def run(self, musicbrainzid, fname):
audioAnalyzer = AudioAnalyzer(verbose=True)
jointAnalyzer = JointAnalyzer(verbose=True)
audioAnalyzer.set_pitch_extractor_params(hop_size=196, bin_resolution=7.5)
# predominant melody extraction
audio_pitch = audioAnalyzer.extract_pitch(fname)
joint_pitch = None
notes = {}
rec_data = dunya.makam.get_recording(musicbrainzid)
for w in rec_data['works']:
symbtr_file = util.docserver_get_symbtrtxt(w['mbid'])
print(symbtr_file)
if symbtr_file:
score_features_file = util.docserver_get_filename(w['mbid'], "scoreanalysis", "metadata", version="0.1")
score_features = json.load(open(score_features_file))
joint_features, features = jointAnalyzer.analyze(
symbtr_file, score_features, fname, audio_pitch)
# redo some steps in audio analysis
features = audioAnalyzer.analyze(
metadata=False, pitch=False, **features)
# get a summary of the analysis
summarized_features = jointAnalyzer.summarize(
score_features=score_features, joint_features=joint_features,
score_informed_audio_features=features)
joint_pitch = summarized_features['audio'].get('pitch', None)
notes[w['mbid']] = summarized_features['joint'].get('notes', None)
if joint_pitch:
audio_pitch = joint_pitch
pitch = [p[1] for p in audio_pitch['pitch']]
# pitches as bytearray
packed_pitch = six.BytesIO()
max_pitch = max(pitch)
temp = [p for p in pitch if p > 0]
min_pitch = min(temp)
height = 255
for p in pitch:
if p < min_pitch:
packed_pitch.write(struct.pack("B", 0))
else:
packed_pitch.write(struct.pack("B", int((p - min_pitch) * 1.0 / (max_pitch - min_pitch) * height)))
output = {}
output['pitch'] = packed_pitch.getvalue()
output['pitchmax'] = {'max': max_pitch, 'min': min_pitch}
return output
def run(self, musicbrainzid, fname):
server_name = socket.gethostname()
subprocess_env = os.environ.copy()
subprocess_env["MCR_CACHE_ROOT"] = "/tmp/emptydir"
subprocess_env["LD_LIBRARY_PATH"] = (
"/mnt/compmusic/%s/MATLAB/MATLAB_Compiler_Runtime/v85/runtime/glnxa64:/mnt/compmusic/%s/MATLAB/MATLAB_Compiler_Runtime/v85/bin/glnxa64:/mnt/compmusic/%s/MATLAB/MATLAB_Compiler_Runtime/v85/sys/os/glnxa64"
% ((server_name,) * 3)
)
# subprocess_env["LD_LIBRARY_PATH"] = "/usr/local/MATLAB/MATLAB_Runtime/v85/runtime/glnxa64:/usr/local/MATLAB/MATLAB_Runtime/v85/bin/glnxa64:/usr/local/MATLAB/MATLAB_Runtime/v85/sys/os/glnxa64/:/usr/local/MATLAB/MATLAB_Runtime/v85/sys/java/jre/glnxa64/jre/lib/amd64/:/usr/local/MATLAB/MATLAB_Runtime/v85/sys/java/jre/glnxa64/jre/lib/amd64/server"
rec_data = dunya.makam.get_recording(musicbrainzid)
if len(rec_data["works"]) == 0:
raise Exception("No work on recording %s" % musicbrainzid)
symbtrtxt = util.docserver_get_symbtrtxt(rec_data["works"][0]["mbid"])
if not symbtrtxt:
raise Exception("No work on recording %s" % musicbrainzid)
metadata = util.docserver_get_filename(rec_data["works"][0]["mbid"], "metadata", "metadata", version="0.1")
mp3file = fname
mlbinary = util.docserver_get_filename(musicbrainzid, "makampitch", "matlab", version="0.6")
output = tempfile.mkdtemp()
proc = subprocess.Popen(
["/srv/dunya/extractTonicTempoTuning %s %s %s %s %s" % (symbtrtxt, metadata, mp3file, mlbinary, output)],
stdout=subprocess.PIPE,
shell=True,
env=subprocess_env,
)
(out, err) = proc.communicate()
ret = {}
expected = ["tempo", "tonic", "tuning"]
for f in expected:
if os.path.isfile(os.path.join(output, f + ".json")):
json_file = open(os.path.join(output, f + ".json"))
ret[f] = json.loads(json_file.read())
json_file.close()
os.remove(os.path.join(output, f + ".json"))
else:
raise Exception("Missing output %s file for %s" % (f, musicbrainzid))
os.rmdir(output)
return ret
def run(self, musicbrainzid, fname):
server_name = socket.gethostname()
subprocess_env = os.environ.copy()
subprocess_env["MCR_CACHE_ROOT"] = "/tmp/emptydir"
subprocess_env["LD_LIBRARY_PATH"] = "/mnt/compmusic/%s/MATLAB/MATLAB_Compiler_Runtime/v85/runtime/glnxa64:/mnt/compmusic/%s/MATLAB/MATLAB_Compiler_Runtime/v85/bin/glnxa64:/mnt/compmusic/%s/MATLAB/MATLAB_Compiler_Runtime/v85/sys/os/glnxa64" % ((server_name,)*3)
