 音频波形数组"""
|
||||
|
||||
def process(self, svc_model, speaker_id, f_pitch_change, input_wav_path):
|
||||
import maad
|
||||
audio, sr = torchaudio.load(input_wav_path)
|
||||
audio = audio.cpu().numpy()[0]
|
||||
temp_wav = io.BytesIO()
|
||||
if self.last_chunk is None:
|
||||
input_wav_path.seek(0)
|
||||
audio, sr = svc_model.infer(speaker_id, f_pitch_change, input_wav_path)
|
||||
audio = audio.cpu().numpy()
|
||||
self.last_chunk = audio[-self.pre_len:]
|
||||
self.last_o = audio
|
||||
return audio[-self.chunk_len:]
|
||||
else:
|
||||
audio = np.concatenate([self.last_chunk, audio])
|
||||
soundfile.write(temp_wav, audio, sr, format="wav")
|
||||
temp_wav.seek(0)
|
||||
audio, sr = svc_model.infer(speaker_id, f_pitch_change, temp_wav)
|
||||
audio = audio.cpu().numpy()
|
||||
ret = maad.util.crossfade(self.last_o, audio, self.pre_len)
|
||||
self.last_chunk = audio[-self.pre_len:]
|
||||
self.last_o = audio
|
||||
return ret[self.chunk_len:2 * self.chunk_len]
|
||||
@@ -0,0 +1,160 @@
|
||||
import hashlib
|
||||
import json
|
||||
import logging
|
||||
import os
|
||||
import time
|
||||
from pathlib import Path
|
||||
import io
|
||||
import librosa
|
||||
import maad
|
||||
import numpy as np
|
||||
from inference import slicer
|
||||
import parselmouth
|
||||
import soundfile
|
||||
import torch
|
||||
import torchaudio
|
||||
|
||||
from hubert import hubert_model
|
||||
import utils
|
||||
from models import SynthesizerTrn
|
||||
logging.getLogger('numba').setLevel(logging.WARNING)
|
||||
logging.getLogger('matplotlib').setLevel(logging.WARNING)
|
||||
|
||||
def resize2d_f0(x, target_len):
|
||||
source = np.array(x)
|
||||
source[source < 0.001] = np.nan
|
||||
target = np.interp(np.arange(0, len(source) * target_len, len(source)) / target_len, np.arange(0, len(source)),
|
||||
source)
|
||||
res = np.nan_to_num(target)
|
||||
return res
|
||||
|
||||
def get_f0(x, p_len,f0_up_key=0):
|
||||
|
||||
time_step = 160 / 16000 * 1000
|
||||
f0_min = 50
|
||||
f0_max = 1100
|
||||
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
|
||||
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
|
||||
|
||||
f0 = parselmouth.Sound(x, 16000).to_pitch_ac(
|
||||
time_step=time_step / 1000, voicing_threshold=0.6,
|
||||
pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
|
||||
|
||||
pad_size=(p_len - len(f0) + 1) // 2
|
||||
if(pad_size>0 or p_len - len(f0) - pad_size>0):
|
||||
f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
|
||||
|
||||
f0 *= pow(2, f0_up_key / 12)
|
||||
f0_mel = 1127 * np.log(1 + f0 / 700)
|
||||
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * 254 / (f0_mel_max - f0_mel_min) + 1
|
||||
f0_mel[f0_mel <= 1] = 1
|
||||
f0_mel[f0_mel > 255] = 255
|
||||
f0_coarse = np.rint(f0_mel).astype(np.int)
|
||||
return f0_coarse, f0
|
||||
|
||||
def clean_pitch(input_pitch):
|
||||
num_nan = np.sum(input_pitch == 1)
|
||||
if num_nan / len(input_pitch) > 0.9:
|
||||
input_pitch[input_pitch != 1] = 1
|
||||
return input_pitch
|
||||
|
||||
|
||||
def plt_pitch(input_pitch):
|
||||
input_pitch = input_pitch.astype(float)
|
||||
input_pitch[input_pitch == 1] = np.nan
|
||||
return input_pitch
|
||||
|
||||
|
||||
def f0_to_pitch(ff):
|
||||
f0_pitch = 69 + 12 * np.log2(ff / 440)
|
||||
return f0_pitch
|
||||
|
||||
|
||||
def fill_a_to_b(a, b):
|
||||
if len(a) < len(b):
|
||||
for _ in range(0, len(b) - len(a)):
|
||||
a.append(a[0])
|
||||
|
||||
|
||||
def mkdir(paths: list):
|
||||
for path in paths:
|
||||
if not os.path.exists(path):
|
||||
os.mkdir(path)
|
||||
|
||||
|
||||
class VitsSvc(object):
|
||||
def __init__(self):
|
||||
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
self.SVCVITS = None
|
||||
self.hps = None
|
||||
self.speakers = None
|
||||
self.hubert_soft = utils.get_hubert_model()
|
||||
|
||||
def set_device(self, device):
|
||||
self.device = torch.device(device)
|
||||
self.hubert_soft.to(self.device)
|
||||
if self.SVCVITS != None:
|
||||
self.SVCVITS.to(self.device)
|
||||
|
||||
def loadCheckpoint(self, path):
|
||||
self.hps = utils.get_hparams_from_file(f"checkpoints/{path}/config.json")
|
||||
self.SVCVITS = SynthesizerTrn(
|
||||
self.hps.data.filter_length // 2 + 1,
|
||||
self.hps.train.segment_size // self.hps.data.hop_length,
|
||||
**self.hps.model)
|
||||
_ = utils.load_checkpoint(f"checkpoints/{path}/model.pth", self.SVCVITS, None)
|
||||
_ = self.SVCVITS.eval().to(self.device)
|
||||
self.speakers = self.hps.spk
|
||||
|
||||
def get_units(self, source, sr):
|
||||
source = source.unsqueeze(0).to(self.device)
|
||||
with torch.inference_mode():
|
||||
units = self.hubert_soft.units(source)
|
||||
return units
|
||||
|
||||
|
||||
def get_unit_pitch(self, in_path, tran):
|
||||
source, sr = torchaudio.load(in_path)
|
||||
source = torchaudio.functional.resample(source, sr, 16000)
|
||||
if len(source.shape) == 2 and source.shape[1] >= 2:
|
||||
source = torch.mean(source, dim=0).unsqueeze(0)
|
||||
soft = self.get_units(source, sr).squeeze(0).cpu().numpy()
|
||||
f0_coarse, f0 = get_f0(source.cpu().numpy()[0], soft.shape[0]*2, tran)
|
||||
return soft, f0
|
||||
|
||||
def infer(self, speaker_id, tran, raw_path):
|
||||
speaker_id = self.speakers[speaker_id]
|
||||
sid = torch.LongTensor([int(speaker_id)]).to(self.device).unsqueeze(0)
|
||||
soft, pitch = self.get_unit_pitch(raw_path, tran)
|
||||
f0 = torch.FloatTensor(clean_pitch(pitch)).unsqueeze(0).to(self.device)
|
||||
stn_tst = torch.FloatTensor(soft)
|
||||
with torch.no_grad():
|
||||
x_tst = stn_tst.unsqueeze(0).to(self.device)
|
||||
x_tst = torch.repeat_interleave(x_tst, repeats=2, dim=1).transpose(1, 2)
|
||||
audio = self.SVCVITS.infer(x_tst, f0=f0, g=sid)[0,0].data.float()
|
||||
return audio, audio.shape[-1]
|
||||
|
||||
def inference(self,srcaudio,chara,tran,slice_db):
|
||||
sampling_rate, audio = srcaudio
|
||||
audio = (audio / np.iinfo(audio.dtype).max).astype(np.float32)
|
||||
if len(audio.shape) > 1:
|
||||
audio = librosa.to_mono(audio.transpose(1, 0))
|
||||
if sampling_rate != 16000:
|
||||
audio = librosa.resample(audio, orig_sr=sampling_rate, target_sr=16000)
|
||||
soundfile.write("tmpwav.wav", audio, 16000, format="wav")
|
||||
chunks = slicer.cut("tmpwav.wav", db_thresh=slice_db)
|
||||
audio_data, audio_sr = slicer.chunks2audio("tmpwav.wav", chunks)
|
||||
audio = []
|
||||
for (slice_tag, data) in audio_data:
|
||||
length = int(np.ceil(len(data) / audio_sr * self.hps.data.sampling_rate))
|
||||
raw_path = io.BytesIO()
|
||||
soundfile.write(raw_path, data, audio_sr, format="wav")
|
||||
raw_path.seek(0)
|
||||
if slice_tag:
|
||||
_audio = np.zeros(length)
|
||||
else:
|
||||
out_audio, out_sr = self.infer(chara, tran, raw_path)
|
||||
_audio = out_audio.cpu().numpy()
|
||||
audio.extend(list(_audio))
|
||||
audio = (np.array(audio) * 32768.0).astype('int16')
|
||||
return (self.hps.data.sampling_rate,audio)
|
||||
@@ -0,0 +1,142 @@
|
||||
import librosa
|
||||
import torch
|
||||
import torchaudio
|
||||
|
||||
|
||||
class Slicer:
|
||||
def __init__(self,
|
||||
sr: int,
|
||||
threshold: float = -40.,
|
||||
min_length: int = 5000,
|
||||
min_interval: int = 300,
|
||||
hop_size: int = 20,
|
||||
max_sil_kept: int = 5000):
|
||||
if not min_length >= min_interval >= hop_size:
|
||||
raise ValueError('The following condition must be satisfied: min_length >= min_interval >= hop_size')
|
||||
if not max_sil_kept >= hop_size:
|
||||
raise ValueError('The following condition must be satisfied: max_sil_kept >= hop_size')
|
||||
min_interval = sr * min_interval / 1000
|
||||
self.threshold = 10 ** (threshold / 20.)
|
||||
self.hop_size = round(sr * hop_size / 1000)
|
||||
self.win_size = min(round(min_interval), 4 * self.hop_size)
|
||||
self.min_length = round(sr * min_length / 1000 / self.hop_size)
|
||||
self.min_interval = round(min_interval / self.hop_size)
|
||||
self.max_sil_kept = round(sr * max_sil_kept / 1000 / self.hop_size)
|
||||
|
||||
def _apply_slice(self, waveform, begin, end):
|
||||
if len(waveform.shape) > 1:
|
||||
return waveform[:, begin * self.hop_size: min(waveform.shape[1], end * self.hop_size)]
|
||||
else:
|
||||
return waveform[begin * self.hop_size: min(waveform.shape[0], end * self.hop_size)]
|
||||
|
||||
# @timeit
|
||||
def slice(self, waveform):
|
||||
if len(waveform.shape) > 1:
|
||||
samples = librosa.to_mono(waveform)
|
||||
else:
|
||||
samples = waveform
|
||||
if samples.shape[0] <= self.min_length:
|
||||
return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}}
|
||||
rms_list = librosa.feature.rms(y=samples, frame_length=self.win_size, hop_length=self.hop_size).squeeze(0)
|
||||
sil_tags = []
|
||||
silence_start = None
|
||||
clip_start = 0
|
||||
for i, rms in enumerate(rms_list):
|
||||
# Keep looping while frame is silent.
|
||||
if rms < self.threshold:
|
||||
# Record start of silent frames.
|
||||
if silence_start is None:
|
||||
silence_start = i
|
||||
continue
|
||||
# Keep looping while frame is not silent and silence start has not been recorded.
|
||||
if silence_start is None:
|
||||
continue
|
||||
# Clear recorded silence start if interval is not enough or clip is too short
|
||||
is_leading_silence = silence_start == 0 and i > self.max_sil_kept
|
||||
need_slice_middle = i - silence_start >= self.min_interval and i - clip_start >= self.min_length
|
||||
if not is_leading_silence and not need_slice_middle:
|
||||
silence_start = None
|
||||
continue
|
||||
# Need slicing. Record the range of silent frames to be removed.
|
||||
if i - silence_start <= self.max_sil_kept:
|
||||
pos = rms_list[silence_start: i + 1].argmin() + silence_start
|
||||
if silence_start == 0:
|
||||
sil_tags.append((0, pos))
|
||||
else:
|
||||
sil_tags.append((pos, pos))
|
||||
clip_start = pos
|
||||
elif i - silence_start <= self.max_sil_kept * 2:
|
||||
pos = rms_list[i - self.max_sil_kept: silence_start + self.max_sil_kept + 1].argmin()
|
||||
pos += i - self.max_sil_kept
|
||||
pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start
|
||||
pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept
|
||||
if silence_start == 0:
|
||||
sil_tags.append((0, pos_r))
|
||||
clip_start = pos_r
|
||||
else:
|
||||
sil_tags.append((min(pos_l, pos), max(pos_r, pos)))
|
||||
clip_start = max(pos_r, pos)
|
||||
else:
|
||||
pos_l = rms_list[silence_start: silence_start + self.max_sil_kept + 1].argmin() + silence_start
|
||||
pos_r = rms_list[i - self.max_sil_kept: i + 1].argmin() + i - self.max_sil_kept
|
||||
if silence_start == 0:
|
||||
sil_tags.append((0, pos_r))
|
||||
else:
|
||||
sil_tags.append((pos_l, pos_r))
|
||||
clip_start = pos_r
|
||||
silence_start = None
|
||||
# Deal with trailing silence.
|
||||
total_frames = rms_list.shape[0]
|
||||
if silence_start is not None and total_frames - silence_start >= self.min_interval:
|
||||
silence_end = min(total_frames, silence_start + self.max_sil_kept)
|
||||
pos = rms_list[silence_start: silence_end + 1].argmin() + silence_start
|
||||
sil_tags.append((pos, total_frames + 1))
|
||||
# Apply and return slices.
|
||||
if len(sil_tags) == 0:
|
||||
return {"0": {"slice": False, "split_time": f"0,{len(waveform)}"}}
|
||||
else:
|
||||
chunks = []
|
||||
# 第一段静音并非从头开始，补上有声片段
|
||||
if sil_tags[0][0]:
|
||||
chunks.append(
|
||||
{"slice": False, "split_time": f"0,{min(waveform.shape[0], sil_tags[0][0] * self.hop_size)}"})
|
||||
for i in range(0, len(sil_tags)):
|
||||
# 标识有声片段（跳过第一段）
|
||||
if i:
|
||||
chunks.append({"slice": False,
|
||||
"split_time": f"{sil_tags[i - 1][1] * self.hop_size},{min(waveform.shape[0], sil_tags[i][0] * self.hop_size)}"})
|
||||
# 标识所有静音片段
|
||||
chunks.append({"slice": True,
|
||||
"split_time": f"{sil_tags[i][0] * self.hop_size},{min(waveform.shape[0], sil_tags[i][1] * self.hop_size)}"})
|
||||
# 最后一段静音并非结尾，补上结尾片段
|
||||
if sil_tags[-1][1] * self.hop_size < len(waveform):
|
||||
chunks.append({"slice": False, "split_time": f"{sil_tags[-1][1] * self.hop_size},{len(waveform)}"})
|
||||
chunk_dict = {}
|
||||
for i in range(len(chunks)):
|
||||
chunk_dict[str(i)] = chunks[i]
|
||||
return chunk_dict
|
||||
|
||||
|
||||
def cut(audio_path, db_thresh=-30, min_len=5000):
|
||||
audio, sr = librosa.load(audio_path, sr=None)
|
||||
slicer = Slicer(
|
||||
sr=sr,
|
||||
threshold=db_thresh,
|
||||
min_length=min_len
|
||||
)
|
||||
chunks = slicer.slice(audio)
|
||||
return chunks
|
||||
|
||||
|
||||
def chunks2audio(audio_path, chunks):
|
||||
chunks = dict(chunks)
|
||||
audio, sr = torchaudio.load(audio_path)
|
||||
if len(audio.shape) == 2 and audio.shape[1] >= 2:
|
||||
audio = torch.mean(audio, dim=0).unsqueeze(0)
|
||||
audio = audio.cpu().numpy()[0]
|
||||
result = []
|
||||
for k, v in chunks.items():
|
||||
tag = v["split_time"].split(",")
|
||||
if tag[0] != tag[1]:
|
||||
result.append((v["slice"], audio[int(tag[0]):int(tag[1])]))
|
||||
return result, sr
|
||||
@@ -0,0 +1,101 @@
|
||||
import io
|
||||
import logging
|
||||
import time
|
||||
from pathlib import Path
|
||||
|
||||
import librosa
|
||||
import matplotlib.pyplot as plt
|
||||
import numpy as np
|
||||
import soundfile
|
||||
|
||||
from inference import infer_tool
|
||||
from inference import slicer
|
||||
from inference.infer_tool import Svc
|
||||
|
||||
logging.getLogger('numba').setLevel(logging.WARNING)
|
||||
chunks_dict = infer_tool.read_temp("inference/chunks_temp.json")
|
||||
|
||||
|
||||
|
||||
def main():
|
||||
import argparse
|
||||
|
||||
parser = argparse.ArgumentParser(description='sovits4 inference')
|
||||
|
||||
# 一定要设置的部分
|
||||
parser.add_argument('-m', '--model_path', type=str, default="logs/44k/G_0.pth", help='模型路径')
|
||||
parser.add_argument('-c', '--config_path', type=str, default="configs/config.json", help='配置文件路径')
|
||||
parser.add_argument('-n', '--clean_names', type=str, nargs='+', default=["君の知らない物語-src.wav"], help='wav文件名列表，放在raw文件夹下')
|
||||
parser.add_argument('-t', '--trans', type=int, nargs='+', default=[0], help='音高调整，支持正负（半音）')
|
||||
parser.add_argument('-s', '--spk_list', type=str, nargs='+', default=['nen'], help='合成目标说话人名称')
|
||||
|
||||
# 可选项部分
|
||||
parser.add_argument('-a', '--auto_predict_f0', action='store_true', default=False,
|
||||
help='语音转换自动预测音高，转换歌声时不要打开这个会严重跑调')
|
||||
parser.add_argument('-cm', '--cluster_model_path', type=str, default="logs/44k/kmeans_10000.pt", help='聚类模型路径，如果没有训练聚类则随便填')
|
||||
parser.add_argument('-cr', '--cluster_infer_ratio', type=float, default=0, help='聚类方案占比，范围0-1，若没有训练聚类模型则填0即可')
|
||||
|
||||
# 不用动的部分
|
||||
parser.add_argument('-sd', '--slice_db', type=int, default=-40, help='默认-40，嘈杂的音频可以-30，干声保留呼吸可以-50')
|
||||
parser.add_argument('-d', '--device', type=str, default=None, help='推理设备，None则为自动选择cpu和gpu')
|
||||
parser.add_argument('-ns', '--noice_scale', type=float, default=0.4, help='噪音级别，会影响咬字和音质，较为玄学')
|
||||
parser.add_argument('-p', '--pad_seconds', type=float, default=0.5, help='推理音频pad秒数，由于未知原因开头结尾会有异响，pad一小段静音段后就不会出现')
|
||||
parser.add_argument('-wf', '--wav_format', type=str, default='flac', help='音频输出格式')
|
||||
|
||||
args = parser.parse_args()
|
||||
|
||||
svc_model = Svc(args.model_path, args.config_path, args.device, args.cluster_model_path)
|
||||
infer_tool.mkdir(["raw", "results"])
|
||||
clean_names = args.clean_names
|
||||
trans = args.trans
|
||||
spk_list = args.spk_list
|
||||
slice_db = args.slice_db
|
||||
wav_format = args.wav_format
|
||||
auto_predict_f0 = args.auto_predict_f0
|
||||
cluster_infer_ratio = args.cluster_infer_ratio
|
||||
noice_scale = args.noice_scale
|
||||
pad_seconds = args.pad_seconds
|
||||
|
||||
infer_tool.fill_a_to_b(trans, clean_names)
|
||||
for clean_name, tran in zip(clean_names, trans):
|
||||
raw_audio_path = f"raw/{clean_name}"
|
||||
if "." not in raw_audio_path:
|
||||
raw_audio_path += ".wav"
|
||||
infer_tool.format_wav(raw_audio_path)
|
||||
wav_path = Path(raw_audio_path).with_suffix('.wav')
|
||||
chunks = slicer.cut(wav_path, db_thresh=slice_db)
|
||||
audio_data, audio_sr = slicer.chunks2audio(wav_path, chunks)
|
||||
|
||||
for spk in spk_list:
|
||||
audio = []
|
||||
for (slice_tag, data) in audio_data:
|
||||
print(f'#=====segment start, {round(len(data) / audio_sr, 3)}s======')
|
||||
|
||||
length = int(np.ceil(len(data) / audio_sr * svc_model.target_sample))
|
||||
if slice_tag:
|
||||
print('jump empty segment')
|
||||
_audio = np.zeros(length)
|
||||
else:
|
||||
# padd
|
||||
pad_len = int(audio_sr * pad_seconds)
|
||||
data = np.concatenate([np.zeros([pad_len]), data, np.zeros([pad_len])])
|
||||
raw_path = io.BytesIO()
|
||||
soundfile.write(raw_path, data, audio_sr, format="wav")
|
||||
raw_path.seek(0)
|
||||
out_audio, out_sr = svc_model.infer(spk, tran, raw_path,
|
||||
cluster_infer_ratio=cluster_infer_ratio,
|
||||
auto_predict_f0=auto_predict_f0,
|
||||
noice_scale=noice_scale
|
||||
)
|
||||
_audio = out_audio.cpu().numpy()
|
||||
pad_len = int(svc_model.target_sample * pad_seconds)
|
||||
_audio = _audio[pad_len:-pad_len]
|
||||
|
||||
audio.extend(list(infer_tool.pad_array(_audio, length)))
|
||||
key = "auto" if auto_predict_f0 else f"{tran}key"
|
||||
cluster_name = "" if cluster_infer_ratio == 0 else f"_{cluster_infer_ratio}"
|
||||
res_path = f'./results/{clean_name}_{key}_{spk}{cluster_name}.{wav_format}'
|
||||
soundfile.write(res_path, audio, svc_model.target_sample, format=wav_format)
|
||||
|
||||
if __name__ == '__main__':
|
||||
main()
|
||||
@@ -0,0 +1,420 @@
|
||||
import copy
|
||||
import math
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
import modules.attentions as attentions
|
||||
import modules.commons as commons
|
||||
import modules.modules as modules
|
||||
|
||||
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
||||
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
||||
|
||||
import utils
|
||||
from modules.commons import init_weights, get_padding
|
||||
from vdecoder.hifigan.models import Generator
|
||||
from utils import f0_to_coarse
|
||||
|
||||
class ResidualCouplingBlock(nn.Module):
|
||||
def __init__(self,
|
||||
channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
n_flows=4,
|
||||
gin_channels=0):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.n_flows = n_flows
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.flows = nn.ModuleList()
|
||||
for i in range(n_flows):
|
||||
self.flows.append(modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels, mean_only=True))
|
||||
self.flows.append(modules.Flip())
|
||||
|
||||
def forward(self, x, x_mask, g=None, reverse=False):
|
||||
if not reverse:
|
||||
for flow in self.flows:
|
||||
x, _ = flow(x, x_mask, g=g, reverse=reverse)
|
||||
else:
|
||||
for flow in reversed(self.flows):
|
||||
x = flow(x, x_mask, g=g, reverse=reverse)
|
||||
return x
|
||||
|
||||
|
||||
class Encoder(nn.Module):
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
gin_channels=0):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = out_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
|
||||
self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
||||
|
||||
def forward(self, x, x_lengths, g=None):
|
||||
# print(x.shape,x_lengths.shape)
|
||||
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
||||
x = self.pre(x) * x_mask
|
||||
x = self.enc(x, x_mask, g=g)
|
||||
