def test_maxpp(self):
for w in 1, 2, 4:
self.assertEqual(audioop.maxpp(b'', w), 0)
self.assertEqual(audioop.maxpp(packs[w](*range(100)), w), 0)
self.assertEqual(audioop.maxpp(packs[w](9, 10, 5, 5, 0, 1), w), 10)
self.assertEqual(audioop.maxpp(datas[w], w),
maxvalues[w] - minvalues[w])
def get_swipe(dev='/dev/audio'):
audio = ossaudiodev.open(dev, 'r')
audio.setparameters(ossaudiodev.AFMT_S16_LE, 1, 44100)
baselines = deque([2**15] * 4)
bias = 0
while 1:
data, power = get_chunk(audio, bias)
baseline = sum(baselines) / len(baselines) * THRESHOLD_FACTOR
print power, baseline, power / (baseline or 1)
chunks = []
while power > baseline:
print power, baseline, power / (baseline or 1), '*'
chunks.append(data)
data, power = get_chunk(audio, bias)
if len(chunks) > 1:
data = old_data + ''.join(chunks) + data
while audioop.maxpp(data[:3000], 2) < baseline / 2:
data = data[1000:]
while audioop.maxpp(data[-3000:], 2) < baseline / 2:
data = data[:-1000]
return audioop.bias(data, 2, -audioop.avg(data, 2))
old_data = data
bias = -audioop.avg(data, 2)
baselines.popleft()
baselines.append(power)
def test_maxpp(self):
for w in 1, 2, 4:
self.assertEqual(audioop.maxpp(b'', w), 0)
self.assertEqual(audioop.maxpp(packs[w](*range(100)), w), 0)
self.assertEqual(audioop.maxpp(packs[w](9, 10, 5, 5, 0, 1), w), 10)
self.assertEqual(audioop.maxpp(datas[w], w),
maxvalues[w] - minvalues[w])
def get_swipe(audio):
print("READY")
baselines = deque([2**15] * 4)
bias = 0
old_data = b""
while 1:
data, power = get_chunk(audio, bias)
baseline = sum(baselines) / len(baselines) * THRESHOLD_FACTOR
logging.debug((power, baseline, power / (baseline or 1)))
chunks = []
while power > baseline:
logging.debug((power, baseline, power / (baseline or 1), "*"))
chunks.append(data)
data, power = get_chunk(audio, bias)
if len(chunks) > 1:
data = old_data + b"".join(chunks) + data
while audioop.maxpp(data[:3000], 2) < baseline / 2:
data = data[1000:]
while audioop.maxpp(data[-3000:], 2) < baseline / 2:
data = data[:-1000]
return audioop.bias(data, 2, -audioop.avg(data, 2))
old_data = data
bias = -audioop.avg(data, 2)
baselines.popleft()
baselines.append(power)
def get_swipe(dev='/dev/audio'):
audio = ossaudiodev.open(dev, 'r')
audio.setparameters(ossaudiodev.AFMT_S16_LE, 1, 44100)
baselines = deque([2**15] * 4)
bias = 0
while 1:
data, power = get_chunk(audio, bias)
baseline = sum(baselines) / len(baselines) * THRESHOLD_FACTOR
print power, baseline, power / (baseline or 1)
chunks = []
while power > baseline:
print power, baseline, power / (baseline or 1), '*'
chunks.append(data)
data, power = get_chunk(audio, bias)
if len(chunks) > 1:
data = old_data + ''.join(chunks) + data
while audioop.maxpp(data[:3000], 2) < baseline / 2:
data = data[1000:]
while audioop.maxpp(data[-3000:], 2) < baseline / 2:
data = data[:-1000]
return audioop.bias(data, 2, -audioop.avg(data, 2))
old_data = data
bias = -audioop.avg(data, 2)
baselines.popleft()
baselines.append(power)
def testmaxpp(data):
if verbose:
print 'maxpp'
if audioop.maxpp(data[0], 1) != 0 or \
audioop.maxpp(data[1], 2) != 0 or \
audioop.maxpp(data[2], 4) != 0:
return 0
return 1
def testmaxpp(data):
if verbose:
print 'maxpp'
if audioop.maxpp(data[0], 1) <> 0 or \
audioop.maxpp(data[1], 2) <> 0 or \
audioop.maxpp(data[2], 4) <> 0:
return 0
return 1
def segment_input_file(threshold, power_threshold, frequency_threshold, begin_threshold, WAVE_INPUT_FILE, WAVE_OUTPUT_FILENAME, WAVE_OUTPUT_FILE_EXTENSION):
audioFrames = []
wf = wave.open(WAVE_INPUT_FILE, 'rb')
