def main():
global waveFrames
global recordNumber
dirName, recordNumber, recordDuration = interactions.getRecordParameters()
print("First we record the background noise, so please don't make any noise.")
print(
"Please note that there is a delay of 0,08 seconds before the recording start to prevent to capture you hitting the Enter key")
# open audio stream with callback function
stream = p.open(format=conf.FORMAT,
channels=conf.CHANNELS,
rate=RATE,
input=True,
frames_per_buffer=conf.CHUNK,
stream_callback=callback,
input_device_index=conf.DEVICE_INDEX)
raw_input("Press Enter key to start recording of background noise.")
# wait to prevent to capture the hitting of enter key
time.sleep(.08)
waveFrames = []
# start audio stream
stream.start_stream()
print("* recording")
# wait for stream to finish
while stream.is_active():
time.sleep(recordDuration)
stream.stop_stream()
print("* done recording")
print()
# close stream
stream.close()
wf = wave.open("tmp/tmp.wav", "wb")
wf.setnchannels(conf.CHANNELS)
wf.setsampwidth(p.get_sample_size(conf.FORMAT))
wf.setframerate(RATE)
wf.writeframes(b''.join(waveFrames))
wf.close
userInput = interactions.wantToStoreRecord(waveFrames)
f.clearTmpFolder()
if userInput == '':
# write audio data to wave file
wf = wave.open(
str(dirName) + "/backgroundNoise.wav", "wb")
wf.setnchannels(conf.CHANNELS)
wf.setsampwidth(p.get_sample_size(conf.FORMAT))
wf.setframerate(RATE)
wf.writeframes(b''.join(waveFrames))
wf.close
while userInput == '': # TODO t':
print("Ready to record %d seconds" % (recordDuration))
print(
"Please note that there is a delay of 0,08 seconds before the recording start to prevent to capture you hitting the Enter key")
# open audio stream with callback function
stream = p.open(format=conf.FORMAT,
channels=conf.CHANNELS,
rate=RATE,
input=True,
frames_per_buffer=conf.CHUNK,
stream_callback=callback,
input_device_index=conf.DEVICE_INDEX)
raw_input("Press Enter key to start " + str(recordNumber) + ". recording.")
# wait to prevent to capture the hitting of enter key
time.sleep(.08)
waveFrames = []
# start audio stream
stream.start_stream()
print("* recording")
# wait for stream to finish
while stream.is_active():
time.sleep(recordDuration)
stream.stop_stream()
print("* done recording")
print()
# close stream
stream.close()
wf = wave.open("tmp/tmp.wav", "wb")
wf.setnchannels(conf.CHANNELS)
wf.setsampwidth(p.get_sample_size(conf.FORMAT))
wf.setframerate(RATE)
wf.writeframes(b''.join(waveFrames))
wf.close
userInput = interactions.wantToStoreRecord(waveFrames)
f.clearTmpFolder()
if userInput == '':
# write audio data to wave file
wf = wave.open(
str(dirName) + "/" + str(recordNumber) + ".wav", "wb")
recordNumber = recordNumber + 1
wf.setnchannels(conf.CHANNELS)
wf.setsampwidth(p.get_sample_size(conf.FORMAT))
wf.setframerate(RATE)
wf.writeframes(b''.join(waveFrames))
wf.close
userInput = raw_input(
"Press Enter to record another record or input anything to quit.")
p.terminate()
print("Quit succefull")
return("0")
def main():
global models
global modelThreshold
global modelScore
print("")
print("This script can only process conf.CHUNK (currently: " + str(conf.CHUNK) +
") frames per loop so if the file contains a number of frames which is not divisible by conf.CHUNK the last few frames are dropped")
model = []
wavenumber = 1
fileName, modelName, optimalFrames, scriptpath = interactions.getTrainParameters()
beginning = time.time()
# do it while there are wave files
wf = []
while os.path.isfile(str(fileName) + "/" + str(wavenumber) + ".wav"):
wf.append((wave.open(str(fileName) + "/" + str(wavenumber) + ".wav"), wavenumber))
print("File " + str(fileName) + "/" + str(wavenumber) +
".wav found.")
wavenumber += 1
for i in wf:
model.append(preprocess(i))
for i in wf:
i[0].close()
wavenumber -= 1
print("Processed " + str(wavenumber) + " files in " + str(time.time() - beginning) + " seconds, minimalize them.")
if model != []:
data = []
for i in range(wavenumber):
data.append(
(model[i],
optimalFrames))
beginning = time.time()
f.clearTmpFolder()
pool = multiprocessing.Pool(processes=4)
result = pool.map(f.minimalizeAndCalcThreshold, data)
minimalizedRecords = []
calculatedThresholds = []
for i in result:
minimalizedRecords.append(i[0])
calculatedThresholds.append(i[1])
zeroFrame = np.zeros(conf.FEATURES_PER_FRAME, dtype=np.float64)
models = []
for i in range(len(minimalizedRecords)):
features = copy.deepcopy(minimalizedRecords[i])
tmpFeatures = [copy.deepcopy(zeroFrame) for number in range(optimalFrames)]
tmpCounter = [0 for number in range(optimalFrames)]
counter = 0.
posCounter = [0 for number in range(len(minimalizedRecords))]
# for every frame in this record try if we find mergable frames
for h in range(optimalFrames):
# we try all recordings
for j in range(len(minimalizedRecords)):
if f.compare(minimalizedRecords[j][h], features[h]) < calculatedThresholds[i][h]:
tmpFeatures[h] += minimalizedRecords[j][h]
tmpCounter[h] += 1
for h in range(optimalFrames):
tmpFeatures[h] = np.divide(tmpFeatures[h], tmpCounter[h])
counter += tmpCounter[h]
counter /= optimalFrames
models.append(modelImport.Model(tmpFeatures, calculatedThresholds[i], modelName, tmpCounter, scriptpath))
print()
print("Computed the models in " + str(time.time() - beginning) + " seconds. Compute their score.")
print()
beginning = time.time()
data = []
for i in range(len(models)):
data.append((models[i], fileName, i))
pool = multiprocessing.Pool(processes=4)
pool.map(q.qualityCheck, data)
models = f.loadModels(tmp=True)
print("Computed the scores in " + str(time.time() - beginning) + " seconds.")
print()
for i in range(len(models)):
print("Model Nr:\t" +
str(i +
1) +
" | Frames:\t" +
str(len(models[i].features)) +
" | Matches:\t" +
str(models[i].matches) +
" | Influenced by:\t" +
str(models[i].influencedBy) +
" | Threshold:\t" +
str(models[i].threshold) +
" | Score:\t" +
str(models[i].score))
# get the model number and substract 1 because list indexing starts
# with 0
modelNumber = interactions.getModelNumber(len(models)+1) - 1
print("You selected Model " + str(modelNumber) + " with " + str(models[modelNumber].matches) + " Matches and a Score of: " + str(models[modelNumber].score))
f.storeModel(models[modelNumber])