def _playAudio(self, audio):
CHUNK = 1024
f = io.BytesIO()
f.write(audio)
f.seek(0)
wf = wave.Wave_read(f)
p = pyaudio.PyAudio()
stream = p.open(format=p.get_format_from_width(wf.getsampwidth()),
channels=wf.getnchannels(),
rate=wf.getframerate(),
output=True)
data = wf.readframes(CHUNK)
while data != b'':
stream.write(data)
data = wf.readframes(CHUNK)
time.sleep(0.2)
stream.stop_stream()
stream.close()
p.terminate()
def generate_transcript(language_code="ro-RO"):
# Creates google client
client = speech.SpeechClient()
# Full path of the audio file, Replace with your file name
file_name = os.path.join(os.path.dirname(__file__), "cache/recording.wav")
wav_file = wave.Wave_read(file_name)
ch = wav_file.getnchannels()
#Loads the audio file into memory
with io.open(file_name, "rb") as audio_file:
content = audio_file.read()
audio = speech.RecognitionAudio(content=content)
config = speech.RecognitionConfig(
encoding=speech.RecognitionConfig.AudioEncoding.LINEAR16,
audio_channel_count=ch,
language_code=language_code,
)
# Sends the request to google to transcribe the audio
response = client.recognize(request={"config": config, "audio": audio})
return response
def Process(self):
wr = wave.Wave_read(self.args.infile)
self.numtracks = wr.getnchannels()
self.framerate = wr.getframerate()
self.sampwidth = wr.getsampwidth()
self.nframes = wr.getnframes()
if self.args.track >= self.numtracks:
print('Error, file only has ' + str(self.numtracks) + ' tracks.')
sys.exit(-1)
self.persample = PerSample(self.args, self.framerate)
offset = self.args.track * self.sampwidth
# Set up the start and finish frame numbers.
frameEnd = int(self.args.end * self.framerate)
if frameEnd < 0:
frameEnd = self.nframes
frameEnd = min(frameEnd, self.nframes)
frameStart = int(self.args.start * self.framerate)
frameStart = max(0, frameStart)
frameStart = min(frameStart, frameEnd)
if frameStart > 0:
wr.readframes(frameStart)
for i in range(frameStart, frameEnd):
frame = wr.readframes(1)
# Get one signed, 16-bit sample
sample = frame[offset] + frame[offset + 1] * 256
if sample > 32767:
sample = sample - 65536
self.persample.Process(i, sample)
def run(self, audio):
audio = normalize_audio(audio)
audio = BytesIO(audio)
with wave.Wave_read(audio) as wav:
audio = np.frombuffer(wav.readframes(wav.getnframes()), np.int16)
result = self.model.stt(audio_buffer=audio)
return result
def setup():
settings = {}
settings['channels'] = 1
settings['bit_depth'] = 3 if USE_24_BIT else 2
settings['profile'] = ask(
'Give the sound profile [0=Sine, 1=Square, 2=Saw, 3=test.wav]',
int,
d=0)
if settings['profile'] != 3:
settings['frequency'] = ask('Give the note frequency [Hz]',
float,
d=440)
settings['sample_rate'] = ask('Give the sampling rate [Hz]',
int,
d=44100)
settings['duration'] = ask('Give the desired test duration [s]',
float,
d=5)
else:
fp = dirname(__file__)
with wave.Wave_read(join_path(fp, 'test.wav')) as wav:
settings['sample_rate'] = wav.getframerate()
settings['duration'] = wav.getnframes() / settings['sample_rate']
settings['channels'] = wav.getnchannels()
settings['bit_depth'] = wav.getsampwidth()
return settings
def getTimes(song, chunk = 2048 * 4):
wav = wave.Wave_read(song)
rate = wav.getframerate()
data = getData(song)
#holds average of the powers of the last 20 chunks
avgList = []
# returns the times when it is considered a beat
timeList = []
i = 0
start = time.time()
# goes through all the chunks in the song
while True:
power = 20*np.log10(np.abs(np.fft.rfft(data[chunk * i: chunk * (1 + i), 0])))
freq = np.linspace(0, rate/2.0, len(power))
if (chunk * (i + 1)) > len(data):
break
avg = abs(sum(power) / len(power))
# compares the power of current chunk to the last 20
if i > 19:
if avg > sum(avgList) / len(avgList):