number_channels = wf.getnchannels()
total_frames = wf.getnframes()
frame_rate = wf.getframerate()
frames_to_read = round( frame_rate * RECORD_SECONDS / SLIDING_WINDOW_AMOUNT )
files_recorded = 0
delay_threshold = 0
if( begin_threshold < 0 ):
delay_threshold = begin_threshold * -1
begin_threshold = 1000
audio = pyaudio.PyAudio()
record_wave_file_count = 0
index = 0
while( wf.tell() < total_frames ):
index = index + 1
raw_wav = wf.readframes(frames_to_read * number_channels)
# If our wav file is shorter than the amount of bytes ( assuming 16 bit ) times the frames, we discard it and assume we arriveed at the end of the file
if (len(raw_wav) != 2 * frames_to_read * number_channels ):
break;
else:
audioFrames.append(raw_wav)
if( len( audioFrames ) >= SLIDING_WINDOW_AMOUNT ):
audioFrames = audioFrames[-SLIDING_WINDOW_AMOUNT:]
intensity = [
audioop.maxpp( audioFrames[0], 4 ) / 32767,
audioop.maxpp( audioFrames[1], 4 ) / 32767
]
highestintensity = np.amax( intensity )
byteString = b''.join(audioFrames)
fftData = np.frombuffer( byteString, dtype=np.int16 )
frequency = get_loudest_freq( fftData, RECORD_SECONDS )
power = get_recording_power( fftData, RECORD_SECONDS )
print( "Segmenting file " + WAVE_INPUT_FILE + ": " + str( math.ceil(wf.tell() / total_frames * 100) ) + "%" , end="\r" )
if( frequency > frequency_threshold and highestintensity > threshold and power > power_threshold ):
record_wave_file_count += 1
if( record_wave_file_count <= begin_threshold and record_wave_file_count > delay_threshold ):
files_recorded += 1
waveFile = wave.open(WAVE_OUTPUT_FILENAME + str(index) + WAVE_OUTPUT_FILE_EXTENSION, 'wb')
waveFile.setnchannels(number_channels)
waveFile.setsampwidth(audio.get_sample_size(FORMAT))
waveFile.setframerate(frame_rate)
waveFile.writeframes(byteString)
waveFile.close()
else:
record_wave_file_count = 0
print( "Extracted " + str(files_recorded) + " segmented files from " + WAVE_INPUT_FILE )
wf.close()
def classify_audioframes(audioQueue, audio_frames, classifier, high_speed):
if (not audioQueue.empty()):
audio_frames.append(audioQueue.get())
# In case we are dealing with frames not being met and a buffer being built up,
# Start skipping every other audio frame to maintain being up to date,
# Trading being up to date over being 100% correct in sequence
if (audioQueue.qsize() > 1):
print("SKIP FRAME", audioQueue.qsize())
audioQueue.get()
if (len(audio_frames) >= 2):
audio_frames = audio_frames[-2:]
highestintensity = np.amax(
audioop.maxpp(audio_frames[1], 4) / 32767)
wavData = b''.join(audio_frames)
# SKIP FEATURE ENGINEERING COMPLETELY WHEN DEALING WITH SILENCE
if (high_speed == True
and highestintensity < SILENCE_INTENSITY_THRESHOLD):
probabilityDict, predicted, frequency = create_empty_probability_dict(
classifier, {}, 0, highestintensity, 0)
else:
power = fftData = np.frombuffer(wavData, dtype=np.int16)
power = get_recording_power(
fftData,
classifier.get_setting('RECORD_SECONDS', RECORD_SECONDS))
probabilityDict, predicted, frequency = predict_raw_data(
wavData, classifier, highestintensity, power)
return probabilityDict, predicted, audio_frames, highestintensity, frequency, wavData
return False, False, audio_frames, False, False, False
def downsampleWav(src,
dst,
inrate=44100,
outrate=22050,
inchannels=1,
outchannels=1):
if not os.path.exists(src):
print('Source not found!')
return False
if not os.path.exists(os.path.dirname(dst)):
os.makedirs(os.path.dirname(dst))
try:
s_read = wave.open(src, 'r')
s_write = wave.open(dst, 'w')
except:
print('Failed to open files!')
