确保法律和政策合规是管理和领导呼叫中心操作的人们的关键问题。在以下文章中，我们将研究深集的语音AI平台如何集成到监视和合规性工作流程中。
每当代理商与客户交谈时，实时获取呼叫笔录并检测代理是否符合标准是有帮助的。例如，每个人在致电客户服务时可能听到的常见短语 - 可以记录此通话是出于质量保证的目的。”大多数情况下，法律要求客户服务代理通知客户记录呼叫。
我们将使用Python和Deepgram的语音到文本API，以了解实时接收带有实时流的成绩单是多么简单。我们还将利用某些功能，这些功能会在对话中识别每个发言人，迅速浏览笔录中的短语，并认识到该模型未经训练或经常遇到的单词。 p>
在您从Python中进行合规监视之前
在这篇文章中，我使用Python 3.10，因此，如果您想跟随，请确保已安装该版本。您还需要抓住Deepgram API Key, which you can get here。
下一步：创建一个目录，我叫Mine monitor_compliance。
然后：转到该目录和create a virtual environment内部，以便所有python库可以安装在此处，而不是在您的计算机上全球安装。要安装虚拟环境在终端中的目录内运行以下命令：python3 -m venv venv。现在通过这样做来激活它：source venv/bin/activate。
安装python软件包，以调查与文本的语音监控
您需要在虚拟环境中安装一些Python软件包才能正常工作。您可以使用Python的pip命令来安装这些软件包。确保您的虚拟环境处于活动状态。然后，从您的终端安装以下内容：
-
pip安装pyaudio
-
PIP安装Websockets
您只需要两个python库，PyAudio和websockets。 Pyaudio库使您可以从计算机的麦克风中获得声音。 Websockets Python库也被使用，因为我们可以使用实时流媒体。 Deepgram也有一个Python SDK，但是在这篇文章中，我们将直接击中API端点。
Python代码依赖性和文件设置
创建一个名为monitor.py的空python文件，并添加以下导入语句：
import pyaudio
import asyncio
import websockets
import os
import json
from pprint import pprint
接下来，添加您的Deepgram API键：
DEEPGRAM_API_KEY=’REPLACE_WITH_YOUR_DEEPGRAM_API_KEY’
定义Python变量
在DEEPGRAM_API_KEY下方，您需要定义一些python变量。常数与Pyaudio相关，Audio_queue是我们在整个代码中使用的异步队列。
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 16000
CHUNK = 8000
audio_queue = asyncio.Queue()
python回调代码，用于与文本的语音监控的合规性监视
当我们创建Pyaudio对象以获取音频时，我们需要此回调作为参数传递。
def callback(input_data, frame_count, time_info, status_flags):
# Put an item into the queue without blocking.
audio_queue.put_nowait(input_data)
return (input_data, pyaudio.paContinue)
在Python中获取麦克风音频
我们在此异步功能中立即连接到麦克风，创建我们的pyaudio对象并打开流。
async def microphone():
audio = pyaudio.PyAudio()
stream = audio.open(
format = FORMAT,
channels = CHANNELS,
rate = RATE,
input = True,
frames_per_buffer = CHUNK,
stream_callback = callback
)
stream.start_stream()
while stream.is_active():
await asyncio.sleep(0.1)
stream.stop_stream()
stream.close()
打开Websocket并连接到DeepGram实时语音到文本
此代码授权Deepgram并打开Websocket以允许实时音频流。我们正在传递API调用中的一些深集功能，例如：
diarize-捕获成绩单中的每个说话者并给他们一个ID。
search-搜索笔录中的短语“可以录制此通话是为了质量和培训目的”。
keywords-正确识别参与者的姓氏和术语
async def process():
extra_headers = {
'Authorization': 'token ' + DEEPGRAM_API_KEY
}
async with websockets.connect('wss://api.deepgram.com/v1/listen?encoding=linear16&sample_rate=16000&channels=1&'\
'&punctuate=true' \
'&diarize=true' \
'&search=this+call+may+be+recorded+for+quality+and+training+purposes' \
'&keywords=Warrens:2' \
'&keyword_boost=standard',
extra_headers = extra_headers) as ws:
async def sender(ws):
try:
while True:
data = await audio_queue.get()
await ws.send(data)
except Exception as e:
print('Error while sending: ', + str(e))
raise
async def receiver(ws): # receives the transcript
async for msg in ws:
msg = json.loads(msg)
pprint(msg)
transcript = msg['channel']['alternatives'][0]['transcript']
words = msg['channel']['alternatives'][0]['words']
for speaker in words:
print(f"Speaker {speaker['speaker']}: {transcript} ")
break
await asyncio.gather(sender(ws), receiver(ws))
运行Python代码以进行合规监视
最后，我们可以运行该项目的代码。为此，添加以下行，从您的终端类型中添加以下命令：python3 monitor.py：
async def run():
await asyncio.gather(microphone(),process())
if __name__ == '__main__':
asyncio.run(run())
根据所使用的流音频，您可以期望得到以下响应：
Diarization
Speaker 0: Hello.
