Saravanan666/pypi-org huggingface dataset:
Package: nendo-plugin-embed-clap
Nendo Plugin Embed CLAPA plugin to create joint embeddings from text and audio using CLAP by LAION.FeaturesCreate joint embeddings from text and audioUse the embeddings in combination with nendo's vector search functionalityRequirementsPlease make sure you have the correct version of Pytorch installed.
Go topytorch.organd select your OS,
package manager and CUDA version to get the correct installation command.InstallationInstall Nendopip install nendo-plugin-embed-clapUsageTake a look at a basic usage example below.
For more detailed information, please refer to thedocumentation.>>>fromnendoimportNendo>>>nd=Nendo(plugins=["nendo_plugin_embed_clap"])>>>track=nd.library.add_track(file_path="path/to/file.mp3")>>>embedding=nd.plugins.embed_clap(track=track)ContributingVisit our docs to learn all about how to contribute to Nendo:ContributingLicenseNendo: MIT LicensePretrained models: The weights are released by LAION under the Apache 2.0 license.
Package: nendo-plugin-fx-core
Nendo Plugin FX CoreNendoFX: Use common audio effects out of the box (based onpedalboardby Spotify Research).FeaturesApply well known audio effects to aNendoTrackmodulation via:chorus,phaserdifferent filters:lowpass,highpass,low_shelf,high_shelfadaptive processing based on MIR features via:limitercustom effects like:stereoalso basics like:distortion,reverb,delay,compressorInstallationInstall Nendopip install nendo-plugin-fx-coreUsageTake a look at a basic usage example below.
For more detailed information, please refer to thedocumentation.For more advanced examples, check out the examples folder.
or try it in colab:fromnendoimportNendo,NendoConfignd=Nendo(config=NendoConfig(plugins=["nendo_plugin_fx_core"]))track=nd.library.add_track(file_path='/path/to/track.mp3')track=nd.plugins.fx_core.compressor(track=track)track=nd.plugins.fx_core.reverb(track=track,wet_level=0.2,dry_level=0.8,room_size=0.1,)track=nd.plugins.fx_core.limiter(track=track)track.play()ContributingVisit our docs to learn all about how to contribute to Nendo:ContributingLicenceNendo: MITPedalboard: GPL3For additional licences checkhttps://github.com/spotify/pedalboard
Package: nendo_plugin_library_postgres
Nendo PostgresDB Library PluginPostgresDB implementation of the Nendo Library Plugin.FeaturesSupports use of a local or remote PostgresDB instance for storing Nendo's library data.Comes with the Nendo Library vector extension, providing support for the storing, querying and retrieval of Embeddings related to Nendo tracks.Provides functions for performing a neighborhood search of similar NendoTracks based on various distance metrics.Provides a set of special filtering functions for more fine-grained search and retrieval of items from the Nendo Library.RequirementsPostgreSQLUsing this plugin requires you to install the postgres client libraries on the local system. Depending on your OS, you can either install just the client libraries or the entire Postgres package, depending on whether you intend to connect to a remote or a local instance of PostgresDB:Ubuntu:sudo apt-get install libpq-dev(client libraries)sudo apt-get install postgresql(complete postgresDB)Mac OS:brew install libpq(client libraries)brew install postgresql@14(complete postgresDB)pgvector extensionFurthermore, the plugin requires thepgvectorextension to be installed. Please follow it's officialinstallation instructionsto install it before proceeding further.After you've installed thepgvectorpackage, you must also enable the extension for thenendodatabase. Connect to your PostgreSQL instancewith an account that has superuser priviledgesand execute:USEnendo;CREATEEXTENSIONIFNOTEXISTSvector;Embedding pluginTo use the vector features of the Nendo Library, you also need to have at least oneNendoEmbeddingPlugininstalled in your environment and configured to be used by Nendo. For example, to use thenendo_plugin_embed_clap,install itand then configure Nendo to also load it on startup. You can use an environment variable for this:exportPLUGINS='["nendo_plugin_embed_clap"]'The PostgresDB Nendo Library plugin will then pick up the embedding plugin automatically.InstallationMake sure to meet therequirements.pip install nendo-plugin-library-postgresHow to useTo enable the postgres library plugin, Nendo must be started with thelibrary_pluginconfiguration variable set tonendo_plugin_library_postgres:fromnendoimportNendo,NendoConfignd=Nendo(config=NendoConfig(library_plugin="nendo_plugin_library_postgres",plugins=["nendo_plugin_embed_clap"]))Alternatively, you can use environment variables to configure Nendo and enable the postgres library plugin. Refer to theconfigurationsection for more information.Plugin configurationPlease use environment variables to configure the plugin:exportLIBRARY_PLUGIN="nendo_plugin_library_postgres"exportPLUGINS='["nendo_plugin_embed_clap"]'exportPOSTGRES_HOST="localhost:5432"exportPOSTGRES_USER="nendo"exportPOSTGRES_PASSWORD="nendo"exportPOSTGRES_DB="nendo"Then, the configuration will be applied once you start Nendo:fromnendoimportNendond=Nendo()# print library infoprint(nd.library)
Package: nendo-plugin-loopify
Nendo Plugin LoopifyAutomatic audio loop extraction.FeaturesAutomatically find loopable sections in aNendoTrackAutomate your sample digging processRequirementsDue tomadmomversions < 0.17 errors with python 3.10, we require the latest version of the  package from git, where this is fixed. See alsothis related issue.Run:pip install git+https://github.com/CPJKU/madmom.git@0551aa8InstallationInstall Nendopip install nendo-plugin-loopifyUsageTake a look at a basic usage example below.