return False
n_frames = s_read.getnframes()
data = s_read.readframes(n_frames)
data = audioop.tomono(data, 2, 0.5, 0.5)
try:
converted = audioop.ratecv(data, 2, inchannels, inrate, outrate, None)
#if outchannels == 1 & inchannels != 1:
#converted[0] = audioop.tomono(converted[0], 2, 1, 0)
except:
print('Failed to downsample wav')
return False
#divide by the max time domain values
try:
max_val = audioop.maxpp(converted[0], 2)
coverted = audioop.mul(converted[0], 2, 1 / max_val)
except:
print('FAILURE IN TIME DOMAIN CONVERSION')
return False
try:
s_write.setparams((outchannels, 2, outrate, 0, 'NONE', 'Uncompressed'))
s_write.writeframes(converted[0])
except:
print('Failed to write wav')
return False
try:
s_read.close()
s_write.close()
except:
print('Failed to close wav files')
return False
return True
def get_highest_intensity_of_wav_file( wav_file ): intensity = [] with wave.open( wav_file ) as fd: params = fd.getparams() for i in range( 0, int(RATE / CHUNK * RECORD_SECONDS)): data = fd.readframes(CHUNK) peak = audioop.maxpp( data, 4 ) / 32767 intensity.append( peak ) return np.amax( intensity )
def get_highest_intensity_of_wav_file(wav_file, record_seconds):
intensity = []
with wave.open(wav_file) as fd:
number_channels = fd.getnchannels()
total_frames = fd.getnframes()
frame_rate = fd.getframerate()
frames_to_read = round(frame_rate * record_seconds)
data = fd.readframes(frames_to_read)
peak = audioop.maxpp(data, 4) / 32767
intensity.append(peak)
return np.amax(intensity)
def test_maxpp(self):
for w in 1, 2, 3, 4:
self.assertEqual(audioop.maxpp(b"", w), 0)
self.assertEqual(audioop.maxpp(bytearray(), w), 0)
self.assertEqual(audioop.maxpp(memoryview(b""), w), 0)
self.assertEqual(audioop.maxpp(packs[w](*range(100)), w), 0)
self.assertEqual(audioop.maxpp(packs[w](9, 10, 5, 5, 0, 1), w), 10)
self.assertEqual(audioop.maxpp(datas[w], w), maxvalues[w] - minvalues[w])
def get_swipe():
p = pyaudio.PyAudio()
stream = p.open(format = FORMAT,
channels = CHANNELS,
rate = RATE,
input = True,
frames_per_buffer = CHUNK)
baselines = deque([2**15] * 4)
bias = 0
while 1:
data, power = get_chunk(stream, bias)
baseline = sum(baselines) / len(baselines) * THRESHOLD_FACTOR
print power, baseline, power / (baseline or 1)
chunks = []
while power > baseline:
print power, baseline, power / (baseline or 1), '*'
chunks.append(data)
data, power = get_chunk(stream, bias)
if len(chunks) > 1:
data = old_data + ''.join(chunks) + data
while audioop.maxpp(data[:3000], 2) < baseline / 2:
data = data[1000:]
while audioop.maxpp(data[-3000:], 2) < baseline / 2:
data = data[:-1000]
return audioop.bias(data, 2, -audioop.avg(data, 2))
old_data = data
bias = -audioop.avg(data, 2)
baselines.popleft()
baselines.append(power)
def get_swipe():
p = pyaudio.PyAudio()
stream = p.open(format=FORMAT,
channels=CHANNELS,
rate=RATE,
input=True,
frames_per_buffer=CHUNK)
baselines = deque([2**15] * 4)
bias = 0
while 1:
data, power = get_chunk(stream, bias)
baseline = sum(baselines) / len(baselines) * THRESHOLD_FACTOR
print power, baseline, power / (baseline or 1)
chunks = []
while power > baseline:
print power, baseline, power / (baseline or 1), '*'