stats = self.proj(x) * x_mask
|
||||
m, logs = torch.split(stats, self.out_channels, dim=1)
|
||||
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
|
||||
return z, m, logs, x_mask
|
||||
|
||||
|
||||
class TextEncoder(nn.Module):
|
||||
def __init__(self,
|
||||
out_channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
n_layers,
|
||||
gin_channels=0,
|
||||
filter_channels=None,
|
||||
n_heads=None,
|
||||
p_dropout=None):
|
||||
super().__init__()
|
||||
self.out_channels = out_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.n_layers = n_layers
|
||||
self.gin_channels = gin_channels
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
||||
self.f0_emb = nn.Embedding(256, hidden_channels)
|
||||
|
||||
self.enc_ = attentions.Encoder(
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout)
|
||||
|
||||
def forward(self, x, x_mask, f0=None, noice_scale=1):
|
||||
x = x + self.f0_emb(f0).transpose(1,2)
|
||||
x = self.enc_(x * x_mask, x_mask)
|
||||
stats = self.proj(x) * x_mask
|
||||
m, logs = torch.split(stats, self.out_channels, dim=1)
|
||||
z = (m + torch.randn_like(m) * torch.exp(logs) * noice_scale) * x_mask
|
||||
|
||||
return z, m, logs, x_mask
|
||||
|
||||
|
||||
|
||||
class DiscriminatorP(torch.nn.Module):
|
||||
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
|
||||
super(DiscriminatorP, self).__init__()
|
||||
self.period = period
|
||||
self.use_spectral_norm = use_spectral_norm
|
||||
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||||
self.convs = nn.ModuleList([
|
||||
norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
||||
norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
||||
norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
||||
norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
||||
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
|
||||
])
|
||||
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
|
||||
# 1d to 2d
|
||||
b, c, t = x.shape
|
||||
if t % self.period != 0: # pad first
|
||||
n_pad = self.period - (t % self.period)
|
||||
x = F.pad(x, (0, n_pad), "reflect")
|
||||
t = t + n_pad
|
||||
x = x.view(b, c, t // self.period, self.period)
|
||||
|
||||
for l in self.convs:
|
||||
x = l(x)
|
||||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x, fmap
|
||||
|
||||
|
||||
class DiscriminatorS(torch.nn.Module):
|
||||
def __init__(self, use_spectral_norm=False):
|
||||
super(DiscriminatorS, self).__init__()
|
||||
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||||
self.convs = nn.ModuleList([
|
||||
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
|
||||
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
|
||||
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
|
||||
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
|
||||
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
|
||||
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
|
||||
])
|
||||
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
|
||||
for l in self.convs:
|
||||
x = l(x)
|
||||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x, fmap
|
||||
|
||||
|
||||
class MultiPeriodDiscriminator(torch.nn.Module):
|
||||
def __init__(self, use_spectral_norm=False):
|
||||
super(MultiPeriodDiscriminator, self).__init__()
|
||||
periods = [2,3,5,7,11]
|
||||
|
||||
discs = [DiscriminatorS(use_spectral_norm=use_spectral_norm)]
|
||||
discs = discs + [DiscriminatorP(i, use_spectral_norm=use_spectral_norm) for i in periods]
|
||||
self.discriminators = nn.ModuleList(discs)
|
||||
|
||||
def forward(self, y, y_hat):
|
||||
y_d_rs = []
|
||||
y_d_gs = []
|
||||
fmap_rs = []
|
||||
fmap_gs = []
|
||||
for i, d in enumerate(self.discriminators):
|
||||
y_d_r, fmap_r = d(y)
|
||||
y_d_g, fmap_g = d(y_hat)
|
||||
y_d_rs.append(y_d_r)
|
||||
y_d_gs.append(y_d_g)
|
||||
fmap_rs.append(fmap_r)
|
||||
fmap_gs.append(fmap_g)
|
||||
|
||||
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
||||
|
||||
|
||||
class SpeakerEncoder(torch.nn.Module):
|
||||
def __init__(self, mel_n_channels=80, model_num_layers=3, model_hidden_size=256, model_embedding_size=256):
|
||||
super(SpeakerEncoder, self).__init__()
|
||||
self.lstm = nn.LSTM(mel_n_channels, model_hidden_size, model_num_layers, batch_first=True)
|
||||
self.linear = nn.Linear(model_hidden_size, model_embedding_size)
|
||||
self.relu = nn.ReLU()
|
||||
|
||||
def forward(self, mels):
|
||||
self.lstm.flatten_parameters()
|
||||
_, (hidden, _) = self.lstm(mels)
|
||||
embeds_raw = self.relu(self.linear(hidden[-1]))
|
||||
return embeds_raw / torch.norm(embeds_raw, dim=1, keepdim=True)
|
||||
|
||||
def compute_partial_slices(self, total_frames, partial_frames, partial_hop):
|
||||
mel_slices = []
|
||||
for i in range(0, total_frames-partial_frames, partial_hop):
|
||||
mel_range = torch.arange(i, i+partial_frames)
|
||||
mel_slices.append(mel_range)
|
||||
|
||||
return mel_slices
|
||||
|
||||
def embed_utterance(self, mel, partial_frames=128, partial_hop=64):
|
||||
mel_len = mel.size(1)
|
||||
last_mel = mel[:,-partial_frames:]
|
||||
|
||||
if mel_len > partial_frames:
|
||||
mel_slices = self.compute_partial_slices(mel_len, partial_frames, partial_hop)
|
||||
mels = list(mel[:,s] for s in mel_slices)
|
||||
mels.append(last_mel)
|
||||
mels = torch.stack(tuple(mels), 0).squeeze(1)
|
||||
|
||||
with torch.no_grad():
|
||||
partial_embeds = self(mels)
|
||||
embed = torch.mean(partial_embeds, axis=0).unsqueeze(0)
|
||||
#embed = embed / torch.linalg.norm(embed, 2)
|
||||
else:
|
||||
with torch.no_grad():
|
||||
embed = self(last_mel)
|
||||
|
||||
return embed
|
||||
|
||||
class F0Decoder(nn.Module):
|
||||
def __init__(self,
|
||||
out_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
spk_channels=0):
|
||||
super().__init__()
|
||||
self.out_channels = out_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.n_heads = n_heads
|
||||
self.n_layers = n_layers
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.spk_channels = spk_channels
|
||||
|
||||
self.prenet = nn.Conv1d(hidden_channels, hidden_channels, 3, padding=1)
|
||||
self.decoder = attentions.FFT(
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout)
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
|
||||
self.f0_prenet = nn.Conv1d(1, hidden_channels , 3, padding=1)
|
||||
self.cond = nn.Conv1d(spk_channels, hidden_channels, 1)
|
||||
|
||||
def forward(self, x, norm_f0, x_mask, spk_emb=None):
|
||||
x = torch.detach(x)
|
||||
if (spk_emb is not None):
|
||||
x = x + self.cond(spk_emb)
|
||||
x += self.f0_prenet(norm_f0)
|
||||
x = self.prenet(x) * x_mask
|
||||
x = self.decoder(x * x_mask, x_mask)
|
||||
x = self.proj(x) * x_mask
|
||||
return x
|
||||
|
||||
|
||||
class SynthesizerTrn(nn.Module):
|
||||
"""
|
||||
Synthesizer for Training
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
spec_channels,
|
||||
segment_size,
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
resblock,
|
||||
resblock_kernel_sizes,
|
||||
resblock_dilation_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
upsample_kernel_sizes,
|
||||
gin_channels,
|
||||
ssl_dim,
|
||||
n_speakers,
|
||||
sampling_rate=44100,
|
||||
**kwargs):
|
||||
|
||||
super().__init__()
|
||||
self.spec_channels = spec_channels
|
||||
self.inter_channels = inter_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.n_heads = n_heads
|
||||
self.n_layers = n_layers
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.resblock = resblock
|
||||
self.resblock_kernel_sizes = resblock_kernel_sizes
|
||||
self.resblock_dilation_sizes = resblock_dilation_sizes
|
||||
self.upsample_rates = upsample_rates
|
||||
self.upsample_initial_channel = upsample_initial_channel
|
||||
self.upsample_kernel_sizes = upsample_kernel_sizes
|
||||
self.segment_size = segment_size
|
||||
self.gin_channels = gin_channels
|
||||
self.ssl_dim = ssl_dim
|
||||
self.emb_g = nn.Embedding(n_speakers, gin_channels)
|
||||
|
||||
self.pre = nn.Conv1d(ssl_dim, hidden_channels, kernel_size=5, padding=2)
|
||||
|
||||
self.enc_p = TextEncoder(
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels=filter_channels,
|
||||
n_heads=n_heads,
|
||||
n_layers=n_layers,
|
||||
kernel_size=kernel_size,
|
||||
p_dropout=p_dropout
|
||||
)
|
||||
hps = {
|
||||
"sampling_rate": sampling_rate,
|
||||
"inter_channels": inter_channels,
|
||||
"resblock": resblock,
|
||||
"resblock_kernel_sizes": resblock_kernel_sizes,
|
||||
"resblock_dilation_sizes": resblock_dilation_sizes,
|
||||
"upsample_rates": upsample_rates,
|
||||
"upsample_initial_channel": upsample_initial_channel,
|
||||
"upsample_kernel_sizes": upsample_kernel_sizes,
|
||||
"gin_channels": gin_channels,
|
||||
}
|
||||
self.dec = Generator(h=hps)
|
||||
self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
|
||||
self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
|
||||
self.f0_decoder = F0Decoder(
|
||||
1,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
spk_channels=gin_channels
|
||||
)
|
||||
self.emb_uv = nn.Embedding(2, hidden_channels)
|
||||
|
||||
def forward(self, c, f0, uv, spec, g=None, c_lengths=None, spec_lengths=None):
|
||||
g = self.emb_g(g).transpose(1,2)
|
||||
# ssl prenet
|
||||
x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype)
|
||||
x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1,2)
|
||||
|
||||
# f0 predict
|
||||
lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500
|
||||
norm_lf0 = utils.normalize_f0(lf0, x_mask, uv)
|
||||
pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g)
|
||||
|
||||
# encoder
|
||||
z_ptemp, m_p, logs_p, _ = self.enc_p(x, x_mask, f0=f0_to_coarse(f0))
|
||||
z, m_q, logs_q, spec_mask = self.enc_q(spec, spec_lengths, g=g)
|
||||
|
||||
# flow
|
||||
z_p = self.flow(z, spec_mask, g=g)
|
||||
z_slice, pitch_slice, ids_slice = commons.rand_slice_segments_with_pitch(z, f0, spec_lengths, self.segment_size)
|
||||
|
||||
# nsf decoder
|
||||
o = self.dec(z_slice, g=g, f0=pitch_slice)
|
||||
|
||||
return o, ids_slice, spec_mask, (z, z_p, m_p, logs_p, m_q, logs_q), pred_lf0, norm_lf0, lf0
|
||||
|
||||
def infer(self, c, f0, uv, g=None, noice_scale=0.35, predict_f0=False):
|
||||
c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
|
||||
g = self.emb_g(g).transpose(1,2)
|
||||
x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype)
|
||||
x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1,2)
|
||||
|
||||
if predict_f0:
|
||||
lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500
|
||||
norm_lf0 = utils.normalize_f0(lf0, x_mask, uv, random_scale=False)
|
||||
pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g)
|
||||
f0 = (700 * (torch.pow(10, pred_lf0 * 500 / 2595) - 1)).squeeze(1)
|
||||
|
||||
z_p, m_p, logs_p, c_mask = self.enc_p(x, x_mask, f0=f0_to_coarse(f0), noice_scale=noice_scale)
|
||||
z = self.flow(z_p, c_mask, g=g, reverse=True)
|
||||
o = self.dec(z * c_mask, g=g, f0=f0)
|
||||
return o
|
||||
@@ -0,0 +1,349 @@
|
||||
import copy
|
||||
import math
|
||||
import numpy as np
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
import modules.commons as commons
|
||||
import modules.modules as modules
|
||||
from modules.modules import LayerNorm
|
||||
|
||||
|
||||
class FFT(nn.Module):
|
||||
def __init__(self, hidden_channels, filter_channels, n_heads, n_layers=1, kernel_size=1, p_dropout=0.,
|
||||
proximal_bias=False, proximal_init=True, **kwargs):
|
||||
super().__init__()
|
||||
self.hidden_channels = hidden_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.n_heads = n_heads
|
||||
self.n_layers = n_layers
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.proximal_bias = proximal_bias
|
||||
self.proximal_init = proximal_init
|
||||
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
self.self_attn_layers = nn.ModuleList()
|
||||
self.norm_layers_0 = nn.ModuleList()
|
||||
self.ffn_layers = nn.ModuleList()
|
||||
self.norm_layers_1 = nn.ModuleList()
|
||||
for i in range(self.n_layers):
|
||||
self.self_attn_layers.append(
|
||||
MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias,
|
||||
proximal_init=proximal_init))
|
||||
self.norm_layers_0.append(LayerNorm(hidden_channels))
|
||||
self.ffn_layers.append(
|
||||
FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
|
||||
self.norm_layers_1.append(LayerNorm(hidden_channels))
|
||||
|
||||
def forward(self, x, x_mask):
|
||||
"""
|
||||
x: decoder input
|
||||
h: encoder output
|
||||
"""
|
||||
self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
|
||||
x = x * x_mask
|
||||
for i in range(self.n_layers):
|
||||
y = self.self_attn_layers[i](x, x, self_attn_mask)
|
||||
y = self.drop(y)
|
||||
x = self.norm_layers_0[i](x + y)
|
||||
|
||||
y = self.ffn_layers[i](x, x_mask)
|
||||
y = self.drop(y)
|
||||
x = self.norm_layers_1[i](x + y)
|
||||
x = x * x_mask
|
||||
return x
|
||||
|
||||
|
||||
class Encoder(nn.Module):
|
||||
def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., window_size=4, **kwargs):
|
||||
super().__init__()
|
||||
self.hidden_channels = hidden_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.n_heads = n_heads
|
||||
self.n_layers = n_layers
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.window_size = window_size
|
||||
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
self.attn_layers = nn.ModuleList()
|
||||
self.norm_layers_1 = nn.ModuleList()
|
||||
self.ffn_layers = nn.ModuleList()
|
||||
self.norm_layers_2 = nn.ModuleList()
|
||||
for i in range(self.n_layers):
|
||||
self.attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, window_size=window_size))
|
||||
self.norm_layers_1.append(LayerNorm(hidden_channels))
|
||||
self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout))
|
||||
self.norm_layers_2.append(LayerNorm(hidden_channels))
|
||||
|
||||
def forward(self, x, x_mask):
|
||||
attn_mask = x_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
|
||||
x = x * x_mask
|
||||
for i in range(self.n_layers):
|
||||
y = self.attn_layers[i](x, x, attn_mask)
|
||||
y = self.drop(y)
|
||||
x = self.norm_layers_1[i](x + y)
|
||||
|
||||
y = self.ffn_layers[i](x, x_mask)
|
||||
y = self.drop(y)
|
||||
x = self.norm_layers_2[i](x + y)
|
||||
x = x * x_mask
|
||||
return x
|
||||
|
||||
|
||||
class Decoder(nn.Module):
|
||||
def __init__(self, hidden_channels, filter_channels, n_heads, n_layers, kernel_size=1, p_dropout=0., proximal_bias=False, proximal_init=True, **kwargs):
|
||||
super().__init__()
|
||||
self.hidden_channels = hidden_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.n_heads = n_heads
|
||||
self.n_layers = n_layers
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.proximal_bias = proximal_bias
|
||||
self.proximal_init = proximal_init
|
||||
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
self.self_attn_layers = nn.ModuleList()
|
||||
self.norm_layers_0 = nn.ModuleList()
|
||||
self.encdec_attn_layers = nn.ModuleList()
|
||||
self.norm_layers_1 = nn.ModuleList()
|
||||
self.ffn_layers = nn.ModuleList()
|
||||
self.norm_layers_2 = nn.ModuleList()
|
||||
for i in range(self.n_layers):
|
||||
self.self_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout, proximal_bias=proximal_bias, proximal_init=proximal_init))
|
||||
self.norm_layers_0.append(LayerNorm(hidden_channels))
|
||||
self.encdec_attn_layers.append(MultiHeadAttention(hidden_channels, hidden_channels, n_heads, p_dropout=p_dropout))
|
||||
self.norm_layers_1.append(LayerNorm(hidden_channels))
|
||||
self.ffn_layers.append(FFN(hidden_channels, hidden_channels, filter_channels, kernel_size, p_dropout=p_dropout, causal=True))
|
||||
self.norm_layers_2.append(LayerNorm(hidden_channels))
|
||||
|
||||
def forward(self, x, x_mask, h, h_mask):
|
||||
"""
|
||||
x: decoder input
|
||||
h: encoder output
|
||||
"""
|
||||
self_attn_mask = commons.subsequent_mask(x_mask.size(2)).to(device=x.device, dtype=x.dtype)
|
||||
encdec_attn_mask = h_mask.unsqueeze(2) * x_mask.unsqueeze(-1)
|
||||
x = x * x_mask
|
||||
for i in range(self.n_layers):
|
||||
y = self.self_attn_layers[i](x, x, self_attn_mask)
|
||||
y = self.drop(y)
|
||||
x = self.norm_layers_0[i](x + y)
|
||||
|
||||
y = self.encdec_attn_layers[i](x, h, encdec_attn_mask)
|
||||
y = self.drop(y)
|
||||
x = self.norm_layers_1[i](x + y)
|
||||
|
||||
y = self.ffn_layers[i](x, x_mask)
|
||||
y = self.drop(y)
|
||||
x = self.norm_layers_2[i](x + y)
|
||||
x = x * x_mask
|
||||
return x
|
||||
|
||||
|
||||
class MultiHeadAttention(nn.Module):
|
||||
def __init__(self, channels, out_channels, n_heads, p_dropout=0., window_size=None, heads_share=True, block_length=None, proximal_bias=False, proximal_init=False):
|
||||
super().__init__()
|
||||
assert channels % n_heads == 0
|
||||
|
||||
self.channels = channels
|
||||
self.out_channels = out_channels
|
||||
self.n_heads = n_heads
|
||||
self.p_dropout = p_dropout
|
||||
self.window_size = window_size
|
||||
self.heads_share = heads_share
|
||||
self.block_length = block_length
|
||||
self.proximal_bias = proximal_bias
|
||||
self.proximal_init = proximal_init
|
||||
self.attn = None
|
||||
|
||||
self.k_channels = channels // n_heads
|
||||
self.conv_q = nn.Conv1d(channels, channels, 1)
|
||||
self.conv_k = nn.Conv1d(channels, channels, 1)
|
||||
self.conv_v = nn.Conv1d(channels, channels, 1)
|
||||
self.conv_o = nn.Conv1d(channels, out_channels, 1)
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
|
||||
if window_size is not None:
|
||||
n_heads_rel = 1 if heads_share else n_heads
|
||||
rel_stddev = self.k_channels**-0.5
|
||||
self.emb_rel_k = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
|
||||
self.emb_rel_v = nn.Parameter(torch.randn(n_heads_rel, window_size * 2 + 1, self.k_channels) * rel_stddev)
|
||||
|
||||
nn.init.xavier_uniform_(self.conv_q.weight)
|
||||
nn.init.xavier_uniform_(self.conv_k.weight)
|
||||
nn.init.xavier_uniform_(self.conv_v.weight)
|
||||
if proximal_init:
|
||||
with torch.no_grad():
|
||||
self.conv_k.weight.copy_(self.conv_q.weight)
|
||||
self.conv_k.bias.copy_(self.conv_q.bias)
|
||||
|
||||
def forward(self, x, c, attn_mask=None):
|
||||
q = self.conv_q(x)
|
||||
k = self.conv_k(c)
|
||||
v = self.conv_v(c)
|
||||
|
||||
x, self.attn = self.attention(q, k, v, mask=attn_mask)
|
||||
|
||||
x = self.conv_o(x)
|
||||
return x
|
||||
|
||||
def attention(self, query, key, value, mask=None):
|
||||
# reshape [b, d, t] -> [b, n_h, t, d_k]
|
||||
b, d, t_s, t_t = (*key.size(), query.size(2))
|
||||
query = query.view(b, self.n_heads, self.k_channels, t_t).transpose(2, 3)
|
||||
key = key.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
||||
value = value.view(b, self.n_heads, self.k_channels, t_s).transpose(2, 3)
|
||||
|
||||
scores = torch.matmul(query / math.sqrt(self.k_channels), key.transpose(-2, -1))
|
||||
if self.window_size is not None:
|
||||
assert t_s == t_t, "Relative attention is only available for self-attention."
|
||||
key_relative_embeddings = self._get_relative_embeddings(self.emb_rel_k, t_s)
|
||||
rel_logits = self._matmul_with_relative_keys(query /math.sqrt(self.k_channels), key_relative_embeddings)
|
||||
scores_local = self._relative_position_to_absolute_position(rel_logits)
|
||||
scores = scores + scores_local
|
||||
if self.proximal_bias:
|
||||
assert t_s == t_t, "Proximal bias is only available for self-attention."
