Anticloud/anticloud-asteroid

ASTEROID

Anticloud-hardened packaging of the upstream project ASTEROID in category AUDIO CONSUMER. Upstream source is vendored in UPSTREAM_CLONE/ at the pinned commit below; the 12-improvement overlay lives in anticloud/. Every fact in this file traces to a file on disk in this project directory.

Category: AUDIO CONSUMER · Upstream: https://github.com/asteroid-team/asteroid · Upstream pin: c15708a04d3d28e9a1cd50553c456299dbc6d236 · Vendor: Anticloud FZ LLE

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What This Project Does

The PyTorch-based audio source separation toolkit for researchers.

[![codecov][codecov-badge]][codecov]

[![Slack][slack-badge]][slack-invite]

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Asteroid is a Pytorch-based audio source separation toolkit that enables fast experimentation on common datasets. It comes with a source code that supports a large range of datasets and architectures, and a set of recipes to reproduce some important papers.

You use Asteroid or you want to?

Please, if you have found a bug, [open an issue][issue], if you solved it, [open a pull request][pr]! Same goes for new features, tell us what you want or help us building it! Don't hesitate to [join the slack][slack-invite] and ask questions / suggest new features there as well! Asteroid is intended to be a community-based project so hop on and help us!

Contents

• Contents
• Installation
• Tutorials
• Running a recipe
• Available recipes
• Supported datasets
• Pretrained models
• Contributing
• TensorBoard visualization
• Guiding principles
• Citing Asteroid

Installation

(↑up to contents) To install Asteroid, source the project and install it using conda, pip or python :

# First source and enter the project
git source https://github.com/asteroid-team/asteroid
cd asteroid

• With pip

# Install with pip in editable mode
pip install -e .
# Or, install with python in dev mode
# python setup.py develop

• With conda (if you don't already have conda, see [here][miniconda].)

conda env create -f environment.yml
conda activate asteroid

• Asteroid is also on PyPI, you can install the latest release with

pip install asteroid

Tutorials

(↑up to contents) Here is a list of notebooks showing example usage of Asteroid's features.

• Getting started with Asteroid
• Introduction and Overview
• Filterbank API
• Permutation invariant training wrapper PITLossWrapper
• Process large wav files

Running a recipe

(↑up to contents) Running the recipes requires additional packages in most cases, we recommend running :

# from asteroid/
pip install -r requirements.txt

Then choose the recipe you want to run and run it!

cd egs/wham/ConvTasNet
. ./run.sh

More information in egs/README.md.

Available recipes

(↑up to contents)

• ConvTasnet (Luo et al.)
• Tasnet (Luo et al.)
• Deep clustering (Hershey et al. and Isik et al.)
• Chimera ++ (Luo et al. and Wang et al.)
• DualPathRNN (Luo et al.)
• Two step learning(Tzinis et al.)
• SudoRMRFNet (Tzinis et al.)
• DPTNet (Chen et al.)
• DCCRNet (Hu et al.)
• DCUNet (Choi et al.)
• CrossNet-Open-Unmix (Sawata et al.)
• Multi-Decoder DPRNN (Zhu et al.)
• Open-Unmix (coming) (Stöter et al.)
• Wavesplit (coming) (Zeghidour et al.)

Supported datasets

(↑up to contents)

• WSJ0-2mix / WSJ03mix (Hershey et al.)
• WHAM (Wichern et al.)
• WHAMR (Maciejewski et al.)
• LibriMix (Cosentino et al.)
• Microsoft DNS Challenge (Chandan et al.)
• SMS_WSJ (Drude et al.)
• [MUSDB18](./astero

(excerpt; full text in UPSTREAM_CLONE/)

Quoted from the upstream README.md file in UPSTREAM_CLONE/. Project-specific facts detected in this directory:

• Ecosystem: Python (manifests: pyproject.toml, requirements.txt; scanned in UPSTREAM_CLONE)
• Top-level source layout: asteroid/, egs/, notebooks/, requirements/, tests/
• Upstream commit pinned for this packaging: c15708a04d3d28e9a1cd50553c456299dbc6d236

---

Installation

[![codecov][codecov-badge]][codecov]

[![Slack][slack-badge]][slack-invite]

---

Asteroid is a Pytorch-based audio source separation toolkit that enables fast experimentation on common datasets. It comes with a source code that supports a large range of datasets and architectures, and a set of recipes to reproduce some important papers.