chunks.append(data)
data, power = get_chunk(stream, bias)
if len(chunks) > 1:
data = old_data + ''.join(chunks) + data
while audioop.maxpp(data[:3000], 2) < baseline / 2:
data = data[1000:]
while audioop.maxpp(data[-3000:], 2) < baseline / 2:
data = data[:-1000]
return audioop.bias(data, 2, -audioop.avg(data, 2))
old_data = data
bias = -audioop.avg(data, 2)
baselines.popleft()
baselines.append(power)
def test_maxpp(self):
for w in (1, 2, 3, 4):
self.assertEqual(audioop.maxpp(b'', w), 0)
self.assertEqual(audioop.maxpp(bytearray(), w), 0)
self.assertEqual(audioop.maxpp(memoryview(b''), w), 0)
self.assertEqual(audioop.maxpp(packs[w](*range(100)), w), 0)
self.assertEqual(audioop.maxpp(packs[w](9, 10, 5, 5, 0, 1), w), 10)
self.assertEqual(audioop.maxpp(datas[w], w),
maxvalues[w] - minvalues[w])
def get_stream_wav_segment(stream, frames):
stream.start_stream()
range_length = int(RATE / CHUNK * RECORD_SECONDS)
remove_half = int(range_length / 2)
frames = frames[remove_half:]
frame_length = len(frames)
intensity = []
for i in range(frame_length, range_length):
data = stream.read(CHUNK)
peak = audioop.maxpp(data, 4) / 32767
intensity.append(peak)
frames.append(data)
highestintensity = np.amax(intensity)
stream.stop_stream()
return frames, highestintensity
def voice_detection(self, in_data):
# look for high input level
input_frames = np.fromstring(in_data, dtype=np.uint16)
level = audioop.maxpp(input_frames, 2)
#print("length: %d, avg: %f" % (len(in_data), level))
if level > VoiceDetector.THRESHOLD_ACTIVATION_LEVEL or \
(level > VoiceDetector.THRESHOLD_DEACTIVATION_LEVEL and self.active):
if not self.active:
print("Voice detected")
self.active = True
self.active_ts = time.time()
if self.active and (
(time.time() - self.active_ts) > VoiceDetector.MIN_DURATION):
if self.active:
print("Voice stopped")
self.active = False
return self.active
def get_peaks(data):
peak_threshold = audioop.maxpp(data[:1000], 2) * FIRST_PEAK_FACTOR
samples = get_samples(data)
i = 0
old_i = 0
sign = 1
while i < len(samples):
peak = 0
while samples[i] * sign > peak_threshold:
peak = max(samples[i] * sign, peak)
i += 1
if peak:
if old_i:
yield i - old_i
old_i = i
sign *= -1
peak_threshold = peak * SECOND_PEAK_FACTOR
i += 1
def get_peaks(data):
peak_threshold = audioop.maxpp(data[:1000], 2) * FIRST_PEAK_FACTOR
samples = get_samples(data)
i = 0
old_i = 0
sign = 1
while i < len(samples):
peak = 0
while samples[i] * sign > peak_threshold:
peak = max(samples[i] * sign, peak)
i += 1
if peak:
if old_i:
yield i - old_i
old_i = i
sign *= -1
peak_threshold = peak * SECOND_PEAK_FACTOR
i += 1
def Ignite():
global button
global key
global set_frequency
global initial_volume
global set_volume
if (set_frequency == 0):
tkMessageBox.showwarning("Tune First",
"You must run the tuning setup first.")
else:
e = Tkinter.Button(top,
text="Cancel",
command=lambda *args: ButtonBack('exit'))
e.grid(row=1, column=4)
b = Tkinter.Button(top,
text="Fine-tune",
command=lambda *args: ButtonBack('R'))
b.grid(row=2, column=4)
set_volume = initial_volume
Note(set_frequency, set_volume).play(-1)
tkMessageBox.showinfo("Inject bubble", "Inject a bubble now.")