|
||||
scores = scores + self._attention_bias_proximal(t_s).to(device=scores.device, dtype=scores.dtype)
|
||||
if mask is not None:
|
||||
scores = scores.masked_fill(mask == 0, -1e4)
|
||||
if self.block_length is not None:
|
||||
assert t_s == t_t, "Local attention is only available for self-attention."
|
||||
block_mask = torch.ones_like(scores).triu(-self.block_length).tril(self.block_length)
|
||||
scores = scores.masked_fill(block_mask == 0, -1e4)
|
||||
p_attn = F.softmax(scores, dim=-1) # [b, n_h, t_t, t_s]
|
||||
p_attn = self.drop(p_attn)
|
||||
output = torch.matmul(p_attn, value)
|
||||
if self.window_size is not None:
|
||||
relative_weights = self._absolute_position_to_relative_position(p_attn)
|
||||
value_relative_embeddings = self._get_relative_embeddings(self.emb_rel_v, t_s)
|
||||
output = output + self._matmul_with_relative_values(relative_weights, value_relative_embeddings)
|
||||
output = output.transpose(2, 3).contiguous().view(b, d, t_t) # [b, n_h, t_t, d_k] -> [b, d, t_t]
|
||||
return output, p_attn
|
||||
|
||||
def _matmul_with_relative_values(self, x, y):
|
||||
"""
|
||||
x: [b, h, l, m]
|
||||
y: [h or 1, m, d]
|
||||
ret: [b, h, l, d]
|
||||
"""
|
||||
ret = torch.matmul(x, y.unsqueeze(0))
|
||||
return ret
|
||||
|
||||
def _matmul_with_relative_keys(self, x, y):
|
||||
"""
|
||||
x: [b, h, l, d]
|
||||
y: [h or 1, m, d]
|
||||
ret: [b, h, l, m]
|
||||
"""
|
||||
ret = torch.matmul(x, y.unsqueeze(0).transpose(-2, -1))
|
||||
return ret
|
||||
|
||||
def _get_relative_embeddings(self, relative_embeddings, length):
|
||||
max_relative_position = 2 * self.window_size + 1
|
||||
# Pad first before slice to avoid using cond ops.
|
||||
pad_length = max(length - (self.window_size + 1), 0)
|
||||
slice_start_position = max((self.window_size + 1) - length, 0)
|
||||
slice_end_position = slice_start_position + 2 * length - 1
|
||||
if pad_length > 0:
|
||||
padded_relative_embeddings = F.pad(
|
||||
relative_embeddings,
|
||||
commons.convert_pad_shape([[0, 0], [pad_length, pad_length], [0, 0]]))
|
||||
else:
|
||||
padded_relative_embeddings = relative_embeddings
|
||||
used_relative_embeddings = padded_relative_embeddings[:,slice_start_position:slice_end_position]
|
||||
return used_relative_embeddings
|
||||
|
||||
def _relative_position_to_absolute_position(self, x):
|
||||
"""
|
||||
x: [b, h, l, 2*l-1]
|
||||
ret: [b, h, l, l]
|
||||
"""
|
||||
batch, heads, length, _ = x.size()
|
||||
# Concat columns of pad to shift from relative to absolute indexing.
|
||||
x = F.pad(x, commons.convert_pad_shape([[0,0],[0,0],[0,0],[0,1]]))
|
||||
|
||||
# Concat extra elements so to add up to shape (len+1, 2*len-1).
|
||||
x_flat = x.view([batch, heads, length * 2 * length])
|
||||
x_flat = F.pad(x_flat, commons.convert_pad_shape([[0,0],[0,0],[0,length-1]]))
|
||||
|
||||
# Reshape and slice out the padded elements.
|
||||
x_final = x_flat.view([batch, heads, length+1, 2*length-1])[:, :, :length, length-1:]
|
||||
return x_final
|
||||
|
||||
def _absolute_position_to_relative_position(self, x):
|
||||
"""
|
||||
x: [b, h, l, l]
|
||||
ret: [b, h, l, 2*l-1]
|
||||
"""
|
||||
batch, heads, length, _ = x.size()
|
||||
# padd along column
|
||||
x = F.pad(x, commons.convert_pad_shape([[0, 0], [0, 0], [0, 0], [0, length-1]]))
|
||||
x_flat = x.view([batch, heads, length**2 + length*(length -1)])
|
||||
# add 0's in the beginning that will skew the elements after reshape
|
||||
x_flat = F.pad(x_flat, commons.convert_pad_shape([[0, 0], [0, 0], [length, 0]]))
|
||||
x_final = x_flat.view([batch, heads, length, 2*length])[:,:,:,1:]
|
||||
return x_final
|
||||
|
||||
def _attention_bias_proximal(self, length):
|
||||
"""Bias for self-attention to encourage attention to close positions.
|
||||
Args:
|
||||
length: an integer scalar.
|
||||
Returns:
|
||||
a Tensor with shape [1, 1, length, length]
|
||||
"""
|
||||
r = torch.arange(length, dtype=torch.float32)
|
||||
diff = torch.unsqueeze(r, 0) - torch.unsqueeze(r, 1)
|
||||
return torch.unsqueeze(torch.unsqueeze(-torch.log1p(torch.abs(diff)), 0), 0)
|
||||
|
||||
|
||||
class FFN(nn.Module):
|
||||
def __init__(self, in_channels, out_channels, filter_channels, kernel_size, p_dropout=0., activation=None, causal=False):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = out_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.activation = activation
|
||||
self.causal = causal
|
||||
|
||||
if causal:
|
||||
self.padding = self._causal_padding
|
||||
else:
|
||||
self.padding = self._same_padding
|
||||
|
||||
self.conv_1 = nn.Conv1d(in_channels, filter_channels, kernel_size)
|
||||
self.conv_2 = nn.Conv1d(filter_channels, out_channels, kernel_size)
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
|
||||
def forward(self, x, x_mask):
|
||||
x = self.conv_1(self.padding(x * x_mask))
|
||||
if self.activation == "gelu":
|
||||
x = x * torch.sigmoid(1.702 * x)
|
||||
else:
|
||||
x = torch.relu(x)
|
||||
x = self.drop(x)
|
||||
x = self.conv_2(self.padding(x * x_mask))
|
||||
return x * x_mask
|
||||
|
||||
def _causal_padding(self, x):
|
||||
if self.kernel_size == 1:
|
||||
return x
|
||||
pad_l = self.kernel_size - 1
|
||||
pad_r = 0
|
||||
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
||||
x = F.pad(x, commons.convert_pad_shape(padding))
|
||||
return x
|
||||
|
||||
def _same_padding(self, x):
|
||||
if self.kernel_size == 1:
|
||||
return x
|
||||
pad_l = (self.kernel_size - 1) // 2
|
||||
pad_r = self.kernel_size // 2
|
||||
padding = [[0, 0], [0, 0], [pad_l, pad_r]]
|
||||
x = F.pad(x, commons.convert_pad_shape(padding))
|
||||
return x
|
||||
@@ -0,0 +1,188 @@
|
||||
import math
|
||||
import numpy as np
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
def slice_pitch_segments(x, ids_str, segment_size=4):
|
||||
ret = torch.zeros_like(x[:, :segment_size])
|
||||
for i in range(x.size(0)):
|
||||
idx_str = ids_str[i]
|
||||
idx_end = idx_str + segment_size
|
||||
ret[i] = x[i, idx_str:idx_end]
|
||||
return ret
|
||||
|
||||
def rand_slice_segments_with_pitch(x, pitch, x_lengths=None, segment_size=4):
|
||||
b, d, t = x.size()
|
||||
if x_lengths is None:
|
||||
x_lengths = t
|
||||
ids_str_max = x_lengths - segment_size + 1
|
||||
ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
|
||||
ret = slice_segments(x, ids_str, segment_size)
|
||||
ret_pitch = slice_pitch_segments(pitch, ids_str, segment_size)
|
||||
return ret, ret_pitch, ids_str
|
||||
|
||||
def init_weights(m, mean=0.0, std=0.01):
|
||||
classname = m.__class__.__name__
|
||||
if classname.find("Conv") != -1:
|
||||
m.weight.data.normal_(mean, std)
|
||||
|
||||
|
||||
def get_padding(kernel_size, dilation=1):
|
||||
return int((kernel_size*dilation - dilation)/2)
|
||||
|
||||
|
||||
def convert_pad_shape(pad_shape):
|
||||
l = pad_shape[::-1]
|
||||
pad_shape = [item for sublist in l for item in sublist]
|
||||
return pad_shape
|
||||
|
||||
|
||||
def intersperse(lst, item):
|
||||
result = [item] * (len(lst) * 2 + 1)
|
||||
result[1::2] = lst
|
||||
return result
|
||||
|
||||
|
||||
def kl_divergence(m_p, logs_p, m_q, logs_q):
|
||||
"""KL(P||Q)"""
|
||||
kl = (logs_q - logs_p) - 0.5
|
||||
kl += 0.5 * (torch.exp(2. * logs_p) + ((m_p - m_q)**2)) * torch.exp(-2. * logs_q)
|
||||
return kl
|
||||
|
||||
|
||||
def rand_gumbel(shape):
|
||||
"""Sample from the Gumbel distribution, protect from overflows."""
|
||||
uniform_samples = torch.rand(shape) * 0.99998 + 0.00001
|
||||
return -torch.log(-torch.log(uniform_samples))
|
||||
|
||||
|
||||
def rand_gumbel_like(x):
|
||||
g = rand_gumbel(x.size()).to(dtype=x.dtype, device=x.device)
|
||||
return g
|
||||
|
||||
|
||||
def slice_segments(x, ids_str, segment_size=4):
|
||||
ret = torch.zeros_like(x[:, :, :segment_size])
|
||||
for i in range(x.size(0)):
|
||||
idx_str = ids_str[i]
|
||||
idx_end = idx_str + segment_size
|
||||
ret[i] = x[i, :, idx_str:idx_end]
|
||||
return ret
|
||||
|
||||
|
||||
def rand_slice_segments(x, x_lengths=None, segment_size=4):
|
||||
b, d, t = x.size()
|
||||
if x_lengths is None:
|
||||
x_lengths = t
|
||||
ids_str_max = x_lengths - segment_size + 1
|
||||
ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
|
||||
ret = slice_segments(x, ids_str, segment_size)
|
||||
return ret, ids_str
|
||||
|
||||
|
||||
def rand_spec_segments(x, x_lengths=None, segment_size=4):
|
||||
b, d, t = x.size()
|
||||
if x_lengths is None:
|
||||
x_lengths = t
|
||||
ids_str_max = x_lengths - segment_size
|
||||
ids_str = (torch.rand([b]).to(device=x.device) * ids_str_max).to(dtype=torch.long)
|
||||
ret = slice_segments(x, ids_str, segment_size)
|
||||
return ret, ids_str
|
||||
|
||||
|
||||
def get_timing_signal_1d(
|
||||
length, channels, min_timescale=1.0, max_timescale=1.0e4):
|
||||
position = torch.arange(length, dtype=torch.float)
|
||||
num_timescales = channels // 2
|
||||
log_timescale_increment = (
|
||||
math.log(float(max_timescale) / float(min_timescale)) /
|
||||
(num_timescales - 1))
|
||||
inv_timescales = min_timescale * torch.exp(
|
||||
torch.arange(num_timescales, dtype=torch.float) * -log_timescale_increment)
|
||||
scaled_time = position.unsqueeze(0) * inv_timescales.unsqueeze(1)
|
||||
signal = torch.cat([torch.sin(scaled_time), torch.cos(scaled_time)], 0)
|
||||
signal = F.pad(signal, [0, 0, 0, channels % 2])
|
||||
signal = signal.view(1, channels, length)
|
||||
return signal
|
||||
|
||||
|
||||
def add_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4):
|
||||
b, channels, length = x.size()
|
||||
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
|
||||
return x + signal.to(dtype=x.dtype, device=x.device)
|
||||
|
||||
|
||||
def cat_timing_signal_1d(x, min_timescale=1.0, max_timescale=1.0e4, axis=1):
|
||||
b, channels, length = x.size()
|
||||
signal = get_timing_signal_1d(length, channels, min_timescale, max_timescale)
|
||||
return torch.cat([x, signal.to(dtype=x.dtype, device=x.device)], axis)
|
||||
|
||||
|
||||
def subsequent_mask(length):
|
||||
mask = torch.tril(torch.ones(length, length)).unsqueeze(0).unsqueeze(0)
|
||||
return mask
|
||||
|
||||
|
||||
@torch.jit.script
|
||||
def fused_add_tanh_sigmoid_multiply(input_a, input_b, n_channels):
|
||||
n_channels_int = n_channels[0]
|
||||
in_act = input_a + input_b
|
||||
t_act = torch.tanh(in_act[:, :n_channels_int, :])
|
||||
s_act = torch.sigmoid(in_act[:, n_channels_int:, :])
|
||||
acts = t_act * s_act
|
||||
return acts
|
||||
|
||||
|
||||
def convert_pad_shape(pad_shape):
|
||||
l = pad_shape[::-1]
|
||||
pad_shape = [item for sublist in l for item in sublist]
|
||||
return pad_shape
|
||||
|
||||
|
||||
def shift_1d(x):
|
||||
x = F.pad(x, convert_pad_shape([[0, 0], [0, 0], [1, 0]]))[:, :, :-1]
|
||||
return x
|
||||
|
||||
|
||||
def sequence_mask(length, max_length=None):
|
||||
if max_length is None:
|
||||
max_length = length.max()
|
||||
x = torch.arange(max_length, dtype=length.dtype, device=length.device)
|
||||
return x.unsqueeze(0) < length.unsqueeze(1)
|
||||
|
||||
|
||||
def generate_path(duration, mask):
|
||||
"""
|
||||
duration: [b, 1, t_x]
|
||||
mask: [b, 1, t_y, t_x]
|
||||
"""
|
||||
device = duration.device
|
||||
|
||||
b, _, t_y, t_x = mask.shape
|
||||
cum_duration = torch.cumsum(duration, -1)
|
||||
|
||||
cum_duration_flat = cum_duration.view(b * t_x)
|
||||
path = sequence_mask(cum_duration_flat, t_y).to(mask.dtype)
|
||||
path = path.view(b, t_x, t_y)
|
||||
path = path - F.pad(path, convert_pad_shape([[0, 0], [1, 0], [0, 0]]))[:, :-1]
|
||||
path = path.unsqueeze(1).transpose(2,3) * mask
|
||||
return path
|
||||
|
||||
|
||||
def clip_grad_value_(parameters, clip_value, norm_type=2):
|
||||
if isinstance(parameters, torch.Tensor):
|
||||
parameters = [parameters]
|
||||
parameters = list(filter(lambda p: p.grad is not None, parameters))
|
||||
norm_type = float(norm_type)
|
||||
if clip_value is not None:
|
||||
clip_value = float(clip_value)
|
||||
|
||||
total_norm = 0
|
||||
for p in parameters:
|
||||
param_norm = p.grad.data.norm(norm_type)
|
||||
total_norm += param_norm.item() ** norm_type
|
||||
if clip_value is not None:
|
||||
p.grad.data.clamp_(min=-clip_value, max=clip_value)
|
||||
total_norm = total_norm ** (1. / norm_type)
|
||||
return total_norm
|
||||
@@ -0,0 +1,61 @@
|
||||
import torch
|
||||
from torch.nn import functional as F
|
||||
|
||||
import modules.commons as commons
|
||||
|
||||
|
||||
def feature_loss(fmap_r, fmap_g):
|
||||
loss = 0
|
||||
for dr, dg in zip(fmap_r, fmap_g):
|
||||
for rl, gl in zip(dr, dg):
|
||||
rl = rl.float().detach()
|
||||
gl = gl.float()
|
||||
loss += torch.mean(torch.abs(rl - gl))
|
||||
|
||||
return loss * 2
|
||||
|
||||
|
||||
def discriminator_loss(disc_real_outputs, disc_generated_outputs):
|
||||
loss = 0
|
||||
r_losses = []
|
||||
g_losses = []
|
||||
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
|
||||
dr = dr.float()
|
||||
dg = dg.float()
|
||||
r_loss = torch.mean((1-dr)**2)
|
||||
g_loss = torch.mean(dg**2)
|
||||
loss += (r_loss + g_loss)
|
||||
r_losses.append(r_loss.item())
|
||||
g_losses.append(g_loss.item())
|
||||
|
||||
return loss, r_losses, g_losses
|
||||
|
||||
|
||||
def generator_loss(disc_outputs):
|
||||
loss = 0
|
||||
gen_losses = []
|
||||
for dg in disc_outputs:
|
||||
dg = dg.float()
|
||||
l = torch.mean((1-dg)**2)
|
||||
gen_losses.append(l)
|
||||
loss += l
|
||||
|
||||
return loss, gen_losses
|
||||
|
||||
|
||||
def kl_loss(z_p, logs_q, m_p, logs_p, z_mask):
|
||||
"""
|
||||
z_p, logs_q: [b, h, t_t]
|
||||
m_p, logs_p: [b, h, t_t]
|
||||
"""
|
||||
z_p = z_p.float()
|
||||
logs_q = logs_q.float()
|
||||
m_p = m_p.float()
|
||||
logs_p = logs_p.float()
|
||||
z_mask = z_mask.float()
|
||||
#print(logs_p)
|
||||
kl = logs_p - logs_q - 0.5
|
||||
kl += 0.5 * ((z_p - m_p)**2) * torch.exp(-2. * logs_p)
|
||||
kl = torch.sum(kl * z_mask)
|
||||
l = kl / torch.sum(z_mask)
|
||||
return l
|
||||
@@ -0,0 +1,112 @@
|
||||
import math
|
||||
import os
|
||||
import random
|
||||
import torch
|
||||
from torch import nn
|
||||
import torch.nn.functional as F
|
||||
import torch.utils.data
|
||||
import numpy as np
|
||||
import librosa
|
||||
import librosa.util as librosa_util
|
||||
from librosa.util import normalize, pad_center, tiny
|
||||
from scipy.signal import get_window
|
||||
from scipy.io.wavfile import read
|
||||
from librosa.filters import mel as librosa_mel_fn
|
||||
|
||||
MAX_WAV_VALUE = 32768.0
|
||||
|
||||
|
||||
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
|
||||
"""
|
||||
PARAMS
|
||||
------
|
||||
C: compression factor
|
||||
"""
|
||||
return torch.log(torch.clamp(x, min=clip_val) * C)
|
||||
|
||||
|
||||
def dynamic_range_decompression_torch(x, C=1):
|
||||
"""
|
||||
PARAMS
|
||||
------
|
||||
C: compression factor used to compress
|
||||
"""
|
||||
return torch.exp(x) / C
|
||||
|
||||
|
||||
def spectral_normalize_torch(magnitudes):
|
||||
output = dynamic_range_compression_torch(magnitudes)
|
||||
return output
|
||||
|
||||
|
||||
def spectral_de_normalize_torch(magnitudes):
|
||||
output = dynamic_range_decompression_torch(magnitudes)
|
||||
return output
|
||||
|
||||
|
||||
mel_basis = {}
|
||||
hann_window = {}
|
||||
|
||||
|
||||
def spectrogram_torch(y, n_fft, sampling_rate, hop_size, win_size, center=False):
|
||||
if torch.min(y) < -1.:
|
||||
print('min value is ', torch.min(y))
|
||||
if torch.max(y) > 1.:
|
||||
print('max value is ', torch.max(y))
|
||||
|
||||
global hann_window
|
||||
dtype_device = str(y.dtype) + '_' + str(y.device)
|
||||
wnsize_dtype_device = str(win_size) + '_' + dtype_device
|
||||
if wnsize_dtype_device not in hann_window:
|
||||
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
|
||||
|
||||
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
|
||||
y = y.squeeze(1)
|
||||
|
||||
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
|
||||
center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
|
||||
|
||||
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
|
||||
return spec
|
||||
|
||||
|
||||
def spec_to_mel_torch(spec, n_fft, num_mels, sampling_rate, fmin, fmax):
|
||||
global mel_basis
|
||||
dtype_device = str(spec.dtype) + '_' + str(spec.device)
|
||||
fmax_dtype_device = str(fmax) + '_' + dtype_device
|
||||
if fmax_dtype_device not in mel_basis:
|
||||
mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
|
||||
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=spec.dtype, device=spec.device)
|
||||
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
|
||||
spec = spectral_normalize_torch(spec)
|
||||
return spec
|
||||
|
||||
|
||||
def mel_spectrogram_torch(y, n_fft, num_mels, sampling_rate, hop_size, win_size, fmin, fmax, center=False):
|
||||
if torch.min(y) < -1.:
|
||||
print('min value is ', torch.min(y))
|
||||
if torch.max(y) > 1.:
|
||||
print('max value is ', torch.max(y))
|
||||
|
||||
global mel_basis, hann_window
|
||||
dtype_device = str(y.dtype) + '_' + str(y.device)
|
||||
fmax_dtype_device = str(fmax) + '_' + dtype_device
|
||||
wnsize_dtype_device = str(win_size) + '_' + dtype_device
|
||||
if fmax_dtype_device not in mel_basis:
|
||||
mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=num_mels, fmin=fmin, fmax=fmax)
|
||||
mel_basis[fmax_dtype_device] = torch.from_numpy(mel).to(dtype=y.dtype, device=y.device)
|
||||
if wnsize_dtype_device not in hann_window:
|
||||
hann_window[wnsize_dtype_device] = torch.hann_window(win_size).to(dtype=y.dtype, device=y.device)
|
||||
|
||||
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_size)/2), int((n_fft-hop_size)/2)), mode='reflect')
|
||||
y = y.squeeze(1)
|
||||
|
||||
spec = torch.stft(y, n_fft, hop_length=hop_size, win_length=win_size, window=hann_window[wnsize_dtype_device],
|
||||
center=center, pad_mode='reflect', normalized=False, onesided=True, return_complex=False)
|
||||
|
||||
spec = torch.sqrt(spec.pow(2).sum(-1) + 1e-6)
|
||||
|
||||
spec = torch.matmul(mel_basis[fmax_dtype_device], spec)
|
||||
spec = spectral_normalize_torch(spec)
|
||||
|
||||
return spec
|
||||
@@ -0,0 +1,342 @@
|
||||
import copy
|
||||
import math
|
||||
import numpy as np
|
||||
import scipy
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
||||
from torch.nn.utils import weight_norm, remove_weight_norm
|
||||
|
||||
import modules.commons as commons
|
||||
from modules.commons import init_weights, get_padding
|
||||
|
||||
|
||||
LRELU_SLOPE = 0.1
|
||||
|
||||
|
||||
class LayerNorm(nn.Module):
|
||||
def __init__(self, channels, eps=1e-5):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.eps = eps
|
||||
|
||||
self.gamma = nn.Parameter(torch.ones(channels))
|
||||
self.beta = nn.Parameter(torch.zeros(channels))
|
||||
|
||||
def forward(self, x):
|
||||
x = x.transpose(1, -1)
|
||||
x = F.layer_norm(x, (self.channels,), self.gamma, self.beta, self.eps)
|
||||
return x.transpose(1, -1)
|
||||
|
||||
|
||||
class ConvReluNorm(nn.Module):
|
||||
def __init__(self, in_channels, hidden_channels, out_channels, kernel_size, n_layers, p_dropout):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.out_channels = out_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.n_layers = n_layers
|
||||
self.p_dropout = p_dropout
|
||||
assert n_layers > 1, "Number of layers should be larger than 0."
