树莓派上的语音识别一

Posted on Sat 25 May 2024 in tech

Abstract 树莓派上的语音识别一
Authors Walter Fan
 Category    learning note  
Status v1.0
Updated 2024-05-25
License CC-BY-NC-ND 4.0

ASR(自动语音识别) 作为日常的应用已经飞入寻常百姓家了, 小米音箱, Apple Siri, 汽车上的各种语音小助手.

这些技术其实并不复杂, 普通工程师花个一两天, 就可以做个原型出来. 开源的语音识别库的精度并不理想, 而讯飞或者百度的 API 所获得的识别率还挺高的.

之前买了一个树莓派, 想给它做一个类似于智能音箱的功能, 采用 Gstreamer 结合讯飞云来构建一个如下的管道

gst-asr.png

  • 捕获来自麦克风的音频流,建立audio pipeline
  • 录制音频流到本地的音频文件(m3u8/ts)
  • 同时编写一个 plugin (AsrSink) 与讯飞云建立 secure web socket 连接
  • 发送 audio frame 到讯飞云
  • 接收来自讯飞云的语音识别结果
  • 通过 websocket 发回语音识别结果

讯飞的 API 参见, 可以申请一个免费的帐号, 不过对调用次数和时长有一定限制

https://www.xfyun.cn/doc/asr/voicedictation/API.html

asr_cloud.png

@startuml
participant ASR_Agent as agent
participant ASR_Cloud as asr_cloud
participant ASR_Web as web

agent -> agent: start_audio_record
note right of agent: capture audio from mic and record into disk
agent -> asr_cloud: connect over wss
agent -> asr_cloud: send audio data(status=0), first frame
agent -> asr_cloud: send audio data(status=1), continue frame
agent -> asr_cloud: send audio data(status=2), last frame
asr_cloud --> agent: ASR result(text)
agent --> web: ASR result over ws
@enduml

调用的 python 示例代码如下

# -*- coding:utf-8 -*-
#
#   author: iflytek
#
#  本demo测试时运行的环境为:Windows + Python3.7
#  本demo测试成功运行时所安装的第三方库及其版本如下,您可自行逐一或者复制到一个新的txt文件利用pip一次性安装:
#   cffi==1.12.3
#   gevent==1.4.0
#   greenlet==0.4.15
#   pycparser==2.19
#   six==1.12.0
#   websocket==0.2.1
#   websocket-client==0.56.0
#
#  语音听写流式 WebAPI 接口调用示例 接口文档(必看):https://doc.xfyun.cn/rest_api/语音听写(流式版).html
#  webapi 听写服务参考帖子(必看):http://bbs.xfyun.cn/forum.php?mod=viewthread&tid=38947&extra=
#  语音听写流式WebAPI 服务,热词使用方式:登陆开放平台https://www.xfyun.cn/后,找到控制台--我的应用---语音听写(流式)---服务管理--个性化热词,
#  设置热词
#  注意:热词只能在识别的时候会增加热词的识别权重,需要注意的是增加相应词条的识别率,但并不是绝对的,具体效果以您测试为准。
#  语音听写流式WebAPI 服务,方言试用方法:登陆开放平台https://www.xfyun.cn/后,找到控制台--我的应用---语音听写(流式)---服务管理--识别语种列表
#  可添加语种或方言,添加后会显示该方言的参数值
#  错误码链接:https://www.xfyun.cn/document/error-code (code返回错误码时必看)
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
import websocket
import datetime
import hashlib
import base64
import hmac
import json
import os
from urllib.parse import urlencode
import time
import ssl
from wsgiref.handlers import format_date_time
from datetime import datetime
from time import mktime
import _thread as thread

STATUS_FIRST_FRAME = 0  # 第一帧的标识
STATUS_CONTINUE_FRAME = 1  # 中间帧标识
STATUS_LAST_FRAME = 2  # 最后一帧的标识


class Ws_Param(object):
    # 初始化
    def __init__(self, APPID, APIKey, APISecret, AudioFile):
        self.APPID = APPID
        self.APIKey = APIKey
        self.APISecret = APISecret
        self.AudioFile = AudioFile

        # 公共参数(common)
        self.CommonArgs = {"app_id": self.APPID}
        # 业务参数(business),更多个性化参数可在官网查看
        self.BusinessArgs = {"domain": "iat", "language": "zh_cn", "accent": "mandarin", "vinfo":1,"vad_eos":10000}

    # 生成url
    def create_url(self):
        url = 'wss://ws-api.xfyun.cn/v2/iat'
        # 生成RFC1123格式的时间戳
        now = datetime.now()
        date = format_date_time(mktime(now.timetuple()))