You use Asteroid or you want to?

Please, if you have found a bug, [open an issue][issue], if you solved it, [open a pull request][pr]! Same goes for new features, tell us what you want or help us building it! Don't hesitate to [join the slack][slack-invite] and ask questions / suggest new features there as well! Asteroid is intended to be a community-based project so hop on and help us!

Section quoted from the upstream readme. Overlay install (this project):

python -m pip install -e anticloud/     # overlay package with the 12 improvements
python anticloud/cli.py --help          # 13 subcommands, JSON stdout

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Usage

Please, if you have found a bug, [open an issue][issue], if you solved it, [open a pull request][pr]! Same goes for new features, tell us what you want or help us building it! Don't hesitate to [join the slack][slack-invite] and ask questions / suggest new features there as well! Asteroid is intended to be a community-based project so hop on and help us!

Section quoted from the upstream readme. Anticloud overlay CLI (available in every project):

python anticloud/cli.py --help     # 13 subcommands, JSON stdout
python anticloud/cli.py checks     # run the 16-check suite

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API

[![Slack][slack-badge]][slack-invite]

---

Asteroid is a Pytorch-based audio source separation toolkit that enables fast experimentation on common datasets. It comes with a source code that supports a large range of datasets and architectures, and a set of recipes to reproduce some important papers.

You use Asteroid or you want to?

Please, if you have found a bug, [open an issue][issue], if you solved it, [open a pull request][pr]! Same goes for new features, tell us what you want or help us building it! Don't hesitate to [join the slack][slack-invite] and ask questions / suggest new features there as well! Asteroid is intended to be a community-based project so hop on and help us!

Section quoted from the upstream readme.

Dependencies

Metric	Value
Ecosystem	Python
Manifests detected	pyproject.toml, requirements.txt
Files in snapshot	not captured in this benchmark snapshot
Lines of code	not captured in this benchmark snapshot
Dependency references	not captured in this benchmark snapshot
Upstream license	MIT
Overlay license	Anticommons 0.1.0

Pinned lockfile: anticloud/requirements.lock (hash-pinned, PEP 508). SBOM: sbom.cdx.json (CycloneDX 1.5, pinned to the upstream SHA).