|
||||
|
||||
self.conv_layers = nn.ModuleList()
|
||||
self.norm_layers = nn.ModuleList()
|
||||
self.conv_layers.append(nn.Conv1d(in_channels, hidden_channels, kernel_size, padding=kernel_size//2))
|
||||
self.norm_layers.append(LayerNorm(hidden_channels))
|
||||
self.relu_drop = nn.Sequential(
|
||||
nn.ReLU(),
|
||||
nn.Dropout(p_dropout))
|
||||
for _ in range(n_layers-1):
|
||||
self.conv_layers.append(nn.Conv1d(hidden_channels, hidden_channels, kernel_size, padding=kernel_size//2))
|
||||
self.norm_layers.append(LayerNorm(hidden_channels))
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
|
||||
self.proj.weight.data.zero_()
|
||||
self.proj.bias.data.zero_()
|
||||
|
||||
def forward(self, x, x_mask):
|
||||
x_org = x
|
||||
for i in range(self.n_layers):
|
||||
x = self.conv_layers[i](x * x_mask)
|
||||
x = self.norm_layers[i](x)
|
||||
x = self.relu_drop(x)
|
||||
x = x_org + self.proj(x)
|
||||
return x * x_mask
|
||||
|
||||
|
||||
class DDSConv(nn.Module):
|
||||
"""
|
||||
Dialted and Depth-Separable Convolution
|
||||
"""
|
||||
def __init__(self, channels, kernel_size, n_layers, p_dropout=0.):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.kernel_size = kernel_size
|
||||
self.n_layers = n_layers
|
||||
self.p_dropout = p_dropout
|
||||
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
self.convs_sep = nn.ModuleList()
|
||||
self.convs_1x1 = nn.ModuleList()
|
||||
self.norms_1 = nn.ModuleList()
|
||||
self.norms_2 = nn.ModuleList()
|
||||
for i in range(n_layers):
|
||||
dilation = kernel_size ** i
|
||||
padding = (kernel_size * dilation - dilation) // 2
|
||||
self.convs_sep.append(nn.Conv1d(channels, channels, kernel_size,
|
||||
groups=channels, dilation=dilation, padding=padding
|
||||
))
|
||||
self.convs_1x1.append(nn.Conv1d(channels, channels, 1))
|
||||
self.norms_1.append(LayerNorm(channels))
|
||||
self.norms_2.append(LayerNorm(channels))
|
||||
|
||||
def forward(self, x, x_mask, g=None):
|
||||
if g is not None:
|
||||
x = x + g
|
||||
for i in range(self.n_layers):
|
||||
y = self.convs_sep[i](x * x_mask)
|
||||
y = self.norms_1[i](y)
|
||||
y = F.gelu(y)
|
||||
y = self.convs_1x1[i](y)
|
||||
y = self.norms_2[i](y)
|
||||
y = F.gelu(y)
|
||||
y = self.drop(y)
|
||||
x = x + y
|
||||
return x * x_mask
|
||||
|
||||
|
||||
class WN(torch.nn.Module):
|
||||
def __init__(self, hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=0, p_dropout=0):
|
||||
super(WN, self).__init__()
|
||||
assert(kernel_size % 2 == 1)
|
||||
self.hidden_channels =hidden_channels
|
||||
self.kernel_size = kernel_size,
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.gin_channels = gin_channels
|
||||
self.p_dropout = p_dropout
|
||||
|
||||
self.in_layers = torch.nn.ModuleList()
|
||||
self.res_skip_layers = torch.nn.ModuleList()
|
||||
self.drop = nn.Dropout(p_dropout)
|
||||
|
||||
if gin_channels != 0:
|
||||
cond_layer = torch.nn.Conv1d(gin_channels, 2*hidden_channels*n_layers, 1)
|
||||
self.cond_layer = torch.nn.utils.weight_norm(cond_layer, name='weight')
|
||||
|
||||
for i in range(n_layers):
|
||||
dilation = dilation_rate ** i
|
||||
padding = int((kernel_size * dilation - dilation) / 2)
|
||||
in_layer = torch.nn.Conv1d(hidden_channels, 2*hidden_channels, kernel_size,
|
||||
dilation=dilation, padding=padding)
|
||||
in_layer = torch.nn.utils.weight_norm(in_layer, name='weight')
|
||||
self.in_layers.append(in_layer)
|
||||
|
||||
# last one is not necessary
|
||||
if i < n_layers - 1:
|
||||
res_skip_channels = 2 * hidden_channels
|
||||
else:
|
||||
res_skip_channels = hidden_channels
|
||||
|
||||
res_skip_layer = torch.nn.Conv1d(hidden_channels, res_skip_channels, 1)
|
||||
res_skip_layer = torch.nn.utils.weight_norm(res_skip_layer, name='weight')
|
||||
self.res_skip_layers.append(res_skip_layer)
|
||||
|
||||
def forward(self, x, x_mask, g=None, **kwargs):
|
||||
output = torch.zeros_like(x)
|
||||
n_channels_tensor = torch.IntTensor([self.hidden_channels])
|
||||
|
||||
if g is not None:
|
||||
g = self.cond_layer(g)
|
||||
|
||||
for i in range(self.n_layers):
|
||||
x_in = self.in_layers[i](x)
|
||||
if g is not None:
|
||||
cond_offset = i * 2 * self.hidden_channels
|
||||
g_l = g[:,cond_offset:cond_offset+2*self.hidden_channels,:]
|
||||
else:
|
||||
g_l = torch.zeros_like(x_in)
|
||||
|
||||
acts = commons.fused_add_tanh_sigmoid_multiply(
|
||||
x_in,
|
||||
g_l,
|
||||
n_channels_tensor)
|
||||
acts = self.drop(acts)
|
||||
|
||||
res_skip_acts = self.res_skip_layers[i](acts)
|
||||
if i < self.n_layers - 1:
|
||||
res_acts = res_skip_acts[:,:self.hidden_channels,:]
|
||||
x = (x + res_acts) * x_mask
|
||||
output = output + res_skip_acts[:,self.hidden_channels:,:]
|
||||
else:
|
||||
output = output + res_skip_acts
|
||||
return output * x_mask
|
||||
|
||||
def remove_weight_norm(self):
|
||||
if self.gin_channels != 0:
|
||||
torch.nn.utils.remove_weight_norm(self.cond_layer)
|
||||
for l in self.in_layers:
|
||||
torch.nn.utils.remove_weight_norm(l)
|
||||
for l in self.res_skip_layers:
|
||||
torch.nn.utils.remove_weight_norm(l)
|
||||
|
||||
|
||||
class ResBlock1(torch.nn.Module):
|
||||
def __init__(self, channels, kernel_size=3, dilation=(1, 3, 5)):
|
||||
super(ResBlock1, self).__init__()
|
||||
self.convs1 = nn.ModuleList([
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
||||
padding=get_padding(kernel_size, dilation[0]))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
||||
padding=get_padding(kernel_size, dilation[1]))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
|
||||
padding=get_padding(kernel_size, dilation[2])))
|
||||
])
|
||||
self.convs1.apply(init_weights)
|
||||
|
||||
self.convs2 = nn.ModuleList([
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
||||
padding=get_padding(kernel_size, 1))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
||||
padding=get_padding(kernel_size, 1))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
||||
padding=get_padding(kernel_size, 1)))
|
||||
])
|
||||
self.convs2.apply(init_weights)
|
||||
|
||||
def forward(self, x, x_mask=None):
|
||||
for c1, c2 in zip(self.convs1, self.convs2):
|
||||
xt = F.leaky_relu(x, LRELU_SLOPE)
|
||||
if x_mask is not None:
|
||||
xt = xt * x_mask
|
||||
xt = c1(xt)
|
||||
xt = F.leaky_relu(xt, LRELU_SLOPE)
|
||||
if x_mask is not None:
|
||||
xt = xt * x_mask
|
||||
xt = c2(xt)
|
||||
x = xt + x
|
||||
if x_mask is not None:
|
||||
x = x * x_mask
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for l in self.convs1:
|
||||
remove_weight_norm(l)
|
||||
for l in self.convs2:
|
||||
remove_weight_norm(l)
|
||||
|
||||
|
||||
class ResBlock2(torch.nn.Module):
|
||||
def __init__(self, channels, kernel_size=3, dilation=(1, 3)):
|
||||
super(ResBlock2, self).__init__()
|
||||
self.convs = nn.ModuleList([
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
||||
padding=get_padding(kernel_size, dilation[0]))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
||||
padding=get_padding(kernel_size, dilation[1])))
|
||||
])
|
||||
self.convs.apply(init_weights)
|
||||
|
||||
def forward(self, x, x_mask=None):
|
||||
for c in self.convs:
|
||||
xt = F.leaky_relu(x, LRELU_SLOPE)
|
||||
if x_mask is not None:
|
||||
xt = xt * x_mask
|
||||
xt = c(xt)
|
||||
x = xt + x
|
||||
if x_mask is not None:
|
||||
x = x * x_mask
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for l in self.convs:
|
||||
remove_weight_norm(l)
|
||||
|
||||
|
||||
class Log(nn.Module):
|
||||
def forward(self, x, x_mask, reverse=False, **kwargs):
|
||||
if not reverse:
|
||||
y = torch.log(torch.clamp_min(x, 1e-5)) * x_mask
|
||||
logdet = torch.sum(-y, [1, 2])
|
||||
return y, logdet
|
||||
else:
|
||||
x = torch.exp(x) * x_mask
|
||||
return x
|
||||
|
||||
|
||||
class Flip(nn.Module):
|
||||
def forward(self, x, *args, reverse=False, **kwargs):
|
||||
x = torch.flip(x, [1])
|
||||
if not reverse:
|
||||
logdet = torch.zeros(x.size(0)).to(dtype=x.dtype, device=x.device)
|
||||
return x, logdet
|
||||
else:
|
||||
return x
|
||||
|
||||
|
||||
class ElementwiseAffine(nn.Module):
|
||||
def __init__(self, channels):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.m = nn.Parameter(torch.zeros(channels,1))
|
||||
self.logs = nn.Parameter(torch.zeros(channels,1))
|
||||
|
||||
def forward(self, x, x_mask, reverse=False, **kwargs):
|
||||
if not reverse:
|
||||
y = self.m + torch.exp(self.logs) * x
|
||||
y = y * x_mask
|
||||
logdet = torch.sum(self.logs * x_mask, [1,2])
|
||||
return y, logdet
|
||||
else:
|
||||
x = (x - self.m) * torch.exp(-self.logs) * x_mask
|
||||
return x
|
||||
|
||||
|
||||
class ResidualCouplingLayer(nn.Module):
|
||||
def __init__(self,
|
||||
channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
p_dropout=0,
|
||||
gin_channels=0,
|
||||
mean_only=False):
|
||||
assert channels % 2 == 0, "channels should be divisible by 2"
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.half_channels = channels // 2
|
||||
self.mean_only = mean_only
|
||||
|
||||
self.pre = nn.Conv1d(self.half_channels, hidden_channels, 1)
|
||||
self.enc = WN(hidden_channels, kernel_size, dilation_rate, n_layers, p_dropout=p_dropout, gin_channels=gin_channels)
|
||||
self.post = nn.Conv1d(hidden_channels, self.half_channels * (2 - mean_only), 1)
|
||||
self.post.weight.data.zero_()
|
||||
self.post.bias.data.zero_()
|
||||
|
||||
def forward(self, x, x_mask, g=None, reverse=False):
|
||||
x0, x1 = torch.split(x, [self.half_channels]*2, 1)
|
||||
h = self.pre(x0) * x_mask
|
||||
h = self.enc(h, x_mask, g=g)
|
||||
stats = self.post(h) * x_mask
|
||||
if not self.mean_only:
|
||||
m, logs = torch.split(stats, [self.half_channels]*2, 1)
|
||||
else:
|
||||
m = stats
|
||||
logs = torch.zeros_like(m)
|
||||
|
||||
if not reverse:
|
||||
x1 = m + x1 * torch.exp(logs) * x_mask
|
||||
x = torch.cat([x0, x1], 1)
|
||||
logdet = torch.sum(logs, [1,2])
|
||||
return x, logdet
|
||||
else:
|
||||
x1 = (x1 - m) * torch.exp(-logs) * x_mask
|
||||
x = torch.cat([x0, x1], 1)
|
||||
return x
|
||||
@@ -0,0 +1,94 @@
|
||||
import torch
|
||||
from torchaudio.models.wav2vec2.utils import import_fairseq_model
|
||||
from fairseq import checkpoint_utils
|
||||
from onnxexport.model_onnx import SynthesizerTrn
|
||||
import utils
|
||||
|
||||
def get_hubert_model():
|
||||
vec_path = "hubert/checkpoint_best_legacy_500.pt"
|
||||
print("load model(s) from {}".format(vec_path))
|
||||
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
|
||||
[vec_path],
|
||||
suffix="",
|
||||
)
|
||||
model = models[0]
|
||||
model.eval()
|
||||
return model
|
||||
|
||||
|
||||
def main(HubertExport, NetExport):
|
||||
path = "SoVits4.0"
|
||||
|
||||
'''if HubertExport:
|
||||
device = torch.device("cpu")
|
||||
vec_path = "hubert/checkpoint_best_legacy_500.pt"
|
||||
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
|
||||
[vec_path],
|
||||
suffix="",
|
||||
)
|
||||
original = models[0]
|
||||
original.eval()
|
||||
model = original
|
||||
test_input = torch.rand(1, 1, 16000)
|
||||
model(test_input)
|
||||
torch.onnx.export(model,
|
||||
test_input,
|
||||
"hubert4.0.onnx",
|
||||
export_params=True,
|
||||
opset_version=16,
|
||||
do_constant_folding=True,
|
||||
input_names=['source'],
|
||||
output_names=['embed'],
|
||||
dynamic_axes={
|
||||
'source':
|
||||
{
|
||||
2: "sample_length"
|
||||
},
|
||||
}
|
||||
)'''
|
||||
if NetExport:
|
||||
device = torch.device("cpu")
|
||||
hps = utils.get_hparams_from_file(f"checkpoints/{path}/config.json")
|
||||
SVCVITS = SynthesizerTrn(
|
||||
hps.data.filter_length // 2 + 1,
|
||||
hps.train.segment_size // hps.data.hop_length,
|
||||
**hps.model)
|
||||
_ = utils.load_checkpoint(f"checkpoints/{path}/model.pth", SVCVITS, None)
|
||||
_ = SVCVITS.eval().to(device)
|
||||
for i in SVCVITS.parameters():
|
||||
i.requires_grad = False
|
||||
test_hidden_unit = torch.rand(1, 10, 256)
|
||||
test_pitch = torch.rand(1, 10)
|
||||
test_mel2ph = torch.LongTensor([0, 1, 2, 3, 4, 5, 6, 7, 8, 9]).unsqueeze(0)
|
||||
test_uv = torch.ones(1, 10, dtype=torch.float32)
|
||||
test_noise = torch.randn(1, 192, 10)
|
||||
test_sid = torch.LongTensor([0])
|
||||
input_names = ["c", "f0", "mel2ph", "uv", "noise", "sid"]
|
||||
output_names = ["audio", ]
|
||||
SVCVITS.eval()
|
||||
torch.onnx.export(SVCVITS,
|
||||
(
|
||||
test_hidden_unit.to(device),
|
||||
test_pitch.to(device),
|
||||
test_mel2ph.to(device),
|
||||
test_uv.to(device),
|
||||
test_noise.to(device),
|
||||
test_sid.to(device)
|
||||
),
|
||||
f"checkpoints/{path}/model.onnx",
|
||||
dynamic_axes={
|
||||
"c": [0, 1],
|
||||
"f0": [1],
|
||||
"mel2ph": [1],
|
||||
"uv": [1],
|
||||
"noise": [2],
|
||||
},
|
||||
do_constant_folding=False,
|
||||
opset_version=16,
|
||||
verbose=False,
|
||||
input_names=input_names,
|
||||
output_names=output_names)
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
main(False, True)
|
||||
@@ -0,0 +1,335 @@
|
||||
import torch
|
||||
from torch import nn
|
||||
from torch.nn import functional as F
|
||||
|
||||
import modules.attentions as attentions
|
||||
import modules.commons as commons
|
||||
import modules.modules as modules
|
||||
|
||||
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
||||
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
||||
|
||||
import utils
|
||||
from modules.commons import init_weights, get_padding
|
||||
from vdecoder.hifigan.models import Generator
|
||||
from utils import f0_to_coarse
|
||||
|
||||
|
||||
class ResidualCouplingBlock(nn.Module):
|
||||
def __init__(self,
|
||||
channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
n_flows=4,
|
||||
gin_channels=0):
|
||||
super().__init__()
|
||||
self.channels = channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.n_flows = n_flows
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.flows = nn.ModuleList()
|
||||
for i in range(n_flows):
|
||||
self.flows.append(
|
||||
modules.ResidualCouplingLayer(channels, hidden_channels, kernel_size, dilation_rate, n_layers,
|
||||
gin_channels=gin_channels, mean_only=True))
|
||||
self.flows.append(modules.Flip())
|
||||
|
||||
def forward(self, x, x_mask, g=None, reverse=False):
|
||||
if not reverse:
|
||||
for flow in self.flows:
|
||||
x, _ = flow(x, x_mask, g=g, reverse=reverse)
|
||||
else:
|
||||
for flow in reversed(self.flows):
|
||||
x = flow(x, x_mask, g=g, reverse=reverse)
|
||||
return x
|
||||
|
||||
|
||||
class Encoder(nn.Module):
|
||||
def __init__(self,
|
||||
in_channels,
|
||||
out_channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
dilation_rate,
|
||||
n_layers,
|
||||
gin_channels=0):
|
||||
super().__init__()
|
||||
self.in_channels = in_channels
|
||||
self.out_channels = out_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.dilation_rate = dilation_rate
|
||||
self.n_layers = n_layers
|
||||
self.gin_channels = gin_channels
|
||||
|
||||
self.pre = nn.Conv1d(in_channels, hidden_channels, 1)
|
||||
self.enc = modules.WN(hidden_channels, kernel_size, dilation_rate, n_layers, gin_channels=gin_channels)
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
||||
|
||||
def forward(self, x, x_lengths, g=None):
|
||||
# print(x.shape,x_lengths.shape)
|
||||
x_mask = torch.unsqueeze(commons.sequence_mask(x_lengths, x.size(2)), 1).to(x.dtype)
|
||||
x = self.pre(x) * x_mask
|
||||
x = self.enc(x, x_mask, g=g)
|
||||
stats = self.proj(x) * x_mask
|
||||
m, logs = torch.split(stats, self.out_channels, dim=1)
|
||||
z = (m + torch.randn_like(m) * torch.exp(logs)) * x_mask
|
||||
return z, m, logs, x_mask
|
||||
|
||||
|
||||
class TextEncoder(nn.Module):
|
||||
def __init__(self,
|
||||
out_channels,
|
||||
hidden_channels,
|
||||
kernel_size,
|
||||
n_layers,
|
||||
gin_channels=0,
|
||||
filter_channels=None,
|
||||
n_heads=None,
|
||||
p_dropout=None):
|
||||
super().__init__()
|
||||
self.out_channels = out_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.kernel_size = kernel_size
|
||||
self.n_layers = n_layers
|
||||
self.gin_channels = gin_channels
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels * 2, 1)
|
||||
self.f0_emb = nn.Embedding(256, hidden_channels)
|
||||
|
||||
self.enc_ = attentions.Encoder(
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout)
|
||||
|
||||
def forward(self, x, x_mask, f0=None, z=None):
|
||||
x = x + self.f0_emb(f0).transpose(1, 2)
|
||||
x = self.enc_(x * x_mask, x_mask)
|
||||
stats = self.proj(x) * x_mask
|
||||
m, logs = torch.split(stats, self.out_channels, dim=1)
|
||||
z = (m + z * torch.exp(logs)) * x_mask
|
||||
return z, m, logs, x_mask
|
||||
|
||||
|
||||
class DiscriminatorP(torch.nn.Module):
|
||||
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
|
||||
super(DiscriminatorP, self).__init__()
|
||||
self.period = period
|
||||
self.use_spectral_norm = use_spectral_norm
|
||||
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||||
self.convs = nn.ModuleList([
|
||||
norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
||||
norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
||||
norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
||||
norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(kernel_size, 1), 0))),
|
||||
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(get_padding(kernel_size, 1), 0))),
|
||||
])
|
||||
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
|
||||
# 1d to 2d
|
||||
b, c, t = x.shape
|
||||
if t % self.period != 0: # pad first
|
||||
n_pad = self.period - (t % self.period)
|
||||
x = F.pad(x, (0, n_pad), "reflect")
|
||||
t = t + n_pad
|
||||
x = x.view(b, c, t // self.period, self.period)
|
||||
|
||||
for l in self.convs:
|
||||
x = l(x)
|
||||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x, fmap
|
||||
|
||||
|
||||
class DiscriminatorS(torch.nn.Module):
|
||||
def __init__(self, use_spectral_norm=False):
|
||||
super(DiscriminatorS, self).__init__()
|
||||
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||||
self.convs = nn.ModuleList([
|
||||
norm_f(Conv1d(1, 16, 15, 1, padding=7)),
|
||||
norm_f(Conv1d(16, 64, 41, 4, groups=4, padding=20)),
|
||||