For more detailed information, please refer to thedocumentation.For more advanced examples, check out the examples folder.
or try it in colab:fromnendoimportNendo,NendoConfignd=Nendo(config=NendoConfig(plugins=["nendo_plugin_loopify"]))track=nd.library.add_track(file_path='/path/to/track.mp3')generated_loops=nd.plugins.loopify(track=track,n_loops=4,beats_per_loop=8)generated_loops[0].loop()ContributingVisit our docs to learn all about how to contribute to Nendo:ContributingLicenseNendo: MIT LicenseMadmom: BSD LicensePretrained models: The weights are released under the CC-BY-NC 4.0 license
Package: nendo-plugin-musicgen
Nendo Plugin MusicgenMusicGen: A state-of-the-art controllable text-to-music model (byMeta Research)FeaturesGenerate conditional and unconditional musicGenerate outpaintings from a prompt and aNendoTrackUse aNendoTrackas melody conditioning to generate musicUse custom finetuned musicgen modelsCommunity Finetunes 🔥For a list of community finetunes check outcommunity_finetunes.json.
If you want to contribute one to the list, open a PR
or contact us viaTwitterorDiscord!RequirementsSince we depend onaudiocraft, please make sure that you fulfill their requirements.
You need Pytorch 2.0.0 or higher, which can be installed viapip install "torch>=2.0"Note: On Mac OSX, the instructions for installing pytorch differ. Please refer to thepytorch installation instructions. For all other problems please refer to theaudiocraft repository.To run the plugin you also need to installaudiocraftby Meta AI Research, run:pip install git+https://github.com/facebookresearch/audiocraftInstallationInstall Nendopip install nendo-plugin-musicgenUsageTake a look at a basic usage example below.
For more detailed information, please refer to thedocumentation.For more advanced examples, check out the examples folder.
or try it in colab:fromnendoimportNendo,NendoConfignd=Nendo(config=NendoConfig(plugins=["nendo_plugin_musicgen"]))# load tracktrack=nd.library.add_track(file_path='/path/to/track.mp3')# run musicgen with custom modelgenerated_collection=nd.plugins.musicgen(track=track,n_samples=5,prompt="janelle monae, rnb, funky, fast, futuristic",bpm=116,key="C",scale="Major",duration=30,conditioning_length=10)generated_collection[0].play()ContributingVisit our docs to learn all about how to contribute to Nendo:ContributingLicenseNendo: MIT LicenseAudioCraft: MIT LicensePretrained models: The weights are released under the CC-BY-NC 4.0 license
Package: nendo-plugin-quantize-core
Nendo Plugin: Core QuantizerAudio quantization with grid detection and time-stretching
(usingrubberband).FeaturesQuantize aNendoTrackor aNendoCollectionto a given BPM and gridUse it in chains to automatically create quantized collections of your favorite tracks and loopsInstallationThis plugin requires therubberbandpackage to be installed in your system. Please refer to therubberband documentationfor further information.Install Nendopip install nendo-plugin-quantize-coreUsageTake a look at a basic usage example below.
For more detailed information, please refer to thedocumentation.For more advanced examples, check out the examples folder.
or try it in colab:fromnendoimportNendo,NendoConfignd=Nendo(config=NendoConfig(plugins=["nendo_plugin_quantize_core"]))track=nd.library.add_track(file_path='/path/to/track.mp3')quantized_track=nd.plugins.quantize_core(track=track)ContributingVisit our docs to learn all about how to contribute to Nendo:ContributingLicenseNendo: MIT Licenserubberband: GPL-2.0 License
Package: nendo-plugin-stemify-demucs
Nendo Plugin Stemify DemucsNendo Plugin for Music Source Separation (based on Metademucs)FeaturesExtract up to six different audio stemsInstallationInstall Nendopip install nendo-plugin-stemify-demucsUsageTake a look at a basic usage example below.
