【小沐学Python】Python实现语音识别（SpeechRecognition）

文章目录

• 1、简介
• 2、安装和测试
• • 2.1 安装python
  • 2.2 安装SpeechRecognition
  • 2.3 安装pyaudio
  • 2.4 安装pocketsphinx（offline）
  • 2.5 安装Vosk （offline）
  • 2.6 安装Whisper（offline）
  • 3 测试
  • • 3.1 命令
    • 3.2 fastapi
    • 3.3 google
    • 3.4 recognize_sphinx
    • 3.5 语音生成音频文件
    • ���语

      1、简介

      https://pypi.org/project/SpeechRecognition/

      https://github.com/Uberi/speech_recognition

      SpeechRecognition用于执行语音识别的库，支持多个引擎和 API，在线和离线。

      Speech recognition engine/API 支持如下接口:

      recognize_bing()：Microsoft Bing Speechrecognize_google()： Google Web Speech APIrecognize_google_cloud()：Google Cloud Speech - requires installation of the google-cloud-speech packagerecognize_houndify()： Houndify by SoundHoundrecognize_ibm()：IBM Speech to Textrecognize_sphinx()：CMU Sphinx - requires installing PocketSphinxrecognize_wit()：Wit.ai

      2、安装和测试

      • Python 3.8+ (required)

      • PyAudio 0.2.11+ (required only if you need to use microphone input, Microphone)

      • PocketSphinx (required only if you need to use the Sphinx recognizer, recognizer_instance.recognize_sphinx)

      • Google API Client Library for Python (required only if you need to use the Google Cloud Speech API, recognizer_instance.recognize_google_cloud)

      • FLAC encoder (required only if the system is not x86-based Windows/Linux/OS X)

      • Vosk (required only if you need to use Vosk API speech recognition recognizer_instance.recognize_vosk)

      • Whisper (required only if you need to use Whisper recognizer_instance.recognize_whisper)

      • openai (required only if you need to use Whisper API speech recognition recognizer_instance.recognize_whisper_api)

        2.1 安装python

        https://www.python.org/downloads/

        2.2 安装SpeechRecognition

        安装库SpeechRecognition：

        #python -m pip install --upgrade pip#pip install 包名 -i https://pypi.tuna.tsinghua.edu.cn/simple/#pip install 包名 -i http://pypi.douban.com/simple/ --trusted-host pypi.douban.com#pip install 包名 -i https://pypi.org/simplepip install SpeechRecognition

        import speech_recognition as srprint(sr.__version__)

        麦克风的特定于硬件的索引获取：

        import speech_recognition as srfor index, name in enumerate(sr.Microphone.list_microphone_names()):    print("Microphone with name \"{1}\" found for `Microphone(device_index={0})`".format(index, name))

        2.3 安装pyaudio

        pip install pyaudio

        2.4 安装pocketsphinx（offline）

        pip install pocketsphinx

        或者https://www.lfd.uci.edu/~gohlke/pythonlibs/#pocketsphinx找到编译好的本地库文件进行安装。

        在这里使用的是recognize_sphinx()语音识别器，它可以脱机工作，但是必须安装pocketsphinx库.

        若要进行中文识别，还需要两样东西。

        1、语音文件（SpeechRecognition对文件格式有要求）；

        SpeechRecognition支持语音文件类型：

        WAV: 必须是 PCM/LPCM 格式AIFFAIFF-CFLAC: 必须是初始 FLAC 格式；OGG-FLAC 格式不可用

        2、中文声学模型、语言模型和字典文件;

        pocketsphinx需要安装的中文语言、声学模型。

        https://sourceforge.net/projects/cmusphinx/files/Acoustic%20and%20Language%20Models/Mandarin/

        下载cmusphinx-zh-cn-5.2.tar.gz并解压：

        在python安装目录下找到Lib\site-packages\speech_recognition：

        点击进入pocketsphinx-data文件夹，并新建文件夹zh-CN：

        在这个文件夹中添加进入刚刚解压的文件，需要注意：把解压出来的zh_cn.cd_cont_5000文件夹重命名为acoustic-model、zh_cn.lm.bin命名为language-model.lm.bin、zh_cn.dic中dic改为pronounciation-dictionary.dict格式。