norm_f(Conv1d(64, 256, 41, 4, groups=16, padding=20)),
|
||||
norm_f(Conv1d(256, 1024, 41, 4, groups=64, padding=20)),
|
||||
norm_f(Conv1d(1024, 1024, 41, 4, groups=256, padding=20)),
|
||||
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
|
||||
])
|
||||
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
|
||||
for l in self.convs:
|
||||
x = l(x)
|
||||
x = F.leaky_relu(x, modules.LRELU_SLOPE)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x, fmap
|
||||
|
||||
|
||||
class F0Decoder(nn.Module):
|
||||
def __init__(self,
|
||||
out_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
spk_channels=0):
|
||||
super().__init__()
|
||||
self.out_channels = out_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.n_heads = n_heads
|
||||
self.n_layers = n_layers
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.spk_channels = spk_channels
|
||||
|
||||
self.prenet = nn.Conv1d(hidden_channels, hidden_channels, 3, padding=1)
|
||||
self.decoder = attentions.FFT(
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout)
|
||||
self.proj = nn.Conv1d(hidden_channels, out_channels, 1)
|
||||
self.f0_prenet = nn.Conv1d(1, hidden_channels, 3, padding=1)
|
||||
self.cond = nn.Conv1d(spk_channels, hidden_channels, 1)
|
||||
|
||||
def forward(self, x, norm_f0, x_mask, spk_emb=None):
|
||||
x = torch.detach(x)
|
||||
if spk_emb is not None:
|
||||
x = x + self.cond(spk_emb)
|
||||
x += self.f0_prenet(norm_f0)
|
||||
x = self.prenet(x) * x_mask
|
||||
x = self.decoder(x * x_mask, x_mask)
|
||||
x = self.proj(x) * x_mask
|
||||
return x
|
||||
|
||||
|
||||
class SynthesizerTrn(nn.Module):
|
||||
"""
|
||||
Synthesizer for Training
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
spec_channels,
|
||||
segment_size,
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
resblock,
|
||||
resblock_kernel_sizes,
|
||||
resblock_dilation_sizes,
|
||||
upsample_rates,
|
||||
upsample_initial_channel,
|
||||
upsample_kernel_sizes,
|
||||
gin_channels,
|
||||
ssl_dim,
|
||||
n_speakers,
|
||||
sampling_rate=44100,
|
||||
**kwargs):
|
||||
super().__init__()
|
||||
self.spec_channels = spec_channels
|
||||
self.inter_channels = inter_channels
|
||||
self.hidden_channels = hidden_channels
|
||||
self.filter_channels = filter_channels
|
||||
self.n_heads = n_heads
|
||||
self.n_layers = n_layers
|
||||
self.kernel_size = kernel_size
|
||||
self.p_dropout = p_dropout
|
||||
self.resblock = resblock
|
||||
self.resblock_kernel_sizes = resblock_kernel_sizes
|
||||
self.resblock_dilation_sizes = resblock_dilation_sizes
|
||||
self.upsample_rates = upsample_rates
|
||||
self.upsample_initial_channel = upsample_initial_channel
|
||||
self.upsample_kernel_sizes = upsample_kernel_sizes
|
||||
self.segment_size = segment_size
|
||||
self.gin_channels = gin_channels
|
||||
self.ssl_dim = ssl_dim
|
||||
self.emb_g = nn.Embedding(n_speakers, gin_channels)
|
||||
|
||||
self.pre = nn.Conv1d(ssl_dim, hidden_channels, kernel_size=5, padding=2)
|
||||
|
||||
self.enc_p = TextEncoder(
|
||||
inter_channels,
|
||||
hidden_channels,
|
||||
filter_channels=filter_channels,
|
||||
n_heads=n_heads,
|
||||
n_layers=n_layers,
|
||||
kernel_size=kernel_size,
|
||||
p_dropout=p_dropout
|
||||
)
|
||||
hps = {
|
||||
"sampling_rate": sampling_rate,
|
||||
"inter_channels": inter_channels,
|
||||
"resblock": resblock,
|
||||
"resblock_kernel_sizes": resblock_kernel_sizes,
|
||||
"resblock_dilation_sizes": resblock_dilation_sizes,
|
||||
"upsample_rates": upsample_rates,
|
||||
"upsample_initial_channel": upsample_initial_channel,
|
||||
"upsample_kernel_sizes": upsample_kernel_sizes,
|
||||
"gin_channels": gin_channels,
|
||||
}
|
||||
self.dec = Generator(h=hps)
|
||||
self.enc_q = Encoder(spec_channels, inter_channels, hidden_channels, 5, 1, 16, gin_channels=gin_channels)
|
||||
self.flow = ResidualCouplingBlock(inter_channels, hidden_channels, 5, 1, 4, gin_channels=gin_channels)
|
||||
self.f0_decoder = F0Decoder(
|
||||
1,
|
||||
hidden_channels,
|
||||
filter_channels,
|
||||
n_heads,
|
||||
n_layers,
|
||||
kernel_size,
|
||||
p_dropout,
|
||||
spk_channels=gin_channels
|
||||
)
|
||||
self.emb_uv = nn.Embedding(2, hidden_channels)
|
||||
self.predict_f0 = False
|
||||
|
||||
def forward(self, c, f0, mel2ph, uv, noise=None, g=None):
|
||||
|
||||
decoder_inp = F.pad(c, [0, 0, 1, 0])
|
||||
mel2ph_ = mel2ph.unsqueeze(2).repeat([1, 1, c.shape[-1]])
|
||||
c = torch.gather(decoder_inp, 1, mel2ph_).transpose(1, 2) # [B, T, H]
|
||||
|
||||
c_lengths = (torch.ones(c.size(0)) * c.size(-1)).to(c.device)
|
||||
g = g.unsqueeze(0)
|
||||
g = self.emb_g(g).transpose(1, 2)
|
||||
x_mask = torch.unsqueeze(commons.sequence_mask(c_lengths, c.size(2)), 1).to(c.dtype)
|
||||
x = self.pre(c) * x_mask + self.emb_uv(uv.long()).transpose(1, 2)
|
||||
|
||||
if self.predict_f0:
|
||||
lf0 = 2595. * torch.log10(1. + f0.unsqueeze(1) / 700.) / 500
|
||||
norm_lf0 = utils.normalize_f0(lf0, x_mask, uv, random_scale=False)
|
||||
pred_lf0 = self.f0_decoder(x, norm_lf0, x_mask, spk_emb=g)
|
||||
f0 = (700 * (torch.pow(10, pred_lf0 * 500 / 2595) - 1)).squeeze(1)
|
||||
|
||||
z_p, m_p, logs_p, c_mask = self.enc_p(x, x_mask, f0=f0_to_coarse(f0), z=noise)
|
||||
z = self.flow(z_p, c_mask, g=g, reverse=True)
|
||||
o = self.dec(z * c_mask, g=g, f0=f0)
|
||||
return o
|
||||
@@ -0,0 +1,83 @@
|
||||
import os
|
||||
import argparse
|
||||
import re
|
||||
|
||||
from tqdm import tqdm
|
||||
from random import shuffle
|
||||
import json
|
||||
import wave
|
||||
|
||||
config_template = json.load(open("configs/config.json"))
|
||||
|
||||
pattern = re.compile(r'^[\.a-zA-Z0-9_\/]+$')
|
||||
|
||||
def get_wav_duration(file_path):
|
||||
with wave.open(file_path, 'rb') as wav_file:
|
||||
# 获取音频帧数
|
||||
n_frames = wav_file.getnframes()
|
||||
# 获取采样率
|
||||
framerate = wav_file.getframerate()
|
||||
# 计算时长（秒）
|
||||
duration = n_frames / float(framerate)
|
||||
return duration
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--train_list", type=str, default="./filelists/train.txt", help="path to train list")
|
||||
parser.add_argument("--val_list", type=str, default="./filelists/val.txt", help="path to val list")
|
||||
parser.add_argument("--test_list", type=str, default="./filelists/test.txt", help="path to test list")
|
||||
parser.add_argument("--source_dir", type=str, default="./dataset/44k", help="path to source dir")
|
||||
args = parser.parse_args()
|
||||
|
||||
train = []
|
||||
val = []
|
||||
test = []
|
||||
idx = 0
|
||||
spk_dict = {}
|
||||
spk_id = 0
|
||||
for speaker in tqdm(os.listdir(args.source_dir)):
|
||||
spk_dict[speaker] = spk_id
|
||||
spk_id += 1
|
||||
wavs = ["/".join([args.source_dir, speaker, i]) for i in os.listdir(os.path.join(args.source_dir, speaker))]
|
||||
new_wavs = []
|
||||
for file in wavs:
|
||||
if not file.endswith("wav"):
|
||||
continue
|
||||
if not pattern.match(file):
|
||||
print(f"warning：文件名{file}中包含非字母数字下划线，可能会导致错误。（也可能不会）")
|
||||
if get_wav_duration(file) < 0.3:
|
||||
print("skip too short audio:", file)
|
||||
continue
|
||||
new_wavs.append(file)
|
||||
wavs = new_wavs
|
||||
shuffle(wavs)
|
||||
train += wavs[2:-2]
|
||||
val += wavs[:2]
|
||||
test += wavs[-2:]
|
||||
|
||||
shuffle(train)
|
||||
shuffle(val)
|
||||
shuffle(test)
|
||||
|
||||
print("Writing", args.train_list)
|
||||
with open(args.train_list, "w") as f:
|
||||
for fname in tqdm(train):
|
||||
wavpath = fname
|
||||
f.write(wavpath + "\n")
|
||||
|
||||
print("Writing", args.val_list)
|
||||
with open(args.val_list, "w") as f:
|
||||
for fname in tqdm(val):
|
||||
wavpath = fname
|
||||
f.write(wavpath + "\n")
|
||||
|
||||
print("Writing", args.test_list)
|
||||
with open(args.test_list, "w") as f:
|
||||
for fname in tqdm(test):
|
||||
wavpath = fname
|
||||
f.write(wavpath + "\n")
|
||||
|
||||
config_template["spk"] = spk_dict
|
||||
print("Writing configs/config.json")
|
||||
with open("configs/config.json", "w") as f:
|
||||
json.dump(config_template, f, indent=2)
|
||||
@@ -0,0 +1,62 @@
|
||||
import math
|
||||
import multiprocessing
|
||||
import os
|
||||
import argparse
|
||||
from random import shuffle
|
||||
|
||||
import torch
|
||||
from glob import glob
|
||||
from tqdm import tqdm
|
||||
|
||||
import utils
|
||||
import logging
|
||||
logging.getLogger('numba').setLevel(logging.WARNING)
|
||||
import librosa
|
||||
import numpy as np
|
||||
|
||||
hps = utils.get_hparams_from_file("configs/config.json")
|
||||
sampling_rate = hps.data.sampling_rate
|
||||
hop_length = hps.data.hop_length
|
||||
|
||||
|
||||
def process_one(filename, hmodel):
|
||||
# print(filename)
|
||||
wav, sr = librosa.load(filename, sr=sampling_rate)
|
||||
soft_path = filename + ".soft.pt"
|
||||
if not os.path.exists(soft_path):
|
||||
devive = torch.device("cuda" if torch.cuda.is_available() else "cpu")
|
||||
wav16k = librosa.resample(wav, orig_sr=sampling_rate, target_sr=16000)
|
||||
wav16k = torch.from_numpy(wav16k).to(devive)
|
||||
c = utils.get_hubert_content(hmodel, wav_16k_tensor=wav16k)
|
||||
torch.save(c.cpu(), soft_path)
|
||||
f0_path = filename + ".f0.npy"
|
||||
if not os.path.exists(f0_path):
|
||||
f0 = utils.compute_f0_dio(wav, sampling_rate=sampling_rate, hop_length=hop_length)
|
||||
np.save(f0_path, f0)
|
||||
|
||||
|
||||
def process_batch(filenames):
|
||||
print("Loading hubert for content...")
|
||||
device = "cuda" if torch.cuda.is_available() else "cpu"
|
||||
hmodel = utils.get_hubert_model().to(device)
|
||||
print("Loaded hubert.")
|
||||
for filename in tqdm(filenames):
|
||||
process_one(filename, hmodel)
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--in_dir", type=str, default="dataset/44k", help="path to input dir")
|
||||
|
||||
args = parser.parse_args()
|
||||
filenames = glob(f'{args.in_dir}/*/*.wav', recursive=True) # [:10]
|
||||
shuffle(filenames)
|
||||
multiprocessing.set_start_method('spawn',force=True)
|
||||
|
||||
num_processes = 1
|
||||
chunk_size = int(math.ceil(len(filenames) / num_processes))
|
||||
chunks = [filenames[i:i + chunk_size] for i in range(0, len(filenames), chunk_size)]
|
||||
print([len(c) for c in chunks])
|
||||
processes = [multiprocessing.Process(target=process_batch, args=(chunk,)) for chunk in chunks]
|
||||
for p in processes:
|
||||
p.start()
|
||||
@@ -0,0 +1,19 @@
|
||||
Flask
|
||||
Flask_Cors
|
||||
gradio
|
||||
numpy
|
||||
#pyworld==0.2.5
|
||||
pyworld==0.2.12
|
||||
scipy==1.7.3
|
||||
SoundFile==0.12.1
|
||||
torch==1.13.1
|
||||
torchaudio==0.13.1
|
||||
tqdm
|
||||
scikit-maad
|
||||
praat-parselmouth
|
||||
onnx
|
||||
onnxsim
|
||||
onnxoptimizer
|
||||
fairseq==0.12.2
|
||||
librosa==0.8.1
|
||||
tensorboard
|
||||
@@ -0,0 +1,9 @@
|
||||
numpy==1.20.3
|
||||
pyworld==0.2.5
|
||||
scipy==1.7.3
|
||||
SoundFile==0.12.1
|
||||
tqdm
|
||||
scikit-maad
|
||||
praat-parselmouth
|
||||
fairseq==0.12.2
|
||||
librosa==0.8.1
|
||||
@@ -0,0 +1,23 @@
|
||||
librosa==0.9.2
|
||||
fairseq==0.12.2
|
||||
Flask==2.1.2
|
||||
Flask_Cors==3.0.10
|
||||
gradio==3.4.1
|
||||
numpy==1.20.0
|
||||
playsound==1.3.0
|
||||
PyAudio==0.2.12
|
||||
pydub==0.25.1
|
||||
pyworld==0.3.0
|
||||
requests==2.28.1
|
||||
scipy==1.7.3
|
||||
sounddevice==0.4.5
|
||||
SoundFile==0.10.3.post1
|
||||
starlette==0.19.1
|
||||
torch==1.12.0+cu116
|
||||
torchaudio==0.12.0+cu116
|
||||
tqdm==4.63.0
|
||||
scikit-maad
|
||||
praat-parselmouth
|
||||
onnx
|
||||
onnxsim
|
||||
onnxoptimizer
|
||||
@@ -0,0 +1,48 @@
|
||||
import os
|
||||
import argparse
|
||||
import librosa
|
||||
import numpy as np
|
||||
from multiprocessing import Pool, cpu_count
|
||||
from scipy.io import wavfile
|
||||
from tqdm import tqdm
|
||||
|
||||
|
||||
def process(item):
|
||||
spkdir, wav_name, args = item
|
||||
# speaker 's5', 'p280', 'p315' are excluded,
|
||||
speaker = spkdir.replace("\\", "/").split("/")[-1]
|
||||
wav_path = os.path.join(args.in_dir, speaker, wav_name)
|
||||
if os.path.exists(wav_path) and '.wav' in wav_path:
|
||||
os.makedirs(os.path.join(args.out_dir2, speaker), exist_ok=True)
|
||||
wav, sr = librosa.load(wav_path, sr=None)
|
||||
wav, _ = librosa.effects.trim(wav, top_db=20)
|
||||
peak = np.abs(wav).max()
|
||||
if peak > 1.0:
|
||||
wav = 0.98 * wav / peak
|
||||
wav2 = librosa.resample(wav, orig_sr=sr, target_sr=args.sr2)
|
||||
wav2 /= max(wav2.max(), -wav2.min())
|
||||
save_name = wav_name
|
||||
save_path2 = os.path.join(args.out_dir2, speaker, save_name)
|
||||
wavfile.write(
|
||||
save_path2,
|
||||
args.sr2,
|
||||
(wav2 * np.iinfo(np.int16).max).astype(np.int16)
|
||||
)
|
||||
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument("--sr2", type=int, default=44100, help="sampling rate")
|
||||
parser.add_argument("--in_dir", type=str, default="./dataset_raw", help="path to source dir")
|
||||
parser.add_argument("--out_dir2", type=str, default="./dataset/44k", help="path to target dir")
|
||||
args = parser.parse_args()
|
||||
processs = cpu_count()-2 if cpu_count() >4 else 1
|
||||
pool = Pool(processes=processs)
|
||||
|
||||
for speaker in os.listdir(args.in_dir):
|
||||
spk_dir = os.path.join(args.in_dir, speaker)
|
||||
if os.path.isdir(spk_dir):
|
||||
print(spk_dir)
|
||||
for _ in tqdm(pool.imap_unordered(process, [(spk_dir, i, args) for i in os.listdir(spk_dir) if i.endswith("wav")])):
|
||||
pass
|
||||
@@ -0,0 +1,22 @@
|
||||
from data_utils import TextAudioSpeakerLoader
|
||||
import json
|
||||
from tqdm import tqdm
|
||||
|
||||
from utils import HParams
|
||||
|
||||
config_path = 'configs/config.json'
|
||||
with open(config_path, "r") as f:
|
||||
data = f.read()
|
||||
config = json.loads(data)
|
||||
hps = HParams(**config)
|
||||
|
||||
train_dataset = TextAudioSpeakerLoader("filelists/train.txt", hps)
|
||||
test_dataset = TextAudioSpeakerLoader("filelists/test.txt", hps)
|
||||
eval_dataset = TextAudioSpeakerLoader("filelists/val.txt", hps)
|
||||
|
||||
for _ in tqdm(train_dataset):
|
||||
pass
|
||||
for _ in tqdm(eval_dataset):
|
||||
pass
|
||||
for _ in tqdm(test_dataset):
|
||||
pass
|
||||
@@ -0,0 +1,310 @@
|
||||
import logging
|
||||
import multiprocessing
|
||||
import time
|
||||
|
||||
logging.getLogger('matplotlib').setLevel(logging.WARNING)
|
||||
import os
|
||||
import json
|
||||
import argparse
|
||||
import itertools
|
||||
import math
|
||||
import torch
|
||||
from torch import nn, optim
|
||||
from torch.nn import functional as F
|
||||
from torch.utils.data import DataLoader
|
||||
from torch.utils.tensorboard import SummaryWriter
|
||||
import torch.multiprocessing as mp
|
||||
import torch.distributed as dist
|
||||
from torch.nn.parallel import DistributedDataParallel as DDP
|
||||
from torch.cuda.amp import autocast, GradScaler
|
||||
|
||||
import modules.commons as commons
|
||||
import utils
|
||||
from data_utils import TextAudioSpeakerLoader, TextAudioCollate
|
||||
from models import (
|
||||
SynthesizerTrn,
|
||||
MultiPeriodDiscriminator,
|
||||
)
|
||||
from modules.losses import (
|
||||
kl_loss,
|
||||
generator_loss, discriminator_loss, feature_loss
|
||||
)
|
||||
|
||||
from modules.mel_processing import mel_spectrogram_torch, spec_to_mel_torch
|
||||
|
||||
torch.backends.cudnn.benchmark = True
|
||||
global_step = 0
|
||||
start_time = time.time()
|
||||
|
||||
# os.environ['TORCH_DISTRIBUTED_DEBUG'] = 'INFO'
|
||||
|
||||
|
||||
def main():
|
||||
"""Assume Single Node Multi GPUs Training Only"""
|
||||
assert torch.cuda.is_available(), "CPU training is not allowed."
|
||||
hps = utils.get_hparams()
|
||||
|
||||
n_gpus = torch.cuda.device_count()
|
||||
os.environ['MASTER_ADDR'] = 'localhost'
|
||||
os.environ['MASTER_PORT'] = hps.train.port
|
||||
|
||||
mp.spawn(run, nprocs=n_gpus, args=(n_gpus, hps,))
|
||||
|
||||
|
||||
def run(rank, n_gpus, hps):
|
||||
global global_step
|
||||
if rank == 0:
|
||||
logger = utils.get_logger(hps.model_dir)
|
||||
logger.info(hps)
|
||||
utils.check_git_hash(hps.model_dir)
|
||||
writer = SummaryWriter(log_dir=hps.model_dir)
|
||||
writer_eval = SummaryWriter(log_dir=os.path.join(hps.model_dir, "eval"))
|
||||
|
||||
# for pytorch on win, backend use gloo
|
||||
dist.init_process_group(backend= 'gloo' if os.name == 'nt' else 'nccl', init_method='env://', world_size=n_gpus, rank=rank)
|
||||
torch.manual_seed(hps.train.seed)
|
||||
torch.cuda.set_device(rank)
|
||||
collate_fn = TextAudioCollate()
|
||||
train_dataset = TextAudioSpeakerLoader(hps.data.training_files, hps)
|
||||
num_workers = 5 if multiprocessing.cpu_count() > 4 else multiprocessing.cpu_count()
|
||||
train_loader = DataLoader(train_dataset, num_workers=num_workers, shuffle=False, pin_memory=True,
|
||||
batch_size=hps.train.batch_size, collate_fn=collate_fn)
|
||||
if rank == 0:
|
||||
eval_dataset = TextAudioSpeakerLoader(hps.data.validation_files, hps)
|
||||
eval_loader = DataLoader(eval_dataset, num_workers=1, shuffle=False,
|
||||
batch_size=1, pin_memory=False,
|
||||
drop_last=False, collate_fn=collate_fn)
|
||||
|
||||
net_g = SynthesizerTrn(
|
||||
hps.data.filter_length // 2 + 1,
|
||||
hps.train.segment_size // hps.data.hop_length,
|
||||
**hps.model).cuda(rank)
|
||||
net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
|
||||
optim_g = torch.optim.AdamW(
|
||||
net_g.parameters(),
|
||||
hps.train.learning_rate,
|
||||
betas=hps.train.betas,
|
||||
eps=hps.train.eps)
|
||||
optim_d = torch.optim.AdamW(
|
||||
net_d.parameters(),
|
||||
hps.train.learning_rate,
|
||||
betas=hps.train.betas,
|
||||
eps=hps.train.eps)
|
||||
net_g = DDP(net_g, device_ids=[rank]) # , find_unused_parameters=True)
|
||||
net_d = DDP(net_d, device_ids=[rank])
|
||||
|
||||
skip_optimizer = False
|
||||
try:
|
||||
_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g,
|
||||
optim_g, skip_optimizer)
|
||||
_, _, _, epoch_str = utils.load_checkpoint(utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d,
|
||||
optim_d, skip_optimizer)
|
||||
epoch_str = max(epoch_str, 1)
|
||||
global_step = (epoch_str - 1) * len(train_loader)
|
||||
except:
|
||||
print("load old checkpoint failed...")