        # 拼接字符串
        signature_origin = "host: " + "ws-api.xfyun.cn" + "\n"
        signature_origin += "date: " + date + "\n"
        signature_origin += "GET " + "/v2/iat " + "HTTP/1.1"
        # 进行hmac-sha256进行加密
        signature_sha = hmac.new(self.APISecret.encode('utf-8'), signature_origin.encode('utf-8'),
                                 digestmod=hashlib.sha256).digest()
        signature_sha = base64.b64encode(signature_sha).decode(encoding='utf-8')

        authorization_origin = "api_key=\"%s\", algorithm=\"%s\", headers=\"%s\", signature=\"%s\"" % (
            self.APIKey, "hmac-sha256", "host date request-line", signature_sha)
        authorization = base64.b64encode(authorization_origin.encode('utf-8')).decode(encoding='utf-8')
        # 将请求的鉴权参数组合为字典
        v = {
            "authorization": authorization,
            "date": date,
            "host": "ws-api.xfyun.cn"
        }
        # 拼接鉴权参数,生成url
        url = url + '?' + urlencode(v)
        # print("date: ",date)
        # print("v: ",v)
        # 此处打印出建立连接时候的url,参考本demo的时候可取消上方打印的注释,比对相同参数时生成的url与自己代码生成的url是否一致
        # print('websocket url :', url)
        return url


# 收到websocket消息的处理
def on_message(ws, message):
    try:
        code = json.loads(message)["code"]
        sid = json.loads(message)["sid"]
        if code != 0:
            errMsg = json.loads(message)["message"]
            print("sid:%s call error:%s code is:%s" % (sid, errMsg, code))

        else:
            data = json.loads(message)["data"]["result"]["ws"]
            # print(json.loads(message))
            result = ""
            for i in data:
                for w in i["cw"]:
                    result += w["w"]
            print("sid:%s call success!,data is:%s" % (sid, json.dumps(data, ensure_ascii=False)))
    except Exception as e:
        print("receive msg,but parse exception:", e)



# 收到websocket错误的处理
def on_error(ws, error):
    print("### error:", error)


# 收到websocket关闭的处理
def on_close(ws,a,b):
    print("### closed ###")


# 收到websocket连接建立的处理
def on_open(ws):
    def run(*args):
        frameSize = 8000  # 每一帧的音频大小
        intervel = 0.04  # 发送音频间隔(单位:s)
        status = STATUS_FIRST_FRAME  # 音频的状态信息,标识音频是第一帧,还是中间帧、最后一帧

        with open(wsParam.AudioFile, "rb") as fp:
            while True:
                buf = fp.read(frameSize)
                # 文件结束
                if not buf:
                    status = STATUS_LAST_FRAME
                # 第一帧处理
                # 发送第一帧音频,带business 参数
                # appid 必须带上,只需第一帧发送
                if status == STATUS_FIRST_FRAME:

                    d = {"common": wsParam.CommonArgs,
                         "business": wsParam.BusinessArgs,
                         "data": {"status": 0, "format": "audio/L16;rate=16000",
                                  "audio": str(base64.b64encode(buf), 'utf-8'),
                                  "encoding": "raw"}}
                    d = json.dumps(d)
                    ws.send(d)
                    status = STATUS_CONTINUE_FRAME
                # 中间帧处理
                elif status == STATUS_CONTINUE_FRAME:
                    d = {"data": {"status": 1, "format": "audio/L16;rate=16000",
                                  "audio": str(base64.b64encode(buf), 'utf-8'),
                                  "encoding": "raw"}}
                    ws.send(json.dumps(d))
                # 最后一帧处理
                elif status == STATUS_LAST_FRAME:
                    d = {"data": {"status": 2, "format": "audio/L16;rate=16000",
                                  "audio": str(base64.b64encode(buf), 'utf-8'),
                                  "encoding": "raw"}}
                    ws.send(json.dumps(d))
                    time.sleep(1)
                    break
                # 模拟音频采样间隔
                time.sleep(intervel)
        ws.close()

    thread.start_new_thread(run, ())


if __name__ == "__main__":
    # 测试时候在此处正确填写相关信息即可运行
    time1 = datetime.now()

    appId = os.getenv("XF_APPID")
    apiSecret = os.getenv("XF_APISecret")
    apiKey = os.getenv("XF_APIKey")
    audioFile = "iat_pcm_16k.pcm"

    wsParam = Ws_Param(APPID=appId, APISecret=apiSecret,
                       APIKey=apiKey,
                       AudioFile=audioFile)
    websocket.enableTrace(False)
    wsUrl = wsParam.create_url()
    ws = websocket.WebSocketApp(wsUrl, on_message=on_message, on_error=on_error, on_close=on_close)
    ws.on_open = on_open
    ws.run_forever(sslopt={"cert_reqs": ssl.CERT_NONE})
    time2 = datetime.now()
    print(time2-time1)

当然自己也可以用开源的 library 实现语音识别, 例如 whipser , 只是它的实时性还有待提高.