        编写脚本测试：

        import speech_recognition as srr = sr.Recognizer()    #调用识别器test = sr.AudioFile("chinese.flac")   #导入语音文件with test as source:       # r.adjust_for_ambient_noise(source)    audio = r.record(source) #使用 record() 从文件中获取数据type(audio)# c=r.recognize_sphinx(audio, language='zh-cn')     #识别输出c=r.recognize_sphinx(audio, language='en-US')     #识别输出print(c)

        import speech_recognition as sr# obtain path to "english.wav" in the same folder as this scriptfrom os import pathAUDIO_FILE = path.join(path.dirname(path.realpath(__file__)), "english.wav")# AUDIO_FILE = path.join(path.dirname(path.realpath(__file__)), "french.aiff")# AUDIO_FILE = path.join(path.dirname(path.realpath(__file__)), "chinese.flac")# use the audio file as the audio sourcer = sr.Recognizer()with sr.AudioFile(AUDIO_FILE) as source:    audio = r.record(source)  # read the entire audio file# recognize speech using Sphinxtry:    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))

        import speech_recognition as srrecognizer = sr.Recognizer()with sr.Microphone() as source:# recognizer.adjust_for_ambient_noise(source)    audio = recognizer.listen(source)c=recognizer.recognize_sphinx(audio, language='zh-cn')     #识别输出# c=r.recognize_sphinx(audio, language='en-US')     #识别输出print(c)

        import speech_recognition as sr# obtain audio from the microphoner = sr.Recognizer()with sr.Microphone() as source:    print("Say something!")    audio = r.listen(source)# recognize speech using Sphinxtry:    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))

        2.5 安装Vosk （offline）

        python3 -m pip install vosk

        您还必须安装 Vosk 模型：

        以下是可供下载的模型。您必须将它们放在项目的模型文件夹中，例如“your-project-folder/models/your-vosk-model”

        https://alphacephei.com/vosk/models

        在测试脚本的所在文件夹，新建model子文件夹，然后把上面下载的模型解压到里面如下：

        编写脚本：

        import speech_recognition as srfrom vosk import KaldiRecognizer, Modelr = sr.Recognizer()with sr.Microphone() as source:    audio = r.listen(source, timeout=3, phrase_time_limit=3)r.vosk_model = Model(model_name="vosk-model-small-cn-0.22")text=r.recognize_vosk(audio, language='zh-cn') print(text)

        2.6 安装Whisper（offline）

        pip install zhconvpip install whisperpip install -U openai-whisperpip3 install wheelpip install soundfile

        编写脚本：

        import speech_recognition as srfrom vosk import KaldiRecognizer, Modelr = sr.Recognizer()with sr.Microphone() as source:    audio = r.listen(source, timeout=3, phrase_time_limit=5)# recognize speech using whispertry:    print("Whisper thinks you said: " + r.recognize_whisper(audio, language="chinese"))except sr.UnknownValueError:    print("Whisper could not understand audio")except sr.RequestError as e:    print("Could not request results from Whisper")

