def pre_process_video_file(self, inputfile = "audio.mp4", outfile_name = None, randtoken = False, callnumber = 1):
"""
This function takes a video file, parses out its respective audio channesl, runs voice activity detection on those channels, and then generates 5 minute clips
for each channel representing audio with the highest voice activity
"""
log.info("input before file " + inputfile)
#initialize inputs
self.inputfile = inputfile
self.source_file = self.initial_directory + "/" + self.inputfile
log.info("inputfile " + self.inputfile)
log.info("source file " + self.source_file)
# ------------ Find the length of your file -------------
lengthoffile = int(ffmpeg_calls.retrieve_len(self.source_file))
log.info("Length of file " + str(lengthoffile))
if lengthoffile == 0:
log.info("ERROR: Length of file is 0" + inputfile)
raise Exception
#TODO: create a different function for second time processing.
# Transcodes the video file into a wav file for audio analysis
# and outputs it to the Wav-Encoded directory.
if callnumber >= 2:
wav_encoded_files = [self.initial_directory + "/" + inputfile]
else:
try:
wav_encoded_files = self.shorten_given_file(self.source_file, lengthoffile=lengthoffile, cuttosize=2400) #shortens a given file and returns filepath
except Exception as e:
log.debug("ERROR: Can't shorten given file")
log.debug(e)
raise Exception
log.info("wav_encoded_files " + str(wav_encoded_files))
# Only do audio analysis on a file longer than 5 minutes.
if lengthoffile > 300:
threads = []
for wav_file in wav_encoded_files:
threads.append(threading.Thread(target=self.create_voice_activity_clip, args = (wav_file, outfile_name,)))
print(threads)
for i in threads:
i.start()
for i in threads:
i.join()
else:
log.warning("File is short. Uploading the wav file without audio analysis")
# Copies all the output.wav files to the Wav-Clips to be uploaded without further
# processing for a file that is 5 or fewer minutes long.
for filename in os.listdir(self.wav_directory):
if "output.wav" in filename:
dest_path = self.targetdirectory + "/" + "cut_stream" + filename[-11] + "_" + self.inputfile[:-4] + ".wav"
cleanup.copy_file(self.wav_directory + "/" + filename, dest_path)
def retrieve_confidence(id, filename):
#----- Retrieves Confidence For All Files Uploaded
log.info("Getting confidence for file: " + filename + " id: " + id)
language, confidence, response_json = VI_API.new_get_language(str(id))
log.info("File: " + filename + " language: " + str(language) + " confidence: " + str(confidence))
## -------- Reuploads video using a 5 min segment comprising random 20 second samples if the confidence is too low ----------------------
cnt = 2
try:
if confidence < 0.50:
log.info("Low confidence score, redoing processing -- this will take ~10 mins")
#Get the full audio filename corresponding to the clip
stream_number = str(filename.split("_")[1][-1])
encoded_file_name = stream_number + "output.wav"
encoded_file_path = wav_files_directory + "/" + encoded_file_name
file_length = ffmpeg_calls.retrieve_len(encoded_file_path)
#print("Failed to find video length")
new_language, new_confidence, new_json = 0, 0, {}
if file_length > MINIMUM_FILE_LENGTH_THRESHOLD:
# Take the shorter length
temp_cut_size = min(file_length, 2400)
log.info("Going to cut " + str(temp_cut_size) + " from the file")
#random_startpoint = random.randint(60, int(file_length - (temp_cut_size + 10)))
