def process_track(file_struct, boundaries_id, labels_id, config):
# Only analize files with annotated beats
if config["annot_beats"]:
jam = jams2.load(file_struct.ref_file)
if len(jam.beats) > 0 and len(jam.beats[0].data) > 0:
pass
else:
logging.warning("No beat information in file %s" %
file_struct.ref_file)
return
logging.info("Segmenting %s" % file_struct.audio_file)
# Compute features if needed
if not os.path.isfile(file_struct.features_file):
featextract.compute_all_features(file_struct)
# Get estimations
est_times, est_labels = run_algorithms(file_struct.audio_file,
boundaries_id, labels_id, config)
# Save
logging.info("Writing results in: %s" % file_struct.est_file)
est_inters = utils.times_to_intervals(est_times)
io.save_estimations(file_struct.est_file, est_inters, est_labels,
boundaries_id, labels_id, **config)
return est_times, est_labels
def process_track(file_struct, boundaries_id, labels_id, config,
annotator_id=0, plot=False):
"""Prepares the parameters, runs the algorithms, and saves results.
Parameters
----------
file_struct: Object
FileStruct containing the paths of the input files (audio file,
features file, reference file, output estimation file).
boundaries_id: str
Identifier of the boundaries algorithm to use ("gt" for ground truth).
labels_id: str
Identifier of the labels algorithm to use (None for not labeling).
config: dict
Dictionary containing the custom parameters of the algorithms to use.
annotator_id: int
Annotator identificator in the ground truth.
Returns
-------
est_times: np.array
List of estimated times for the segment boundaries.
est_labels: np.array
List of all the labels associated segments.
"""
# Only analize files with annotated beats
if config["annot_beats"]:
jam = jams2.load(file_struct.ref_file)
if len(jam.beats) > 0 and len(jam.beats[0].data) > 0:
pass
else:
logging.warning("No beat information in file %s" %
file_struct.ref_file)
return
logging.info("Segmenting %s" % file_struct.audio_file)
# Compute features if needed
if not os.path.isfile(file_struct.features_file):
featextract.compute_all_features(file_struct)
# Get estimations
est_times, est_labels = run_algorithms(file_struct.audio_file,
boundaries_id, labels_id, config,
annotator_id=annotator_id)
# Save
logging.info("Writing results in: %s" % file_struct.est_file)
io.save_estimations(file_struct.est_file, est_times, est_labels,
boundaries_id, labels_id, **config)
if plot:
audio_name = splitext(basename(file_struct.audio_file))[0]
plot_name = join(dirname(dirname(file_struct.audio_file)), 'plots', audio_name+'_'+'mfcc'+'_'+boundaries_id+'.pdf')
dataset_name = basename(dirname(dirname(file_struct.audio_file)))
plotting.plot_one_track(plot_name, file_struct, est_times, est_labels, boundaries_id, labels_id, dataset_name)
return est_times, est_labels
def process_track(file_struct, boundaries_id, labels_id, config,
annotator_id=0):
"""Prepares the parameters, runs the algorithms, and saves results.
Parameters
----------
file_struct: Object
FileStruct containing the paths of the input files (audio file,
features file, reference file, output estimation file).
boundaries_id: str
Identifier of the boundaries algorithm to use ("gt" for ground truth).
labels_id: str
Identifier of the labels algorithm to use (None for not labeling).
config: dict
Dictionary containing the custom parameters of the algorithms to use.
annotator_id: int
Annotator identificator in the ground truth.
Returns
-------
est_times: np.array
List of estimated times for the segment boundaries.
est_labels: np.array
List of all the labels associated segments.
