大家好,我是痞子衡,是正经搞技术的痞子。今天痞子衡给大家介绍的是语音处理工具Jays-PySPEECH诞生之语音识别实现。
语音识别是Jays-PySPEECH的核心功能,Jays-PySPEECH借助的是SpeechRecognition系统以及CMU Sphinx引擎来实现的语音识别功能,今天痞子衡为大家介绍语音识别在Jays-PySPEECH中是如何实现的。
一、SpeechRecognition系统简介
SpeechRecognition是一套基于python实现语音识别的系统,该系统的设计者为 Anthony Zhang (Uberi),该库从2014年开始推出,一直持续更新至今,Jays-PySPEECH使用的是SpeechRecognition 3.8.1。
SpeechRecognition系统的官方主页如下:
- SpeechRecognition官方主页: https://github.com/Uberi/speech_recognition
- SpeechRecognition安装方法: https://pypi.org/project/SpeechRecognition/
SpeechRecognition系统自身并没有语音识别功能,其主要是调用第三方语音识别引擎来实现语音识别,SpeechRecognition支持的语音识别引擎非常多,有如下8种:
- CMU Sphinx (works offline)
- Google Speech Recognition
- Google Cloud Speech API
- Wit.ai
- Microsoft Bing Voice Recognition
- Houndify API
- IBM Speech to Text
- Snowboy Hotword Detection (works offline)
不管是选用哪一种语音识别引擎,在SpeechRecognition里调用接口都是一致的,我们以实现音频文件转文字的示例代码 audio_transcribe.py 为例了解SpeechRecognition的用法,截取audio_transcribe.py部分内容如下:
import speech_recognition as sr
# 指定要转换的音频源文件(english.wav)
from os import path
AUDIO_FILE = path.join(path.dirname(path.realpath(__file__)), "english.wav")
# 定义SpeechRecognition对象并获取音频源文件(english.wav)中的数据
r = sr.Recognizer()
with sr.AudioFile(AUDIO_FILE) as source:
audio = r.record(source) # read the entire audio file
# 使用CMU Sphinx引擎去识别音频
try:
print("Sphinx thinks you said " + r.recognize_sphinx(audio))
except sr.UnknownValueError:
print("Sphinx could not understand audio")
except sr.RequestError as e:
print("Sphinx error; {0}".format(e))
# 使用Microsoft Bing Voice Recognition引擎去识别音频
BING_KEY = "INSERT BING API KEY HERE" # Microsoft Bing Voice Recognition API keys 32-character lowercase hexadecimal strings
try:
print("Microsoft Bing Voice Recognition thinks you said " + r.recognize_bing(audio, key=BING_KEY))
except sr.UnknownValueError:
print("Microsoft Bing Voice Recognition could not understand audio")
except sr.RequestError as e:
print("Could not request results from Microsoft Bing Voice Recognition service; {0}".format(e))
