98 lines
3.5 KiB
Python
98 lines
3.5 KiB
Python
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import os
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import alsaaudio
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import audioop
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import numpy
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import shutil
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import asyncio
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import time
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import datetime
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import collections
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import scipy.io.wavfile as wavfile
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from pyAudioAnalysis import MidTermFeatures as mtF
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from pyAudioAnalysis import audioTrainTest as aT
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fs = 44100
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start_time = 0
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daily_counter = 0
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std = None
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mean = None
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classifier = None
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sec_60_que = None
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class_names = None
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microphone = 'hw:CARD=LX3000,DEV=0'
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record_path = "Last60Sample/"
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permanent_path = "DailySamples/"
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conv = datetime.datetime.fromtimestamp
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def background( f ):
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def wrapped(*args,**kwargs):
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return asyncio.get_event_loop().run_in_executor(None,f,*args,*kwargs)
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return wrapped
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def record_analyze_audio( mid_term_buffer_size_in_sec ):
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print("Press Ctr+C to stop recording")
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inp = alsaaudio.PCM(alsaaudio.PCM_CAPTURE, alsaaudio.PCM_NONBLOCK,device=microphone)
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inp.setchannels(1)
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inp.setrate(fs)
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inp.setformat(alsaaudio.PCM_FORMAT_S16_LE)
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inp.setperiodsize(512)
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mid_term_buffer_size = int(mid_term_buffer_size_in_sec * fs)
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cur_window = []
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append = cur_window.append
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mid_term_buffer = []
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d = os.path.dirname(record_path)
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if os.path.exists(d) and record_path!=".":
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shutil.rmtree(record_path)
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os.makedirs(record_path)
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d = os.path.dirname(permanent_path)
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if os.path.exists(d) and permanent_path!=".":
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shutil.rmtree(permanent_path)
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os.makedirs(permanent_path)
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while 1:
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l,data = inp.read()
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if l :
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[append(audioop.getsample(data, 2, i)) for i in range(l)]
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if (len(cur_window)+len(mid_term_buffer)>mid_term_buffer_size):
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samples_to_copy_to_mid_buffer = mid_term_buffer_size - len(mid_term_buffer)
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else:
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samples_to_copy_to_mid_buffer = len(cur_window)
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mid_term_buffer += cur_window[0:samples_to_copy_to_mid_buffer]
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del(cur_window[0:samples_to_copy_to_mid_buffer])
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if len(mid_term_buffer) == mid_term_buffer_size:
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predict(mid_term_buffer)
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mid_term_buffer = []
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@background
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def predict( mid_term_buffer ):
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global sec_60_que
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global daily_counter
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[mt_features, st_features, _] = mtF.mid_feature_extraction(mid_term_buffer, fs, 2.0*fs, 2.0*fs, 0.020*fs, 0.020*fs)
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curFV = (mt_features[:,0] - mean) / std
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[result,P] = aT.classifier_wrapper(classifier, 'svm', curFV)
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cur_wav_file_name = "%s%s_%s.wav"%(record_path,conv(time.time()).strftime('%S,%f'),str(class_names[int(result)]))
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permanent_name = "%s/%s_%s.wav"%(permanent_path,conv(time.time()).strftime('%H:%M_%S,%f'),str(class_names[int(result)]))
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mid_term_buffer_array = numpy.int16(mid_term_buffer)
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wav_to_remove=sec_60_que.popleft()
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if os.path.exists(str(wav_to_remove)):
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os.remove(wav_to_remove)
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if daily_counter != conv(time.time()).strftime('%d'):
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shutil.rmtree(permanent_path)
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daily_counter = conv(time.time()).strftime('%d')
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wavfile.write(cur_wav_file_name, fs, mid_term_buffer_array)
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if not os.path.exists(permanent_path) :
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os.makedirs(permanent_path)
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wavfile.write(permanent_name, fs, mid_term_buffer_array)
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sec_60_que.append(cur_wav_file_name)
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def start( duration_of_record,name_of_model ):
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global classifier
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global mean
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global std
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global class_names
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global sec_60_que
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duration = int(duration_of_record)
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model_name = name_of_model
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sec_60_que = collections.deque(int(60/duration)*[0],int(60/duration))
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[classifier, mean,std,class_names,mt_win,mt_step,st_win,st_step, compute_beat] = aT.load_model(model_name)
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record_analyze_audio( mid_term_buffer_size_in_sec = duration )
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