# chunk * i / rate = time in seconds
timeS = chunk * i / rate
timeMS = timeS * 1000
timeList.append(timeMS)
avgList.pop(0)
avgList.append(avg)
i += 1
end = time.time()
print(end - start, 'seconds to run')
wav.close()
return timeList
def BASE_WAVEFORM(ts, **kwarks):
choice = kwarks.get('profile', 1)
if choice == 1: # Square
return np.sign(np.sin(2 * np.pi * ts * kwarks.get('frequency', 440)))
elif choice == 2: # Sawtooth
ret = np.mod(ts, 1 / kwarks.get('frequency', 440))
return ret / np.max(np.abs(ret)) - 0.5
elif choice == 3:
fp = dirname(__file__)
with wave.Wave_read(join_path(fp, 'test.wav')) as wav:
audio = wav.readframes(wav.getnframes())
bit_depth = kwarks['bit_depth']
if bit_depth == 3:
audio = np.frombuffer(b''.join([
audio[3 * i:3 * (i + 1)] + b'\x00'
for i in range(len(audio))
]),
dtype=np.int32) # Not tested at all
elif bit_depth == 2:
audio = np.frombuffer(audio, dtype=np.int16)
else:
raise Exception('Non-supported bit depth')
audio = audio.astype(float)
return (audio -
(audio.max() - audio.min()) / 2) / np.max(np.abs(audio))
else: # If choice is not 1, 2 or 3 the selected profile is sine wave.
return np.sin(2 * np.pi * ts * kwarks.get('frequency', 440))
def run_with_metadata(self, audio) -> Metadata:
normalized_audio = normalize_audio_input(audio)
audio_streams = BytesIO(normalized_audio)
with wave.Wave_read(audio_streams) as wav:
audio_streams = np.frombuffer(wav.readframes(wav.getnframes()),
np.int16)
results = self.model.sttWithMetadata(audio_buffer=audio_streams)
return results
def run(self, audio):
"""Perform speech-to-text transcription"""
audio = normalize_audio(audio)
audio = BytesIO(audio)
with wave.Wave_read(audio) as wav:
audio = np.frombuffer(wav.readframes(wav.getnframes()), np.int16)
result = self.model.stt(audio_buffer=audio)
return result
def loadwav(fname):
with wave.Wave_read(fname) as f:
assert f.getframerate(
) == sp.sf, 'sampling rate is different (' + fname + ')'
assert f.getnchannels(
) == 1, 'channel is not 1 (' + fname + ')'
T = numpy.frombuffer(f.readframes(f.getnframes()),
numpy.int16).astype(numpy.float32)
return T
def load_waveform_from_wave_file(self, filename, dtype=np.float):
"""Opens wave file and reads, assuming signed shorts"""
wr = wave.Wave_read(filename)
fs = wr.getframerate()
sig = np.array(struct.unpack('%dh' % wr.getnframes(),
wr.readframes(wr.getnframes())),
dtype=dtype)
wr.close()
return sig, fs
def is_valid_wav(filename):
# check the sampling rate and number bits of the WAV
try:
wav_file = wave.Wave_read(filename)
except:
return False
if wav_file.getframerate() != 16000 or wav_file.getsampwidth() != 2 or wav_file.getnchannels() != 1 \
or wav_file.getcomptype() != 'NONE':
return False
return True
def run(self, audio) -> str:
""" Receives the audio, normalizes it and is sent to the model to be transcribed. Returns the result of the
transcribe audio in string format."""
normalized_audio = normalize_audio_input(audio)
audio_streams = BytesIO(normalized_audio)
with wave.Wave_read(audio_streams) as wav:
audio_streams = np.frombuffer(wav.readframes(wav.getnframes()),
np.int16)
results = self.model.stt(audio_buffer=audio_streams)
return results
def testWaveFile(filename):
w = wave.Wave_read(filename)
bitrate = w.getframerate()
channels = w.getnchannels()
bits = w.getsampwidth()*8
if not bitrate==8000 or not channels==1 or not bits==16:
newFilename = filename[:-4] + "_8000.wav"
returnValue = os.system(SOXCOMMAND.format(filename, newFilename))
if returnValue:
raise(SOX_Exception("Something went wrong calling sox: SOXCOMMAND.format(filename, newFilename"))
filename = newFilename
return(filename)
def read_wave_file(file, data_min_proportion=1.0):
"""
Reads a wave file and returns it as a NumPy array.
