svoice | PyTorch implementation of the paper Voice Separation | Speech library

by facebookresearch Python Version: Current License: Non-SPDX

X-Ray Key Features Code Snippets Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | svoice Summary

svoice is a Python library typically used in Telecommunications, Media, Media, Entertainment, Artificial Intelligence, Speech, Deep Learning, Pytorch applications. svoice has no bugs, it has no vulnerabilities, it has build file available and it has medium support. However svoice has a Non-SPDX License. You can download it from GitHub.

We provide a PyTorch implementation of our speaker voice separation research work. In Voice Separation with an Unknown Number of Multiple Speakers, we present a new method for separating a mixed audio sequence, in which multiple voices speak simultaneously. The new method employs gated neural networks that are trained to separate the voices at multiple processing steps, while maintaining the speaker in each output channel fixed. A different model is trained for every number of possible speakers, and the model with the largest number of speakers is employed to select the actual number of speakers in a given sample. Our method greatly outperforms the current state of the art, which, as we show, is not competitive for more than two speakers. Please note that this implementation does not contain the "IDloss" as described in the paper. Audio samples can be found here: Samples.

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svoice has a medium active ecosystem.

It has 1029 star(s) with 159 fork(s). There are 24 watchers for this library.

It had no major release in the last 6 months.

There are 26 open issues and 59 have been closed. On average issues are closed in 60 days. There are 1 open pull requests and 0 closed requests.

It has a neutral sentiment in the developer community.

The latest version of svoice is current.

Quality

svoice has 0 bugs and 0 code smells.

Security

svoice has no vulnerabilities reported, and its dependent libraries have no vulnerabilities reported.

svoice code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

svoice has a Non-SPDX License.

Non-SPDX licenses can be open source with a non SPDX compliant license, or non open source licenses, and you need to review them closely before use.

Reuse

svoice releases are not available. You will need to build from source code and install.

Build file is available. You can build the component from source.

Installation instructions, examples and code snippets are available.

svoice saves you 610 person hours of effort in developing the same functionality from scratch.

It has 1607 lines of code, 104 functions and 17 files.

It has high code complexity. Code complexity directly impacts maintainability of the code.

Top functions reviewed by kandi - BETA

kandi has reviewed svoice and discovered the below as its top functions. This is intended to give you an instant insight into svoice implemented functionality, and help decide if they suit your requirements.

Evaluate automatic selection
Create a DataLoader from dataset
Compute pairwise pairwise pairwise pairwise
Average metrics
Evaluate a model
Generate a scene
Generate a random room
Fetch signals from the speaker
Fetch noise
Convert a model into a dataset
Remove padding
Determine mix directory and mix files
R Write a wavs estimate to files
Collate a batch of input sequences
Loads mixing files from the given batch
Pad a list
Calculate the metrics for a model
Calculate SISNR
Calculate the SISNR index
Calculate the PESQ
Main entry point
Compute and return a dataset
Finds audio files in path
Forward the RNN model into chunks
Preprocess one or more directories
Preprocess files
Concatenate Estimator

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svoice Key Features

No Key Features are available at this moment for svoice.

svoice Examples and Code Snippets

No Code Snippets are available at this moment for svoice.

Community Discussions

Trending Discussions on svoice

Explaining this Bash Script

QUESTION

Explaining this Bash Script

Asked 2019-Sep-20 at 13:36

Alright i was flashing a debloater script on my S6 EDGE, but i was curious about how these commands acutally works so i decided to ask for some help...

update-script

...

ANSWER

Answered 2019-Sep-20 at 10:20

the things you don't understand can be divide into two command group,

rm group
/sbin/rm -fr delete directory and files silently
mount group
/sbin/mount -t auto mount partitions under auto configured parameters, mostly related with mtab and fstab

you can do more research on quoted terminology above

if you cant see the rm and mount file under /sbin, there are two explanation i can think of

only app with effective root privilege can do that, and you are viewing in an app that does not holding root privilege when scanning /sbin.
rooting device is by placing a special binary file su under special dir.
app has to call exec("su") explicitly to start a process with root privilege
so that it can delete system files, mount or umount partitions, etc.
you are viewing in a chroot environment, the real /sbin is outside the environment.
which is less likely to happen on your phone,
it is a way of relocating / directory for process(and its sub-process) .

Source https://stackoverflow.com/questions/58023670

Community Discussions, Code Snippets contain sources that include Stack Exchange Network

Vulnerabilities

No vulnerabilities reported

Install svoice

First, install Python 3.7 (recommended with Anaconda). Clone this repository and install the dependencies. We recommend using a fresh virtualenv or Conda environment.
If you want to train using a new dataset, you can:. You also need to generate the relevant .jsonfiles in the egs/folder. For that purpose you can use the python -m svoice.data.audio command that will scan the given folders and output the required metadata as json. For instance, if your mixture files are located in $mix and the separated files are in $spk1 and $spk2, you can do.
Create a separate config file for it.
Place the new config files under the dset folder. Check conf/dset/debug.yaml for more details on configuring your dataset.
Point to it either in the general config file or via the command line, e.g. ./train.py dset=name_of_dset.
Notice, we already provided the yaml file for it. Can be found under conf/dset/debug.yaml.
Run ./make_debug.sh to generate json files for the toy dataset.
Run python train.py

Support

For any new features, suggestions and bugs create an issue on GitHub. If you have any questions check and ask questions on community page Stack Overflow .

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