# 使用其他引擎去识别音频
# ... ...
有木有觉得SpeechRecognition使用起来特别简单?是的,这正是SpeechRecognition系统强大之处,更多示例可见 https://github.com/Uberi/speech_recognition/tree/master/examples。
1.1 选用CMU Sphinx引擎
前面痞子衡讲了SpeechRecognition系统自身并没有语音识别功能,因此我们需要为SpeechRecognition安装一款语音识别引擎,痞子衡为JaysPySPEECH选用的是可离线工作的CMU Sphinx。
CMU Sphinx是卡内基梅隆大学开发的一款开源语音识别引擎,该引擎可以离线工作,并且支持多语种(英语、中文、法语等)。CMU Sphinx引擎的官方主页如下:
- CMU Sphinx官方主页: https://cmusphinx.github.io/
- CMU Sphinx官方下载: https://sourceforge.net/projects/cmusphinx/
由于JaysPySPEECH是基于Python环境开发的,因此我们不能直接用CMU Sphinx,那该怎么办?别着急,Dmitry Prazdnichnov大牛为CMU Sphinx写了Python封装接口,即PocketSphinx,其官方主页如下:
- PocketSphinx官方主页: https://github.com/bambocher/pocketsphinx-python
- PocketSphinx安装方法: https://pypi.org/project/pocketsphinx/
我们在JaysPySPEECH诞生系列文章第一篇 环境搭建 里已经安装了SpeechRecognition和PocketSphinx,痞子衡的安装路径为C:\tools_mcu\Python27\Lib\site-packages下的\speech_recognition与\pocketsphinx,安装好这两个包,引擎便选好了。
1.2 为PocketSphinx引擎增加中文语言包
默认情况下,PocketSphinx仅支持US English语言的识别,在C:\tools_mcu\Python27\Lib\site-packages\speech_recognition\pocketsphinx-data目录下仅能看到en-US文件夹,先来看一下这个文件夹里有什么:
\pocketsphinx-data\en-US
\acoustic-model --声学模型
\feat.params --HMM模型的特征参数
\mdef --模型定义文件
\means --混合高斯模型的均值
\mixture_weights --混合权重
\noisedict --噪声也就是非语音字典
\sendump --从声学模型中获取混合权重
\transition_matrices --HMM模型的状态转移矩阵
\variances --混合高斯模型的方差
\language-model.lm.bin --语言模型
\pronounciation-dictionary.dict --拼音字典
看到这一堆文件是不是觉得有点难懂?这其实跟CMU Sphinx引擎的语音识别原理有关,此处我们暂且不深入了解,对我们调用API的应用来说只需要关于如何为CMU Sphinx增加其他语言包(比如中文包)。
要想增加其他语言,首先得要有语言包数据,CMU Sphinx主页提供了12种主流语言包的下载 https://sourceforge.net/projects/cmusphinx/files/Acoustic_and_Language_Models/,因为JaysPySPEECH需要支持中文识别,因此我们需要下载\Mandarin下面的三个文件:
\Mandarin
\zh_broadcastnews_16k_ptm256_8000.tar.bz2 --声学模型
\zh_broadcastnews_64000_utf8.DMP --语言模型
\zh_broadcastnews_utf8.dic --拼音字典
有了中文语言包数据,然后我们需要根据 Notes on using PocketSphinx 里指示的步骤操作,痞子衡整理如下:
- \speech_recognition\pocketsphinx-data目录下创建zh-CN文件夹
- 将zh_broadcastnews_16k_ptm256_8000.tar.bz2解压缩并里面所有文件放入\zh-CN\acoustic-model文件夹下
- 将zh_broadcastnews_utf8.dic重命名为pronounciation-dictionary.dict并放入\zh-CN文件夹下
- 借助SphinxBase工具将zh_broadcastnews_64000_utf8.DMP转换成language-model.lm.bin并放入\zh-CN文件夹下
关于第4步里提到的SphinxBase工具,我们需要从 https://github.com/cmusphinx/sphinxbase 里下载源码,然后使用Visual Studio 2010(或以上)打开\sphinxbase\sphinxbase.sln工程Rebuild All后会在\sphinxbase\bin\Release\x64下看到生成了如下6个工具:
\\sphinxbase\bin\Release\x64
\sphinx_cepview.exe
\sphinx_fe.exe
\sphinx_jsgf2fsg.exe
\sphinx_lm_convert.exe
\sphinx_pitch.exe
\sphinx_seg.exe
我们主要使用sphinx_lm_convert.exe工具完成转换工作生成language-model.lm.bin,具体命令如下:
PS C:\tools_mcu\sphinxbase\bin\Release\x64> .\sphinx_lm_convert.exe -i .\zh_broadcastnews_64000_utf8.DMP -o language-model.lm - ofmt arpa
Current configuration: [NAME] [DEFLT] [VALUE] -case -help no no -i .\zh_broadcastnews_64000_utf8.DMP -ifmt -logbase 1.0001 1.000100e+00 -mmap no no -o language-model.lm -ofmt arpa INFO: ngram_model_trie.c(354): Trying to read LM in trie binary format INFO: ngram_model_trie.c(365): Header doesn't match INFO: ngram_model_trie.c(177): Trying to read LM in arpa format INFO: ngram_model_trie.c(70): No \data\ mark in LM file INFO: ngram_model_trie.c(445): Trying to read LM in dmp format INFO: ngram_model_trie.c(527): ngrams 1=63944, 2=16600781, 3=20708460 INFO: lm_trie.c(474): Training quantizer INFO: lm_trie.c(482): Building LM trie