Args:
file: Either a filename or a file object
Returns a 2-tuple of:
(samprate, data)
where samprate is the sampling rate as in integer (e.g. 16000), and `data`
is a numpy array with dtype int16 and shape (num_channels, num_samples).
Raises:
RuntimeError: if an error occurred while reading the data.
(Note: if more than `data_min_proportion` of the
expected data was read, it will succeed even if
the file was truncated.)
wave.Error: whatever errors the wave module encountered
OsError (via wave module), if a file could not be opened.
"""
wave_reader = wave.Wave_read(file)
(nchannels, sampwidth, framerate, nframes, comptype,
compname) = wave_reader.getparams()
if comptype != 'NONE':
raise RuntimeError(
"Wave file has compression, which is unsupported: comptype={},"
"compname={}".format(comptype, compname))
if sampwidth != 2:
raise RuntimeError(
"Wave file has sample width of {}, expected 2.".format(sampwidth))
data_as_bytes = wave_reader.readframes(nframes)
nframes_read = len(data_as_bytes) // (sampwidth * nchannels)
assert nframes_read <= nframes
if nframes_read < data_min_proportion * nframes:
raise RuntimeError(
"Reading data from {0}, read too little data: {1} != {2} "
"(min allowed proportion: {3})".format(file, nframes_read, nframes,
dat_min_proportion))
dt = np.dtype('int16')
if sys.byteorder == 'big':
# Make sure to interpret the data as little-endian even if the machine
# is big endian.
dt = dt.newbyteorder('<')
array = np.frombuffer(data_as_bytes, dt)
# order='F' because the frame has a higher stride than the channel.
return (framerate, array.reshape((nchannels, nframes_read), order='F'))
def build_data(wav, begin=None, end=None):
wav_in_file = wave.Wave_read(wav)
wav_in_num_samples = wav_in_file.getnframes()
N = wav_in_file.getnframes()
dstr = wav_in_file.readframes(N)
data = np.fromstring(dstr, np.int16)
if begin is not None and end is not None:
return data[begin * 16000:end * 16000]
X = []
l = len(data)
for i in range(0, l - 100, 160):
X.append(data[i:i + 480])
return X
def is_valid_wav(filename):
# check the sampling rate and number bits of the WAV
try:
wav_file = wave.Wave_read(filename)
if wav_file.getframerate() != 16000 or wav_file.getsampwidth() != 2 or wav_file.getnchannels() != 1 \
or wav_file.getcomptype() != 'NONE':
wav_file.close()
return False
wav_file.close()
return True
except Exception:
if 'wav_file' in locals():
wav_file.close()
return False
def fixWaveFile(filename):
w = wave.Wave_read(filename)
bitrate = w.getframerate()
channels = w.getnchannels()
bits = w.getsampwidth() * 8
if not bitrate == 8000 or not channels == 1 or not bits == 16:
newFilename = filename[:-4] + "_8000.wav"
returnValue = os.system(SOXCOMMAND.format(filename, newFilename))
if returnValue:
raise (SOX_Exception("""Something went wrong calling sox:
SOXCOMMAND.format(filename, newFilename
Is sox installed? If not, just make sure that you've saved 8kHz mono wav files."""
))
filename = newFilename
return (filename)
def fixWaveFile(filename):
w = wave.Wave_read(filename)
bitrate = w.getframerate()
channels = w.getnchannels()
bits = w.getsampwidth() * 8
if not bitrate == 8000 or not channels == 1 or not bits == 16:
newFilename = filename[:-4] + "_8000.wav"
returnValue = os.system(SOXCOMMAND.format(filename, newFilename))
if returnValue:
raise (SOX_Exception("""Nie udało się wywołać programu sox:
SOXCOMMAND.format(filename, newFilename
Czy program sox jest zainstalowany? Sprawdź też, czy pliki dźwiękowe mają format wav 8kHz mono."""