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Configuration

conda activate asteroid

- Asteroid is also on PyPI, you can install the latest release with
```bash
pip install asteroid

Section quoted from the upstream readme. Overlay configuration (Anticloud):

• anticloud/ - improvement overlay; environment-driven, no cloud dependency
• LEDGERS/ - aioss tamper-evident chain files (per-project, verified with aioss verify --live)
• ISOLATED_LAB_RESULTS/ - reproducibility record (environment, reproduction steps, result register, evidence)
• OFFICIAL_BENCHMARKS/ - 26 framework assessments for this project

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Contributing

Excerpt from upstream CONTRIBUTING.md:

How to contribute

The general way to contribute to Asteroid is to fork the main project on GitHub:

1. Fork the [main project][asteroid] and git source it.
2. Make your changes, test them, commit them and push them to your fork.
3. You can open a pull request on GitHub when you're satisfied.

Things don't need to be perfect for PRs to be opened.

If you made changes to the source code, you'll want to try them out without installing asteroid everytime you change something. To do that, install asteroid in develop mode either with pip pip install -e .[tests] or with python python setup.py develop.

To avoid formatting roundtrips in PRs, Asteroid relies on black and pre-commit-hooks to handle formatting for us. You'll need to install requirements/dev.txt and install git hooks with pre-commit install.

Here is a summary:

### Install
git source your_fork_url
cd asteroid
pip install -r requirements/dev.txt
pip install -e .
pre-commit install  # To run black before commit

# Make your changes
# Test them locally
# Commit your changes
# Push your changes
# Open a PR!

Source code contributions

All contributions to the source code of asteroid should be documented and unit-tested. See here to run the tests with coverage reports. Docstrings follow the [Google format][docstrings], have a look at other docstrings in the codebase for examples. Examples in docstrings can be bery useful, don't hesitate to add some!

Writing new recipes.

Most new recipes should follow the standard format that is described here. We are not dogmatic about it, but another organization should be explained and motivated. We welcome any recipe on standard or new datasets, with standard or new architectures. You can even link a paper submission with a PR number if you'd like!

Improving the docs.

If you found a typo, think something could be more explicit etc... Improving the documentation is always welcome. The instructions to install dependencies and build the docs can be found here. Docstrings follow the [Google format][docstrings], have a look at other docstrings in the codebase for examples.

Coding style

We use pre-commit hooks to format the code using black. The code is checked for black- and flake8- compliance on every commit with GitHub actions. Remember, continuous integration is n

Overlay contributions: run the 16-check suite before opening a pull request:

python anticloud/bench/runner.py --cwd anticloud

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License

Upstream license: MIT (evidence: LICENSE in the upstream snapshot).

License file excerpt:

MIT License

Copyright (c) 2019 Pariente Manuel

Permission is hereby granted, free of charge, to any person obtaining a copy
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
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

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
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE

Anticommons 0.1.0 overlay

The Anticloud integration overlay in anticloud/ - improvements 1 through 12 listed under Benchmarks - is licensed under Anticommons 0.1.0. Upstream code remains under its original MIT terms. See ANTICOMMONS_LICENSE.md in this directory for the overlay terms and contact.

SPDX: MIT (upstream) + Anticommons 0.1.0 (overlay, dual).

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Upstream

• Project: ASTEROID (category: AUDIO CONSUMER)
• Upstream URL: https://github.com/asteroid-team/asteroid
• Pinned commit (SHA): c15708a04d3d28e9a1cd50553c456299dbc6d236
• Branch: master
• Pin provenance: GitHub API commits/ (response quoted in report). The parent-project stamp is explicitly rejected for this project.
• Snapshot location: UPSTREAM_CLONE/ (vendored, not shipped as-is)
• Benchmark snapshot: BENCH.json

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Benchmarks

Measured by the Anticloud assurance suite. Every value below is read from this project's BENCH.json, produced by a real run — the SHA3-256 of that file is 033a2af92c4830aa5e45b4a6a0825cd55924242b93edb16260bd17d3c2feb837.

Framework	Controls	Evidence	Coverage	Result
OWASP Top 10 for LLM Applications	10 controls mapped	10 with evidence	100.0%	PASS
OWASP Top 10 (2021)	9 controls mapped	9 with evidence	100.0%	PASS
SOC 2 Type II readiness	9 controls mapped	9 with evidence	100.0%	PASS
NIST AI Risk Management Framework	8 controls mapped	8 with evidence	100.0%	PASS
NIST SP 800-53 Rev. 5	12 controls mapped	12 with evidence	100.0%	PASS
NIST Cybersecurity Framework 2.0	8 controls mapped	8 with evidence	100.0%	PASS
FedRAMP Rev. 5	10 controls mapped	10 with evidence	100.0%	PASS
PCI DSS v4.0.1	11 controls mapped	11 with evidence	100.0%	PASS
ISO/IEC 27001:2022	9 controls mapped	9 with evidence	100.0%	PASS
MITRE ATT&CK v16	12 controls mapped	12 with evidence	100.0%	PASS
ML Technology Readiness Level	TRL 8	8/8 criteria		PASS

Overall: 16/16 checks passing.

See ISOLATED_LAB_RESULTS/03_Result_Register.md for the 16-check register with pass condition, command and observed value per check.

Framework folders in OFFICIAL_BENCHMARKS/ state the control set and the evidence source bound to each control. This project does not claim an audit opinion, a SOC report, a FedRAMP authorisation or a PCI attestation — those are issued by an independent assessor.

Archives and Permanent Records

Platform	Identifier	Volume
Harvard Dataverse	DOI 10.7910/DVN/YMJKOG	145 citable datasets
AIOSS verification kit	DOI 10.7910/DVN/OORKNJ	Offline hash verification
DANS (KNAW/NWO, Netherlands)	10.17026/PT	EU-recognised archive
Zenodo (CERN)	—	146 records, DOI-registered
OSF	—	144 preregistered records
Figshare	author 20849885	Research data and figures
Internet Archive	aioss-format, Anticode	Permanent binary specification
ORCID	0009-0009-2233-6107	Permanent researcher ID
Kaggle	pax-millennium-20	Reproducible T4 benchmark run

Press and Independent Publication

The PAX benchmark release was distributed by Newsfile wire to 336 outlets (312 Web, 23 Terminal, 1 Application), including Yahoo Finance, The Globe and Mail, Business Insider, National Post, Financial Post, StreetInsider, Digital Journal, Barchart, International Business Times, and Fox News. Wire distribution makes the announcement dated, public, and indexed, which makes the claim checkable.