#subprocess_env["LD_LIBRARY_PATH"] = "/usr/local/MATLAB/MATLAB_Runtime/v85/runtime/glnxa64:/usr/local/MATLAB/MATLAB_Runtime/v85/bin/glnxa64:/usr/local/MATLAB/MATLAB_Runtime/v85/sys/os/glnxa64/:/usr/local/MATLAB/MATLAB_Runtime/v85/sy s/java/jre/glnxa64/jre/lib/amd64/:/usr/local/MATLAB/MATLAB_Runtime/v85/sys/java/jre/glnxa64/jre/lib/amd64/server"
rec_data = dunya.makam.get_recording(musicbrainzid)
if len(rec_data['works']) == 0:
raise Exception('No work on recording %s' % musicbrainzid)
symbtrtxt =util.docserver_get_symbtrtxt(rec_data['works'][0]['mbid'])
if not symbtrtxt:
raise Exception('No work on recording %s' % musicbrainzid)
metadata = util.docserver_get_filename(rec_data['works'][0]['mbid'], "metadata", "metadata", version="0.1")
tonic = util.docserver_get_filename(musicbrainzid, "tonictempotuning", "tonic", version="0.1")
tempo = util.docserver_get_filename(musicbrainzid, "tonictempotuning", "tempo", version="0.1")
tuning = util.docserver_get_filename(musicbrainzid, "tonictempotuning", "tuning", version="0.1")
melody = util.docserver_get_filename(musicbrainzid, "makampitch", "matlab", version="0.6")
mp3file = fname
output = tempfile.mkdtemp()
print "/srv/dunya/alignAudioScore %s %s '' %s %s %s %s %s %s" % (symbtrtxt, metadata, mp3file, melody, tonic, tempo, tuning, output)
proc = subprocess.Popen(["/srv/dunya/alignAudioScore %s %s '' %s %s %s %s %s %s" % (symbtrtxt, metadata, mp3file, melody, tonic, tempo, tuning, output)], stdout=subprocess.PIPE, shell=True, env=subprocess_env)
(out, err) = proc.communicate()
ret = {}
if os.path.isfile(os.path.join(output, 'alignedNotes.json')):
json_file = open(os.path.join(output, 'alignedNotes.json'))
ret['notesalign'] = json.loads(json_file.read())
json_file.close()
os.remove(os.path.join(output, 'alignedNotes.json'))
if os.path.isfile(os.path.join(output, 'sectionLinks.json')):
json_file = open(os.path.join(output, 'sectionLinks.json'))
ret['sectionlinks'] = json.loads(json_file.read())
json_file.close()
os.remove(os.path.join(output, 'sectionLinks.json'))
os.rmdir(output)
return ret
def run(self, musicbrainzid, fname):
output = {
"works_intervals": {},
"tonic": {},
"pitch": {},
"melodic_progression": {},
"tempo": {},
"pitch_distribution": {},
"pitch_class_distribution": {},
"transposition": {},
"makam": {},
"note_models": {},
"notes": {},
"sections": {}
}
audioAnalyzer = AudioAnalyzer(verbose=True)
jointAnalyzer = JointAnalyzer(verbose=True)
# predominant melody extraction
pitchfile = util.docserver_get_filename(musicbrainzid,
"audioanalysis",
"pitch",
version="0.1")
audio_pitch = json.load(open(pitchfile))
output['pitch'] = None
rec_data = dunya.makam.get_recording(musicbrainzid)
for w in rec_data['works']:
symbtr_file = util.docserver_get_symbtrtxt(w['mbid'])
print symbtr_file
if symbtr_file:
score_features_file = util.docserver_get_filename(
w['mbid'], "scoreanalysis", "metadata", version="0.1")
score_features = json.load(open(score_features_file))
joint_features, features = jointAnalyzer.analyze(
symbtr_file, score_features, fname, audio_pitch)
# redo some steps in audio analysis
features = audioAnalyzer.analyze(metadata=False,
pitch=False,
**features)
# get a summary of the analysis
summarized_features = jointAnalyzer.summarize(
score_features=score_features,
joint_features=joint_features,
score_informed_audio_features=features)
audio_pitch = summarized_features['audio'].get('pitch', None)
pitch = summarized_features['audio'].get('pitch', None)
if pitch:
pitch['pitch'] = pitch['pitch'].tolist()
melodic_progression = features.get('melodic_progression', None)
tonic = features.get('tonic', None)
tempo = features.get('tempo', None)
pitch_distribution = features.get('pitch_distribution', None)
pitch_class_distribution = features.get(
'pitch_class_distribution', None)
transposition = features.get('transposition', None)
makam = features.get('makam', None)
note_models = features.get('note_models', None)
notes = summarized_features['joint'].get('notes', None)
sections = summarized_features['joint'].get('sections', None)
if pitch_distribution:
pitch_distribution = pitch_distribution.to_dict()
if pitch_class_distribution:
pitch_class_distribution = pitch_class_distribution.to_dict(
)
if note_models:
note_models = to_dict(note_models)
if melodic_progression:
AudioSeyirAnalyzer.serialize(melodic_progression)
if notes:
min_interval = 9999
max_interval = 0
for i in notes:
if i['interval'][0] < min_interval:
min_interval = i['interval'][0]
if i['interval'][1] > max_interval:
max_interval = i['interval'][1]
output["works_intervals"][w['mbid']] = {
"from": min_interval,
"to": max_interval
}
output["pitch"] = pitch
output["melodic_progression"] = melodic_progression
output["pitch_distribution"] = pitch_distribution
output["pitch_class_distribution"] = pitch_class_distribution
output["tempo"][w['mbid']] = tempo
output["tonic"][w['mbid']] = tonic
output["transposition"][w['mbid']] = transposition
output["makam"][w['mbid']] = makam
output["note_models"][w['mbid']] = note_models
output["notes"][w['mbid']] = notes
output["sections"][w['mbid']] = sections
return output
def run(self, musicbrainzid, fname):
output = {
"works_intervals": {},
"tonic": {},
"pitch": {},
"melodic_progression": {},
"tempo": {},
"pitch_distribution": {},
"pitch_class_distribution": {},
"transposition": {},
"makam": {},
"note_models": {},
"notes": {},
"sections": {}
}
audioAnalyzer = AudioAnalyzer(verbose=True)
jointAnalyzer = JointAnalyzer(verbose=True)
# predominant melody extraction
pitchfile = util.docserver_get_filename(musicbrainzid, "audioanalysis", "pitch", version="0.1")
audio_pitch = json.load(open(pitchfile))
output['pitch'] = None
rec_data = dunya.makam.get_recording(musicbrainzid)
for w in rec_data['works']:
symbtr_file = util.docserver_get_symbtrtxt(w['mbid'])
print(symbtr_file)
if symbtr_file:
score_features_file = util.docserver_get_filename(w['mbid'], "scoreanalysis", "metadata", version="0.1")
score_features = json.load(open(score_features_file))
joint_features, features = jointAnalyzer.analyze(
symbtr_file, score_features, fname, audio_pitch)
# redo some steps in audio analysis
features = audioAnalyzer.analyze(
metadata=False, pitch=False, **features)
# get a summary of the analysis
summarized_features = jointAnalyzer.summarize(
score_features=score_features, joint_features=joint_features,
score_informed_audio_features=features)
audio_pitch = summarized_features['audio'].get('pitch', None)
pitch = summarized_features['audio'].get('pitch', None)
if pitch:
pitch['pitch'] = pitch['pitch'].tolist()
melodic_progression = features.get('melodic_progression', None)
tonic = features.get('tonic', None)
tempo = features.get('tempo', None)
pitch_distribution = features.get('pitch_distribution', None)
pitch_class_distribution = features.get('pitch_class_distribution', None)
transposition = features.get('transposition', None)
makam = features.get('makam', None)
note_models = features.get('note_models', None)
notes = summarized_features['joint'].get('notes', None)
sections = summarized_features['joint'].get('sections', None)
if pitch_distribution:
pitch_distribution = pitch_distribution.to_dict()
if pitch_class_distribution:
pitch_class_distribution = pitch_class_distribution.to_dict()
if note_models:
note_models = to_dict(note_models)
if melodic_progression:
AudioSeyirAnalyzer.serialize(melodic_progression)
if notes:
min_interval = 9999
max_interval = 0
for i in notes:
if i['interval'][0] < min_interval:
min_interval = i['interval'][0]
if i['interval'][1] > max_interval:
max_interval = i['interval'][1]
output["works_intervals"][w['mbid']] = {"from": min_interval, "to": max_interval}
output["pitch"] = pitch
output["melodic_progression"] = melodic_progression
output["pitch_distribution"] = pitch_distribution
output["pitch_class_distribution"] = pitch_class_distribution
output["tempo"][w['mbid']] = tempo
output["tonic"][w['mbid']] = tonic
output["transposition"][w['mbid']] = transposition
output["makam"][w['mbid']] = makam
output["note_models"][w['mbid']] = note_models
output["notes"][w['mbid']] = notes
output["sections"][w['mbid']] = sections
return output
