700字范文 > python分割语音端点检测_python的webrtc库如何实现语音端点检测科大讯飞输入法PC体

python分割语音端点检测_python的webrtc库如何实现语音端点检测科大讯飞输入法PC体

时间：2020-09-22 12:34:22

python的webrtc库如何实现语音端点检测科大讯飞输入法PC体验版下载：语音+手写+

-03-05出处：网络整理：

延伸：科大讯飞输入法PC体验版下载：语音+手写+键盘

描述：...• 首款“云计算”智能语音输入法• 语音流式识别，边说边识别• 语音端点智能检测手写• 键盘上直接手写，同一界面多种输入• 笔画顺序随意组合，结果照样准确• 竖屏叠写，长句手写一气呵成• 横屏连写，写完左边写右边键盘• 拼音/英文/字母独立面板• 底部滑动切换键盘• 九宫格式表情、标...

现在基于云端语义库的聊天机器人层出不穷,其中最著名的当属amazon的 Alexa/Echo 智能音箱.

国内如雨后春笋般出现了各种搭载语音聊天的智能音箱(如前几天在知乎上广告的若琪机器人)和各类智能机器人产品.国内语音服务提供商主要面对中文语音服务,由于语音不像图像有分辨率等等较为客观的指标,很多时候凭主观判断,所以较难判断各家语音识别和合成技术的好坏.但是我个人认为,国内的中文语音服务和国外的英文语音服务,在某些方面已经有超越的趋势.

通常搭建机器人聊天系统主要包括以下三个方面:

语音转文字(ASR/STT)

语义内容(NLU/NLP)

文字转语音(TTS)

语音转文字(ASR/STT)

在将语音传给云端API之前,是本地前端的语音采集,这部分主要包括如下几个方面:

麦克风降噪

声源定位

回声消除

唤醒词

语音端点检测

音频格式压缩

python 端点检测

由于实际应用中,单纯依靠能量检测特征检测等方法很难判断人声说话的起始点,所以市面上大多数的语音产品都是使用唤醒词判断语音起始.另外加上声音回路,还可以做语音打断.这样的交互方式可能有些傻,每次必须喊一下唤醒词才能继续聊天.这种方式聊多了,个人感觉会嘴巴疼:-O .现在github上有snowboy唤醒词的开源库,大家可以登录snowboy官网训练自己的唤醒词模型.

Kitt-AI : Snowboy

Sensory : Sensory

考虑到用唤醒词嘴巴会累,所以大致调研了一下,Python拥有丰富的库,直接import就能食用.这种方式容易受强噪声干扰,适合一个人在家玩玩.

pyaudio: pip install pyaudio 可以从设备节点读取原始音频流数据,音频编码是PCM格式;

webrtcvad: pip install webrtcvad 检测判断一组语音数据是否为空语音;

当检测到持续时间长度 T1 vad检测都有语音活动,可以判定为语音起始;

当检测到持续时间长度 T2 vad检测都没有有语音活动,可以判定为语音结束;

完整程序代码可以从我的github下载

程序很简单,相信看一会儿就明白了

'''

Requirements:

+ pyaudio - `pip install pyaudio`

+ py-webrtcvad - `pip install webrtcvad`

'''

import webrtcvad

import collections

import sys

import signal

import pyaudio

from array import array

from struct import pack

import wave

import time

FORMAT = pyaudio.paInt16

CHANNELS = 1

RATE = 16000

CHUNK_DURATION_MS = 30 # supports 10, 20 and 30 (ms)

PADDING_DURATION_MS = 1500 # 1 sec jugement

CHUNK_SIZE = int(RATE CHUNK_DURATION_MS / 1000) # chunk to read

CHUNK_BYTES = CHUNK_SIZE 2 # 16bit = 2 bytes, PCM

NUM_PADDING_CHUNKS = int(PADDING_DURATION_MS / CHUNK_DURATION_MS)

# NUM_WINDOW_CHUNKS = int

(240 / CHUNK_DURATION_MS)

