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