def analyze(video_id):
print "Beginning audio analysis...\n"
file_name = audio_scraper.get_wav_from_vid(video_id)
decision_tree = tree.generate()
audio_file = AudioFile.open(file_name)
frames = audio_file.frames(16384)
save_spectrum_image(audio_file)
analysis = []
i = 0
for frame in frames:
#print "Processing [audio frame {}]...".format(i)
frequencies, levels = get_power_spectrum(frame)
data = training_data.generate(frequencies, levels, file_name)
try:
analysis.append(decision_tree.predict(data))
except ValueError as e:
print "Encountered error: {0}".format(e)
i += 1
print "Audio analysis complete.\n"
return analysis, frames
def import_data(video_id):
file_name = audio_scraper.download_song(video_id)
audio_file = AudioFile.open(file_name)
frames = audio_file.frames(16384)
for frame in frames:
import_frame(frame)
def analyze(video_id):
print "Beginning audio analysis...\n"
file_name = audio_scraper.get_wav_from_vid(video_id)
decision_tree = tree.generate()
audio_file = AudioFile.open(file_name)
frames = audio_file.frames(16384)
save_spectrum_image(audio_file)
analysis = []
i = 0
for frame in frames:
# print "Processing [audio frame {}]...".format(i)
frequencies, levels = get_power_spectrum(frame)
data = training_data.generate(frequencies, levels, file_name)
try:
analysis.append(decision_tree.predict(data))
except ValueError as e:
print "Encountered error: {0}".format(e)
i += 1
print "Audio analysis complete.\n"
return analysis, frames
def import_data(video_id):
file_name = audio_scraper.download_song(video_id)
audio_file = AudioFile.open(file_name)
frames = audio_file.frames(16384)
for frame in frames:
import_frame(frame)
