wavencoder | Python library for encoding audio signals | Speech library

by shangeth Python Version: 0.1.3 License: MIT

X-Ray Key Features Code Snippets(3)Community Discussions(1)Vulnerabilities Install Support

kandi X-RAY | wavencoder Summary

wavencoder is a Python library typically used in Artificial Intelligence, Speech, Deep Learning, Pytorch applications. wavencoder has no bugs, it has no vulnerabilities, it has build file available, it has a Permissive License and it has low support. You can install using 'pip install wavencoder' or download it from GitHub, PyPI.

WavEncoder is a Python library for encoding audio signals, transforms for audio augmentation, and training audio classification models with PyTorch backend.

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Quality

Security

License

Reuse

Support

wavencoder has a low active ecosystem.

It has 36 star(s) with 6 fork(s). There are 4 watchers for this library.

It had no major release in the last 12 months.

There are 3 open issues and 9 have been closed. On average issues are closed in 45 days. There are no pull requests.

It has a neutral sentiment in the developer community.

The latest version of wavencoder is 0.1.3

Quality

wavencoder has 0 bugs and 0 code smells.

Security

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

wavencoder code analysis shows 0 unresolved vulnerabilities.

There are 0 security hotspots that need review.

License

wavencoder is licensed under the MIT License. This license is Permissive.

Permissive licenses have the least restrictions, and you can use them in most projects.

Reuse

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

Deployable package is available in PyPI.

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

Installation instructions, examples and code snippets are available.

It has 1560 lines of code, 102 functions and 30 files.

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

Top functions reviewed by kandi - BETA

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

Train a model
Backward computation
Download noise dataset
Decorator for reporthook
Download IR files from Voxel
Runs test prediction on the given device
Get a sample wav file from the TIMIT corpus
Compute the attention weights
Compute the attention matrix
Compute attention weights
Get attention weights
Compute weights for query
Forward computation
Compute weights from query
Calculate the weighted sum of values
Get weights for query
Return the contents of the README md file

Get all kandi verified functions for this library.

wavencoder Key Features

No Key Features are available at this moment for wavencoder.

wavencoder Examples and Code Snippets

WavEncoder,Usage,Use wavencoder with PyTorch Sequential or class modules

Python

Lines of Code : 32

License : Permissive (MIT)

Copy

import torch
import torch.nn as nn
import wavencoder

model = nn.Sequential(
        wavencoder.models.Wav2Vec(),
        wavencoder.models.LSTM_Attn_Classifier(512, 64, 2,                          
                                               retu

WavEncoder,Usage,Train the encoder-classifier models

Python

Lines of Code : 14

License : Permissive (MIT)

Copy

from wavencoder.models import Wav2Vec, LSTM_Attn_Classifier
from wavencoder.trainer import train, test_evaluate_classifier, test_predict_classifier

model = nn.Sequential(
    Wav2Vec(pretrained=False),
    LSTM_Attn_Classifier(512, 64, 2)
)

trainlo

WavEncoder,Usage,Add Transforms to your DataLoader for Augmentation/Processing the wav signal

Python

Lines of Code : 13

License : Permissive (MIT)

Copy

from wavencoder.transforms import Compose, AdditiveNoise, SpeedChange, Clipping, PadCrop, Reverberation

audio, _ = torchaudio.load('test.wav')

transforms = Compose([
                    AdditiveNoise('path-to-noise-folder', p=0.5, snr_levels=[5, 10

Community Discussions

Trending Discussions on wavencoder

I get some difference between a buffer of recorded sound and stored sound file

QUESTION

I get some difference between a buffer of recorded sound and stored sound file

Asked 2019-Sep-09 at 03:00

I am trying to develop a music recognition web application using ACRCloud audio recognition.

I have a client side in React that records the music from the laptop's microphone and sends it to the server side:

...

ANSWER

Answered 2019-Sep-09 at 03:00

identifyAudio(buffer), the buffer has no header, so "can't generate fingerprint". You encoded it with wav-encoder and get the 'No result' response. The WAV file you generated may have problems. So you can save the source audio buffer and the WAV file you generated, and send them to "support@acrcloud.com", and I will test it.

Source https://stackoverflow.com/questions/57838576

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

Vulnerabilities

No vulnerabilities reported

Install wavencoder

Use the package manager pip to install wavencoder.

Support

Pull requests are welcome. For major changes, please open an issue first to discuss what you would like to change. Please make sure to update tests as appropriate.

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