))
filename = newFilename
return (filename)
def convert(self):
print("convert")
try:
pathname = os.path.normpath(self.pathname + '/../')
language = self.ui.comboBox_language.currentText()
for i in range(0, 101):
file = wave.Wave_read(
os.path.join(pathname, 'Languages', language,
'{:04d}.wav'.format(i)))
file1 = wave.Wave_write(
os.path.join(pathname, 'Languages', language, 'work',
str(i) + '.wav'))
file1.setparams(file.getparams())
file1.writeframes(file.readframes(file.getnframes()))
file1.close()
file.close()
except TypeError as e:
print("wavefile : ", e)
def crop_file(input_wav, output_wav, start_time, end_time):
wav_in_file = wave.Wave_read(input_wav)
wav_in_num_samples = wav_in_file.getnframes()
wav_out_file = wave.Wave_write(output_wav)
wav_out_file.setparams(
(wav_in_file.getnchannels(), wav_in_file.getsampwidth(),
wav_in_file.getframerate(), float(end_time) - float(start_time) + 1,
'NONE', 'noncompressed'))
start_sample = int(float(start_time) * wav_in_file.getframerate())
end_sample = int(float(end_time) * wav_in_file.getframerate())
# writing the wav file from the given regions
for i in range(0, wav_in_num_samples):
samples = wav_in_file.readframes(1)
if start_sample <= i <= end_sample:
wav_out_file.writeframes(samples)
# samples_unpacked = struct.unpack("<h", samples)
# print i, int(samples_unpacked[0])
wav_in_file.close()
wav_out_file.close()
def play_audio(self,
audio_data_src: http.client.HTTPResponse or io.BytesIO,
format: str = 'wav') -> None:
'''
Plays audio_data with format=format
Args:
audio_data: binary audio source
format: audio format
'''
try:
if format == 'wav':
#define stream chunk
chunk = 1024
#open a wav format music
f = wave.Wave_read(audio_data_src)
#instantiate PyAudio
p = pyaudio.PyAudio()
#open stream
stream = p.open(format=p.get_format_from_width(
f.getsampwidth()),
channels=f.getnchannels(),
rate=f.getframerate(),
output=True)
#read data
data = f.readframes(chunk)
#play stream
while data:
stream.write(data)
data = f.readframes(chunk)
#stop stream
stream.stop_stream()
stream.close()
#close PyAudio
p.terminate()
except Exception as e:
ErrorLogger(__file__, e)
def read_wav_file(file: Text) -> Tuple[int, np.ndarray]:
"""
Reads a wave file and returns it as a NumPy array.
Args:
file: Filepath to a .wav file.
Returns: (samprate, data) where samprate is the sampling frequency and data
is a numpy array with dtype int16 and shape (num_channels, num_samples).
Raises:
RuntimeError: if an error occurred while reading the data.
wave.Error: whatever errors the wave module encountered
OsError (via wave module), if a file could not be opened.
"""
wave_reader = wave.Wave_read(file)
(nchannels, sampwidth, framerate, nframes, comptype,
compname) = wave_reader.getparams()
if comptype != 'NONE':
raise RuntimeError(
"Wave file has compression, which is unsupported: comptype={},"
"compname={}".format(comptype, compname))
# Expect 16-bit magnitude sampling.
if sampwidth != 2:
raise RuntimeError(
"Wave file has sample width of {}, expected 2.".format(sampwidth))
data_as_bytes = wave_reader.readframes(nframes)
nframes_read = len(data_as_bytes) // (sampwidth * nchannels)
assert nframes_read <= nframes
dt = np.dtype('int16')
if sys.byteorder == 'big':
# Make sure to interpret the data as little-endian even if the machine
# is big endian.
dt = dt.newbyteorder('<')
array = np.frombuffer(data_as_bytes, dt)
