150 lines
5.1 KiB
Python
150 lines
5.1 KiB
Python
# utils_asr.py
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# 同济子豪兄 2024-5-22
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# 录音+语音识别
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print('导入录音+语音识别模块')
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# import pyaudio
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import wave
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import numpy as np
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import os
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import sys
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from API_KEY import *
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# 确定麦克风索引号
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# import sounddevice as sd
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# print(sd.query_devices())
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def record(MIC_INDEX=0, DURATION=5):
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'''
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调用麦克风录音，需用arecord -l命令获取麦克风ID
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DURATION，录音时长
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'''
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print('开始 {} 秒录音'.format(DURATION))
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os.system('sudo arecord -D "plughw:{}" -f dat -c 1 -r 16000 -d {} temp/speech_record.wav'.format(MIC_INDEX, DURATION))
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print('录音结束')
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def record_auto(MIC_INDEX=1):
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'''
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开启麦克风录音，保存至'temp/speech_record.wav'音频文件
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音量超过阈值自动开始录音，低于阈值一段时间后自动停止录音
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MIC_INDEX：麦克风设备索引号
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'''
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CHUNK = 1024 # 采样宽度
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RATE = 16000 # 采样率
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QUIET_DB = 2000 # 分贝阈值，大于则开始录音，否则结束
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delay_time = 1 # 声音降至分贝阈值后，经过多长时间，自动终止录音
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FORMAT = pyaudio.paInt16
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CHANNELS = 1 if sys.platform == 'darwin' else 2 # 采样通道数
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# 初始化录音
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p = pyaudio.PyAudio()
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stream = p.open(format=FORMAT,
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channels=CHANNELS,
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rate=RATE,
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input=True,
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frames_per_buffer=CHUNK,
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input_device_index=MIC_INDEX
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)
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frames = [] # 所有音频帧
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flag = False # 是否已经开始录音
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quiet_flag = False # 当前音量小于阈值
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temp_time = 0 # 当前时间是第几帧
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last_ok_time = 0 # 最后正常是第几帧
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START_TIME = 0 # 开始录音是第几帧
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END_TIME = 0 # 结束录音是第几帧
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print('可以说话啦！')
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while True:
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# 获取当前chunk的声音
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data = stream.read(CHUNK, exception_on_overflow=False)
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frames.append(data)
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# 获取当前chunk的音量分贝值
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temp_volume = np.max(np.frombuffer(data, dtype=np.short))
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if temp_volume > QUIET_DB and flag==False:
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print("音量高于阈值，开始录音")
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flag =True
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START_TIME = temp_time
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last_ok_time = temp_time
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if flag: # 录音中的各种情况
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if(temp_volume < QUIET_DB and quiet_flag==False):
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print("录音中，当前音量低于阈值")
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quiet_flag = True
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last_ok_time = temp_time
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if(temp_volume > QUIET_DB):
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# print('录音中，当前音量高于阈值，正常录音')
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quiet_flag = False
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last_ok_time = temp_time
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if(temp_time > last_ok_time + delay_time*15 and quiet_flag==True):
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print("音量低于阈值{:.2f}秒后，检测当前音量".format(delay_time))
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if(quiet_flag and temp_volume < QUIET_DB):
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print("当前音量仍然小于阈值，录音结束")
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END_TIME = temp_time
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break
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else:
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print("当前音量重新高于阈值，继续录音中")
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quiet_flag = False
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last_ok_time = temp_time
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# print('当前帧 {} 音量 {}'.format(temp_time+1, temp_volume))
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temp_time += 1
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if temp_time > 150: # 超时直接退出
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END_TIME = temp_time
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print('超时，录音结束')
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break
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# 停止录音
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stream.stop_stream()
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stream.close()
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p.terminate()
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# 导出wav音频文件
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output_path = 'temp/speech_record.wav'
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wf = wave.open(output_path, 'wb')
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wf.setnchannels(CHANNELS)
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wf.setsampwidth(p.get_sample_size(FORMAT))
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wf.setframerate(RATE)
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wf.writeframes(b''.join(frames[START_TIME-2:END_TIME]))
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wf.close()
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print('保存录音文件', output_path)
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# import appbuilder
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# # 配置密钥
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# os.environ["APPBUILDER_TOKEN"] = APPBUILDER_TOKEN
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# asr = appbuilder.ASR() # 语音识别组件
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# def speech_recognition(audio_path='temp/speech_record.wav'):
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# '''
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# AppBuilder-SDK语音识别组件
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# '''
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# print('开始语音识别')
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# # 载入wav音频文件
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# with wave.open(audio_path, 'rb') as wav_file:
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# # 获取音频文件的基本信息
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# num_channels = wav_file.getnchannels()
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# sample_width = wav_file.getsampwidth()
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# framerate = wav_file.getframerate()
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# num_frames = wav_file.getnframes()
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# # 获取音频数据
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# frames = wav_file.readframes(num_frames)
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# # 向API发起请求
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# content_data = {"audio_format": "wav", "raw_audio": frames, "rate": 16000}
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# message = appbuilder.Message(content_data)
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# speech_result = asr.run(message).content['result'][0]
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# print('语音识别结果：', speech_result)
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# return speech_result |