| |
| |
| ''' |
| MIT License |
| |
| Copyright (c) 2018 Mauricio |
| |
| Permission is hereby granted, free of charge, to any person obtaining a copy |
| of this software and associated documentation files (the "Software"), to deal |
| in the Software without restriction, including without limitation the rights |
| to use, copy, modify, merge, publish, distribute, sublicense, and/or sell |
| copies of the Software, and to permit persons to whom the Software is |
| furnished to do so, subject to the following conditions: |
| |
| The above copyright notice and this permission notice shall be included in all |
| copies or substantial portions of the Software. |
| |
| THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR |
| IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, |
| FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE |
| AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER |
| LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, |
| OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE |
| SOFTWARE. |
| |
| Adapted from https://github.com/mauriciovander/silence-removal/blob/master/vad.py |
| ''' |
| import numpy |
|
|
| class VoiceActivityDetection: |
|
|
| def __init__(self): |
| self.__step = 160 |
| self.__buffer_size = 160 |
| self.__buffer = numpy.array([],dtype=numpy.int16) |
| self.__out_buffer = numpy.array([],dtype=numpy.int16) |
| self.__n = 0 |
| self.__VADthd = 0. |
| self.__VADn = 0. |
| self.__silence_counter = 0 |
|
|
| |
| |
| def vad(self, _frame, sc_threshold=20): |
| frame = numpy.array(_frame) ** 2. |
| result = True |
| threshold = 0.2 |
| thd = numpy.min(frame) + numpy.ptp(frame) * threshold |
| self.__VADthd = (self.__VADn * self.__VADthd + thd) / float(self.__VADn + 1.) |
| self.__VADn += 1. |
|
|
| if numpy.mean(frame) <= self.__VADthd: |
| self.__silence_counter += 1 |
| else: |
| self.__silence_counter = 0 |
| if self.__silence_counter > sc_threshold: |
| result = False |
| return result |
|
|
| |
| def add_samples(self, data): |
| self.__buffer = numpy.append(self.__buffer, data) |
| result = len(self.__buffer) >= self.__buffer_size |
| |
| return result |
|
|
| |
| |
| |
| def get_frame(self): |
| window = self.__buffer[:self.__buffer_size] |
| self.__buffer = self.__buffer[self.__step:] |
| |
| return window |
|
|
| |
| |
| def process(self, data, sc_threshold): |
| self.__buffer = numpy.array([],dtype=numpy.int16) |
| self.__out_buffer = numpy.array([],dtype=numpy.int16) |
| if self.add_samples(data): |
| while len(self.__buffer) >= self.__buffer_size: |
| |
| window = self.get_frame() |
| if self.vad(window, sc_threshold): |
| self.__out_buffer = numpy.append(self.__out_buffer, window) |
| return self.__out_buffer |
|
|
