def files_to_data(files, file_labels, threshold): uniqueLabels = np.unique(file_labels) labelString2Int = {s:i for i,s in enumerate(uniqueLabels)} labels=[] data=[] for file,file_label in zip(files,file_labels): wav = segment.wav_to_np(file)[:,0]/32768. # left CH chunks = segment.chop_all(wav, threshold, afterlength=700, prelength=0) chunks = map(transform.sndFeature, chunks) data += list(chunks) labels += [labelString2Int[file_label],]*len(chunks) print 'Chunked {} examples for {}'.format(len(chunks),file) X = np.array(data) y = np.array(labels) return X,y,uniqueLabels
from sklearn.externals import joblib import sys tap_recog = joblib.load('forest_recog.bin') p = pyaudio.PyAudio() realtime = True over=False if realtime: stream = p.open(format=p.get_format_from_width(2), channels=1, rate=44100, input=True) else: chunk = segment.wav_to_np('snaps/gss.wav')[:,0]/32768. # segment.play_wav(chunk) frames = [] CHUNK = 10000 threshold = 0.4 text=[] try: last_click = None while True: print 'loop' if realtime: chunk = stream.read(CHUNK) chunk = segment.decode(chunk, 1)[:,0]/32678. elif over: break