# order='F' because the frame has a higher stride than the channel.
return framerate, array.reshape((nchannels, nframes_read), order='F')
def wf_and_sr_from_filepath(filepath, **kwargs):
must_ensure_mono = kwargs.pop('ensure_mono', True)
if is_wav_file(filepath):
kwargs = dict({'always_2d': False}, **kwargs)
if 'offset_s' in kwargs.keys() or 'duration' in kwargs.keys():
sample_rate = wave.Wave_read(filepath).getframerate()
start = int(round(kwargs.pop('offset_s', 0) * sample_rate))
kwargs['start'] = start
duration = kwargs.pop('duration', None)
if duration is not None:
kwargs['stop'] = int(start + round(duration * sample_rate))
kwargs = filter_kwargs_to_func_arguments(sf.read, kwargs)
wf, sr = sf.read(filepath, **kwargs)
else:
kwargs['offset'] = kwargs.pop('offset_s', 0.0)
wf, sr = librosa.load(filepath, **kwargs)
if must_ensure_mono:
wf = ensure_mono(wf)
return wf, sr
def FileSelected(self, filename):
"""
Set a sound file.
@param filename (string) is an audio file name (a wave is expected).
"""
# we already opened the same file
if filename == self._filename and self._mediaplayer is not None:
logging.info(' ... SndPlayer: file %s was already opened. [WARNING]' % (filename))
return
try:
m = wx.media.MediaCtrl(self, style=wx.NO_BORDER)
m.Load(filename)
self._length = m.Length()
if self._length == 0: # **** BUG of the MediaPlayer! ****
import wave
w = wave.Wave_read(filename)
self._length = int(1000 * float(w.getnframes())/float(w.getframerate()))
logging.info(" ... File %s successfully loaded. [ OK ]" % (filename))
except Exception as e:
logging.info(" ... File %s not loaded. [ ERROR ]" % (filename))
ShowInformation(self, self._prefs, 'Error loading: '+filename+': '+str(e), style=wx.ICON_ERROR)
return False
# set mediaplayer with the new one
self._filename = filename
self._mediaplayer = m
self.ActivateButtons(True)
self._offsets = (0,self._length)
if self._playbackSlider is not None:
self._playbackSlider.SetRange(0, self._length)
self._playbackSlider.SetTickFreq(int(self._length/10), 1)
self._timer.Start(self._refreshTimer)
self.Refresh()
phoneme_classifier_SIGMA = "4.3589"
phoneme_classifier_C = "1"
phoneme_classifier_B = "0.8"
phoneme_classifier_epochs = "1"
phoneme_classifier_model = "models/pa_phoeneme_frame_based.C_%s.B_%s.sigma_%s.pad_%s.epochs_%s.model" % \
(phoneme_classifier_C, phoneme_classifier_B, phoneme_classifier_SIGMA,
phoneme_classifier_pad, phoneme_classifier_epochs)
# generate intermediate files from a temp filename
(tmp_fd, tmp_filename) = tempfile.mkstemp()
wav_filename = tmp_filename + ".16kHz.wav"
mfc_filename = tmp_filename + ".mfc"
mfcc_tmp_file = tmp_filename + ".mfc_delta"
# read Wav file parameters
wave_file = wave.Wave_read(args.wav_filename)
wave_sampling_rate = wave_file.getframerate()
wave_file.close()
# converts WAV to 16kHz
if wave_sampling_rate != 16000:
cmd = "%s %s -r 16k %s remix 1" % (sox_bin, args.wav_filename, wav_filename)
easy_call(cmd)
rm_wav_file = True
else:
wav_filename = args.wav_filename
rm_wav_file = False
# extract MFCC features using HCopy utility
cmd_params = "%s -C %s %s %s" % (hcopy_bin, htk_config, wav_filename, mfc_filename)
easy_call(cmd_params)
def unpackMono(waveFile):
w = wave.Wave_read(waveFile)
data = []
for i in range(w.getnframes()):
data.append(unpack("B", w.readframes(1))[0])
return (data)
playsound('indian.wav')
# wav file says 'sorry!'
# Lets try sorry.html
url = f"{standard_url}sorry.html"
response = requests.get(url, auth=HTTPBasicAuth('butter', 'fly'))
page_contents = BeautifulSoup(response.text, 'html.parser')
print(page_contents)
#* - "what are you apologizing for?"