PS C:\tools_mcu\sphinxbase\bin\Release\x64> .\sphinx_lm_convert.exe -i .\language-model.lm -o language-model.lm.bin
Current configuration: [NAME] [DEFLT] [VALUE] -case -help no no -i .\language-model.lm -ifmt -logbase 1.0001 1.000100e+00 -mmap no no -o language-model.lm.bin -ofmt INFO: ngram_model_trie.c(354): Trying to read LM in trie binary format INFO: ngram_model_trie.c(365): Header doesn't match INFO: ngram_model_trie.c(177): Trying to read LM in arpa format INFO: ngram_model_trie.c(193): LM of order 3 INFO: ngram_model_trie.c(195): #1-grams: 63944 INFO: ngram_model_trie.c(195): #2-grams: 16600781 INFO: ngram_model_trie.c(195): #3-grams: 20708460 INFO: lm_trie.c(474): Training quantizer INFO: lm_trie.c(482): Building LM trie
二、Jays-PySPEECH语音识别实现
语音识别代码实现其实很简单,直接调用speech_recognition里的API即可,目前仅实现了CMU Sphinx引擎,并且仅支持中英双语识别。具体到Jays-PySPEECH上主要是实现GUI界面上"ASR"按钮的回调函数,即audioSpeechRecognition(),如果用户选定了配置参数(语言类型、ASR引擎类型),并点击了"ASR"按钮,此时便会触发audioSpeechRecognition()的执行。代码如下:
import speech_recognition
class mainWin(win.speech_win):
def getLanguageSelection(self):
languageType = self.m_choice_lang.GetString(self.m_choice_lang.GetSelection())
if languageType == 'Mandarin Chinese':
languageType = 'zh-CN'
languageName = 'Chinese'
else: # languageType == 'US English':
languageType = 'en-US'
languageName = 'English'
return languageType, languageName
def audioSpeechRecognition( self, event ):
if os.path.isfile(self.wavPath):
# 创建speech_recognition语音识别对象asrObj
asrObj = speech_recognition.Recognizer()
# 获取wav文件里的语音内容
with speech_recognition.AudioFile(self.wavPath) as source:
speechAudio = asrObj.record(source)
self.m_textCtrl_asrttsText.Clear()
# 获取语音语言类型(English/Chinese)
languageType, languageName = self.getLanguageSelection()
engineType = self.m_choice_asrEngine.GetString(self.m_choice_asrEngine.GetSelection())
if engineType == 'CMU Sphinx':
try:
# 调用recognize_sphinx完成语音识别
speechText = asrObj.recognize_sphinx(speechAudio, language=languageType)
# 语音识别结果显示在asrttsText文本框内
self.m_textCtrl_asrttsText.write(speechText)
self.statusBar.SetStatusText("ASR Conversation Info: Successfully")
# 语音识别结果写入指定文件
fileName = self.m_textCtrl_asrFileName.GetLineText(0)
if fileName == '':
fileName = 'asr_untitled1.txt'
asrFilePath = os.path.join(os.path.dirname(os.path.abspath(os.path.dirname(__file__))), 'conv', 'asr', fileName)
asrFileObj = open(asrFilePath, 'wb')
asrFileObj.write(speechText)
asrFileObj.close()
except speech_recognition.UnknownValueError:
self.statusBar.SetStatusText("ASR Conversation Info: Sphinx could not understand audio")
except speech_recognition.RequestError as e:
self.statusBar.SetStatusText("ASR Conversation Info: Sphinx error; {0}".format(e))
else:
self.statusBar.SetStatusText("ASR Conversation Info: Unavailable ASR Engine")
至此,语音处理工具Jays-PySPEECH诞生之语音识别实现痞子衡便介绍完毕了,掌声在哪里~~~