For more detailed information, please refer to thedocumentation.For more advanced examples, check out the examples folder.
or try it in colab:fromnendoimportNendo,NendoConfignd=Nendo(config=NendoConfig(plugins=["nendo_plugin_stemify_demucs"]))# load tracktrack=nd.library.add_track(file_path='/path/to/track.mp3')# run stemification to get 6 stemsstems=nd.plugins.stemify_demucs(track=track,stem_types=["vocals","drums","bass","other","piano","guitar"],model="htdemucs_6s")vocals,drums=stems[0],stems[1]drums.play()# run stemification to get vocals and backgroundstems=nd.plugins.stemify_demucs(track=track,stem_types=["vocals","no_vocals"],model="mdx_extra")background=stems[1]background.play()ContributingVisit our docs to learn all about how to contribute to Nendo:ContributingLicenseNendo: MIT LicenseDemucs: MIT License
Package: nendo-plugin-textgen
Nendo Plugin TextGenA text generation plugin using local LLMs or other text generation methods.
Builds on top oftransformersby Hugging Face.FeaturesGenerate text from a prompt using a local LLMAccess all LLMs from theHugging Face Model HubRequirementsSince we depend ontransformers, please make sure that you fulfill their requirements.
You also need Pytorch installed on your system, please refer to thepytorch installation instructions.InstallationInstall Nendopip install nendo-plugin-textgenIf you have a CUDA GPU you can install the following library for an additional speedup:pip install flash-attn --no-build-isolationThen setATTN_IMPLEMENTATION=flash_attention_2in your environment variables.UsageTake a look at a basic usage example below.
For more detailed information, please refer to thedocumentation.>>>fromnendoimportNendo>>>nd=Nendo(plugins=["nendo_plugin_textgen"])>>>nd.plugins.textgen(prompt=["Tell me about your favorite song."])[0]ContributingVisit our docs to learn all about how to contribute to Nendo:ContributingLicenseNendo: MIT License
Package: nendo-plugin-transcribe-whisper
Nendo Plugin Transcribe WhisperA nendo plugin for speech transcription, based on Whisper by OpenAI.FeaturesFast speech transcription with optional word-level timestamps.RequirementsSince we depend ontransformers, please make sure that you fulfill their requirements.
You also need Pytorch installed on your system, please refer to thepytorch installation instructions.InstallationInstall Nendopip install nendo-plugin-transcribe-whisperIf you have a cuda GPU on your machine you can also installflash-attnto get an additional speedup:pip install flash-attn --no-build-isolationThen setATTN_IMPLEMENTATION=flash_attention_2in your environment variables.Usage>>>fromnendoimportNendo>>>nd=Nendo(plugins=["nendo_plugin_transcribe_whisper"])>>>track=nd.library.add_track(file_path="path/to/file.mp3")>>>nd.plugins.transcribe_whisper(track=track)>>>track.get_plugin_value("transcription")ContributingVisit our docs to learn all about how to contribute to Nendo:ContributingLicenseNendo: MIT LicensePretrained models: The weights are released under the Apache 2.0 license.
Package: nendo-plugin-vampnet
Nendo Plugin VampnetNendo Plugin for VampNet: Music Generation via Masked Acoustic Token Modeling
(byHugo Flores Garcia).FeaturesGenerate unconditional variationsUse custom finetuned vampnet modelsGenerate in- and outpaintings from aNendoTrackRequirementsDue to madmom versions < 0.17 errors with python 3.10, we require the latest version of the package from git, where this is fixed. See also this related issue.Run:pip install git+https://github.com/CPJKU/madmom.git@0551aa8This plugin also requires the manual installation ofvampnetvia its git repository:pip install git+https://github.com/hugofloresgarcia/vampnet.git@0c0c6bcFor further information, please refer to the originalvampnet repository.InstallationInstall Nendopip install nendo-plugin-vampnetUsageTake a look at a basic usage example below.
For more detailed information, please refer to thedocumentation.For more advanced examples, check out the examples folder.
or try it in colab:fromnendoimportNendo,NendoConfignd=Nendo(config=NendoConfig(plugins=["nendo_plugin_vampnet"]))track=nd.library.add_track(file_path='/path/to/track.mp3')vamp=nd.plugins.vampnet(track=track,prefix_secs=2,suffix_secs=0)vamp.play()ContributingVisit our docs to learn all about how to contribute to Nendo:ContributingLicenseNendo: MIT LicenseVampnet: MIT LicensePretrained Models: The weights for the models are licensed CC BY-NC-SA 4.0. Likewise, any VampNet models fine-tuned on the pretrained models are also licensed CC BY-NC-SA 4.0.