# Let's play with wave module
import wave
indian = wave.Wave_read('indian.wav')
print(f'''Number of channels: {indian.getnchannels()}'
Sample width: {indian.getsampwidth()}
Frame rate: {indian.getframerate()}
Number of frames: {indian.getnframes()}''')
# use parameters: .getparams()} to get al once
new = wave.Wave_write('new.wav')
new.setframerate(11025 * 2) # increase frame rate
new.setsampwidth(1) # decrease sample width
new.setnframes(55788)
new.setnchannels(1)
print(f'''Number of channels: {new.getnchannels()}
Sample width: {new.getsampwidth()}
def run(self, musicbrainzid, fname):
baseFname, ext = os.path.splitext(os.path.basename(fname))
wavfname, created = util.docserver_get_wav_filename(musicbrainzid)
panelWidth = 900 # pixels
panelHeight = 255 # pixels
zoomlevels = self._zoom_levels # seconds
options = coll.namedtuple(
'options',
'image_height fft_size image_width f_min f_max scale_exp pallete')
options.image_height = panelHeight
options.fft_size = self._fft_size
options.f_min = self._f_min
options.f_max = self._f_max
options.pallete = self._pallete
options.scale_exp = self._scale_exp
ret = {}
for zoom in zoomlevels:
# At the beginning of each zoom level we reset the image_width
# since we are modifying it at the end of the last zoom level
options.image_width = panelWidth
wvFile = wave.Wave_read(wavfname)
framerate = wvFile.getframerate()
totalframes = wvFile.getnframes()
# We want this many frames per file at this zoom level.
framesperimage = framerate * zoom
wfname = "waveform%s" % zoom
specname = "spectrum%s" % zoom
inv_mfcc_name = "inv_mfcc_spectrum%s" % zoom
wfdata = []
specdata = []
inv_mfcc_data = []
sumframes = 0
while sumframes < totalframes:
if sumframes + framesperimage > totalframes:
remaining_frames = (totalframes - sumframes)
options.image_width = options.image_width * remaining_frames / framesperimage
else:
remaining_frames = framesperimage
fp, smallname = tempfile.mkstemp(".wav")
os.close(fp)
data = wvFile.readframes(remaining_frames)
wavout = wave.open(smallname, "wb")
# This will set nframes, but writeframes resets it
wavout.setparams(wvFile.getparams())
wavout.writeframes(data)
wavout.close()
sumframes += framesperimage
specio = io.BytesIO()
# Set the name attr so that PIL gets the filetype hint
specio.name = "spec.png"
wavio = io.BytesIO()
wavio.name = "wav.png"
in_mfcc_io = io.BytesIO()
in_mfcc_io.name = "melspec.png"
w2png.genimages(smallname, wavio, specio, in_mfcc_io, options)
os.unlink(smallname)
specdata.append(specio.getvalue())
wfdata.append(wavio.getvalue())
inv_mfcc_data.append(in_mfcc_io.getvalue())
ret[wfname] = wfdata
ret[specname] = specdata
ret[inv_mfcc_name] = inv_mfcc_data
ret["smallfull"] = self.make_mini(wavfname)
if created:
os.unlink(wavfname)
return ret
def run(self):
def _frame_index_to_sec(frame_index):
return (float(frame_index * rhino.frame_length) /
float(rhino.sample_rate)) - float(1)
"""
Creates an input audio stream, initializes wake word detection (Porcupine) and speech to intent (Rhino)
engines, and monitors the audio stream for occurrences of the wake word and then infers the intent from speech
command that follows.
