spectra-torch 0.3.0

Spectra Extraction based on PyTorch

spectra_torch

This library provides common spectra features from an audio signal including MFCCs and filter bank energies. This library mimics the library python_speech_features but PyTorch-style.

This library provides voice activity detection (VAD) based on energy. This library mimics the library VAD-python but PyTorch-style.

Use: Rui Wang. (2020, february 26). mechanicalsea/spectra: release v0.2.2 (Version 0.2.2).

Installation

This library is avaliable on pypi.org

To install from Pypi:

pip install --upgrade spectra-torch

Require:

• python: 3.7.3
• torch: 1.4.0
• torchaudio: 0.4.0

Usage

Supported features:

• Mel Frequency Cepstral Coefficients (MFCC)
• Filterbank Energies
• Log Filterbank Energies
• Voice Activity Detection (VAD)

Here are examples.

Easy demo:

import spectra_torch as mm
import torchaudio as ta

sig, sr = ta.load_wav('mywav.wav')
sig = sig[0]
mfcc = mm.mfcc(sig, sr) # MFCC
starts, detection = is_speech(sig, sr, speechlen=1) # VAD

Performance

The difference between spectra_torch and python_speech_features:

• Precision bais: 1e-4
• Speed up: 0.1s/mfcc

MFCC

def mfcc(signal, samplerate=16000, winlen=0.025, hoplen=0.01, 
         numcep=13, nfilt=26, nfft=None, lowfreq=0, highfreq=None, 
         preemph=0.97, ceplifter=22, plusEnergy=True)

Filterbank

def fbank(signal, samplerate=16000, winlen=0.025, hoplen=0.01, 
          nfilt=26, nfft=512, lowfreq=0, highfreq=None, preemph=0.97)

VAD

def is_speech(signal, samplerate=16000, winlen=0.02, hoplen=0.01, 
              thresEnergy=0.6, speechlen=0.5, lowfreq=300, highfreq=3000, 
              preemph=0.97)

Reference

• python_speeck_features: https://github.com/jameslyons/python_speech_features
• VAD-python: https://github.com/marsbroshok/VAD-python
• pythonaudio: https://pytorch.org/audio/_modules/torchaudio/functional.html