def hamming_index(snd_cls, recent_id=None, writeonly=False):
if recent_id:
path = str(HammingIndexPath(recent_id))
else:
path = HammingIndexPath.most_recent_index()
duration_cache = dict()
web_url_cache = dict()
def web_url(doc, ts):
try:
url = web_url_cache[doc._id]
except KeyError:
url = doc.meta['web_url']
web_url_cache[doc._id] = url
return url
def total_duration(doc, ts):
try:
duration = duration_cache[doc._id]
except KeyError:
duration = doc.geom.dimensions[0].end / zounds.Seconds(1)
duration_cache[doc._id] = duration
return duration
logger.debug(
'loading hamming index from {path} in writeonly={writeonly} mode'.
format(**locals()))
return zounds.HammingIndex(snd_cls,
snd_cls.hashed,
version='1',
path=path,
listen=False,
writeonly=writeonly,
web_url=web_url,
total_duration=total_duration)
zounds.Learned,
# this feature will be computed using the learned K-Means clusters
learned=BarkKmeans(),
needs=WithOnsets.bark,
store=True)
pooled = zounds.VariableRateTimeSeriesFeature(
zounds.Pooled,
needs=(bark_kmeans, WithOnsets.slices),
op=np.max,
axis=0,
store=True)
if __name__ == '__main__':
index = zounds.HammingIndex(WithCodes, WithCodes.pooled, listen=True)
zounds.ingest(
zounds.PhatDrumLoops(),
WithOnsets,
multi_threaded=True)
# learn K-Means centroids from the drum hits
if not BarkKmeans.exists():
print('learning K-Means clusters')
BarkKmeans.process(docs=(wo.bark for wo in WithOnsets))
# bark_kmeans = BarkKmeans()
# force the new pooled feature to be computed
for doc in WithCodes:
url = 'https://archive.org/download/FlavioGaete/FlavioGaete22.zip'
filename = os.path.split(urlparse(url).path)[-1]
if not os.path.exists(filename):
resp = requests.get(url, stream=True)
print('Downloading {url} -> {filename}...'.format(**locals()))
with open(filename, 'wb') as f:
for chunk in resp.iter_content(chunk_size=1000000):
f.write(chunk)
return filename
if __name__ == '__main__':
index = zounds.HammingIndex(
WithCodes, WithCodes.bfcc_kmeans_pooled, listen=True)
zip_filename = download_zip_archive()
print('Processing Audio...')
for zf in ff.iter_zip(zip_filename):
if '._' in zf.filename:
continue
try:
print('processing {zf.filename}'.format(**locals()))
WithTimbre.process(
_id=zf.filename, meta=zf, raise_if_exists=True)
except ff.ModelExistsError as e:
print(e)
class Sound(BaseModel):
fake_hash = zounds.ArrayWithUnitsFeature(produce_fake_hash,
needs=BaseModel.fft,
store=True)
if __name__ == '__main__':
zounds.ingest(zounds.InternetArchive('Kevin_Gates_-_By_Any_Means-2014'),
Sound,
multi_threaded=True)
def web_url(doc, ts):
return doc.meta['web_url']
def total_duration(doc, ts):
return doc.fake_hash.dimensions[0].end / zounds.Seconds(1)
index = zounds.HammingIndex(Sound,
Sound.fake_hash,
path='fake_hash_index',
web_url=web_url,
total_duration=total_duration)
if not len(index):
index.add_all()
for i in xrange(1000):
list(index.random_search(n_results=50, sort=True))