|
||||
epoch_str = 1
|
||||
global_step = 0
|
||||
if skip_optimizer:
|
||||
epoch_str = 1
|
||||
global_step = 0
|
||||
|
||||
scheduler_g = torch.optim.lr_scheduler.ExponentialLR(optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
|
||||
scheduler_d = torch.optim.lr_scheduler.ExponentialLR(optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
|
||||
|
||||
scaler = GradScaler(enabled=hps.train.fp16_run)
|
||||
|
||||
for epoch in range(epoch_str, hps.train.epochs + 1):
|
||||
if rank == 0:
|
||||
train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler,
|
||||
[train_loader, eval_loader], logger, [writer, writer_eval])
|
||||
else:
|
||||
train_and_evaluate(rank, epoch, hps, [net_g, net_d], [optim_g, optim_d], [scheduler_g, scheduler_d], scaler,
|
||||
[train_loader, None], None, None)
|
||||
scheduler_g.step()
|
||||
scheduler_d.step()
|
||||
|
||||
|
||||
def train_and_evaluate(rank, epoch, hps, nets, optims, schedulers, scaler, loaders, logger, writers):
|
||||
net_g, net_d = nets
|
||||
optim_g, optim_d = optims
|
||||
scheduler_g, scheduler_d = schedulers
|
||||
train_loader, eval_loader = loaders
|
||||
if writers is not None:
|
||||
writer, writer_eval = writers
|
||||
|
||||
# train_loader.batch_sampler.set_epoch(epoch)
|
||||
global global_step
|
||||
|
||||
net_g.train()
|
||||
net_d.train()
|
||||
for batch_idx, items in enumerate(train_loader):
|
||||
c, f0, spec, y, spk, lengths, uv = items
|
||||
g = spk.cuda(rank, non_blocking=True)
|
||||
spec, y = spec.cuda(rank, non_blocking=True), y.cuda(rank, non_blocking=True)
|
||||
c = c.cuda(rank, non_blocking=True)
|
||||
f0 = f0.cuda(rank, non_blocking=True)
|
||||
uv = uv.cuda(rank, non_blocking=True)
|
||||
lengths = lengths.cuda(rank, non_blocking=True)
|
||||
mel = spec_to_mel_torch(
|
||||
spec,
|
||||
hps.data.filter_length,
|
||||
hps.data.n_mel_channels,
|
||||
hps.data.sampling_rate,
|
||||
hps.data.mel_fmin,
|
||||
hps.data.mel_fmax)
|
||||
|
||||
with autocast(enabled=hps.train.fp16_run):
|
||||
y_hat, ids_slice, z_mask, \
|
||||
(z, z_p, m_p, logs_p, m_q, logs_q), pred_lf0, norm_lf0, lf0 = net_g(c, f0, uv, spec, g=g, c_lengths=lengths,
|
||||
spec_lengths=lengths)
|
||||
|
||||
y_mel = commons.slice_segments(mel, ids_slice, hps.train.segment_size // hps.data.hop_length)
|
||||
y_hat_mel = mel_spectrogram_torch(
|
||||
y_hat.squeeze(1),
|
||||
hps.data.filter_length,
|
||||
hps.data.n_mel_channels,
|
||||
hps.data.sampling_rate,
|
||||
hps.data.hop_length,
|
||||
hps.data.win_length,
|
||||
hps.data.mel_fmin,
|
||||
hps.data.mel_fmax
|
||||
)
|
||||
y = commons.slice_segments(y, ids_slice * hps.data.hop_length, hps.train.segment_size) # slice
|
||||
|
||||
# Discriminator
|
||||
y_d_hat_r, y_d_hat_g, _, _ = net_d(y, y_hat.detach())
|
||||
|
||||
with autocast(enabled=False):
|
||||
loss_disc, losses_disc_r, losses_disc_g = discriminator_loss(y_d_hat_r, y_d_hat_g)
|
||||
loss_disc_all = loss_disc
|
||||
|
||||
optim_d.zero_grad()
|
||||
scaler.scale(loss_disc_all).backward()
|
||||
scaler.unscale_(optim_d)
|
||||
grad_norm_d = commons.clip_grad_value_(net_d.parameters(), None)
|
||||
scaler.step(optim_d)
|
||||
|
||||
with autocast(enabled=hps.train.fp16_run):
|
||||
# Generator
|
||||
y_d_hat_r, y_d_hat_g, fmap_r, fmap_g = net_d(y, y_hat)
|
||||
with autocast(enabled=False):
|
||||
loss_mel = F.l1_loss(y_mel, y_hat_mel) * hps.train.c_mel
|
||||
loss_kl = kl_loss(z_p, logs_q, m_p, logs_p, z_mask) * hps.train.c_kl
|
||||
loss_fm = feature_loss(fmap_r, fmap_g)
|
||||
loss_gen, losses_gen = generator_loss(y_d_hat_g)
|
||||
loss_lf0 = F.mse_loss(pred_lf0, lf0)
|
||||
loss_gen_all = loss_gen + loss_fm + loss_mel + loss_kl + loss_lf0
|
||||
optim_g.zero_grad()
|
||||
scaler.scale(loss_gen_all).backward()
|
||||
scaler.unscale_(optim_g)
|
||||
grad_norm_g = commons.clip_grad_value_(net_g.parameters(), None)
|
||||
scaler.step(optim_g)
|
||||
scaler.update()
|
||||
|
||||
if rank == 0:
|
||||
if global_step % hps.train.log_interval == 0:
|
||||
lr = optim_g.param_groups[0]['lr']
|
||||
losses = [loss_disc, loss_gen, loss_fm, loss_mel, loss_kl]
|
||||
logger.info('Train Epoch: {} [{:.0f}%]'.format(
|
||||
epoch,
|
||||
100. * batch_idx / len(train_loader)))
|
||||
logger.info(f"Losses: {[x.item() for x in losses]}, step: {global_step}, lr: {lr}")
|
||||
|
||||
scalar_dict = {"loss/g/total": loss_gen_all, "loss/d/total": loss_disc_all, "learning_rate": lr,
|
||||
"grad_norm_d": grad_norm_d, "grad_norm_g": grad_norm_g}
|
||||
scalar_dict.update({"loss/g/fm": loss_fm, "loss/g/mel": loss_mel, "loss/g/kl": loss_kl,
|
||||
"loss/g/lf0": loss_lf0})
|
||||
|
||||
# scalar_dict.update({"loss/g/{}".format(i): v for i, v in enumerate(losses_gen)})
|
||||
# scalar_dict.update({"loss/d_r/{}".format(i): v for i, v in enumerate(losses_disc_r)})
|
||||
# scalar_dict.update({"loss/d_g/{}".format(i): v for i, v in enumerate(losses_disc_g)})
|
||||
image_dict = {
|
||||
"slice/mel_org": utils.plot_spectrogram_to_numpy(y_mel[0].data.cpu().numpy()),
|
||||
"slice/mel_gen": utils.plot_spectrogram_to_numpy(y_hat_mel[0].data.cpu().numpy()),
|
||||
"all/mel": utils.plot_spectrogram_to_numpy(mel[0].data.cpu().numpy()),
|
||||
"all/lf0": utils.plot_data_to_numpy(lf0[0, 0, :].cpu().numpy(),
|
||||
pred_lf0[0, 0, :].detach().cpu().numpy()),
|
||||
"all/norm_lf0": utils.plot_data_to_numpy(lf0[0, 0, :].cpu().numpy(),
|
||||
norm_lf0[0, 0, :].detach().cpu().numpy())
|
||||
}
|
||||
|
||||
utils.summarize(
|
||||
writer=writer,
|
||||
global_step=global_step,
|
||||
images=image_dict,
|
||||
scalars=scalar_dict
|
||||
)
|
||||
|
||||
if global_step % hps.train.eval_interval == 0:
|
||||
evaluate(hps, net_g, eval_loader, writer_eval)
|
||||
utils.save_checkpoint(net_g, optim_g, hps.train.learning_rate, epoch,
|
||||
os.path.join(hps.model_dir, "G_{}.pth".format(global_step)))
|
||||
utils.save_checkpoint(net_d, optim_d, hps.train.learning_rate, epoch,
|
||||
os.path.join(hps.model_dir, "D_{}.pth".format(global_step)))
|
||||
keep_ckpts = getattr(hps.train, 'keep_ckpts', 0)
|
||||
if keep_ckpts > 0:
|
||||
utils.clean_checkpoints(path_to_models=hps.model_dir, n_ckpts_to_keep=keep_ckpts, sort_by_time=True)
|
||||
|
||||
global_step += 1
|
||||
|
||||
if rank == 0:
|
||||
global start_time
|
||||
now = time.time()
|
||||
durtaion = format(now - start_time, '.2f')
|
||||
logger.info(f'====> Epoch: {epoch}, cost {durtaion} s')
|
||||
start_time = now
|
||||
|
||||
|
||||
def evaluate(hps, generator, eval_loader, writer_eval):
|
||||
generator.eval()
|
||||
image_dict = {}
|
||||
audio_dict = {}
|
||||
with torch.no_grad():
|
||||
for batch_idx, items in enumerate(eval_loader):
|
||||
c, f0, spec, y, spk, _, uv = items
|
||||
g = spk[:1].cuda(0)
|
||||
spec, y = spec[:1].cuda(0), y[:1].cuda(0)
|
||||
c = c[:1].cuda(0)
|
||||
f0 = f0[:1].cuda(0)
|
||||
uv= uv[:1].cuda(0)
|
||||
mel = spec_to_mel_torch(
|
||||
spec,
|
||||
hps.data.filter_length,
|
||||
hps.data.n_mel_channels,
|
||||
hps.data.sampling_rate,
|
||||
hps.data.mel_fmin,
|
||||
hps.data.mel_fmax)
|
||||
y_hat = generator.module.infer(c, f0, uv, g=g)
|
||||
|
||||
y_hat_mel = mel_spectrogram_torch(
|
||||
y_hat.squeeze(1).float(),
|
||||
hps.data.filter_length,
|
||||
hps.data.n_mel_channels,
|
||||
hps.data.sampling_rate,
|
||||
hps.data.hop_length,
|
||||
hps.data.win_length,
|
||||
hps.data.mel_fmin,
|
||||
hps.data.mel_fmax
|
||||
)
|
||||
|
||||
audio_dict.update({
|
||||
f"gen/audio_{batch_idx}": y_hat[0],
|
||||
f"gt/audio_{batch_idx}": y[0]
|
||||
})
|
||||
image_dict.update({
|
||||
f"gen/mel": utils.plot_spectrogram_to_numpy(y_hat_mel[0].cpu().numpy()),
|
||||
"gt/mel": utils.plot_spectrogram_to_numpy(mel[0].cpu().numpy())
|
||||
})
|
||||
utils.summarize(
|
||||
writer=writer_eval,
|
||||
global_step=global_step,
|
||||
images=image_dict,
|
||||
audios=audio_dict,
|
||||
audio_sampling_rate=hps.data.sampling_rate
|
||||
)
|
||||
generator.train()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
@@ -0,0 +1,18 @@
|
||||
Flask
|
||||
Flask_Cors
|
||||
gradio
|
||||
numpy==1.20.3
|
||||
pyworld==0.2.5
|
||||
scipy==1.7.3
|
||||
SoundFile==0.12.1
|
||||
torch==1.13.1
|
||||
torchaudio==0.13.1
|
||||
tqdm
|
||||
scikit-maad
|
||||
praat-parselmouth
|
||||
onnx
|
||||
onnxsim
|
||||
onnxoptimizer
|
||||
fairseq==0.12.2
|
||||
librosa==0.8.1
|
||||
tensorboard
|
||||
@@ -0,0 +1,502 @@
|
||||
import os
|
||||
import glob
|
||||
import re
|
||||
import sys
|
||||
import argparse
|
||||
import logging
|
||||
import json
|
||||
import subprocess
|
||||
import random
|
||||
|
||||
import librosa
|
||||
import numpy as np
|
||||
from scipy.io.wavfile import read
|
||||
import torch
|
||||
from torch.nn import functional as F
|
||||
from modules.commons import sequence_mask
|
||||
from hubert import hubert_model
|
||||
MATPLOTLIB_FLAG = False
|
||||
|
||||
logging.basicConfig(stream=sys.stdout, level=logging.DEBUG)
|
||||
logger = logging
|
||||
|
||||
f0_bin = 256
|
||||
f0_max = 1100.0
|
||||
f0_min = 50.0
|
||||
f0_mel_min = 1127 * np.log(1 + f0_min / 700)
|
||||
f0_mel_max = 1127 * np.log(1 + f0_max / 700)
|
||||
|
||||
|
||||
# def normalize_f0(f0, random_scale=True):
|
||||
# f0_norm = f0.clone() # create a copy of the input Tensor
|
||||
# batch_size, _, frame_length = f0_norm.shape
|
||||
# for i in range(batch_size):
|
||||
# means = torch.mean(f0_norm[i, 0, :])
|
||||
# if random_scale:
|
||||
# factor = random.uniform(0.8, 1.2)
|
||||
# else:
|
||||
# factor = 1
|
||||
# f0_norm[i, 0, :] = (f0_norm[i, 0, :] - means) * factor
|
||||
# return f0_norm
|
||||
# def normalize_f0(f0, random_scale=True):
|
||||
# means = torch.mean(f0[:, 0, :], dim=1, keepdim=True)
|
||||
# if random_scale:
|
||||
# factor = torch.Tensor(f0.shape[0],1).uniform_(0.8, 1.2).to(f0.device)
|
||||
# else:
|
||||
# factor = torch.ones(f0.shape[0], 1, 1).to(f0.device)
|
||||
# f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1)
|
||||
# return f0_norm
|
||||
def normalize_f0(f0, x_mask, uv, random_scale=True):
|
||||
# calculate means based on x_mask
|
||||
uv_sum = torch.sum(uv, dim=1, keepdim=True)
|
||||
uv_sum[uv_sum == 0] = 9999
|
||||
means = torch.sum(f0[:, 0, :] * uv, dim=1, keepdim=True) / uv_sum
|
||||
|
||||
if random_scale:
|
||||
factor = torch.Tensor(f0.shape[0], 1).uniform_(0.8, 1.2).to(f0.device)
|
||||
else:
|
||||
factor = torch.ones(f0.shape[0], 1).to(f0.device)
|
||||
# normalize f0 based on means and factor
|
||||
f0_norm = (f0 - means.unsqueeze(-1)) * factor.unsqueeze(-1)
|
||||
if torch.isnan(f0_norm).any():
|
||||
exit(0)
|
||||
return f0_norm * x_mask
|
||||
|
||||
|
||||
def plot_data_to_numpy(x, y):
|
||||
global MATPLOTLIB_FLAG
|
||||
if not MATPLOTLIB_FLAG:
|
||||
import matplotlib
|
||||
matplotlib.use("Agg")
|
||||
MATPLOTLIB_FLAG = True
|
||||
mpl_logger = logging.getLogger('matplotlib')
|
||||
mpl_logger.setLevel(logging.WARNING)
|
||||
import matplotlib.pylab as plt
|
||||
import numpy as np
|
||||
|
||||
fig, ax = plt.subplots(figsize=(10, 2))
|
||||
plt.plot(x)
|
||||
plt.plot(y)
|
||||
plt.tight_layout()
|
||||
|
||||
fig.canvas.draw()
|
||||
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
|
||||
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
||||
plt.close()
|
||||
return data
|
||||
|
||||
|
||||
|
||||
def interpolate_f0(f0):
|
||||
'''
|
||||
对F0进行插值处理
|
||||
'''
|
||||
|
||||
data = np.reshape(f0, (f0.size, 1))
|
||||
|
||||
vuv_vector = np.zeros((data.size, 1), dtype=np.float32)
|
||||
vuv_vector[data > 0.0] = 1.0
|
||||
vuv_vector[data <= 0.0] = 0.0
|
||||
|
||||
ip_data = data
|
||||
|
||||
frame_number = data.size
|
||||
last_value = 0.0
|
||||
for i in range(frame_number):
|
||||
if data[i] <= 0.0:
|
||||
j = i + 1
|
||||
for j in range(i + 1, frame_number):
|
||||
if data[j] > 0.0:
|
||||
break
|
||||
if j < frame_number - 1:
|
||||
if last_value > 0.0:
|
||||
step = (data[j] - data[i - 1]) / float(j - i)
|
||||
for k in range(i, j):
|
||||
ip_data[k] = data[i - 1] + step * (k - i + 1)
|
||||
else:
|
||||
for k in range(i, j):
|
||||
ip_data[k] = data[j]
|
||||
else:
|
||||
for k in range(i, frame_number):
|
||||
ip_data[k] = last_value
|
||||
else:
|
||||
ip_data[i] = data[i]
|
||||
last_value = data[i]
|
||||
|
||||
return ip_data[:,0], vuv_vector[:,0]
|
||||
|
||||
|
||||
def compute_f0_parselmouth(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512):
|
||||
import parselmouth
|
||||
x = wav_numpy
|
||||
if p_len is None:
|
||||
p_len = x.shape[0]//hop_length
|
||||
else:
|
||||
assert abs(p_len-x.shape[0]//hop_length) < 4, "pad length error"
|
||||
time_step = hop_length / sampling_rate * 1000
|
||||
f0_min = 50
|
||||
f0_max = 1100
|
||||
f0 = parselmouth.Sound(x, sampling_rate).to_pitch_ac(
|
||||
time_step=time_step / 1000, voicing_threshold=0.6,
|
||||
pitch_floor=f0_min, pitch_ceiling=f0_max).selected_array['frequency']
|
||||
|
||||
pad_size=(p_len - len(f0) + 1) // 2
|
||||
if(pad_size>0 or p_len - len(f0) - pad_size>0):
|
||||
f0 = np.pad(f0,[[pad_size,p_len - len(f0) - pad_size]], mode='constant')
|
||||
return f0
|
||||
|
||||
def resize_f0(x, target_len):
|
||||
source = np.array(x)
|
||||
source[source<0.001] = np.nan
|
||||
target = np.interp(np.arange(0, len(source)*target_len, len(source))/ target_len, np.arange(0, len(source)), source)
|
||||
res = np.nan_to_num(target)
|
||||
return res
|
||||
|
||||
def compute_f0_dio(wav_numpy, p_len=None, sampling_rate=44100, hop_length=512):
|
||||
import pyworld
|
||||
if p_len is None:
|
||||
p_len = wav_numpy.shape[0]//hop_length
|
||||
f0, t = pyworld.dio(
|
||||
wav_numpy.astype(np.double),
|
||||
fs=sampling_rate,
|
||||
f0_ceil=800,
|
||||
frame_period=1000 * hop_length / sampling_rate,
|
||||
)
|
||||
f0 = pyworld.stonemask(wav_numpy.astype(np.double), f0, t, sampling_rate)
|
||||
for index, pitch in enumerate(f0):
|
||||
f0[index] = round(pitch, 1)
|
||||
return resize_f0(f0, p_len)
|
||||
|
||||
def f0_to_coarse(f0):
|
||||
is_torch = isinstance(f0, torch.Tensor)
|
||||
f0_mel = 1127 * (1 + f0 / 700).log() if is_torch else 1127 * np.log(1 + f0 / 700)
|
||||
f0_mel[f0_mel > 0] = (f0_mel[f0_mel > 0] - f0_mel_min) * (f0_bin - 2) / (f0_mel_max - f0_mel_min) + 1
|
||||
|
||||
f0_mel[f0_mel <= 1] = 1
|
||||
f0_mel[f0_mel > f0_bin - 1] = f0_bin - 1
|
||||
f0_coarse = (f0_mel + 0.5).long() if is_torch else np.rint(f0_mel).astype(np.int)
|
||||
assert f0_coarse.max() <= 255 and f0_coarse.min() >= 1, (f0_coarse.max(), f0_coarse.min())
|
||||
return f0_coarse
|
||||
|
||||
|
||||
def get_hubert_model():
|
||||
vec_path = "hubert/checkpoint_best_legacy_500.pt"
|
||||
print("load model(s) from {}".format(vec_path))
|
||||
from fairseq import checkpoint_utils
|
||||
models, saved_cfg, task = checkpoint_utils.load_model_ensemble_and_task(
|
||||
[vec_path],
|
||||
suffix="",
|
||||
)
|
||||
model = models[0]
|
||||
model.eval()
|
||||
return model
|
||||
|
||||
def get_hubert_content(hmodel, wav_16k_tensor):
|
||||
feats = wav_16k_tensor
|
||||
if feats.dim() == 2: # double channels
|
||||
feats = feats.mean(-1)
|
||||
assert feats.dim() == 1, feats.dim()
|
||||
feats = feats.view(1, -1)
|
||||
padding_mask = torch.BoolTensor(feats.shape).fill_(False)
|
||||
inputs = {
|
||||
"source": feats.to(wav_16k_tensor.device),
|
||||
"padding_mask": padding_mask.to(wav_16k_tensor.device),
|
||||
"output_layer": 9, # layer 9
|
||||
}
|
||||
with torch.no_grad():
|
||||
logits = hmodel.extract_features(**inputs)
|
||||
feats = hmodel.final_proj(logits[0])
|
||||
return feats.transpose(1, 2)
|
||||
|
||||
|
||||
def get_content(cmodel, y):
|
||||
with torch.no_grad():
|
||||
c = cmodel.extract_features(y.squeeze(1))[0]
|
||||
c = c.transpose(1, 2)
|
||||
return c
|
||||
|
||||
|
||||
|
||||
def load_checkpoint(checkpoint_path, model, optimizer=None, skip_optimizer=False):
|
||||
assert os.path.isfile(checkpoint_path)
|
||||
checkpoint_dict = torch.load(checkpoint_path, map_location='cpu')
|
||||
iteration = checkpoint_dict['iteration']
|
||||
learning_rate = checkpoint_dict['learning_rate']
|
||||
if optimizer is not None and not skip_optimizer and checkpoint_dict['optimizer'] is not None:
|
||||
optimizer.load_state_dict(checkpoint_dict['optimizer'])
|
||||
saved_state_dict = checkpoint_dict['model']
|
||||
if hasattr(model, 'module'):
|
||||
state_dict = model.module.state_dict()
|
||||
else:
|
||||
state_dict = model.state_dict()
|
||||
new_state_dict = {}
|
||||
for k, v in state_dict.items():
|
||||
try:
|
||||
# assert "dec" in k or "disc" in k
|
||||
# print("load", k)
|