Speaker 0: Can you hear me?
Speaker 0: Hello, and thank you for calling Premier phone service.
Speaker 0: Be aware that this call may be recorded for quality and training purposes. My name is Beth and will be assisting you today.
Speaker 0: How are you doing?
Speaker 1: Not too bad.
Speaker 1: How are you today?
Speaker 0: I'm doing well. Thank you. May I please have your name?
Speaker 1: My name is Blake Warren.
Search
'search': [{'hits': [{'confidence': 0.8900703,
'end': 15.27,
'snippet': 'this call may be recorded for '
'quality and training purposes '
'my name is',
'start': 11.962303},
{'confidence': 0.3164375,
'end': 17.060001,
'snippet': 'and training purposes my name '
'is beth and i will be assisting '
'you today',
'start': 13.546514}],
'query': 'this call may be recorded for quality and '
'training purposes'}]},
扩展项目合规性监控，语音为文本
希望您在这个项目上工作很开心。用Python和Deepgram监视呼叫中心的合规性可以简单明了。您可以使用Deepgram’s other features的一些流媒体进一步扩展项目。
该项目的最终代码如下：
import pyaudio
import asyncio
import websockets
import os
import json
from pprint import pprint
DEEPGRAM_API_KEY = "YOUR_DEEPGRAM_API_KEY"
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 16000
CHUNK = 8000
audio_queue = asyncio.Queue()
def callback(input_data, frame_count, time_info, status_flags):
audio_queue.put_nowait(input_data)
return (input_data, pyaudio.paContinue)
async def microphone():
audio = pyaudio.PyAudio()
stream = audio.open(
format = FORMAT,
channels = CHANNELS,
rate = RATE,
input = True,
frames_per_buffer = CHUNK,
stream_callback = callback
)
stream.start_stream()
while stream.is_active():
await asyncio.sleep(0.1)
stream.stop_stream()
stream.close()
async def process():
extra_headers = {
'Authorization': 'token ' + DEEPGRAM_API_KEY
}
async with websockets.connect('wss://api.deepgram.com/v1/listen?encoding=linear16&sample_rate=16000&channels=1&'\
'&punctuate=true' \
'&diarize=true' \
'&search=this+call+may+be+recorded+for+quality+and+training+purposes' \
'&keywords=Warrens:2' \
'&keyword_boost=standard',
extra_headers = extra_headers) as ws:
async def sender(ws):
try:
while True:
data = await audio_queue.get()
await ws.send(data)
except Exception as e:
print('Error while sending: ', + str(e))
raise
async def receiver(ws):
async for msg in ws:
msg = json.loads(msg)
pprint(msg)
transcript = msg['channel']['alternatives'][0]['transcript']
words = msg['channel']['alternatives'][0]['words']
for speaker in words:
print(f"Speaker {speaker['speaker']}: {transcript} ")
break
await asyncio.gather(sender(ws), receiver(ws))
async def run():
await asyncio.gather(microphone(),process())
if __name__ == '__main__':
asyncio.run(run())
如果您对此帖子或Deepgram周围的其他任何反馈，我们很乐意收到您的来信。请在我们的GitHub discussions中告诉我们。