Package: nendo-plugin-voicegen-styletts2
Nendo Plugin VoiceGen StyleTTS2A text to speech plugin based on StyleTTS2.Requirements[!WARNING]This plugin is currently only supported on Linux.We are actively working on adding support for MacOS.
But since we are usingespeak-ngas a backend, we are limited to Linux.Please install the requirements forStyleTTS2:pipinstallgit+https://github.com/resemble-ai/monotonic_align.git
sudoapt-getinstallespeak-ngInstallationInstall Nendopip install nendo-plugin-voicegen-styletts2UsageTake a look at a basic usage example below.
For more detailed information and other plugin examples, please refer to thedocumentation.>>>fromnendoimportNendo>>>nd=Nendo(plugins=["nendo_plugin_voicegen_styletts2"])>>>track=nd.library.add_track(file_path="path/to/file.mp3")>>>track=nd.plugins.voicegen_styletts2(track=track)>>>track.play()
Package: nene
Nēnē: A no-frills static site generatorDo you lovePelican?
Can't get enough of thatJekyll?
Then Nēnē is probably not for you.Static website generators seem to be the favourite pastime of developers
procrastinating on other more important projects.
So of course, I had to make my own!When I sayno-frills, I mean it. Nēnē has no built-in templates, no special
provisions for blogging, plugins, or the sort of thing that would be useful if
you just want to start a website.
But it also tries to combine several nice features from other generators into
something that will appeal to those who want to build their own templates.Trivia:Nēnēcontinues the long tradition of naming static site
generators built in Python after birds (the nēnē is agoose endemic to Hawai'i).DocumentationInstall instructions and other information can be found in the preliminary
documentation website (built with Nēnē, of course):nene.leouieda.comLooking for an example? Checkout these websites built with Nēnē:https://www.compgeolab.org(source code)https://www.acarolcolombo.com(source code)https://aguspesce.github.io/garabatos(source code)https://www.leouieda.com(source code)WARNING: You probably want to pin Nēnē to a specific version since I will
likely break compatibility between releases.LicenseNēnē is free and open-source software distributed under theMIT License.
Package: nengo-edge
NengoEdge ToolsNengoEdgeis a cloud-based platform for training and
deploying high accuracy, low power audio AI models on edge devices. This package
contains tools and examples to assist in taking a trained model exported from
NengoEdge and deploying it in your own application.To get started running NengoEdge models locally,
set up a Python environment using the installation instructions below.
Then download thelive microphone demo notebookand open it with:jupyter notebook /path/to/microphone-demo.ipynbInstallationNengoEdge models use theTensorFlowmachine learning library. If you already have TensorFlow installed,
then all you need is to:pip install nengo-edgeIf you do not have TensorFlow installed, see the see the fullinstallation instructionsfor more details.Release history23.9.27 (September 27, 2023)AddedAdded warning when a downloaded nengo-edge model artifacts’ version does not
match local environment nengo-edge version. (#6)Addednengo-edgepackage-datasetCLI command, which can be used to validate
and package KWS and ASR datasets. (#10)ChangedSavedModelRunnernow uses Keras’ SavedModel format, instead of the raw
TensorFlow SavedModel format. (#8)SavedModelRunnercan now take raggedobject-arrays as input. (#8)23.7.30 (July 30, 2023)AddedAddedCoralRunnerfor running models exported for the Coral board. (#4)AddedDiscoRunnerfor running models exported for the Disco board. (#4)AddedNordicRunnerfor running models exported for the Nordic board. (#4)Added on-device MFCC extraction code
(device_modules.np_mfcc.LogMelFeatureExtractor). (#4)Added two new examples demonstrating how to run models exported for the
Coral/Disco/Nordic devices. (#4)ChangedRenamedtflite_runner.RunnertoTFLiteRunner. (#4)Renamedsaved_model_runner.RunnertoSavedModelRunner. (#4)TFLiteRunner.reset_statenow takes abatch_sizeargument, which can be used
to prepare the model to run with different batch sizes. (#5)23.2.23 (February 23, 2023)FixedFixed an issue causing pip to refuse to installnengo-edge. (#3)23.1.31 (January 31, 2023)Initial release
Package: nengo-extras
Nengo extrasExtra utilities and add-ons for Nengo.This repository contains utilities that occupy
a liminal space not quite generic enough for inclusion inNengo,
but useful enough that they should be publicly accessible.Some of these utilities may eventually migrate toNengo,
and others may be split off into their own separate repositories.InstallationTo install Nengo extras, we recommend usingpip.pipinstallnengo-extrasUsageExample notebooks can be found
in thedocs/examplesdirectory.For a listing of the contents of this repository,
and information on how to use it,
see thefull documentation.DevelopmentTo run the unit tests:pytestnengo_extras[--plots]To run the static checks:.ci/static.shrunTo build the documentation:sphinx-builddocsdocs/_buildRelease history0.5.0 (January 12, 2022)AddedAdded NengoDL builders for NengoLoihi neuron types. These will automatically be used
by the NengoLoihi repository if it is installed. (#94)FixedUpdated to work with more recent versions of Nengo. (#94)0.4.0 (November 15, 2019)AddedAddednengo_extras.simulators.RealTimeSimulator, which will ensure that
simulations don’t run faster than real time.
