def main():
parser = argparse.ArgumentParser(description='Process some integers.')
parser.add_argument('--input-file', dest='input_file', required=True)
args = parser.parse_args()
input_dir = os.path.split(args.input_file)[0]
temp_file = input_dir + "/temp.wav"
cmd = [
"/usr/bin/ffmpeg", "-i", args.input_file, "-ar", "22050", "-ac", "1",
"-acodec", "pcm_s16le", temp_file, "-y"
]
subprocess.check_call(cmd)
cmd = ["yaafe", "-c", FEATURE_PLAN, "-r", "22050", temp_file]
subprocess.check_output(cmd)
features1 = ["zcr", "flux", "spectral_rollof", "energy_stats"]
features2 = ["mfcc_stats"]
features3 = ["spectral_flatness_per_band"]
features4 = features1 + features2 + features3
FEATURE_GROUPS = [features1, features2, features3, features4]
peaks, convolution_values, timestamps = feat.get_combined_peaks(
temp_file, FEATURE_GROUPS, kernel_type="gaussian")
detected_segments = kernel.calculate_segment_start_end_times_from_peak_positions(
peaks, timestamps)
timestamps, feature_vectors = feat.read_features(features4,
temp_file,
scale=True)
with open("/opt/speech-music-discrimination/pickled/model.pickle",
'r') as f:
trained_model = pickle.load(f)
frame_level_predictions = trained_model.predict(feature_vectors)
annotated_segments = Util.get_annotated_labels_from_predictions_and_sm_segments(
frame_level_predictions, detected_segments, timestamps)
annotated_segments = Util.combine_adjacent_labels_of_the_same_class(
annotated_segments)
annotated_segments = feat.filter_noisy_labels(annotated_segments)
annotated_segments = Util.combine_adjacent_labels_of_the_same_class(
annotated_segments)
Util.write_audacity_labels(annotated_segments,
input_dir + "/annotated-segments.txt")
for f in glob.glob(input_dir + "/*.csv"):
os.remove(f)
os.remove(temp_file)
def main():
subprocess.check_output(["unzip", "muspeak-mirex2015-detection-examples.zip", "-d",
"muspeak-mirex2015-detection-examples"])
mp3_to_wav()
wav_files = glob.glob("./muspeak-mirex2015-detection-examples/*.wav")
for wav_file in wav_files:
print wav_file
label_file = wav_file.replace(".mp3.wav", ".csv")
if not os.path.isfile(label_file):
label_file = label_file.replace(".csv", "_v2.csv")
WavEditor.create_audio_segments(label_file, wav_file, "segments", True, ",", "f2", remove_overlapping=True)
speech_wavs = glob.glob("./segments/*_s.wav")
music_wavs = glob.glob("./segments/*_m.wav")
all_files_dict = {}
for f in speech_wavs:
all_files_dict[f] = "s"
for f in music_wavs:
all_files_dict[f] = "m"
random.seed(2222)
all_files_random_keys = random.sample(all_files_dict.keys(), len(all_files_dict.keys()))
last_seconds = 0
files_to_concatenate = []
labels = []
for v in all_files_random_keys:
duration = float(subprocess.check_output(["soxi", "-D", v]).strip())
segment_start_time = last_seconds
segment_end_time = last_seconds + duration
last_seconds += duration
labels.append(AudacityLabel(segment_start_time, segment_end_time, all_files_dict[v]))
files_to_concatenate.append(v)
audacity_labels = Util.combine_adjacent_labels_of_the_same_class(labels)
Util.write_audacity_labels(audacity_labels, "mirex_combined.txt")
command = []
command.append("sox")
command.extend(files_to_concatenate)
command.append("mirex_combined.wav")
subprocess.check_output(command)
shutil.rmtree("./segments")
shutil.rmtree("./muspeak-mirex2015-detection-examples")
def test_combine_adjacent_labels_of_the_same_class(self):
input_labels = [
AudacityLabel(0, 10, "m"),
AudacityLabel(10, 20, "m"),
AudacityLabel(20, 21, "s"),
AudacityLabel(21, 22, "s"),
AudacityLabel(22, 23, "s"),
AudacityLabel(23, 30, "m")
]
expected_labels = [
AudacityLabel(0, 20, "m"),
AudacityLabel(20, 23, "s"),
AudacityLabel(23, 30, "m"),
]
actual_labels = Util.combine_adjacent_labels_of_the_same_class(input_labels)
self.assertListEqual(expected_labels, actual_labels)
all_files_dict[f] = "m"
random.seed(1111)
all_files_random_keys = random.sample(all_files_dict.keys(),
len(all_files_dict.keys()))
last_seconds = 0
files_to_concatenate = []
labels = []
for v in all_files_random_keys:
duration = float(subprocess.check_output(["soxi", "-D", v]).strip())
segment_start_time = last_seconds
segment_end_time = last_seconds + duration
last_seconds += duration
labels.append(
AudacityLabel(segment_start_time, segment_end_time, all_files_dict[v]))
files_to_concatenate.append(v)
audacity_labels = Util.combine_adjacent_labels_of_the_same_class(labels)
Util.write_audacity_labels(audacity_labels, "gtzan_combined.txt")
command = []
command.append("sox")
command.extend(files_to_concatenate)
command.append("gtzan_combined.wav")
subprocess.check_output(command)
subprocess.call(['chmod', '-R', '777', './music_speech'])
shutil.rmtree("./music_speech")
for f in music_wavs:
all_files_dict[f] = "m"
random.seed(1111)
all_files_random_keys = random.sample(all_files_dict.keys(), len(all_files_dict.keys()))
last_seconds = 0
files_to_concatenate = []
labels = []
for v in all_files_random_keys:
duration = float(subprocess.check_output(["soxi", "-D", v]).strip())
segment_start_time = last_seconds
segment_end_time = last_seconds + duration
last_seconds += duration
labels.append(AudacityLabel(segment_start_time, segment_end_time, all_files_dict[v]))
files_to_concatenate.append(v)
audacity_labels = Util.combine_adjacent_labels_of_the_same_class(labels)
Util.write_audacity_labels(audacity_labels, "gtzan_combined.txt")
command = []
command.append("sox")
command.extend(files_to_concatenate)
command.append("gtzan_combined.wav")
subprocess.check_output(command)
subprocess.call(['chmod', '-R', '777', './music_speech'])
shutil.rmtree("./music_speech")
