语音唤醒
国外主站https://picovoice.ai/ 注册后获取porcupine_key，训练提示词模型，下载ppn模型。
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import pvporcupine
import pyaudio
import struct
import pyttsx3
import keyboard
porcupine_key = "xxxxx"
porcupine_model = 'xxxxxx'
def keyword_wake_up():
porcupine = pvporcupine.create(access_key=porcupine_key, keyword_paths=[porcupine_model])
# 开启录音流
kws_audio = pyaudio.PyAudio()
audio_stream = kws_audio.open(
rate=porcupine.sample_rate,
channels=1,
format=pyaudio.paInt16,
input=True,
frames_per_buffer=porcupine.frame_length,
input_device_index=None,
)
print("等待唤醒中,唤醒词:hello chat...")
while True:
pcm = audio_stream.read(porcupine.frame_length)
_pcm = struct.unpack_from("h" * porcupine.frame_length, pcm)
keyword_index = porcupine.process(_pcm)
if keyword_index >= 0:
print("唤醒了捏！")
engine = pyttsx3.init()
engine.say("唤醒了捏")
engine.runAndWait()
break
audio_stream.stop_stream()
audio_stream.close()
porcupine.delete()
kws_audio.terminate()
def press_key_wake_up():
print("按任意键唤醒...")
keyboard.read_event()
print("唤醒了捏！")
engine = pyttsx3.init()
engine.say("唤醒了捏")
engine.runAndWait()
keyword_wake_up()
语音识别
调用腾讯云的语音识别api，使用工作台的两个参数
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# 腾讯云asr,输入base64编码的wav音频，输出text，此函数需异步调用，以节约请求事件
from tencentcloud.common import credential
from tencentcloud.common.profile.client_profile import ClientProfile
from tencentcloud.common.profile.http_profile import HttpProfile
from tencentcloud.asr.v20190614 import asr_client, models
import struct
import pvporcupine
import pyaudio
import asyncio
import json
import base64
import io
import wave
import pvcobra
#
porcupine_key = "xxxxxxxxxxx"
porcupine_model = 'xxxxxxxxx.ppn'
def sound_record():
porcupine = pvporcupine.create(access_key=porcupine_key, keyword_paths=[porcupine_model])
cobra = pvcobra.create(access_key=porcupine_key)
# 开启录音流
kws_audio = pyaudio.PyAudio()
audio_stream = kws_audio.open(
rate=porcupine.sample_rate,
channels=1,
format=pyaudio.paInt16,
input=True,
frames_per_buffer=porcupine.frame_length,
input_device_index=None,
)
# 开始录音
frames = []
print("开始录音...")
silence_count = 0
is_voiced = 1
while is_voiced:
pcm = audio_stream.read(porcupine.frame_length)
_pcm = struct.unpack_from("h" * porcupine.frame_length, pcm)
is_voiced = cobra.process(_pcm)
silence_count = 0 if is_voiced > 0.5 else silence_count + 1
if silence_count <= 50:
if silence_count < 10:
frames.append(pcm)
else:
break
audio_stream.stop_stream()
audio_stream.close()
porcupine.delete()
kws_audio.terminate()
# 将数据帧编码为base64编码的WAV格式
with io.BytesIO() as wav_buffer:
with wave.open(wav_buffer, 'wb') as wf:
wf.setnchannels(1)
wf.setsampwidth(kws_audio.get_sample_size(pyaudio.paInt16))
wf.setframerate(porcupine.sample_rate)
wf.writeframes(b''.join(frames))
wav_base64 = base64.b64encode(
wav_buffer.getvalue()).decode('utf-8')
#保存的路径
output_file = 'recorded_audio.wav' # 替换成你想要保存的路径
with open(output_file, 'wb') as output_file:
output_file.write(base64.b64decode(wav_base64))
print(f"录音已保存")
return wav_base64
# 腾讯云api的ID和key，用于语音识别
tencent_Id = "xxxxxxx"
tencent_key = "xxxxxxxxx"
async def tencent_asr(wav_base64):
cred = credential.Credential(tencent_Id, tencent_key)
# 实例化一个http选项，可选的，没有特殊需求可以跳过
httpProfile = HttpProfile()
httpProfile.endpoint = "asr.ap-guangzhou.tencentcloudapi.com"
# 实例化一个client选项，可选的，没有特殊需求可以跳过
clientProfile = ClientProfile()
clientProfile.httpProfile = httpProfile
# 实例化要请求产品的client对象,clientProfile是可选的
client = asr_client.AsrClient(cred, "", clientProfile)
# 实例化一个请求对象,每个接口都会对应一个request对象
req = models.SentenceRecognitionRequest()
params = {
"ProjectId": 0,
"SubServiceType": 2,
"EngSerViceType": "16k_zh",
"SourceType": 1,
"VoiceFormat": "wav",
"UsrAudioKey": "0",
"Data": wav_base64, # 音频二进制数据
"DataLen": len(wav_base64) # 音频长度
}
req.from_json_string(json.dumps(params))
response = await asyncio.to_thread(client.SentenceRecognition, req)
if response.Result == "":
print("你什么都没说~")
else:
print("你：" + response.Result)
return response.Result
def listen(model: str = "tencent"):
audio_data = sound_record()
if model == "tencent":
user_words = asyncio.run(tencent_asr(audio_data))
return user_words
#elif model == "whisper":
#user_words = whisper_asr(audio_data)
#return user_words
listen()