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"
boundstat, fldmodel = (None, None)
try:
boundstat = util.docserver_get_filename('d2f729b8-cdc5-4019-ae0d-41695b78ee5b', "trainphraseseg",
"boundstat", version="0.1")
fldmodel = util.docserver_get_filename('d2f729b8-cdc5-4019-ae0d-41695b78ee5b', "trainphraseseg", "fldmodel",
version="0.1")
# boundstat = util.docserver_get_filename('31b52b29-be39-4ccb-98f2-2154140920f9', "trainphraseseg", "boundstat", version="0.1")
# fldmodel = util.docserver_get_filename('31b52b29-be39-4ccb-98f2-2154140920f9', "trainphraseseg", "fldmodel", version="0.1")
except util.NoFileException:
raise Exception('No training files found for recording %s' % musicbrainzid)
files = []
symbtr = compmusic.dunya.makam.get_symbtr(musicbrainzid)
files.append({'path': fname, 'name': symbtr['name']})
fp, files_json = tempfile.mkstemp(".json")
f = open(files_json, 'w')
json.dump(files, f)
f.close()
os.close(fp)
fp, out_json = tempfile.mkstemp(".json")
os.close(fp)
proc = subprocess.Popen(
["/srv/dunya/phraseSeg segmentWrapper %s %s %s %s" % (boundstat, fldmodel, files_json, out_json)],
stdout=subprocess.PIPE, shell=True, env=subprocess_env)
(out, err) = proc.communicate()
ret = {"segments": []}
segments_file = open(out_json, 'r')
segments = segments_file.read()
if segments == "":
segments = "[]"
ret["segments"].append(json.loads(segments))
os.unlink(files_json)
os.unlink(out_json)
return ret
def obtainPitchTonicForRaga(raganame, outputfolder):
if not os.path.exists(outputfolder):
os.makedirs(outputfolder)
#get raga recording mapping
ragaRecordings = obtainRagaRecordingMapping()
print len(ragaRecordings.keys())
for mbid in ragaRecordings[raganame]:
pitchData = util.docserver_get_json(mbid,'pitch','pitch')
tonic = float(util.docserver_get_contents(mbid,'ctonic','tonic'))
HopSize = 196
filename =util.docserver_get_filename(mbid,'mp3')
shutil.copyfile(filename, outputfolder+ '/'+filename.split('/')[-1])
filename = filename.split('/')[-1].split('.')[0]
#TStamps = np.array(range(0,len(pitchData)))*np.float(HopSize)/44100.0
#dump = np.array([TStamps, pitchData]).transpose()
np.savetxt(outputfolder+ '/'+filename+'.pitch', pitchData, delimiter = "\t")
np.savetxt(outputfolder+ '/'+filename+'.tonic', np.array([tonic]), delimiter = "\t")
def run(self, musicbrainzid, fname):
wavfname = util.docserver_get_filename(musicbrainzid, "wav", "wave")
proclist = ["/srv/dunya/PitchCandExt_O3", "-m", "T", "-t", "V", "-i", wavfname]
p = subprocess.Popen(proclist, stdout=subprocess.PIPE)
output = p.communicate()
tonic = output[0]
return {"tonic": str(tonic)}
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):
max_pitch = util.docserver_get_filename(musicbrainzid, "tomatodunya", "pitchmax", part=1, version="0.1")
pitch = json.load(open(max_pitch))
self._f_min = pitch['min']
self._f_max = pitch['max']
ret = super(MakamAudioImage, self).run(musicbrainzid, fname)
try:
pitchfile = util.docserver_get_filename(musicbrainzid, "jointanalysis", "pitch", part=1, version="0.1")
loaded_pitch = json.load(open(pitchfile, 'r'))
# If pitch extraction from jointanalysis failed then load it from audioanlysis
if not loaded_pitch:
pitchfile = util.docserver_get_filename(musicbrainzid, "audioanalysis", "pitch", part=1, version="0.1")
loaded_pitch = json.load(open(pitchfile, 'r'))
except util.NoFileException:
pitchfile = util.docserver_get_filename(musicbrainzid, "audioanalysis", "pitch", part=1, version="0.1")
loaded_pitch = json.load(open(pitchfile, 'r'))
pitch = np.array(loaded_pitch['pitch'])