# generate 20 second ranges between the beginning of the video and the cutsize.
x = range(10,temp_cut_size, 20)
arr = random.sample(x, 15)
cutpoints_file = open(wav_files_directory + "/" + "cutpoints_random_sample.txt", "w")
num_cuts = int(MINIMUM_FILE_LENGTH_THRESHOLD / len(arr))
log.info("Number of cuts " + str(num_cuts))
for i in range(len(arr)):
cutpoints_file.write("file " + str(encoded_file_name) + "\n")
cutpoints_file.write("inpoint " + str(arr[i] - num_cuts) + "\n")
cutpoints_file.write("outpoint " + str(arr[i]) + "\n")
cutpoints_file.close()
log.info("Trying to create the shortened file from the cutpoints")
ffmpeg_calls.create_shortened_file(wav_files_directory, reprocessed_files_directory, filename, "cutpoints_random_sample.txt")
temp_id = VI_API.upload_video_file(filename, reprocessed_files_directory + "/" + filename)
new_language, new_confidence, new_json = VI_API.new_get_language(temp_id)
if new_confidence > confidence:
confidence = new_confidence
language = new_language
response_json = new_json
VI_API.clean_index([id])
else:
if temp_id != "None":
VI_API.clean_index([temp_id])
log.info("Writing the results to json ... ")
write_json_to_file(response_json, detection_results_json_directory + "/" + filename.split(".")[0] + ".json")
log.info("Wrote result to: " + detection_results_json_directory + "/" + filename.split(".")[0] + ".json")
return {
"streamName": filename,
"language": str(language),
"confidence": str(confidence),
"resultJsonFile": filename.split(".")[0] + ".json"
}
except Exception as e:
print("ERROR: Failed to get new confidence for file " + filename)
print e
return {}
def retrieve_confidence(id, filename):
#----- Retrieves Confidence For All Files Uploaded
log.info("Getting confidence for file: " + filename + " id: " + id)
language, confidence, response_json = VI_API.new_get_language(str(id))
log.info("File: " + filename + " language: " + str(language) + " confidence: " + str(confidence))
## -------- Reuploads video using a 5 min segment comprising random 20 second samples if the confidence is too low ----------------------
cnt = 2
try:
#If confidence is below a certain theshold
if confidence < 0.50:
log.info("Low confidence score, redoing processing -- this will take ~10 mins")
#Get the full audio filename corresponding to the clip
#This finds the streamnumber from the description of the file
#the wavfile format is 'cutstream2_output.wav', this takes the 2 from the filesname to find the stream number
stream_number = str(filename.split("_")[1][-1])
#This creates an encoded file name for the file, the encoded file name is simply the stream number + "output.wav". So stream 3 becomes 3output.wav
encoded_file_name = stream_number + "output.wav"
#This gives the full filepath the the newly created wav file
encoded_file_path = wav_files_directory + "/" + encoded_file_name
file_length = ffmpeg_calls.retrieve_len(encoded_file_path)
#print("Failed to find video length")
#initialize langauge and confidence at zero
new_language, new_confidence, new_json = 0, 0, {}
#If the file length is smaller than 5 minutes, don't do any processing
if file_length > MINIMUM_FILE_LENGTH_THRESHOLD:
# Take the shorter length
#The temp_cut_size of the file is the smaller between 20 minutes, and the length of the entire file
#If the file is smaller than 20 minutes, we will not take a random 20 minute sample from the video file and instead process the entire video file
temp_cut_size = min(file_length, 2400)
log.info("Going to cut " + str(temp_cut_size) + " from the file")
#random_startpoint = random.randint(60, int(file_length - (temp_cut_size + 10)))
# generate 20 second ranges between the beginning of the video and the cutsize.
x = range(10,temp_cut_size, 20)
#This takes a random sample from the 20 second segments created above, this is used to generate a random mix of 20 second segments to create a 5 minute file from
arr = random.sample(x, 15)
#Create a file with locations to cut to create a shorter file
cutpoints_file = open(wav_files_directory + "/" + "cutpoints_random_sample.txt", "w")
#Number of cuts is determined by file lenght / length of the array
num_cuts = int(MINIMUM_FILE_LENGTH_THRESHOLD / len(arr))
log.info("Number of cuts " + str(num_cuts))
#Creates a file called cutpoints .txt, which is a helper file used by ffmpeg to figure out where to cut the original file
for i in range(len(arr)):
cutpoints_file.write("file " + str(encoded_file_name) + "\n")
cutpoints_file.write("inpoint " + str(arr[i] - num_cuts) + "\n")
cutpoints_file.write("outpoint " + str(arr[i]) + "\n")
cutpoints_file.close()
log.info("Trying to create the shortened file from the cutpoints")
ffmpeg_calls.create_shortened_file(wav_files_directory, reprocessed_files_directory, filename, "cutpoints_random_sample.txt")
temp_id = VI_API.upload_video_file(filename, reprocessed_files_directory + "/" + filename)
new_language, new_confidence, new_json = VI_API.new_get_language(temp_id)
#If the new confidence generated by the new method is better than old confidence generated by the first method, use results from the new method
if new_confidence > confidence:
confidence = new_confidence
language = new_language
response_json = new_json
VI_API.clean_index([id])
else:
if temp_id != "None":
VI_API.clean_index([temp_id])
log.info("Writing the results to json ... ")
#This writes the results from audio detection to a JSON File
write_json_to_file(response_json, detection_results_json_directory + "/" + filename.split(".")[0] + ".json")
log.info("Wrote result to: " + detection_results_json_directory + "/" + filename.split(".")[0] + ".json")
#This returns the results in JSON Format
return {
"streamName": filename,
"language": str(language),
"confidence": str(confidence),
"resultJsonFile": filename.split(".")[0] + ".json"
}
except Exception as e:
print("ERROR: Failed to get new confidence for file " + filename)
print e
return {}