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

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 Speech
recognize_google()： Google Web Speech API
recognize_google_cloud()：Google Cloud Speech - requires installation of the google-cloud-speech package
recognize_houndify()： Houndify by SoundHound
recognize_ibm()：IBM Speech to Text
recognize_sphinx()：CMU Sphinx - requires installing PocketSphinx
recognize_wit()：Wit.ai
1
2
3
4
5
6
7

以上几个中只有 recognition_sphinx（）可与CMU Sphinx 引擎脱机工作， 其他六个都需要连接互联网。另外，SpeechRecognition 附带 Google Web Speech API 的默认 API 密钥，可直接使用它。其他的 API 都需要使用 API 密钥或用户名/密码组合进行身份验证。

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/simple
pip install SpeechRecognition
1
2
3
4
5

import speech_recognition as sr
print(sr.__version__)
1
2

麦克风的特定于硬件的索引获取：

import speech_recognition as sr
for index, name in enumerate(sr.Microphone.list_microphone_names()):
    print("Microphone with name \"{1}\" found for `Microphone(device_index={0})`".format(index, name))
1
2
3

2.3 安装pyaudio

pip install pyaudio
1

2.4 安装pocketsphinx（offline）

pip install pocketsphinx
1

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
AIFF
AIFF-C
FLAC: 必须是初始 FLAC 格式；OGG-FLAC 格式不可用
1
2
3
4

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 sr

r = 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)
1
2
3
4
5
6
7
8
9
10
11

import speech_recognition as sr

# obtain path to "english.wav" in the same folder as this script
from os import path
AUDIO_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 source
r = sr.Recognizer()
with sr.AudioFile(AUDIO_FILE) 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))
except sr.UnknownValueError:
    print("Sphinx could not understand audio")
except sr.RequestError as e:
    print("Sphinx error; {0}".format(e))
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20

import speech_recognition as sr

recognizer = 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)
1
2
3
4
5
6
7
8
9

import speech_recognition as sr

# obtain audio from the microphone
r = sr.Recognizer()
with sr.Microphone() as source:
    print("Say something!")
    audio = r.listen(source)

# recognize speech using Sphinx
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))
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

2.5 安装Vosk （offline）

python3 -m pip install vosk
1

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 sr
from vosk import KaldiRecognizer, Model

r = 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)
1
2
3
4
5
6
7
8
9
10

2.6 安装Whisper（offline）

pip install zhconv
pip install whisper
pip install -U openai-whisper
pip3 install wheel
pip install soundfile
1
2
3
4
5

编写脚本：

import speech_recognition as sr
from vosk import KaldiRecognizer, Model

r = sr.Recognizer()
with sr.Microphone() as source:
    audio = r.listen(source, timeout=3, phrase_time_limit=5)

# recognize speech using whisper
try:
    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")
1
2
3
4
5
6
7
8
9
10
11
12
13
14

3 测试

3.1 命令

python -m speech_recognition
1

3.2 fastapi

import json
import os
from pprint import pprint

import speech_recognition
import torch
import uvicorn
from fastapi import FastAPI, HTTPException
from pydantic import BaseModel
import soundfile
import whisper
import vosk

class ResponseModel(BaseModel):
    path: str


app = 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))
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81

3.3 google

import speech_recognition as sr
import webbrowser as wb
import speak

chrome_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)
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25

3.4 recognize_sphinx

import logging
import speech_recognition as sr


def 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
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40

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()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15

• 方法2：

import wave
from 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2bytes
FILEPATH = '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() < t + 10:  # 设置录音时间（秒）
    	#循环read，每次read 2000frames
        string_audio_data = stream.read(num_samples)
        my_buf.append(string_audio_data)
    print('录音结束.')
    save_wave_file(FILEPATH, my_buf)
    stream.close()
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38

结语

如果您觉得该方法或代码有一点点用处，可以给作者点个赞，或打赏杯咖啡；╮(￣▽￣)╭
如果您感觉方法或代码不咋地//(ㄒoㄒ)//，就在评论处留言，作者继续改进；o_O???
如果您需要相关功能的代码定制化开发，可以留言私信作者；(✿◡‿◡)
感谢各位大佬童鞋们的支持！( ´ ▽´ )ﾉ ( ´ ▽´)っ！！！