audioSeyirAnalyzer = audioseyiranalyzer.AudioSeyirAnalyzer()
# compute number of frames from some simple rules set by the user
duration = pitch[-1][0]
min_num_frames = 40
max_frame_dur = 30
frame_dur = duration / min_num_frames if duration / min_num_frames <= max_frame_dur else max_frame_dur
frame_dur = int(5 * round(float(frame_dur) / 5)) # round to 5 seconds
if not frame_dur:
frame_dur = 5
seyir_features = audioSeyirAnalyzer.analyze(pitch, frame_dur=frame_dur, hop_ratio=0.5)
fimage = tempfile.NamedTemporaryFile(mode='w+', suffix=".png")
plot(seyir_features, fimage.name)
fimage.flush()
fileContent = None
with open(fimage.name, mode='rb') as file:
file_content = file.read()
if not file_content:
raise Exception("No image generated")
ret['smallfull'] = file_content
return ret
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 test_derived_file_path(self):
pathder = self.df.full_path_for_part(1)
self.assertEqual("/collectionroot/derived/f5/f522f7c6-8299-44e9-889f-063d37526801/derived/0.1/f522f7c6-8299-44e9-889f-063d37526801-derived-0.1-meta-1.json", pathder)
with self.assertRaises(exceptions.NoFileException) as cm:
pathder = self.df.full_path_for_part(3)
self.assertEqual(cm.exception.message, "partnumber is greater than number of parts")
pathder2 = util.docserver_get_filename("f522f7c6-8299-44e9-889f-063d37526801", "derived", "meta", "2")
self.assertEqual("/collectionroot/derived/f5/f522f7c6-8299-44e9-889f-063d37526801/derived/0.1/f522f7c6-8299-44e9-889f-063d37526801-derived-0.1-meta-2.json", pathder2)
def run(self, musicbrainzid, fname):
wavfname = util.docserver_get_filename(musicbrainzid, "wav", "wave")
proclist = [
"/srv/dunya/PitchCandExt_O3", "-m", "T", "-t", "V", "-i", wavfname
]
p = subprocess.Popen(proclist, stdout=subprocess.PIPE)
output = p.communicate()
tonic = output[0]
return {"tonic": str(tonic)}
def run(self, musicbrainzid, fname):
output = super(DunyaPitchMakam, self).run(musicbrainzid, fname)
# Compute the pitch octave correction
tonicfile = util.docserver_get_filename(musicbrainzid, "tonictempotuning", "tonic", version="0.1")
alignednotefile = util.docserver_get_filename(musicbrainzid, "scorealign", "notesalign", version="0.1")
pitch = array(output['pitch'])
out_pitch = [p[1] for p in output["pitch"]]
tonic = json.load(open(tonicfile, 'r'))['scoreInformed']['Value']
notes = json.load(open(alignednotefile, 'r'))['notes']
pitch_corrected, synth_pitch, notes = alignedpitchfilter.correctOctaveErrors(pitch, notes, tonic)
output["pitch_corrected"] = pitch_corrected
output["pitch"] = out_pitch
del output["matlab"]
del output["settings"]
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/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, fname):
yamltonic = self.get_from_file(self.musicbrainz_id)
if yamltonic:
print "Got tonic from a yaml file"
tonic = yamltonic
else:
print "Need to calculate the tonic from scratch"
wavfname = util.docserver_get_filename(self.musicbrainz_id, "wav", "wave")
proclist = ["/srv/dunya/PitchCandExt_O3", "-m", "T", "-t", "V", "-i", wavfname]
p = subprocess.Popen(proclist, stdout=subprocess.PIPE)
output = p.communicate()
tonic = output[0]
return {"tonic": str(tonic)}
def test_derived_file_path(self):
pathder = self.df.full_path_for_part(1)
self.assertEqual(
"/collectionroot/derived/f5/f522f7c6-8299-44e9-889f-063d37526801/derived/0.1/f522f7c6-8299-44e9-889f-063d37526801-derived-0.1-meta-1.json",
pathder)
with self.assertRaises(exceptions.NoFileException) as cm:
pathder = self.df.full_path_for_part(3)
self.assertEqual(cm.exception.message,
