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Prepare code for public release
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‎.dockerignore

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*.pth
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*.wav
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*.dac
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tests/
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runs/

‎.gitattributes

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‎.gitignore

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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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# C extensions
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*.so
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# Distribution / packaging
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.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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# 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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# 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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# Scrapy stuff:
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.scrapy
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# Sphinx documentation
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docs/_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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# Environments
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.env
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.venv
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env/env.sh
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venv/
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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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# Files created by experiments
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output/
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snapshot/
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*.m4a
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*.wav
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notebooks/scratch.ipynb
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notebooks/inspect.ipynb
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notebooks/effects.ipynb
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notebooks/*.ipynb
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notebooks/*.gif
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notebooks/*.wav
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notebooks/*.mp4
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*runs/
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boards/
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samples/
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*.ipynb
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results.json
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metrics.csv
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mprofile_*
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mem.png
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results/
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mprofile*
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*.png
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# do not ignore the test wav file
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!tests/audio/short_test_audio.wav
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!tests/audio/output.wav
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*/.DS_Store
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.DS_Store
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env.sh
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_codebraid/
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**/*.html
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**/*.exec.md
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flagged/
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log.txt
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ckpt/
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.syncthing*
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tests/assets/
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archived/
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*_remote_module_*
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*.zip
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*.pth
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encoded_out/
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recon/
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recons/

‎.pre-commit-config.yaml

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repos:
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- repo: https://github.com/asottile/reorder_python_imports
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rev: v2.5.0
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hooks:
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- id: reorder-python-imports
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- repo: https://github.com/psf/black
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rev: 23.1.0
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hooks:
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- id: black
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language_version: python3
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v4.0.1
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hooks:
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- id: end-of-file-fixer
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- id: trailing-whitespace

‎Dockerfile

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FROM pytorch/pytorch:2.0.1-cuda11.7-cudnn8-runtime
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COPY . /app
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WORKDIR /app
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RUN apt update && apt install -y git
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# install the package
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RUN pip install .
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# cache the model
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RUN python3 -m dac download

‎LICENSE

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MIT License
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Copyright (c) 2023-present, Descript
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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
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in the Software without restriction, including without limitation the rights
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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
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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
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copies or substantial portions of the Software.
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THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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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.

‎README.md

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# Descript Audio Codec (.dac)
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<!-- ![](https://static.arxiv.org/static/browse/0.3.4/images/icons/favicon-32x32.png) -->
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This repository contains training and inference scripts
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for the Descript Audio Codec (.dac), a high fidelity general
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neural audio codec.
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## Usage
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### Installation
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```
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git clone https://github.com/descriptinc/descript-audio-codec
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cd descript-audio-codec
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pip install .
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```
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### Compress audio
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```
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python3 -m dac encode /path/to/input --output /path/to/output/codes
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```
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This command will create `.dac` files with the same name as the input files.
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It will also preserve the directory structure relative to input root and
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re-create it in the output directory. Please use `python -m dac encode --help`
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for more options.
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### Reconstruct audio from compressed codes
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```
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python3 -m dac decode /path/to/output/codes --output /path/to/reconstructed_input
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```
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This command will create `.wav` files with the same name as the input files.
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It will also preserve the directory structure relative to input root and
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re-create it in the output directory. Please use `python -m dac decode --help`
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for more options.
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### Docker image
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We provide a dockerfile to build a docker image with all the necessary
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dependencies.
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1. Building the image.
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```
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docker build -t dac .
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```
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2. Using the image.
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Usage on CPU:
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```
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docker run dac <command>
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```
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Usage on GPU:
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```
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docker run --gpus=all dac <command>
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```
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`<command>` can be one of the compression and reconstruction commands listed
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above. For example, if you want to run compression,
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```
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docker run --gpus=all dac python3 -m dac encode ...
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```
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## Training
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The baseline model configuration can be trained using the following commands.
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### Pre-requisites
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Please install the correct dependencies
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```
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pip install -e ".[dev]"
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```
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### Single GPU training
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```
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export CUDA_VISIBLE_DEVICES=0
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python scripts/train.py --args.load conf/ablations/baseline.yml --save_path runs/baseline/
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```
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### Multi GPU training
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```
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export CUDA_VISIBLE_DEVICES=0,1
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torchrun --nproc_per_node gpu scripts/train.py --args.load conf/ablations/baseline.yml --save_path runs/baseline/
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```
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## Testing
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We provide two test scripts to test CLI + training functionality. Please
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make sure that the trainig pre-requisites are satisfied before launching these
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tests. To launch these tests please run
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```
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python -m pytest tests
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```

‎conf/1gpu.yml

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$include:
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- conf/base.yml
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batch_size: 12
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val_batch_size: 12
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num_workers: 4

‎conf/ablations/baseline.yml

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$include:
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- conf/base.yml
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- conf/1gpu.yml

‎conf/ablations/diff-mb.yml

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$include:
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- conf/base.yml
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- conf/1gpu.yml
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Discriminator.sample_rate: 44100
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Discriminator.fft_sizes: [2048, 1024, 512]
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Discriminator.bands:
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- [0.0, 0.05]
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- [0.05, 0.1]
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- [0.1, 0.25]
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- [0.25, 0.5]
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- [0.5, 1.0]
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# re-weight lambdas to make up for
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# lost discriminators vs baseline
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lambdas:
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mel/loss: 15.0
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adv/feat_loss: 5.0
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adv/gen_loss: 1.0
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vq/commitment_loss: 0.25
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vq/codebook_loss: 1.0

‎conf/ablations/equal-mb.yml

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$include:
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- conf/base.yml
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- conf/1gpu.yml
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Discriminator.sample_rate: 44100
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Discriminator.fft_sizes: [2048, 1024, 512]
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Discriminator.bands:
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- [0.0, 0.2]
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- [0.2, 0.4]
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- [0.4, 0.6]
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- [0.6, 0.8]
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- [0.8, 1.0]
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# re-weight lambdas to make up for
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# lost discriminators vs baseline
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lambdas:
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mel/loss: 15.0
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adv/feat_loss: 5.0
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adv/gen_loss: 1.0
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vq/commitment_loss: 0.25
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vq/codebook_loss: 1.0

‎conf/ablations/no-adv.yml

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$include:
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- conf/base.yml
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- conf/1gpu.yml
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lambdas:
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mel/loss: 1.0
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waveform/loss: 1.0
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vq/commitment_loss: 0.25
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vq/codebook_loss: 1.0

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