        3 测试

        3.1 命令

        python -m speech_recognition

        3.2 fastapi

        import jsonimport osfrom pprint import pprintimport speech_recognitionimport torchimport uvicornfrom fastapi import FastAPI, HTTPExceptionfrom pydantic import BaseModelimport soundfileimport whisperimport voskclass ResponseModel(BaseModel):    path: strapp = FastAPI()def get_path(req: ResponseModel):    path = req.path    if path == "":        raise HTTPException(status_code=400, detail="No path provided")    if not path.endswith(".wav"):        raise HTTPException(status_code=400, detail="Invalid file type")    if not os.path.exists(path):        raise HTTPException(status_code=404, detail="File does not exist")    return path@app.get("/")def root():    return {"message": "speech-recognition api"}@app.post("/recognize-google")def recognize_google(req: ResponseModel):    path = get_path(req)    r = speech_recognition.Recognizer()    with speech_recognition.AudioFile(path) as source:        audio = r.record(source)    return r.recognize_google(audio, language='ja-JP', show_all=True)@app.post("/recognize-vosk")def recognize_vosk(req: ResponseModel):    path = get_path(req)    r = speech_recognition.Recognizer()    with speech_recognition.AudioFile(path) as source:        audio = r.record(source)    return json.loads(r.recognize_vosk(audio, language='ja'))@app.post("/recognize-whisper")def recognize_whisper(req: ResponseModel):    path = get_path(req)    r = speech_recognition.Recognizer()    with speech_recognition.AudioFile(path) as source:        audio = r.record(source)    result = r.recognize_whisper(audio, language='ja')    try:        return json.loads(result)    except:        return {"text": result}if __name__ == "__main__":    host = os.environ.get('HOST', '0.0.0.0')    port: int = os.environ.get('PORT', 8080)    uvicorn.run("main:app", host=host, port=int(port))

        3.3 google

        import speech_recognition as srimport webbrowser as wbimport speakchrome_path = 'C:/Program Files (x86)/Google/Chrome/Application/chrome.exe %s'r = sr.Recognizer()with sr.Microphone() as source:    print ('Say Something!')    audio = r.listen(source)    print ('Done!') try:    text = r.recognize_google(audio)    print('Google thinks you said:\n' + text)    lang = 'en'    speak.tts(text, lang)    f_text = 'https://www.google.co.in/search?q=' + text    wb.get(chrome_path).open(f_text) except Exception as e:    print (e)

        3.4 recognize_sphinx

        import loggingimport speech_recognition as srdef audio_Sphinx(filename):    logging.info('开始识别语音文件...')    # use the audio file as the audio source    r = sr.Recognizer()    with sr.AudioFile(filename) as source:        audio = r.record(source)  # read the entire audio file    # recognize speech using Sphinx    try:        print("Sphinx thinks you said: " + r.recognize_sphinx(audio, language='zh-cn'))    except sr.UnknownValueError:        print("Sphinx could not understand audio")    except sr.RequestError as e:        print("Sphinx error; {0}".format(e))    if __name__ == "__main__":    logging.basicConfig(level=logging.INFO)    wav_num = 0    while True:        r = sr.Recognizer()        #启用麦克风        mic = sr.Microphone()        logging.info('录音中...')        with mic as source:            #降噪            r.adjust_for_ambient_noise(source)            audio = r.listen(source)        with open(f"00{wav_num}.wav", "wb") as f:            #将麦克风录到的声音保存为wav文件            f.write(audio.get_wav_data(convert_rate=16000))        logging.info('录音结束，识别中...')        target = audio_Sphinx(f"00{wav_num}.wav")        wav_num += 1

        3.5 语音生成音频文件

        • 方法1：

          import speech_recognition as sr # Use SpeechRecognition to record 使用语音识别包录制音频def my_record(rate=16000):    r = sr.Recognizer()    with sr.Microphone(sample_rate=rate) as source:        print("please say something")        audio = r.listen(source)     with open("voices/myvoices.wav", "wb") as f:        f.write(audio.get_wav_data())    print("录音完成！") my_record()

          • 方法2：

            import wavefrom pyaudio import PyAudio, paInt16 framerate = 16000  # 采样率num_samples = 2000  # 采样点channels = 1  # 声道sampwidth = 2  # 采样宽度2bytesFILEPATH = 'voices/myvoices.wav'  def save_wave_file(filepath, data):    wf = wave.open(filepath, 'wb')    wf.setnchannels(channels)    wf.setsampwidth(sampwidth)    wf.setframerate(framerate)    wf.writeframes(b''.join(data))    wf.close()  #录音def my_record():    pa = PyAudio()    #打开一个新的音频stream    stream = pa.open(format=paInt16, channels=channels,                     rate=framerate, input=True, frames_per_buffer=num_samples)    my_buf = [] #存放录音数据     t = time.time()    print('正在录音...')     while time.time()  
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