svoice | PyTorch implementation of the paper Voice Separation | Speech library
kandi X-RAY | svoice Summary
kandi X-RAY | svoice Summary
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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Top functions reviewed by kandi - BETA
- 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
svoice Key Features
svoice Examples and Code Snippets
Community Discussions
Trending Discussions on svoice
QUESTION
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 10:20the 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 withmtab
andfstab
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 filesu
under special dir.
app has to callexec("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) .
Community Discussions, Code Snippets contain sources that include Stack Exchange Network
Vulnerabilities
No vulnerabilities reported
Install svoice
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
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