mode = False
desired_amplitude = 0
display_status = Label(top, text="")
display_status.grid(row=0, column=1)
display_status.configure(text="Status: Manual")
while (button != 'exit'):
Note(set_frequency).stop()
Note(set_frequency, set_volume).play(-1)
stream.start_stream()
data = stream.read(2048)
stream.stop_stream()
amplitude = (audioop.maxpp(data, 2) / 65536) * 1.41
display_volume.configure(text="Volume: " + str(set_volume))
display_amplitude.configure(text=("Amplitude: %.2f" % amplitude))
display_frequency.configure(text="Frequency: " +
str(set_frequency))
top.update()
top.update_idletasks()
if (amplitude < desired_amplitude and mode):
max_amplitude_temp = 0
display_status.configure(text="Status: Scanning")
for i in range(set_frequency - 5, set_frequency + 15):
Note(i).stop()
Note(i, set_volume).play(-1)
stream.start_stream()
data = stream.read(2048)
stream.stop_stream()
amplitude = (audioop.maxpp(data, 2) / 65536) * 1.41
if (amplitude > max_amplitude_temp):
max_amplitude_temp = amplitude
set_frequency = i
display_frequency.configure(text="Frequency: " + str(i))
if (amplitude < desired_amplitude):
set_volume += 0.02
elif (mode):
display_status.configure(text="Status: Ampl. Lock")
if (key == 'Up'):
key = ''
set_volume = set_volume + 0.005
if (key == 'Down'):
key = ''
set_volume = set_volume - 0.005
if (key == 'Left'):
key = ''
set_frequency = set_frequency - 1
if (key == 'Right'):
key = ''
set_frequency = set_frequency + 1
if (key == 'space'):
mode = not mode
desired_amplitude = amplitude
if (mode):
display_status.configure(text="Status: Ampl. Lock")
# key = ''
# for frequency in range(set_frequency-20,set_frequency+20,1):
# Note(set_frequency).stop()
# Note(frequency,set_volume).play(-1)
# sleep(0.1)
# stream.start_stream()
# data=stream.read(2048)
# stream.stop_stream()
# amplitude = (audioop.maxpp(data,2)/65536)*1.41
# w.create_rectangle(int(((frequency-frequency_range[0])/float(frequency_range[1]-frequency_range[0]))*500.0), 50, int(((frequency-frequency_range[0])/float(frequency_range[1]-frequency_range[0]))*500.0)+1, 50-((amplitude/1.41)*50), fill="red")
# w.update()
# top.title("PyLuminescence Auto-Tun e Fine")
# display_percentage.configure(text=str(((frequency-(set_frequency-50))))+"% Fine")
# if(amplitude > temp_best_amplitude):
# temp_best_amplitude=amplitude
# temp_best_frequency=frequency
# display_amplitude.configure(text=("Best Amplitude: %.2f" % amplitude))
# display_frequency.configure(text=("Best Frequency: "+ str(frequency)))
# set_frequency = temp_best_frequency
b.destroy()
e.destroy()
chunk = 1024
FORMAT = pyaudio.paInt16
CHANNELS = 1
RATE = 44100
p = pyaudio.PyAudio()
# open stream
stream = p.open(format=FORMAT,
channels=CHANNELS,
rate=RATE,
input=True,
output=True,
frames_per_buffer=chunk)
# read data
while True:
data = stream.read(chunk)
#Return the maximum of the absolute value of all samples in a fragment.
a = audioop.max(data, 2)
#Return the maximum peak-peak value in the sound fragment.
b = audioop.maxpp(data, 2)
#if a>5000:
# print 'a=',a
if b > 5000:
print 'Sound Detected'
time.sleep(0.01)
stream.close()
p.terminate()
def test_maxpp(self):
self.assertEqual(audioop.maxpp(data[0], 1), 0)
self.assertEqual(audioop.maxpp(data[1], 2), 0)
self.assertEqual(audioop.maxpp(data[2], 4), 0)
def test_maxpp(self):
self.assertEqual(audioop.maxpp(data[0], 1), 0)
self.assertEqual(audioop.maxpp(data[1], 2), 0)
self.assertEqual(audioop.maxpp(data[2], 4), 0)
def Tune():
global initial_volume
global set_frequency
volume = initial_volume
display_volume.configure(text="Volume: " + str(volume))
temp_best_amplitude = 0.0
temp_best_frequency = 0.0
display_percentage = Label(top, text="")
display_percentage.grid(row=1, column=4)
display_percentage.configure(text="%")