NUM_WINDOW_CHUNKS = int

(400 / CHUNK_DURATION_MS) # 400 ms/ 30ms ge

NUM_WINDOW_CHUNKS_END = NUM_WINDOW_CHUNKS 2

START_OFFSET = int(NUM_WINDOW_CHUNKS CHUNK_DURATION_MS 0.5 RATE)

vad = webrtcvad.Vad

(1)

pa = pyaudio.PyAudio()

stream = pa.open(format=FORMAT,

channels=CHANNELS,

rate=RATE,

input=True,

start=False,

# input_device_index=2,

frames_per_buffer=CHUNK_SIZE)

got_a_sentence = False

leave = False

def handle_int(sig, chunk):

global leave, got_a_sentence

leave = True

got_a_sentence = True

def record_to_file(path, data, sample_width):

Records from the microphone and outputs the resulting data to 'path'

# sample_width, data = record()

data = pack('' + ('h' len(data)), data)

wf = wave.open(path, 'wb')

wf.setnchannels

(1)

wf.setsampwidth(sample_width)

wf.setframerate(RATE)

wf.writeframes(data)

wf.close()

def normalize(snd_data):

Average the volume out

MAXIMUM = 32767 # 16384

times = float(MAXIMUM) / max(abs(i) for i in snd_data)

r = array('h')

for i in snd_data:

r.append(int(i times))

return r

signal.signal(signal.SIGINT, handle_int)

while not leave:

ring_buffer = collections.deque(maxlen=NUM_PADDING_CHUNKS)

triggered = False

voiced_frames = []

ring_buffer_flags = [0] NUM_WINDOW_CHUNKS

ring_buffer_index = 0

ring_buffer_flags_end = [0] NUM_WINDOW_CHUNKS_END

ring_buffer_index_end = 0

buffer_in = ''

# WangS

raw_data = array('h')

index = 0

start_point = 0

StartTime = time.time()

print( recording: )

stream.start_stream()

while not got_a_sentence and not leave:

chunk = stream.read(CHUNK_SIZE)

# add WangS

raw_data.extend(array('h', chunk))

index += CHUNK_SIZE

TimeUse = time.time() - StartTime

active = vad.is_speech(chunk, RATE)

sys.stdout.write('1' if active else '_')

ring_buffer_flags[ring_buffer_index] = 1 if active else 0

ring_buffer_index += 1

ring_buffer_index %= NUM_WINDOW_CHUNKS

ring_buffer_flags_end[ring_buffer_index_end] = 1 if active else 0

ring_buffer_index_end += 1

ring_buffer_index_end %= NUM_WINDOW_CHUNKS_END

# start point detection

if not triggered:

ring_buffer.append(chunk)

num_voiced = sum(ring_buffer_flags)

if num_voiced 0.8 NUM_WINDOW_CHUNKS:

sys.stdout.write(' Open ')

triggered = True

start_point = index - CHUNK_SIZE 20 # start point

# voiced_frames.extend(ring_buffer)

ring_buffer.clear()

# end point detection

else:

# voiced_frames.append(chunk)

ring_buffer.append(chunk)

num_unvoiced = NUM_WINDOW_CHUNKS_END - sum(ring_buffer_flags_end)

if num_unvoiced 0.90 NUM_WINDOW_CHUNKS_END or TimeUse 10:

sys.stdout.write(' Close ')

triggered = False

got_a_sentence = True

sys.stdout.flush()

sys.stdout.write('\n')

# data = b''.join(voiced_frames)

stream.stop_stream()

print( done recording)

got_a_sentence = False

# write to file

raw_data.reverse()

for index in range(start_point):

raw_data.pop()

raw_data.reverse()

raw_data = normalize(raw_data)

record_to_file(recording.wav, raw_data, 2)

leave = True

stream.close()

程序运行方式sudo python vad.py

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python分割语音端点检测_python的webrtc库如何实现语音端点检测 科大讯飞输入法PC体

python分割语音端点检测_python的webrtc库如何实现语音端点检测科大讯飞输入法PC体