||||
new_state_dict[k] = saved_state_dict[k]
|
||||
assert saved_state_dict[k].shape == v.shape, (saved_state_dict[k].shape, v.shape)
|
||||
except:
|
||||
print("error, %s is not in the checkpoint" % k)
|
||||
logger.info("%s is not in the checkpoint" % k)
|
||||
new_state_dict[k] = v
|
||||
if hasattr(model, 'module'):
|
||||
model.module.load_state_dict(new_state_dict)
|
||||
else:
|
||||
model.load_state_dict(new_state_dict)
|
||||
print("load ")
|
||||
logger.info("Loaded checkpoint '{}' (iteration {})".format(
|
||||
checkpoint_path, iteration))
|
||||
return model, optimizer, learning_rate, iteration
|
||||
|
||||
|
||||
def save_checkpoint(model, optimizer, learning_rate, iteration, checkpoint_path):
|
||||
logger.info("Saving model and optimizer state at iteration {} to {}".format(
|
||||
iteration, checkpoint_path))
|
||||
if hasattr(model, 'module'):
|
||||
state_dict = model.module.state_dict()
|
||||
else:
|
||||
state_dict = model.state_dict()
|
||||
torch.save({'model': state_dict,
|
||||
'iteration': iteration,
|
||||
'optimizer': optimizer.state_dict(),
|
||||
'learning_rate': learning_rate}, checkpoint_path)
|
||||
|
||||
def clean_checkpoints(path_to_models='logs/44k/', n_ckpts_to_keep=2, sort_by_time=True):
|
||||
"""Freeing up space by deleting saved ckpts
|
||||
|
||||
Arguments:
|
||||
path_to_models -- Path to the model directory
|
||||
n_ckpts_to_keep -- Number of ckpts to keep, excluding G_0.pth and D_0.pth
|
||||
sort_by_time -- True -> chronologically delete ckpts
|
||||
False -> lexicographically delete ckpts
|
||||
"""
|
||||
ckpts_files = [f for f in os.listdir(path_to_models) if os.path.isfile(os.path.join(path_to_models, f))]
|
||||
name_key = (lambda _f: int(re.compile('._(\d+)\.pth').match(_f).group(1)))
|
||||
time_key = (lambda _f: os.path.getmtime(os.path.join(path_to_models, _f)))
|
||||
sort_key = time_key if sort_by_time else name_key
|
||||
x_sorted = lambda _x: sorted([f for f in ckpts_files if f.startswith(_x) and not f.endswith('_0.pth')], key=sort_key)
|
||||
to_del = [os.path.join(path_to_models, fn) for fn in
|
||||
(x_sorted('G')[:-n_ckpts_to_keep] + x_sorted('D')[:-n_ckpts_to_keep])]
|
||||
del_info = lambda fn: logger.info(f".. Free up space by deleting ckpt {fn}")
|
||||
del_routine = lambda x: [os.remove(x), del_info(x)]
|
||||
rs = [del_routine(fn) for fn in to_del]
|
||||
|
||||
def summarize(writer, global_step, scalars={}, histograms={}, images={}, audios={}, audio_sampling_rate=22050):
|
||||
for k, v in scalars.items():
|
||||
writer.add_scalar(k, v, global_step)
|
||||
for k, v in histograms.items():
|
||||
writer.add_histogram(k, v, global_step)
|
||||
for k, v in images.items():
|
||||
writer.add_image(k, v, global_step, dataformats='HWC')
|
||||
for k, v in audios.items():
|
||||
writer.add_audio(k, v, global_step, audio_sampling_rate)
|
||||
|
||||
|
||||
def latest_checkpoint_path(dir_path, regex="G_*.pth"):
|
||||
f_list = glob.glob(os.path.join(dir_path, regex))
|
||||
f_list.sort(key=lambda f: int("".join(filter(str.isdigit, f))))
|
||||
x = f_list[-1]
|
||||
print(x)
|
||||
return x
|
||||
|
||||
|
||||
def plot_spectrogram_to_numpy(spectrogram):
|
||||
global MATPLOTLIB_FLAG
|
||||
if not MATPLOTLIB_FLAG:
|
||||
import matplotlib
|
||||
matplotlib.use("Agg")
|
||||
MATPLOTLIB_FLAG = True
|
||||
mpl_logger = logging.getLogger('matplotlib')
|
||||
mpl_logger.setLevel(logging.WARNING)
|
||||
import matplotlib.pylab as plt
|
||||
import numpy as np
|
||||
|
||||
fig, ax = plt.subplots(figsize=(10,2))
|
||||
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
|
||||
interpolation='none')
|
||||
plt.colorbar(im, ax=ax)
|
||||
plt.xlabel("Frames")
|
||||
plt.ylabel("Channels")
|
||||
plt.tight_layout()
|
||||
|
||||
fig.canvas.draw()
|
||||
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
|
||||
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
||||
plt.close()
|
||||
return data
|
||||
|
||||
|
||||
def plot_alignment_to_numpy(alignment, info=None):
|
||||
global MATPLOTLIB_FLAG
|
||||
if not MATPLOTLIB_FLAG:
|
||||
import matplotlib
|
||||
matplotlib.use("Agg")
|
||||
MATPLOTLIB_FLAG = True
|
||||
mpl_logger = logging.getLogger('matplotlib')
|
||||
mpl_logger.setLevel(logging.WARNING)
|
||||
import matplotlib.pylab as plt
|
||||
import numpy as np
|
||||
|
||||
fig, ax = plt.subplots(figsize=(6, 4))
|
||||
im = ax.imshow(alignment.transpose(), aspect='auto', origin='lower',
|
||||
interpolation='none')
|
||||
fig.colorbar(im, ax=ax)
|
||||
xlabel = 'Decoder timestep'
|
||||
if info is not None:
|
||||
xlabel += '\n\n' + info
|
||||
plt.xlabel(xlabel)
|
||||
plt.ylabel('Encoder timestep')
|
||||
plt.tight_layout()
|
||||
|
||||
fig.canvas.draw()
|
||||
data = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8, sep='')
|
||||
data = data.reshape(fig.canvas.get_width_height()[::-1] + (3,))
|
||||
plt.close()
|
||||
return data
|
||||
|
||||
|
||||
def load_wav_to_torch(full_path):
|
||||
sampling_rate, data = read(full_path)
|
||||
return torch.FloatTensor(data.astype(np.float32)), sampling_rate
|
||||
|
||||
|
||||
def load_filepaths_and_text(filename, split="|"):
|
||||
with open(filename, encoding='utf-8') as f:
|
||||
filepaths_and_text = [line.strip().split(split) for line in f]
|
||||
return filepaths_and_text
|
||||
|
||||
|
||||
def get_hparams(init=True):
|
||||
parser = argparse.ArgumentParser()
|
||||
parser.add_argument('-c', '--config', type=str, default="./configs/base.json",
|
||||
help='JSON file for configuration')
|
||||
parser.add_argument('-m', '--model', type=str, required=True,
|
||||
help='Model name')
|
||||
|
||||
args = parser.parse_args()
|
||||
model_dir = os.path.join("./logs", args.model)
|
||||
|
||||
if not os.path.exists(model_dir):
|
||||
os.makedirs(model_dir)
|
||||
|
||||
config_path = args.config
|
||||
config_save_path = os.path.join(model_dir, "config.json")
|
||||
if init:
|
||||
with open(config_path, "r") as f:
|
||||
data = f.read()
|
||||
with open(config_save_path, "w") as f:
|
||||
f.write(data)
|
||||
else:
|
||||
with open(config_save_path, "r") as f:
|
||||
data = f.read()
|
||||
config = json.loads(data)
|
||||
|
||||
hparams = HParams(**config)
|
||||
hparams.model_dir = model_dir
|
||||
return hparams
|
||||
|
||||
|
||||
def get_hparams_from_dir(model_dir):
|
||||
config_save_path = os.path.join(model_dir, "config.json")
|
||||
with open(config_save_path, "r") as f:
|
||||
data = f.read()
|
||||
config = json.loads(data)
|
||||
|
||||
hparams =HParams(**config)
|
||||
hparams.model_dir = model_dir
|
||||
return hparams
|
||||
|
||||
|
||||
def get_hparams_from_file(config_path):
|
||||
with open(config_path, "r") as f:
|
||||
data = f.read()
|
||||
config = json.loads(data)
|
||||
|
||||
hparams =HParams(**config)
|
||||
return hparams
|
||||
|
||||
|
||||
def check_git_hash(model_dir):
|
||||
source_dir = os.path.dirname(os.path.realpath(__file__))
|
||||
if not os.path.exists(os.path.join(source_dir, ".git")):
|
||||
logger.warn("{} is not a git repository, therefore hash value comparison will be ignored.".format(
|
||||
source_dir
|
||||
))
|
||||
return
|
||||
|
||||
cur_hash = subprocess.getoutput("git rev-parse HEAD")
|
||||
|
||||
path = os.path.join(model_dir, "githash")
|
||||
if os.path.exists(path):
|
||||
saved_hash = open(path).read()
|
||||
if saved_hash != cur_hash:
|
||||
logger.warn("git hash values are different. {}(saved) != {}(current)".format(
|
||||
saved_hash[:8], cur_hash[:8]))
|
||||
else:
|
||||
open(path, "w").write(cur_hash)
|
||||
|
||||
|
||||
def get_logger(model_dir, filename="train.log"):
|
||||
global logger
|
||||
logger = logging.getLogger(os.path.basename(model_dir))
|
||||
logger.setLevel(logging.DEBUG)
|
||||
|
||||
formatter = logging.Formatter("%(asctime)s\t%(name)s\t%(levelname)s\t%(message)s")
|
||||
if not os.path.exists(model_dir):
|
||||
os.makedirs(model_dir)
|
||||
h = logging.FileHandler(os.path.join(model_dir, filename))
|
||||
h.setLevel(logging.DEBUG)
|
||||
h.setFormatter(formatter)
|
||||
logger.addHandler(h)
|
||||
return logger
|
||||
|
||||
|
||||
def repeat_expand_2d(content, target_len):
|
||||
# content : [h, t]
|
||||
|
||||
src_len = content.shape[-1]
|
||||
target = torch.zeros([content.shape[0], target_len], dtype=torch.float).to(content.device)
|
||||
temp = torch.arange(src_len+1) * target_len / src_len
|
||||
current_pos = 0
|
||||
for i in range(target_len):
|
||||
if i < temp[current_pos+1]:
|
||||
target[:, i] = content[:, current_pos]
|
||||
else:
|
||||
current_pos += 1
|
||||
target[:, i] = content[:, current_pos]
|
||||
|
||||
return target
|
||||
|
||||
|
||||
class HParams():
|
||||
def __init__(self, **kwargs):
|
||||
for k, v in kwargs.items():
|
||||
if type(v) == dict:
|
||||
v = HParams(**v)
|
||||
self[k] = v
|
||||
|
||||
def keys(self):
|
||||
return self.__dict__.keys()
|
||||
|
||||
def items(self):
|
||||
return self.__dict__.items()
|
||||
|
||||
def values(self):
|
||||
return self.__dict__.values()
|
||||
|
||||
def __len__(self):
|
||||
return len(self.__dict__)
|
||||
|
||||
def __getitem__(self, key):
|
||||
return getattr(self, key)
|
||||
|
||||
def __setitem__(self, key, value):
|
||||
return setattr(self, key, value)
|
||||
|
||||
def __contains__(self, key):
|
||||
return key in self.__dict__
|
||||
|
||||
def __repr__(self):
|
||||
return self.__dict__.__repr__()
|
||||
|
||||
@@ -0,0 +1,15 @@
|
||||
import os
|
||||
import shutil
|
||||
|
||||
|
||||
class AttrDict(dict):
|
||||
def __init__(self, *args, **kwargs):
|
||||
super(AttrDict, self).__init__(*args, **kwargs)
|
||||
self.__dict__ = self
|
||||
|
||||
|
||||
def build_env(config, config_name, path):
|
||||
t_path = os.path.join(path, config_name)
|
||||
if config != t_path:
|
||||
os.makedirs(path, exist_ok=True)
|
||||
shutil.copyfile(config, os.path.join(path, config_name))
|
||||
@@ -0,0 +1,503 @@
|
||||
import os
|
||||
import json
|
||||
from .env import AttrDict
|
||||
import numpy as np
|
||||
import torch
|
||||
import torch.nn.functional as F
|
||||
import torch.nn as nn
|
||||
from torch.nn import Conv1d, ConvTranspose1d, AvgPool1d, Conv2d
|
||||
from torch.nn.utils import weight_norm, remove_weight_norm, spectral_norm
|
||||
from .utils import init_weights, get_padding
|
||||
|
||||
LRELU_SLOPE = 0.1
|
||||
|
||||
|
||||
def load_model(model_path, device='cuda'):
|
||||
config_file = os.path.join(os.path.split(model_path)[0], 'config.json')
|
||||
with open(config_file) as f:
|
||||
data = f.read()
|
||||
|
||||
global h
|
||||
json_config = json.loads(data)
|
||||
h = AttrDict(json_config)
|
||||
|
||||
generator = Generator(h).to(device)
|
||||
|
||||
cp_dict = torch.load(model_path)
|
||||
generator.load_state_dict(cp_dict['generator'])
|
||||
generator.eval()
|
||||
generator.remove_weight_norm()
|
||||
del cp_dict
|
||||
return generator, h
|
||||
|
||||
|
||||
class ResBlock1(torch.nn.Module):
|
||||
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3, 5)):
|
||||
super(ResBlock1, self).__init__()
|
||||
self.h = h
|
||||
self.convs1 = nn.ModuleList([
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
||||
padding=get_padding(kernel_size, dilation[0]))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
||||
padding=get_padding(kernel_size, dilation[1]))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[2],
|
||||
padding=get_padding(kernel_size, dilation[2])))
|
||||
])
|
||||
self.convs1.apply(init_weights)
|
||||
|
||||
self.convs2 = nn.ModuleList([
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
||||
padding=get_padding(kernel_size, 1))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
||||
padding=get_padding(kernel_size, 1))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=1,
|
||||
padding=get_padding(kernel_size, 1)))
|
||||
])
|
||||
self.convs2.apply(init_weights)
|
||||
|
||||
def forward(self, x):
|
||||
for c1, c2 in zip(self.convs1, self.convs2):
|
||||
xt = F.leaky_relu(x, LRELU_SLOPE)
|
||||
xt = c1(xt)
|
||||
xt = F.leaky_relu(xt, LRELU_SLOPE)
|
||||
xt = c2(xt)
|
||||
x = xt + x
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for l in self.convs1:
|
||||
remove_weight_norm(l)
|
||||
for l in self.convs2:
|
||||
remove_weight_norm(l)
|
||||
|
||||
|
||||
class ResBlock2(torch.nn.Module):
|
||||
def __init__(self, h, channels, kernel_size=3, dilation=(1, 3)):
|
||||
super(ResBlock2, self).__init__()
|
||||
self.h = h
|
||||
self.convs = nn.ModuleList([
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[0],
|
||||
padding=get_padding(kernel_size, dilation[0]))),
|
||||
weight_norm(Conv1d(channels, channels, kernel_size, 1, dilation=dilation[1],
|
||||
padding=get_padding(kernel_size, dilation[1])))
|
||||
])
|
||||
self.convs.apply(init_weights)
|
||||
|
||||
def forward(self, x):
|
||||
for c in self.convs:
|
||||
xt = F.leaky_relu(x, LRELU_SLOPE)
|
||||
xt = c(xt)
|
||||
x = xt + x
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
for l in self.convs:
|
||||
remove_weight_norm(l)
|
||||
|
||||
|
||||
def padDiff(x):
|
||||
return F.pad(F.pad(x, (0,0,-1,1), 'constant', 0) - x, (0,0,0,-1), 'constant', 0)
|
||||
|
||||
class SineGen(torch.nn.Module):
|
||||
""" Definition of sine generator
|
||||
SineGen(samp_rate, harmonic_num = 0,
|
||||
sine_amp = 0.1, noise_std = 0.003,
|
||||
voiced_threshold = 0,
|
||||
flag_for_pulse=False)
|
||||
samp_rate: sampling rate in Hz
|
||||
harmonic_num: number of harmonic overtones (default 0)
|
||||
sine_amp: amplitude of sine-wavefrom (default 0.1)
|
||||
noise_std: std of Gaussian noise (default 0.003)
|
||||
voiced_thoreshold: F0 threshold for U/V classification (default 0)
|
||||
flag_for_pulse: this SinGen is used inside PulseGen (default False)
|
||||
Note: when flag_for_pulse is True, the first time step of a voiced
|
||||
segment is always sin(np.pi) or cos(0)
|
||||
"""
|
||||
|
||||
def __init__(self, samp_rate, harmonic_num=0,
|
||||
sine_amp=0.1, noise_std=0.003,
|
||||
voiced_threshold=0,
|
||||
flag_for_pulse=False):
|
||||
super(SineGen, self).__init__()
|
||||
self.sine_amp = sine_amp
|
||||
self.noise_std = noise_std
|
||||
self.harmonic_num = harmonic_num
|
||||
self.dim = self.harmonic_num + 1
|
||||
self.sampling_rate = samp_rate
|
||||
self.voiced_threshold = voiced_threshold
|
||||
self.flag_for_pulse = flag_for_pulse
|
||||
|
||||
def _f02uv(self, f0):
|
||||
# generate uv signal
|
||||
uv = (f0 > self.voiced_threshold).type(torch.float32)
|
||||
return uv
|
||||
|
||||
def _f02sine(self, f0_values):
|
||||
""" f0_values: (batchsize, length, dim)
|
||||
where dim indicates fundamental tone and overtones
|
||||
"""
|
||||
# convert to F0 in rad. The interger part n can be ignored
|
||||
# because 2 * np.pi * n doesn't affect phase
|
||||
rad_values = (f0_values / self.sampling_rate) % 1
|
||||
|
||||
# initial phase noise (no noise for fundamental component)
|
||||
rand_ini = torch.rand(f0_values.shape[0], f0_values.shape[2], \
|
||||
device=f0_values.device)
|
||||
rand_ini[:, 0] = 0
|
||||
rad_values[:, 0, :] = rad_values[:, 0, :] + rand_ini
|
||||
|
||||
# instantanouse phase sine[t] = sin(2*pi \sum_i=1 ^{t} rad)
|
||||
if not self.flag_for_pulse:
|
||||
# for normal case
|
||||
|
||||
# To prevent torch.cumsum numerical overflow,
|
||||
# it is necessary to add -1 whenever \sum_k=1^n rad_value_k > 1.
|
||||
# Buffer tmp_over_one_idx indicates the time step to add -1.
|
||||
# This will not change F0 of sine because (x-1) * 2*pi = x * 2*pi
|
||||
tmp_over_one = torch.cumsum(rad_values, 1) % 1
|
||||
tmp_over_one_idx = (padDiff(tmp_over_one)) < 0
|
||||
cumsum_shift = torch.zeros_like(rad_values)
|
||||
cumsum_shift[:, 1:, :] = tmp_over_one_idx * -1.0
|
||||
|
||||
sines = torch.sin(torch.cumsum(rad_values + cumsum_shift, dim=1)
|
||||
* 2 * np.pi)
|
||||
else:
|
||||
# If necessary, make sure that the first time step of every
|
||||
# voiced segments is sin(pi) or cos(0)
|
||||
# This is used for pulse-train generation
|
||||
|
||||
# identify the last time step in unvoiced segments
|
||||
uv = self._f02uv(f0_values)
|
||||
uv_1 = torch.roll(uv, shifts=-1, dims=1)
|
||||
uv_1[:, -1, :] = 1
|
||||
u_loc = (uv < 1) * (uv_1 > 0)
|
||||
|
||||
# get the instantanouse phase
|
||||
tmp_cumsum = torch.cumsum(rad_values, dim=1)
|
||||
# different batch needs to be processed differently
|
||||
for idx in range(f0_values.shape[0]):
|
||||
temp_sum = tmp_cumsum[idx, u_loc[idx, :, 0], :]
|
||||
temp_sum[1:, :] = temp_sum[1:, :] - temp_sum[0:-1, :]