for frequency in range(21000, 25000, 10):
Note(frequency, volume).play(-1)
sleep(0.1)
stream.start_stream()
data = stream.read(2048)
stream.stop_stream()
amplitude = (audioop.maxpp(data, 2) / 65536) * 1.41
Note(frequency).stop()
w.create_rectangle(
int(((frequency - frequency_range[0]) /
float(frequency_range[1] - frequency_range[0])) * 500.0),
50,
int(((frequency - frequency_range[0]) /
float(frequency_range[1] - frequency_range[0])) * 500.0) + 1,
50 - ((amplitude / 1.41) * 50),
fill="blue")
w.update()
top.title("PyLuminescence Auto-Tune Coarse")
#w.delete("all")
display_percentage.configure(text=str(
int(((frequency - frequency_range[0]) /
float(frequency_range[1] - frequency_range[0])) * 100.0)) +
"% Coarse")
if (amplitude > temp_best_amplitude):
temp_best_amplitude = amplitude
temp_best_frequency = frequency
display_amplitude.configure(text=("Best Amplitude: %.2f" %
amplitude))
display_frequency.configure(text=("Best Frequency: " +
str(frequency)))
set_frequency = temp_best_frequency
for frequency in range(set_frequency - 50, set_frequency + 50, 1):
Note(frequency, volume).play(-1)
sleep(0.1)
stream.start_stream()
data = stream.read(2048)
stream.stop_stream()
amplitude = (audioop.maxpp(data, 2) / 65536) * 1.41
Note(frequency).stop()
w.create_rectangle(
int(((frequency - frequency_range[0]) /
float(frequency_range[1] - frequency_range[0])) * 500.0),
50,
int(((frequency - frequency_range[0]) /
float(frequency_range[1] - frequency_range[0])) * 500.0) + 1,
50 - ((amplitude / 1.41) * 50),
fill="red")
w.update()
top.title("PyLuminescence Auto-Tune Fine")
display_percentage.configure(
text=str(((frequency - (set_frequency - 50)))) + "% Fine")
if (amplitude > temp_best_amplitude):
temp_best_amplitude = amplitude
temp_best_frequency = frequency
display_amplitude.configure(text=("Best Amplitude: %.2f" %
amplitude))
display_frequency.configure(text=("Best Frequency: " +
str(frequency)))
set_frequency = temp_best_frequency
display_percentage.destroy()
def get_chunk(src, bias):
data = audioop.bias(src.read(10000), 2, bias)
return data, audioop.maxpp(data, 2)
def audioPP(mode, fData):
return min(audioop.maxpp(fData, 2), 100)
# Downloading an audio file from s3 to local system
bucket.download_file('****{wave file URL ob s3}****.wav',
r'../path_for_local_system/recording.wav')
with open(r'../../decibel_values.csv', 'a') as f:
f.write('recording_name', 'sample_width', 'n_frames', 'avg', 'rms',
'avgpp', 'zero_crossings', 'maxpp', 'min_max')
f.write(
str(a, wav.getsampwidth(), wav.getnframes(),
audioop.avg(string_wav, wav.getsampwidth()),
audioop.rms(string_wav, wav.getsampwidth()),
audioop.avgpp(string_wav, wav.getsampwidth()),
audioop.cross(string_wav, wav.getsampwidth()),
audioop.maxpp(string_wav, wav.getsampwidth()),
audioop.minmax(string_wav, wav.getsampwidth())))
wav = wave.open(r'../../recordings.wav')
string_wav = wav.readframes(wav.getnframes())
print('getsampwidth', wav.getsampwidth())
print('get n frmaes', wav.getnframes())
print('avg: ', audioop.avg(string_wav, wav.getsampwidth()))
print('rms: ', audioop.rms(string_wav, wav.getsampwidth()))
print('avgpp: ', audioop.avgpp(string_wav, wav.getsampwidth()))
print('zero_crossings: ', audioop.cross(string_wav, wav.getsampwidth()))
print('maxpp: ', audioop.maxpp(string_wav, wav.getsampwidth()))
print('max min: ', audioop.minmax(string_wav, wav.getsampwidth()))
def record_consumer(threshold, power_threshold, frequency_threshold,
begin_threshold, WAVE_OUTPUT_FILENAME,
WAVE_OUTPUT_FILE_EXTENSION, FULL_WAVE_OUTPUT_FILENAME,
audio, stream):
global recordQueue
files_recorded = 0
j = 0
record_wave_file_count = 0
audioFrames = []
# Set the proper thresholds for starting recordings
delay_threshold = 0
