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Fixed rematching flag in kdtree and added match algorithm selection flag

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This commit is contained in:
Pezz89
2016-03-25 12:38:40 +00:00
parent 997a91ce1b
commit fec05bbcb7
4 changed files with 59 additions and 22 deletions
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src/sppysound/concatenator.py
+28 -2
View File
@@ -14,6 +14,14 @@ import json
modpath = sys.argv[0]
modpath = os.path.splitext(modpath)[0]+'.log'
class SmartFormatter(argparse.HelpFormatter):
def _split_lines(self, text, width):
# this is the RawTextHelpFormatter._split_lines
if text.startswith('R|'):
return text[2:].splitlines()
return argparse.HelpFormatter._split_lines(self, text, width)
def parse_sub_args(args, analysis):
try:
args = getattr(args, analysis)
@@ -53,6 +61,7 @@ def parse_arguments():
# TODO: Write program description.
parser = argparse.ArgumentParser(
description='',
formatter_class=SmartFormatter
)
parser.add_argument(
@@ -157,7 +166,7 @@ def parse_arguments():
parser.add_argument(
"--enforcef0",
action="store_true",
help="This flag enables pitch shifting of matched grainsto better match the target."
help="This flag enables pitch shifting of matched grains to better match the target."
)
parser.add_argument(
@@ -166,12 +175,24 @@ def parse_arguments():
help="This flag enables scaling of matched grains to better match the target's volume."
)
parser.add_argument(
"--match_method",
type=str,
metavar='',
help="R|Choose the algorithm to use when matching analyses. Available "
"algorithms are:\nBrute force: \'bruteforce\'\nK-d Tree Search: "
"'kdtree'",
)
parser.add_argument('--verbose', '-v', action='count')
args = parser.parse_args()
for item in config_items:
parse_sub_args(args, item)
if args.match_method:
config.matcher["method"] = args.match_method
if args.rematch:
config.matcher["rematch"] = True
@@ -251,9 +272,14 @@ def main():
rematch=args.rematch
)
match_method_dict = {
'bruteforce': matcher.brute_force_matcher,
'kdtree': matcher.kdtree_matcher
}
# Perform matching on databases using the method specified.
matcher.match(
matcher.knn_matcher,
match_method_dict[config.matcher["method"]],
grain_size=config.matcher["grain_size"],
overlap=config.matcher["overlap"]
)
src/sppysound/config.py
+10 -7
View File
@@ -1,21 +1,21 @@
rms = {
"window_size": 70,
"overlap": 2,
"overlap": 8,
}
variance = {
"window_size": 70,
"overlap": 2
"overlap": 8
}
kurtosis = {
"window_size": 70,
"overlap": 2
"overlap": 8
}
skewness = {
"window_size": 70,
"overlap": 2
"overlap": 8
}
fft = {
@@ -64,10 +64,13 @@ analysis = {
matcher = {
"rematch": True,
"grain_size": 70,
"overlap": 2,
"overlap": 8,
# Defines the number of matches to keep for synthesis. Note that this must
# also be specified in the synthesis config
"match_quantity": 1
"match_quantity": 1,
# Choose the algorithm used to perform matching. kdtree is recommended for
# larger datasets.
"method": 'kdtree'
}
synthesizer = {
@@ -76,7 +79,7 @@ synthesizer = {
"enforce_f0": True,
"enf_f0_ratio_limit": 10.,
"grain_size": 70,
"overlap": 2,
"overlap": 8,
"normalize" : True,
# Defines the number of potential grains to choose from matches when
# synthesizing output.
src/sppysound/database.py
+14 -12
View File
@@ -355,18 +355,16 @@ class Matcher:
grain_indexes[:, 0] = grain_indexes[:, 1] - grain_indexes[:, 0]
return grain_indexes
def knn_matcher(self, grain_size, overlap):
def kdtree_matcher(self, grain_size, overlap):
# Count grains of the source database
source_sample_indexes = self.count_grains(self.source_db, grain_size, overlap)
try:
self.output_db.data.create_group("match")
except ValueError:
self.logger.debug("Match group already exists in the {0} HDF5 file.".format(self.output_db))
self.logger.info("Match group already exists in the {0} HDF5 file.".format(self.output_db))
if self.rematch:
self.output_db.data["match"].clear()
#
final_match_indexes = []
if self.config:
weightings = self.config.matcher_weightings
@@ -374,6 +372,14 @@ class Matcher:
weightings = {x: 1. for x in self.matcher_analyses}
for tind, target_entry in enumerate(self.target_db.analysed_audio):
# Check if match data already exists and use it rather than
# regenerating if it does.
if target_entry.name in self.output_db.data["match"].keys():
self.logger.info("Match data already exists for {0}. Using this "
"data. Run with the \'--rematch\' flag to "
"overwrite.".format(self.output_db))
continue
# Create an array of grain times for target sample
target_times = target_entry.generate_grain_times(grain_size, overlap, save_times=True)
x_size = target_times.shape[0]
@@ -381,6 +387,7 @@ class Matcher:
match_vals = np.empty((x_size, self.match_quantity))
match_vals.fill(np.inf)
# Allocate memory for target analyses.
all_target_analyses = np.empty((len(self.matcher_analyses), target_times.shape[0]))
for i, analysis in enumerate(self.matcher_analyses):
@@ -390,10 +397,8 @@ class Matcher:
all_target_analyses[i] = target_data
imp = Imputer(axis=1)
# Impute values for Nans
all_target_analyses = imp.fit_transform(all_target_analyses)
# all_target_analyses[np.isnan(all_target_analyses)] = np.inf
# all_target_analyses = np.nan_to_num(all_target_analyses)
for sind, source_entry in enumerate(self.source_db.analysed_audio):
# Create an array of grain times for source sample
@@ -406,10 +411,9 @@ class Matcher:
source_data, s = source_entry.analysis_data_grains(source_times, analysis, format=analysis_formatting)
all_source_analyses[i] = source_data
self.logger.info("Matching \"{0}\" for: {1} to {2}".format(analysis, source_entry.name, target_entry.name))
# all_source_analyses[np.isnan(all_source_analyses)] = np.inf
# all_source_analyses = np.nan_to_num(all_source_analyses)
self.logger.info("K-d Tree Matching: {0} to {1}".format(source_entry.name, target_entry.name))
# Impute values for Nans
all_source_analyses = imp.fit_transform(all_source_analyses)
source_tree = spatial.cKDTree(all_source_analyses.T, leafsize=100)
@@ -458,8 +462,6 @@ class Matcher:
if self.rematch:
self.output_db.data["match"].clear()
#
final_match_indexes = []
if self.config:
weightings = self.config.matcher_weightings
src/sppysound/docs/api.rst
+7 -1
View File
@@ -86,7 +86,13 @@ concatenate.py Script Usage
--enforcerms This flag enables scaling of matched grains to better
match the target's volume.
--match_method Choose the algorithm to use when matching analyses. Available algorithms are:
Brute force: 'bruteforce'
K-d Tree Search: 'kdtree'
--verbose, -v Specify the verbosity of the script's output. Additional
v will produce greater levels of detail ie. -vvvvv will
produce all messages.