"partnumber is greater than number of parts")
pathder2 = util.docserver_get_filename(
"f522f7c6-8299-44e9-889f-063d37526801", "derived", "meta", "2")
self.assertEqual(
"/collectionroot/derived/f5/f522f7c6-8299-44e9-889f-063d37526801/derived/0.1/f522f7c6-8299-44e9-889f-063d37526801-derived-0.1-meta-2.json",
pathder2)
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):
max_pitch = util.docserver_get_filename(musicbrainzid,
"tomatodunya",
"pitchmax",
part=1,
version="0.1")
pitch = json.load(open(max_pitch))
self._f_min = pitch['min']
self._f_max = pitch['max']
ret = super(MakamAudioImage, self).run(musicbrainzid, fname)
try:
pitchfile = util.docserver_get_filename(musicbrainzid,
"jointanalysis",
"pitch",
part=1,
version="0.1")
loaded_pitch = json.load(open(pitchfile, 'r'))
# If pitch extraction from jointanalysis failed then load it from audioanlysis
if not loaded_pitch:
pitchfile = util.docserver_get_filename(musicbrainzid,
"audioanalysis",
"pitch",
part=1,
version="0.1")
loaded_pitch = json.load(open(pitchfile, 'r'))
except util.NoFileException:
pitchfile = util.docserver_get_filename(musicbrainzid,
"audioanalysis",
"pitch",
part=1,
version="0.1")
loaded_pitch = json.load(open(pitchfile, 'r'))
pitch = np.array(loaded_pitch['pitch'])
audioSeyirAnalyzer = audioseyiranalyzer.AudioSeyirAnalyzer()
# compute number of frames from some simple rules set by the user
duration = pitch[-1][0]
min_num_frames = 40
max_frame_dur = 30
frame_dur = duration / min_num_frames if duration / min_num_frames <= max_frame_dur else max_frame_dur
frame_dur = int(5 * round(float(frame_dur) / 5)) # round to 5 seconds
if not frame_dur:
frame_dur = 5
seyir_features = audioSeyirAnalyzer.analyze(pitch,
frame_dur=frame_dur,
hop_ratio=0.5)
fimage = tempfile.NamedTemporaryFile(mode='w+', suffix=".png")
plot(seyir_features, fimage.name)
fimage.flush()
fileContent = None
with open(fimage.name, mode='rb') as file:
file_content = file.read()
if not file_content:
raise Exception("No image generated")
ret['smallfull'] = file_content
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):
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"
boundstat, fldmodel = (None, None)
try:
boundstat = util.docserver_get_filename(
'd2f729b8-cdc5-4019-ae0d-41695b78ee5b',
"trainphraseseg",
"boundstat",
version="0.1")
fldmodel = util.docserver_get_filename(
'd2f729b8-cdc5-4019-ae0d-41695b78ee5b',
"trainphraseseg",
"fldmodel",
version="0.1")
#boundstat = util.docserver_get_filename('31b52b29-be39-4ccb-98f2-2154140920f9', "trainphraseseg", "boundstat", version="0.1")
#fldmodel = util.docserver_get_filename('31b52b29-be39-4ccb-98f2-2154140920f9', "trainphraseseg", "fldmodel", version="0.1")
except util.NoFileException:
raise Exception('No training files found for recording %s' %
musicbrainzid)
files = []
symbtr = compmusic.dunya.makam.get_symbtr(musicbrainzid)
files.append({'path': fname, 'name': symbtr['name']})
fp, files_json = tempfile.mkstemp(".json")
f = open(files_json, 'w')
json.dump(files, f)
f.close()
os.close(fp)
fp, out_json = tempfile.mkstemp(".json")
os.close(fp)
proc = subprocess.Popen([
"/srv/dunya/phraseSeg segmentWrapper %s %s %s %s" %
(boundstat, fldmodel, files_json, out_json)
],
stdout=subprocess.PIPE,
shell=True,
env=subprocess_env)
(out, err) = proc.communicate()
ret = {"segments": []}
segments_file = open(out_json, 'r')
segments = segments_file.read()
if segments == "":
segments = "[]"
ret["segments"].append(json.loads(segments))
os.unlink(files_json)
os.unlink(out_json)
return ret
