def convert_callback(self):
if not self._file_url.get():
self._status.set("please choose audio file")
elif not self._Dist_url.get():
self._status.set("please choose Destination directory")
else:
self._status.set("processing")
self.freeze_controls()
AUDIO_FILE = str(self._file_url.get())
filename = "game_log_" + datetime.datetime.now().strftime(
"%Y-%m-%d_%H-%M-%S") + ".pdf"
destination = self._Dist_url.get()
kss = Keyword_Spotting_Service()
myaudio = AudioSegment.from_file(AUDIO_FILE, "wav")
chunk_length_ms = 1000 # pydub calculates in millisec
chunks = make_chunks(myaudio,
chunk_length_ms) # Make chunks of one sec
# Export all of the individual chunks as wav files
predictedSentence = ""
for i, chunk in enumerate(chunks):
chunk_name = "sample_AI\chunk{0}.wav".format(i)
chunk.export(chunk_name, format="wav")
for i, chunk in enumerate(chunks):
predictedSentence += kss.predict("sample_AI\chunk" + str(i) +
".wav") + " "
self._pdf = pdfkit.from_string(
"prediction is: " + predictedSentence,
path.join(destination, filename))
self._status.set("Log file generated!")
self.unfreeze_controls()
def predict():
# get file from POST request and save it
audio_file = request.files["file"]
file_name = str(random.randint(0, 100000))
audio_file.save(file_name)
# instantiate keyword spotting service singleton and get prediction
kss = Keyword_Spotting_Service()
predicted_keyword = kss.predict(file_name)
# we don't need the audio file any more - let's delete it!
os.remove(file_name)
# send back result as a json file
result = {"keyword": predicted_keyword}
return jsonify(result)
def predict():
audio_file = request.files["file"]
file_name = str(random.randint(0, 100000))
audio_file.save(file_name)
kss = Keyword_Spotting_Service()
predicted_keyword = kss.predict(file_name)
os.remove(file_name)
data = {"keyword": predicted_keyword}
return jsonify(data)
def main_func(file):
list_text = []
dangerous_word = 'left'
convert_audio_format(file)
main_dir = file.replace('ogg', 'wav')
Preprocess_Signal(main_dir)
files = order_files_by(main_dir)
kss = Keyword_Spotting_Service()
for i in range(len(files)):
list_text.append(kss.predict(files[i]))
print(list_text)
for j in range(len(list_text)):
if list_text[j] == dangerous_word:
alert_massage()
def predict():
# get audio file and save it
audio_file = request.files["file"]
file_name = str(random.randint(0, 1000000))
audio_file.save(file_name)
# invoke keyword spotting service
kss = Keyword_Spotting_Service()
# make a prediction
predicted_keyword = kss.predict(file_name)
# remove de audio file
os.remove(file_name)
# send back the predicted keyword to the client
data = {"keyword": predicted_keyword}
return jsonify(data)
def predict():
# Get audio file and save it
audio_file = request.files["file"]
file_name = str(random.randint(0, 100000))
audio_file.save(file_name)
# Invoke keyword spotting service
kss = Keyword_Spotting_Service()
# Make a prediction
predicted_keyword = kss.predict(file_name)
# Remove the audio file
os.remove(file_name)
# Send back the predicted Keyword in Json format
data = {"keyword": predicted_keyword}
return jsonify(data)
def predict():
"""Endpoint to predict keyword
:return (json): This endpoint returns a json file with the following format:
{
"keyword": "down"
}
"""
# get file from POST request and save it
audio_file = request.files["file"]
file_name = str(random.randint(0, 100000))
audio_file.save(file_name)
# instantiate keyword spotting service singleton and get prediction
kss = Keyword_Spotting_Service()
predicted_keyword = kss.predict(file_name)
# we don't need the audio file any more - let's delete it!
os.remove(file_name)
# send back result as a json file
result = {"keyword": predicted_keyword.split("\\")[-1]}
return jsonify(result)