(#85,#151)Addednengo_extras.neurons.NumbaLIF, which is a numba-accelerated
drop-in replacement for thenengo.LIFneuron model (requiresnumbato
be installed).
(#86)FixedFixed some Nengo 3.0.0 compatibility issues.
(#90)0.3.0 (June 4, 2018)ChangedSubmodules are no longer automatically imported into thenengo_extrasnamespace, as it can be difficult to install
requirements for the various tools in Nengo Extras.
(#77,#78)0.2.0 (May 31, 2018)AddedAdded the association matrix learning rule (AML)
to learn associations from cue vectors to target vectors
in a one-shot fashion without catastrophic forgetting.
(#72)Added classes to convert Nengo models to GEXF for visualization with Gephi.
(#54)Added aCameraprocess to stream images from a camera to Nengo.
(#61)0.1.0 (March 14, 2018)Initial release of Nengo Extras!
Tested with Nengo 2.7.0, but should work with earlier versions.
If you run into any issues, pleasefile a bug report.
Package: nengolib
import nengolibAdditional extensions and tools for modelling dynamical systems inNengo.DocumentationThis project’s documentation is hosted on GitHub.IO:https://arvoelke.github.io/nengolib-docs/.DevelopmentTo install the development version of nengolib:git clone https://github.com/arvoelke/nengolib
cd nengolib
python setup.py developNotebooks can be run manually indocs/notebooksby running:pip install jupyter
jupyter notebookRelease History0.5.2 (September 10, 2019)FixedSolved an issue where scipy.misc imports were relocated.
(#182)0.5.1 (April 17, 2019)Tested against Nengo versions 2.2.0-2.8.0. Requiresnengo<3.0.FixedA variety of miscellaneous fixes were made to the documentation.
Thenengolib.networks.RollingWindowdocumentation references the
shifted Legendre polynomial equations forlegendre == True.
(#176)0.5.0 (March 9, 2019)Tested against Nengo versions 2.2.0-2.8.0.
We now requirenumpy>=1.13.0,scipy>=0.19.0, andnengo>=2.2.0.AddedAdded thenengolib.RLS()recursive least-squares (RLS)
learning rule. This can be substituted fornengo.PES().
Seenotebooks/examples/full_force_learning.ipynbfor an
example that uses this to implement spiking FORCE in Nengo.
(#133)Added thenengolib.stats.Rd()method for quasi-random sampling of
arbitrarily high-dimensional vectors. It is now the default method for
scattered sampling of encoders and evaluation points.
The method can be manually switched back tonengolib.stats.Sobol().
(#153)Added thenengolib.neuron.init_lif(sim, ens)helper function
for initializing the neural state of aLIFensemble, from within
a simulator block, to represent0uniformly at the start.
(#156)Addednengolib.synapses.LegendreDelayas an alternative tonengolib.synapses.PadeDelay– it has an equivalent transfer function
but a state-space realization corresponding to the shifted
Legendre basis.
The networknengolib.networks.RollingWindowsupportlegendre=Trueto make this system the default realization.
(#161)FixedRelease no longer requirespytest.
(#156)0.4.2 (May 18, 2018)Tested against Nengo versions 2.1.0-2.7.0.AddedSolving for connection weights by accounting for the neural
dynamics. To use, pass innengolib.Temporal()tonengo.Connectionfor thesolverparameter.
Requiresnengo>=2.5.0.
(#137)0.4.1 (December 5, 2017)Tested against Nengo versions 2.1.0-2.6.0.FixedCompatible with newest SciPy release (1.0.0).
(#130)0.4.0b (June 7, 2017)Initial beta release of nengolib.
Tested against Nengo versions 2.1.0-2.4.0.