|
||||
# stores the accumulation of i.phase within
|
||||
# each voiced segments
|
||||
tmp_cumsum[idx, :, :] = 0
|
||||
tmp_cumsum[idx, u_loc[idx, :, 0], :] = temp_sum
|
||||
|
||||
# rad_values - tmp_cumsum: remove the accumulation of i.phase
|
||||
# within the previous voiced segment.
|
||||
i_phase = torch.cumsum(rad_values - tmp_cumsum, dim=1)
|
||||
|
||||
# get the sines
|
||||
sines = torch.cos(i_phase * 2 * np.pi)
|
||||
return sines
|
||||
|
||||
def forward(self, f0):
|
||||
""" sine_tensor, uv = forward(f0)
|
||||
input F0: tensor(batchsize=1, length, dim=1)
|
||||
f0 for unvoiced steps should be 0
|
||||
output sine_tensor: tensor(batchsize=1, length, dim)
|
||||
output uv: tensor(batchsize=1, length, 1)
|
||||
"""
|
||||
with torch.no_grad():
|
||||
f0_buf = torch.zeros(f0.shape[0], f0.shape[1], self.dim,
|
||||
device=f0.device)
|
||||
# fundamental component
|
||||
fn = torch.multiply(f0, torch.FloatTensor([[range(1, self.harmonic_num + 2)]]).to(f0.device))
|
||||
|
||||
# generate sine waveforms
|
||||
sine_waves = self._f02sine(fn) * self.sine_amp
|
||||
|
||||
# generate uv signal
|
||||
# uv = torch.ones(f0.shape)
|
||||
# uv = uv * (f0 > self.voiced_threshold)
|
||||
uv = self._f02uv(f0)
|
||||
|
||||
# noise: for unvoiced should be similar to sine_amp
|
||||
# std = self.sine_amp/3 -> max value ~ self.sine_amp
|
||||
# . for voiced regions is self.noise_std
|
||||
noise_amp = uv * self.noise_std + (1 - uv) * self.sine_amp / 3
|
||||
noise = noise_amp * torch.randn_like(sine_waves)
|
||||
|
||||
# first: set the unvoiced part to 0 by uv
|
||||
# then: additive noise
|
||||
sine_waves = sine_waves * uv + noise
|
||||
return sine_waves, uv, noise
|
||||
|
||||
|
||||
class SourceModuleHnNSF(torch.nn.Module):
|
||||
""" SourceModule for hn-nsf
|
||||
SourceModule(sampling_rate, harmonic_num=0, sine_amp=0.1,
|
||||
add_noise_std=0.003, voiced_threshod=0)
|
||||
sampling_rate: sampling_rate in Hz
|
||||
harmonic_num: number of harmonic above F0 (default: 0)
|
||||
sine_amp: amplitude of sine source signal (default: 0.1)
|
||||
add_noise_std: std of additive Gaussian noise (default: 0.003)
|
||||
note that amplitude of noise in unvoiced is decided
|
||||
by sine_amp
|
||||
voiced_threshold: threhold to set U/V given F0 (default: 0)
|
||||
Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
|
||||
F0_sampled (batchsize, length, 1)
|
||||
Sine_source (batchsize, length, 1)
|
||||
noise_source (batchsize, length 1)
|
||||
uv (batchsize, length, 1)
|
||||
"""
|
||||
|
||||
def __init__(self, sampling_rate, harmonic_num=0, sine_amp=0.1,
|
||||
add_noise_std=0.003, voiced_threshod=0):
|
||||
super(SourceModuleHnNSF, self).__init__()
|
||||
|
||||
self.sine_amp = sine_amp
|
||||
self.noise_std = add_noise_std
|
||||
|
||||
# to produce sine waveforms
|
||||
self.l_sin_gen = SineGen(sampling_rate, harmonic_num,
|
||||
sine_amp, add_noise_std, voiced_threshod)
|
||||
|
||||
# to merge source harmonics into a single excitation
|
||||
self.l_linear = torch.nn.Linear(harmonic_num + 1, 1)
|
||||
self.l_tanh = torch.nn.Tanh()
|
||||
|
||||
def forward(self, x):
|
||||
"""
|
||||
Sine_source, noise_source = SourceModuleHnNSF(F0_sampled)
|
||||
F0_sampled (batchsize, length, 1)
|
||||
Sine_source (batchsize, length, 1)
|
||||
noise_source (batchsize, length 1)
|
||||
"""
|
||||
# source for harmonic branch
|
||||
sine_wavs, uv, _ = self.l_sin_gen(x)
|
||||
sine_merge = self.l_tanh(self.l_linear(sine_wavs))
|
||||
|
||||
# source for noise branch, in the same shape as uv
|
||||
noise = torch.randn_like(uv) * self.sine_amp / 3
|
||||
return sine_merge, noise, uv
|
||||
|
||||
|
||||
class Generator(torch.nn.Module):
|
||||
def __init__(self, h):
|
||||
super(Generator, self).__init__()
|
||||
self.h = h
|
||||
|
||||
self.num_kernels = len(h["resblock_kernel_sizes"])
|
||||
self.num_upsamples = len(h["upsample_rates"])
|
||||
self.f0_upsamp = torch.nn.Upsample(scale_factor=np.prod(h["upsample_rates"]))
|
||||
self.m_source = SourceModuleHnNSF(
|
||||
sampling_rate=h["sampling_rate"],
|
||||
harmonic_num=8)
|
||||
self.noise_convs = nn.ModuleList()
|
||||
self.conv_pre = weight_norm(Conv1d(h["inter_channels"], h["upsample_initial_channel"], 7, 1, padding=3))
|
||||
resblock = ResBlock1 if h["resblock"] == '1' else ResBlock2
|
||||
self.ups = nn.ModuleList()
|
||||
for i, (u, k) in enumerate(zip(h["upsample_rates"], h["upsample_kernel_sizes"])):
|
||||
c_cur = h["upsample_initial_channel"] // (2 ** (i + 1))
|
||||
self.ups.append(weight_norm(
|
||||
ConvTranspose1d(h["upsample_initial_channel"] // (2 ** i), h["upsample_initial_channel"] // (2 ** (i + 1)),
|
||||
k, u, padding=(k - u) // 2)))
|
||||
if i + 1 < len(h["upsample_rates"]): #
|
||||
stride_f0 = np.prod(h["upsample_rates"][i + 1:])
|
||||
self.noise_convs.append(Conv1d(
|
||||
1, c_cur, kernel_size=stride_f0 * 2, stride=stride_f0, padding=stride_f0 // 2))
|
||||
else:
|
||||
self.noise_convs.append(Conv1d(1, c_cur, kernel_size=1))
|
||||
self.resblocks = nn.ModuleList()
|
||||
for i in range(len(self.ups)):
|
||||
ch = h["upsample_initial_channel"] // (2 ** (i + 1))
|
||||
for j, (k, d) in enumerate(zip(h["resblock_kernel_sizes"], h["resblock_dilation_sizes"])):
|
||||
self.resblocks.append(resblock(h, ch, k, d))
|
||||
|
||||
self.conv_post = weight_norm(Conv1d(ch, 1, 7, 1, padding=3))
|
||||
self.ups.apply(init_weights)
|
||||
self.conv_post.apply(init_weights)
|
||||
self.cond = nn.Conv1d(h['gin_channels'], h['upsample_initial_channel'], 1)
|
||||
|
||||
def forward(self, x, f0, g=None):
|
||||
# print(1,x.shape,f0.shape,f0[:, None].shape)
|
||||
f0 = self.f0_upsamp(f0[:, None]).transpose(1, 2) # bs,n,t
|
||||
# print(2,f0.shape)
|
||||
har_source, noi_source, uv = self.m_source(f0)
|
||||
har_source = har_source.transpose(1, 2)
|
||||
x = self.conv_pre(x)
|
||||
x = x + self.cond(g)
|
||||
# print(124,x.shape,har_source.shape)
|
||||
for i in range(self.num_upsamples):
|
||||
x = F.leaky_relu(x, LRELU_SLOPE)
|
||||
# print(3,x.shape)
|
||||
x = self.ups[i](x)
|
||||
x_source = self.noise_convs[i](har_source)
|
||||
# print(4,x_source.shape,har_source.shape,x.shape)
|
||||
x = x + x_source
|
||||
xs = None
|
||||
for j in range(self.num_kernels):
|
||||
if xs is None:
|
||||
xs = self.resblocks[i * self.num_kernels + j](x)
|
||||
else:
|
||||
xs += self.resblocks[i * self.num_kernels + j](x)
|
||||
x = xs / self.num_kernels
|
||||
x = F.leaky_relu(x)
|
||||
x = self.conv_post(x)
|
||||
x = torch.tanh(x)
|
||||
|
||||
return x
|
||||
|
||||
def remove_weight_norm(self):
|
||||
print('Removing weight norm...')
|
||||
for l in self.ups:
|
||||
remove_weight_norm(l)
|
||||
for l in self.resblocks:
|
||||
l.remove_weight_norm()
|
||||
remove_weight_norm(self.conv_pre)
|
||||
remove_weight_norm(self.conv_post)
|
||||
|
||||
|
||||
class DiscriminatorP(torch.nn.Module):
|
||||
def __init__(self, period, kernel_size=5, stride=3, use_spectral_norm=False):
|
||||
super(DiscriminatorP, self).__init__()
|
||||
self.period = period
|
||||
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||||
self.convs = nn.ModuleList([
|
||||
norm_f(Conv2d(1, 32, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
|
||||
norm_f(Conv2d(32, 128, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
|
||||
norm_f(Conv2d(128, 512, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
|
||||
norm_f(Conv2d(512, 1024, (kernel_size, 1), (stride, 1), padding=(get_padding(5, 1), 0))),
|
||||
norm_f(Conv2d(1024, 1024, (kernel_size, 1), 1, padding=(2, 0))),
|
||||
])
|
||||
self.conv_post = norm_f(Conv2d(1024, 1, (3, 1), 1, padding=(1, 0)))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
|
||||
# 1d to 2d
|
||||
b, c, t = x.shape
|
||||
if t % self.period != 0: # pad first
|
||||
n_pad = self.period - (t % self.period)
|
||||
x = F.pad(x, (0, n_pad), "reflect")
|
||||
t = t + n_pad
|
||||
x = x.view(b, c, t // self.period, self.period)
|
||||
|
||||
for l in self.convs:
|
||||
x = l(x)
|
||||
x = F.leaky_relu(x, LRELU_SLOPE)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x, fmap
|
||||
|
||||
|
||||
class MultiPeriodDiscriminator(torch.nn.Module):
|
||||
def __init__(self, periods=None):
|
||||
super(MultiPeriodDiscriminator, self).__init__()
|
||||
self.periods = periods if periods is not None else [2, 3, 5, 7, 11]
|
||||
self.discriminators = nn.ModuleList()
|
||||
for period in self.periods:
|
||||
self.discriminators.append(DiscriminatorP(period))
|
||||
|
||||
def forward(self, y, y_hat):
|
||||
y_d_rs = []
|
||||
y_d_gs = []
|
||||
fmap_rs = []
|
||||
fmap_gs = []
|
||||
for i, d in enumerate(self.discriminators):
|
||||
y_d_r, fmap_r = d(y)
|
||||
y_d_g, fmap_g = d(y_hat)
|
||||
y_d_rs.append(y_d_r)
|
||||
fmap_rs.append(fmap_r)
|
||||
y_d_gs.append(y_d_g)
|
||||
fmap_gs.append(fmap_g)
|
||||
|
||||
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
||||
|
||||
|
||||
class DiscriminatorS(torch.nn.Module):
|
||||
def __init__(self, use_spectral_norm=False):
|
||||
super(DiscriminatorS, self).__init__()
|
||||
norm_f = weight_norm if use_spectral_norm == False else spectral_norm
|
||||
self.convs = nn.ModuleList([
|
||||
norm_f(Conv1d(1, 128, 15, 1, padding=7)),
|
||||
norm_f(Conv1d(128, 128, 41, 2, groups=4, padding=20)),
|
||||
norm_f(Conv1d(128, 256, 41, 2, groups=16, padding=20)),
|
||||
norm_f(Conv1d(256, 512, 41, 4, groups=16, padding=20)),
|
||||
norm_f(Conv1d(512, 1024, 41, 4, groups=16, padding=20)),
|
||||
norm_f(Conv1d(1024, 1024, 41, 1, groups=16, padding=20)),
|
||||
norm_f(Conv1d(1024, 1024, 5, 1, padding=2)),
|
||||
])
|
||||
self.conv_post = norm_f(Conv1d(1024, 1, 3, 1, padding=1))
|
||||
|
||||
def forward(self, x):
|
||||
fmap = []
|
||||
for l in self.convs:
|
||||
x = l(x)
|
||||
x = F.leaky_relu(x, LRELU_SLOPE)
|
||||
fmap.append(x)
|
||||
x = self.conv_post(x)
|
||||
fmap.append(x)
|
||||
x = torch.flatten(x, 1, -1)
|
||||
|
||||
return x, fmap
|
||||
|
||||
|
||||
class MultiScaleDiscriminator(torch.nn.Module):
|
||||
def __init__(self):
|
||||
super(MultiScaleDiscriminator, self).__init__()
|
||||
self.discriminators = nn.ModuleList([
|
||||
DiscriminatorS(use_spectral_norm=True),
|
||||
DiscriminatorS(),
|
||||
DiscriminatorS(),
|
||||
])
|
||||
self.meanpools = nn.ModuleList([
|
||||
AvgPool1d(4, 2, padding=2),
|
||||
AvgPool1d(4, 2, padding=2)
|
||||
])
|
||||
|
||||
def forward(self, y, y_hat):
|
||||
y_d_rs = []
|
||||
y_d_gs = []
|
||||
fmap_rs = []
|
||||
fmap_gs = []
|
||||
for i, d in enumerate(self.discriminators):
|
||||
if i != 0:
|
||||
y = self.meanpools[i - 1](y)
|
||||
y_hat = self.meanpools[i - 1](y_hat)
|
||||
y_d_r, fmap_r = d(y)
|
||||
y_d_g, fmap_g = d(y_hat)
|
||||
y_d_rs.append(y_d_r)
|
||||
fmap_rs.append(fmap_r)
|
||||
y_d_gs.append(y_d_g)
|
||||
fmap_gs.append(fmap_g)
|
||||
|
||||
return y_d_rs, y_d_gs, fmap_rs, fmap_gs
|
||||
|
||||
|
||||
def feature_loss(fmap_r, fmap_g):
|
||||
loss = 0
|
||||
for dr, dg in zip(fmap_r, fmap_g):
|
||||
for rl, gl in zip(dr, dg):
|
||||
loss += torch.mean(torch.abs(rl - gl))
|
||||
|
||||
return loss * 2
|
||||
|
||||
|
||||
def discriminator_loss(disc_real_outputs, disc_generated_outputs):
|
||||
loss = 0
|
||||
r_losses = []
|
||||
g_losses = []
|
||||
for dr, dg in zip(disc_real_outputs, disc_generated_outputs):
|
||||
r_loss = torch.mean((1 - dr) ** 2)
|
||||
g_loss = torch.mean(dg ** 2)
|
||||
loss += (r_loss + g_loss)
|
||||
r_losses.append(r_loss.item())
|
||||
g_losses.append(g_loss.item())
|
||||
|
||||
return loss, r_losses, g_losses
|
||||
|
||||
|
||||
def generator_loss(disc_outputs):
|
||||
loss = 0
|
||||
gen_losses = []
|
||||
for dg in disc_outputs:
|
||||
l = torch.mean((1 - dg) ** 2)
|
||||
gen_losses.append(l)
|
||||
loss += l
|
||||
|
||||
return loss, gen_losses
|
||||
@@ -0,0 +1,111 @@
|
||||
import math
|
||||
import os
|
||||
os.environ["LRU_CACHE_CAPACITY"] = "3"
|
||||
import random
|
||||
import torch
|
||||
import torch.utils.data
|
||||
import numpy as np
|
||||
import librosa
|
||||
from librosa.util import normalize
|
||||
from librosa.filters import mel as librosa_mel_fn
|
||||
from scipy.io.wavfile import read
|
||||
import soundfile as sf
|
||||
|
||||
def load_wav_to_torch(full_path, target_sr=None, return_empty_on_exception=False):
|
||||
sampling_rate = None
|
||||
try:
|
||||
data, sampling_rate = sf.read(full_path, always_2d=True)# than soundfile.
|
||||
except Exception as ex:
|
||||
print(f"'{full_path}' failed to load.\nException:")
|
||||
print(ex)
|
||||
if return_empty_on_exception:
|
||||
return [], sampling_rate or target_sr or 32000
|
||||
else:
|
||||
raise Exception(ex)
|
||||
|
||||
if len(data.shape) > 1:
|
||||
data = data[:, 0]
|
||||
assert len(data) > 2# check duration of audio file is > 2 samples (because otherwise the slice operation was on the wrong dimension)
|
||||
|
||||
if np.issubdtype(data.dtype, np.integer): # if audio data is type int
|
||||
max_mag = -np.iinfo(data.dtype).min # maximum magnitude = min possible value of intXX
|
||||
else: # if audio data is type fp32
|
||||
max_mag = max(np.amax(data), -np.amin(data))
|
||||
max_mag = (2**31)+1 if max_mag > (2**15) else ((2**15)+1 if max_mag > 1.01 else 1.0) # data should be either 16-bit INT, 32-bit INT or [-1 to 1] float32
|
||||
|
||||
data = torch.FloatTensor(data.astype(np.float32))/max_mag
|
||||
|
||||
if (torch.isinf(data) | torch.isnan(data)).any() and return_empty_on_exception:# resample will crash with inf/NaN inputs. return_empty_on_exception will return empty arr instead of except
|
||||
return [], sampling_rate or target_sr or 32000
|
||||
if target_sr is not None and sampling_rate != target_sr:
|
||||
data = torch.from_numpy(librosa.core.resample(data.numpy(), orig_sr=sampling_rate, target_sr=target_sr))
|
||||
sampling_rate = target_sr
|
||||
|
||||
return data, sampling_rate
|
||||
|
||||
def dynamic_range_compression(x, C=1, clip_val=1e-5):
|
||||
return np.log(np.clip(x, a_min=clip_val, a_max=None) * C)
|
||||
|
||||
def dynamic_range_decompression(x, C=1):
|
||||
return np.exp(x) / C
|
||||
|
||||
def dynamic_range_compression_torch(x, C=1, clip_val=1e-5):
|
||||
return torch.log(torch.clamp(x, min=clip_val) * C)
|
||||
|
||||
def dynamic_range_decompression_torch(x, C=1):
|
||||
return torch.exp(x) / C
|
||||
|
||||
class STFT():
|
||||
def __init__(self, sr=22050, n_mels=80, n_fft=1024, win_size=1024, hop_length=256, fmin=20, fmax=11025, clip_val=1e-5):
|
||||
self.target_sr = sr
|
||||
|
||||
self.n_mels = n_mels
|
||||
self.n_fft = n_fft
|
||||
self.win_size = win_size
|
||||
self.hop_length = hop_length
|
||||
self.fmin = fmin
|
||||
self.fmax = fmax
|
||||
self.clip_val = clip_val
|
||||
self.mel_basis = {}
|
||||
self.hann_window = {}
|
||||
|
||||
def get_mel(self, y, center=False):
|
||||
sampling_rate = self.target_sr
|
||||
n_mels = self.n_mels
|
||||
n_fft = self.n_fft
|
||||
win_size = self.win_size
|
||||
hop_length = self.hop_length
|
||||
fmin = self.fmin
|
||||
fmax = self.fmax
|
||||
clip_val = self.clip_val
|
||||
|
||||
if torch.min(y) < -1.:
|
||||
print('min value is ', torch.min(y))
|
||||
if torch.max(y) > 1.:
|
||||
print('max value is ', torch.max(y))
|
||||
|
||||
if fmax not in self.mel_basis:
|
||||
mel = librosa_mel_fn(sr=sampling_rate, n_fft=n_fft, n_mels=n_mels, fmin=fmin, fmax=fmax)
|
||||
self.mel_basis[str(fmax)+'_'+str(y.device)] = torch.from_numpy(mel).float().to(y.device)
|
||||
self.hann_window[str(y.device)] = torch.hann_window(self.win_size).to(y.device)
|
||||
|
||||
y = torch.nn.functional.pad(y.unsqueeze(1), (int((n_fft-hop_length)/2), int((n_fft-hop_length)/2)), mode='reflect')
|
||||
y = y.squeeze(1)
|
||||
|
||||
spec = torch.stft(y, n_fft, hop_length=hop_length, win_length=win_size, window=self.hann_window[str(y.device)],
|
||||
center=center, pad_mode='reflect', normalized=False, onesided=True)
|
||||
# print(111,spec)
|
||||
spec = torch.sqrt(spec.pow(2).sum(-1)+(1e-9))
|
||||
# print(222,spec)
|
||||
spec = torch.matmul(self.mel_basis[str(fmax)+'_'+str(y.device)], spec)
|
||||
# print(333,spec)
|
||||
spec = dynamic_range_compression_torch(spec, clip_val=clip_val)
|
||||
# print(444,spec)
|
||||
return spec
|
||||
|
||||
def __call__(self, audiopath):
|
||||
audio, sr = load_wav_to_torch(audiopath, target_sr=self.target_sr)
|
||||
spect = self.get_mel(audio.unsqueeze(0)).squeeze(0)
|
||||
return spect
|
||||
|
||||
stft = STFT()
|
||||
@@ -0,0 +1,68 @@
|
||||
import glob
|
||||
import os
|
||||
import matplotlib
|
||||
import torch
|
||||
from torch.nn.utils import weight_norm
|
||||
# matplotlib.use("Agg")
|
||||
import matplotlib.pylab as plt
|
||||
|
||||
|
||||
def plot_spectrogram(spectrogram):
|
||||
fig, ax = plt.subplots(figsize=(10, 2))
|
||||
im = ax.imshow(spectrogram, aspect="auto", origin="lower",
|
||||
interpolation='none')
|
||||
plt.colorbar(im, ax=ax)
|
||||
|
||||
fig.canvas.draw()
|
||||
plt.close()
|
||||
|
||||
return fig
|
||||
|
||||
|
||||
def init_weights(m, mean=0.0, std=0.01):
|
||||
classname = m.__class__.__name__
|
||||
if classname.find("Conv") != -1:
|
||||
m.weight.data.normal_(mean, std)
|
||||
|
||||
|
||||
def apply_weight_norm(m):
|
||||
classname = m.__class__.__name__
|
||||
if classname.find("Conv") != -1:
|
||||
weight_norm(m)
|
||||
|
||||
|
||||
def get_padding(kernel_size, dilation=1):
|
||||
return int((kernel_size*dilation - dilation)/2)
|
||||
|
||||
|
||||
def load_checkpoint(filepath, device):
|
||||
assert os.path.isfile(filepath)
|
||||
print("Loading '{}'".format(filepath))
|
||||
checkpoint_dict = torch.load(filepath, map_location=device)
|
||||
print("Complete.")
|
||||
return checkpoint_dict
|
||||
|
||||
|
||||
def save_checkpoint(filepath, obj):
|
||||
print("Saving checkpoint to {}".format(filepath))
|
||||
torch.save(obj, filepath)
|
||||
print("Complete.")
|
||||
|
||||
|
||||
def del_old_checkpoints(cp_dir, prefix, n_models=2):
|
||||
pattern = os.path.join(cp_dir, prefix + '????????')
|
||||
cp_list = glob.glob(pattern) # get checkpoint paths
|
||||
cp_list = sorted(cp_list)# sort by iter
|
||||
if len(cp_list) > n_models: # if more than n_models models are found
|
||||
for cp in cp_list[:-n_models]:# delete the oldest models other than lastest n_models
|
||||
open(cp, 'w').close()# empty file contents
|
||||
os.unlink(cp)# delete file (move to trash when using Colab)
|
||||
|
||||
|
||||
def scan_checkpoint(cp_dir, prefix):
|
||||
pattern = os.path.join(cp_dir, prefix + '????????')
|
||||
cp_list = glob.glob(pattern)
|
||||
if len(cp_list) == 0:
|
||||
return None
|
||||
return sorted(cp_list)[-1]
|
||||
|
||||
Reference in New Issue
Block a user