if (begin_threshold < 0):
delay_threshold = begin_threshold * -1
begin_threshold = 1000
totalAudioFrames = []
try:
with KeyPoller() as key_poller:
while (True):
if (not recordQueue.empty()):
audioFrames.append(recordQueue.get())
totalAudioFrames.append(audioFrames[-1])
if (len(audioFrames) >= SLIDING_WINDOW_AMOUNT):
j += 1
audioFrames = audioFrames[-SLIDING_WINDOW_AMOUNT:]
intensity = [
audioop.maxpp(audioFrames[0], 4) / 32767,
audioop.maxpp(audioFrames[1], 4) / 32767
]
highestintensity = np.amax(intensity)
byteString = b''.join(audioFrames)
fftData = np.frombuffer(byteString, dtype=np.int16)
frequency = get_loudest_freq(fftData, RECORD_SECONDS)
power = get_recording_power(fftData, RECORD_SECONDS)
fileid = "%0.2f" % ((j) * RECORD_SECONDS)
if (record_controls(key_poller, recordQueue) == False):
stream.stop_stream()
break
if (frequency > frequency_threshold
and highestintensity > threshold
and power > power_threshold):
record_wave_file_count += 1
if (record_wave_file_count <= begin_threshold and
record_wave_file_count > delay_threshold):
files_recorded += 1
print(
"Files recorded: %0d - Power: %0d - Freq: %0d - Saving %s"
%
(files_recorded, power, frequency, fileid))
waveFile = wave.open(
WAVE_OUTPUT_FILENAME + fileid +
WAVE_OUTPUT_FILE_EXTENSION, 'wb')
waveFile.setnchannels(CHANNELS)
waveFile.setsampwidth(
audio.get_sample_size(FORMAT))
waveFile.setframerate(RATE)
waveFile.writeframes(byteString)
waveFile.close()
else:
print(
"Files recorded: %0d - Power: %0d - Freq: %0d"
% (files_recorded, power, frequency))
else:
record_wave_file_count = 0
print(
"Files recorded: %0d - Power: %0d - Freq: %0d"
% (files_recorded, power, frequency))
# Persist the total wave only once every six frames
if (len(totalAudioFrames) % 6):
byteString = b''.join(totalAudioFrames)
waveFile = wave.open(FULL_WAVE_OUTPUT_FILENAME,
'wb')
waveFile.setnchannels(CHANNELS)
waveFile.setsampwidth(
audio.get_sample_size(FORMAT))
waveFile.setframerate(RATE)
waveFile.writeframes(byteString)
waveFile.close()
except Exception as e:
print("----------- ERROR DURING RECORDING -------------- ")
exc_type, exc_value, exc_tb = sys.exc_info()
traceback.print_exception(exc_type, exc_value, exc_tb)
stream.stop_stream()
frames_per_buffer=chunk)
pre_init(96000, -16, 1, 1024)
pygame.init()
best_frequency = 0
amplitude = 0
#msgbox(msg="First, I need to tune.", title="", ok_button="Begin!")
for frequency in range(22700,23500,5):
data=stream.read(6048)
Note(frequency).stop()
Note(frequency).play(-1)
sleep(0.05)
print(audioop.maxpp(data,2))
if(audioop.maxpp(data,2) > amplitude):
amplitude = audioop.maxpp(data,2)
best_frequency = frequency
frequency = best_frequency
Note(frequency,0.8).play(-1)
print(frequency)
msgbox(msg="Now, inject a bubble.", title="", ok_button="Done!")
volume = 0.1
while(True):
Note(frequency,volume).play(-1)
volume = volume+0.05
sleep(0.1)
msgbox('Lit')
def get_chunk(src, bias):
data = audioop.bias(src.read(10000), 2, bias)
return data, audioop.maxpp(data, 2)
def testmaxpp(data): if audioop.maxpp(data[0], 1) <> 0 or \ audioop.maxpp(data[1], 2) <> 0 or \ audioop.maxpp(data[2], 4) <> 0: return 0 return 1
l[i], data[i] = mic[i].read()
# greedy algorithm which selects most loud sound
#rms = [aud.rms(data[i],1) for i in range(micnum)]
#rmssorted = sorted(rms)
'''
L = 100
mxix = [aud.findmax(data[i],L) for i in range(micnum)]
prms = [aud.rms(data[i][mxix[i]*2:(mxix[i]+L)*2],1) for i in range(micnum)]
prmssorted = sorted(prms)
print prmssorted
'''
direction = 'UNDEFINED'
ind = 0
#if prmssorted[2] - prmssorted[0] > 20:
maxpp = [aud.maxpp(data[i],1) for i in range(micnum)]
print maxpp
maxppsorted = sorted(maxpp)
if maxppsorted[2] - maxppsorted[1] > 20:
# possible value of dirscore: 4,5,6,8,9,10
#dirscore = 4 * prms.index(prmssorted[2]) + 2 * prms.index(prmssorted[1]) + prms.index(prmssorted[0])
dirscore = 4 * maxpp.index(maxppsorted[2]) + 2 * maxpp.index(maxppsorted[1]) + maxpp.index(maxppsorted[0])
print dirscore
dictionary = {'0':'UNDEFINED',
'1':'UNDEFINED',
'2':'UNDEFINED',
'3':'UNDEFINED',
'4':'LEFT',
'5':'BACKLEFT',
'6':'FRONTLEFT',
'7':'UNDEFINED',
def _callback( self, data, frame_count, time_info, status):
# Если микрофон замьючен - ничего не делаем
if self.muted :
self.buffer.clear()
self.ignoreFirstFrame = True
return None, pyaudio.paContinue
# А еще игнорируем первый фрейм после unmute:
if self.ignoreFirstFrame :
self.ignoreFirstFrame = False
return None, pyaudio.paContinue
# Контролируем размер буфера. В режиме ожидания 1с, в активном режиме 3с
maxBufferSize = int(CHUNKS_PER_SECOND * (3 if self.active else 1) )
while len(self.buffer)>maxBufferSize :
self.buffer.pop(0)
data = numpy.fromstring( data, dtype='int16')
if self.sampleRate != VOICE_SAMPLING_RATE:
data, self.__ratecvState = audioop.ratecv( data.tobytes(), 2, self.channels, self.sampleRate, VOICE_SAMPLING_RATE, self.__ratecvState )
data = numpy.fromstring( data, dtype='int16')
#print(f"channels:")
# Раскидываем на каналы
channels = [0]*self.channels
for ch in range(self.channels):
channels[ch] = data[ch::self.channels].tobytes()
#print(numpy.fromstring(channels[ch], dtype='int16'))
# "Оптимальный уровень громкости"
if config.micSelection == "rms":
# Вариант 2: "наибольший RMS, но без искажений".
chBest = -1
rmsBest = 0
maxBest = 0
chGood = -1
rmsGood = 100000
maxGood = 0
for ch in config.microphones:
__rms = audioop.rms( channels[ch], self.sampleSize )
__maxpp = audioop.maxpp( channels[ch], self.sampleSize )
if (__rms>rmsBest) and (__rms<5000) and (__maxpp<64000) :
rmsBest = __rms
maxBest = __maxpp
chBest = ch
if (chGood<0) or (__rms < rmsGood) :
rmsGood = __rms
rmsBest = __maxpp
chGood = ch
#print(f'rms:[{__rms[0]},{__rms[1]}], maxpp:[{__maxpp[0]},{__maxpp[1]}], rmsBest={rmsBest}({chBest}), rmsGood={rmsGood}({chGood})')
#print(f'rmsBest={rmsBest}({chBest}), rmsGood={rmsGood}({chGood})')
if chBest>=0:
self.channel = chBest
self.__rms = rmsBest
self.__maxpp = maxBest
else:
self.channel = chGood
self.__rms = rmsGood
self.__maxpp = maxGood
data = channels[self.channel]
# "Среднее по микрофонным каналам":
else :
self.channel = "avg"
factor = 1.0 / len(config.microphones)
#print(f'factor={factor} ')
data = None
for ch in config.microphones :
if data==None :
data = audioop.mul( channels[ch], 2, factor )
else :
data = audioop.add( data, audioop.mul( channels[ch], 2, factor ), 2 )
self.__rms = audioop.rms( data, self.sampleSize )
self.__maxpp = audioop.maxpp( data, self.sampleSize )
#print(f"Final data: channel={self.channel}, rms={self.rms}, maxpp={self.maxpp} ")
#print(numpy.fromstring(data, dtype='int16'))
# Сохранить фрагмент в буфере:
self.buffer.append( data )
if not self.active:
# Если уровень звука "немного меньше" фонового шума - снизить значение порогового шума
if self.__rms + config.noiseThreshold < self.__noiseLevel:
self.__noiseLevel = self.__rms + int( config.noiseThreshold / 4 )
# Посчитать VAD index (self.vadLevel)
vadFrameSize = int(VOICE_SAMPLING_RATE*self.sampleSize/1000 * VAD_FRAME)
p = 0
voiceFrames = 0
totalFrames = 0
while p+vadFrameSize <= len(data):
totalFrames += 1
if self.vad.is_speech( data[p:p+vadFrameSize], VOICE_SAMPLING_RATE ): voiceFrames += 1
p += vadFrameSize
self.vadLevel = int(voiceFrames*100/totalFrames)
isVoice = (totalFrames>0) and (self.vadLevel>=config.vadConfidence)
if isVoice and (self.rms > self.triggerLevel):
self.active = True
else:
if self.__rms + config.noiseThreshold > self.__noiseLevel :
self.__noiseLevel = self.__rms
return None, pyaudio.paContinue