"""
porcupine = None
rhino = None
pa = None
audio_stream = None
wake_phrase_detected = True
intent_extraction_is_finalized = False
Apath = Video_to_Audio(self._video_path)
wf = wave.Wave_read(Apath)
ww, sr = soundfile.read(Video_to_Audio(self._video_path))
print(len(ww))
try:
porcupine = Porcupine(
library_path=self._porcupine_library_path,
model_file_path=self._porcupine_model_file_path,
keyword_file_paths=[self._porcupine_keyword_file_path],
sensitivities=[0.5],
)
rhino = Rhino(
library_path=self._rhino_library_path,
model_path=self._rhino_model_file_path,
context_path=self._rhino_context_file_path,
sensitivity=0.6,
)
print()
print(
"****************************** context ******************************"
)
print(rhino.context_info)
print(
"*********************************************************************"
)
print()
pa = pyaudio.PyAudio()
audio_stream = pa.open(
rate=porcupine.sample_rate,
channels=1,
format=pyaudio.paInt16,
input=True,
frames_per_buffer=porcupine.frame_length,
input_device_index=self._input_device_index,
)
test = 0
Tpath = Apath.replace("wav", "txt")
f = open(Tpath, "w")
ouput = ""
classtr = ""
startcount = 0
endcount = 0
cango = 1
checkfirst = 0
data_csv = [["Class_num", "Start_time", "End_time"]]
ClassNum = None
Start_time = None
Start_time2 = None
ClassNum2 = None
rm = None
# NOTE: This is true now and will be correct possibly forever. If it changes the logic below need to change.
assert porcupine.frame_length == rhino.frame_length
try:
while True:
date = wf.readframes(porcupine.frame_length)
pcm = audio_stream.read(porcupine.frame_length,
exception_on_overflow=False)
pcm = struct.unpack_from("h" * porcupine.frame_length,
date)
if self._output_path is not None:
self._recorded_frames.append(pcm)
if not wake_phrase_detected:
wake_phrase_detected = porcupine.process(pcm)
if wake_phrase_detected:
print("detected wake phrase")
elif not intent_extraction_is_finalized:
intent_extraction_is_finalized = rhino.process(pcm)
else:
if rhino.is_understood():
cango = 1
intent, slot_values = rhino.get_intent()
print()
if intent == "EndWork":
endcount += 1
classstr = " - %s" % _frame_index_to_sec(test)
else:
checkfirst += 1
startcount += 1
endcount = 0
for slot, value in slot_values.items():
print("%s: %s" % (slot, value))
classstr = ("%s: %s" % (slot, value)) + (
" start time is %s" %
_frame_index_to_sec(test))
if startcount == 2:
Start_time2 = Start_time
ClassNum2 = ClassNum
Start_time = _frame_index_to_sec(test)
ClassNum = value
print()
print("intent : %s at time: %f" %
(intent, _frame_index_to_sec(test)))
print()
else:
print("didn't understand the command")
cango = 0
rhino.reset()
wake_phrase_detected = True
intent_extraction_is_finalized = False
print(startcount, endcount)
print(ouput)
if cango:
if endcount == 1 and startcount == 0:
ouput = classstr
f.write("-1 class end at" + ouput + "\n")
endcount = 0
ouput = ""
data_csv.append(
["-1", "-1",
_frame_index_to_sec(test)])
elif ouput == "" and endcount == 0 and startcount == 1:
ouput = classstr
elif ouput != "" and endcount == 1:
try:
data_csv.remove(rm)
except:
pass
data_csv.append([
ClassNum, Start_time,
_frame_index_to_sec(test)
])
ouput += classstr
endcount = 0
startcount = 0
f.write(ouput + "\n")
ouput = ""
elif endcount == 0 and startcount == 2:
if checkfirst == 2:
data_csv.append(
[ClassNum2, Start_time2, "-1"])
f.write(ouput + "\n")
data_csv.append([ClassNum, Start_time, "-1"])
rm = [ClassNum, Start_time, "-1"]
ouput = classstr
f.write(ouput + "\n")
startcount = 1
test += 1
except:
print("EOF")
print(_frame_index_to_sec(test))
data_csv.append(["Maybe miss", classstr, classstr])
f.write("Могла быть упущенная метка : %s" % classstr)
with open("sw_data_new.csv", "w") as f:
writer = csv.writer(f)
for row in data_csv:
writer.writerow(row)
except KeyboardInterrupt:
print("stopping ...")
finally:
if porcupine is not None:
porcupine.delete()
if rhino is not None:
rhino.delete()
if audio_stream is not None:
audio_stream.close()
if pa is not None:
pa.terminate()
if self._output_path is not None and len(
self._recorded_frames) > 0:
recorded_audio = np.concatenate(self._recorded_frames,
axis=0).astype(np.int16)
soundfile.write(
os.path.expanduser(self._output_path),
recorded_audio,
samplerate=porcupine.sample_rate,
subtype="PCM_16",
)