# sudo apt update && sudo apt install ffmpeg
# pip install -U openai-whisper

import whisper

model = whisper.load_model("base")
result = model.transcribe("talk.mp3",  fp16=False)
print(result["text"])

百度的 API 更加简洁一些, 示例代码如下

# -*- coding: utf-8 -*-
"""
实时流式识别
需要安装websocket-client库
使用方式 python realtime_asr.py 16k-0.pcm
"""
import websocket

import threading
import time
import uuid
import json
import logging
import sys

import const

if len(sys.argv) < 2:
    pcm_file = "16k-0.pcm"
else:
    pcm_file = sys.argv[1]

logger = logging.getLogger()

"""

1. 连接 ws_app.run_forever()
2. 连接成功后发送数据 on_open()
2.1 发送开始参数帧 send_start_params()
2.2 发送音频数据帧 send_audio()
2.3 库接收识别结果 on_message()
2.4 发送结束帧 send_finish()
3. 关闭连接 on_close()

库的报错 on_error()
"""


def send_start_params(ws):
    """
    开始参数帧
    :param websocket.WebSocket ws:
    :return:
    """
    req = {
        "type": "START",
        "data": {
            "appid": const.APPID,  # 网页上的appid
            "appkey": const.APPKEY,  # 网页上的appid对应的appkey
            "dev_pid": const.DEV_PID,  # 识别模型
            "cuid": "yourself_defined_user_id",  # 随便填不影响使用。机器的mac或者其它唯一id,百度计算UV用。
            "sample": 16000,  # 固定参数
            "format": "pcm"  # 固定参数
        }
    }
    body = json.dumps(req)
    ws.send(body, websocket.ABNF.OPCODE_TEXT)
    logger.info("send START frame with params:" + body)


def send_audio(ws):
    """
    发送二进制音频数据,注意每个帧之间需要有间隔时间
    :param  websocket.WebSocket ws:
    :return:
    """
    chunk_ms = 160  # 160ms的录音
    chunk_len = int(16000 * 2 / 1000 * chunk_ms)
    with open(pcm_file, 'rb') as f:
        pcm = f.read()

    index = 0
    total = len(pcm)
    logger.info("send_audio total={}".format(total))
    while index < total:
        end = index + chunk_len
        if end >= total:
            # 最后一个音频数据帧
            end = total
        body = pcm[index:end]
        logger.debug("try to send audio length {}, from bytes [{},{})".format(len(body), index, end))
        ws.send(body, websocket.ABNF.OPCODE_BINARY)
        index = end
        time.sleep(chunk_ms / 1000.0)  # ws.send 也有点耗时,这里没有计算


def send_finish(ws):
    """
    发送结束帧
    :param websocket.WebSocket ws:
    :return:
    """
    req = {
        "type": "FINISH"
    }
    body = json.dumps(req)
    ws.send(body, websocket.ABNF.OPCODE_TEXT)
    logger.info("send FINISH frame")


def send_cancel(ws):
    """
    发送取消帧
    :param websocket.WebSocket ws:
    :return:
    """
    req = {
        "type": "CANCEL"
    }
    body = json.dumps(req)
    ws.send(body, websocket.ABNF.OPCODE_TEXT)
    logger.info("send Cancel frame")


def on_open(ws):
    """
    连接后发送数据帧
    :param  websocket.WebSocket ws:
    :return:
    """

    def run(*args):
        """
        发送数据帧
        :param args:
        :return:
        """
        send_start_params(ws)
        send_audio(ws)
        send_finish(ws)
        logger.debug("thread terminating")

    threading.Thread(target=run).start()


def on_message(ws, message):
    """
    接收服务端返回的消息
    :param ws:
    :param message: json格式,自行解析
    :return:
    """
    logger.info("Response: " + message)


def on_error(ws, error):
    """
    库的报错,比如连接超时
    :param ws:
    :param error: json格式,自行解析
    :return:
        """
    logger.error("error: " + str(error))


def on_close(ws):
    """
    Websocket关闭
    :param websocket.WebSocket ws:
    :return:
    """
    logger.info("ws close ...")
    # ws.close()


if __name__ == "__main__":
    logging.basicConfig(format='[%(asctime)-15s] [%(funcName)s()][%(levelname)s] %(message)s')
    logger.setLevel(logging.DEBUG)  # 调整为logging.INFO,日志会少一点
    logger.info("begin")
    # websocket.enableTrace(True)
    uri = const.URI + "?sn=" + str(uuid.uuid1())
    logger.info("uri is "+ uri)
    ws_app = websocket.WebSocketApp(uri,
                                    on_open=on_open,  # 连接建立后的回调
                                    on_message=on_message,  # 接收消息的回调
                                    on_error=on_error,  # 库遇见错误的回调
                                    on_close=on_close)  # 关闭后的回调
    ws_app.run_forever()


本作品采用知识共享署名-非商业性使用-禁止演绎 4.0 国际许可协议进行许可。