"""
# Only analize files with annotated beats
if config["annot_beats"]:
jam = jams.load(file_struct.ref_file)
annots = jam.search(namespace="beat.*")
if not annots:
logging.warning("No beat information in file %s" %
file_struct.ref_file)
return np.array([]), np.array([])
logging.info("Segmenting %s" % file_struct.audio_file)
# Compute features if needed
if not os.path.isfile(file_struct.features_file):
featextract.compute_all_features(file_struct)
# Get estimations
est_times, est_labels = run_algorithms(file_struct.audio_file,
boundaries_id, labels_id, config,
annotator_id=annotator_id)
# Save
logging.info("Writing results in: %s" % file_struct.est_file)
io.save_estimations(file_struct, est_times, est_labels,
boundaries_id, labels_id, **config)
return est_times, est_labels
def process_track(file_struct,
boundaries_id,
labels_id,
config,
annotator_id=0):
"""Prepares the parameters, runs the algorithms, and saves results.
Parameters
----------
file_struct: `msaf.io.FileStruct`
FileStruct containing the paths of the input files (audio file,
features file, reference file, output estimation file).
boundaries_id: str
Identifier of the boundaries algorithm to use ("gt" for ground truth).
labels_id: str
Identifier of the labels algorithm to use (None for not labeling).
config: dict
Dictionary containing the custom parameters of the algorithms to use.
annotator_id: int
Annotator identificator in the ground truth.
Returns
-------
est_times: np.array
List of estimated times for the segment boundaries.
est_labels: np.array
List of all the labels associated segments.
"""
logging.info("Segmenting %s" % file_struct.audio_file)
# Get features
config["features"] = Features.select_features(config["feature"],
file_struct,
config["annot_beats"],
config["framesync"])
# Get estimations
est_times, est_labels = run_algorithms(file_struct,
boundaries_id,
labels_id,
config,
annotator_id=annotator_id)
# Save
logging.info("Writing results in: %s" % file_struct.est_file)
io.save_estimations(file_struct, est_times, est_labels, boundaries_id,
labels_id, **config)
return est_times, est_labels
def process_track(file_struct, boundaries_id, labels_id, config,
annotator_id=0):
"""Prepares the parameters, runs the algorithms, and saves results.
Parameters
----------
file_struct: `msaf.io.FileStruct`
FileStruct containing the paths of the input files (audio file,
features file, reference file, output estimation file).
boundaries_id: str
Identifier of the boundaries algorithm to use ("gt" for ground truth).
labels_id: str
Identifier of the labels algorithm to use (None for not labeling).
config: dict
Dictionary containing the custom parameters of the algorithms to use.
annotator_id: int
Annotator identificator in the ground truth.
Returns
-------
est_times: np.array
List of estimated times for the segment boundaries.
est_labels: np.array
List of all the labels associated segments.
"""
logging.info("Segmenting %s" % file_struct.audio_file)
# Get features
config["features"] = Features.select_features(
config["feature"], file_struct, config["annot_beats"],
config["framesync"])
# Get estimations
est_times, est_labels = run_algorithms(file_struct,
boundaries_id, labels_id, config,
annotator_id=annotator_id)
# Save
logging.info("Writing results in: %s" % file_struct.est_file)
io.save_estimations(file_struct, est_times, est_labels,
boundaries_id, labels_id, **config)
return est_times, est_labels
def process(
in_path,
annot_beats=False,
feature="hpcp",
ds_name="*",
framesync=False,
boundaries_id=msaf.DEFAULT_BOUND_ID,
labels_id=msaf.DEFAULT_LABEL_ID,
hier=False,
sonify_bounds=False,
plot=False,
n_jobs=4,
annotator_id=0,
config=None,
out_bounds="out_bounds.wav",
out_sr=22050,
):
"""Main process to segment a file or a collection of files.
Parameters
----------
in_path: str
Input path. If a directory, MSAF will function in collection mode.
If audio file, MSAF will be in single file mode.
annot_beats: bool
Whether to use annotated beats or not. Only available in collection
mode.
feature: str
String representing the feature to be used (e.g. hpcp, mfcc, tonnetz)
ds_name: str
Prefix of the dataset to be used (e.g. SALAMI, Isophonics)
framesync: str
Whether to use framesync features or not (default: False -> beatsync)
boundaries_id: str
Identifier of the boundaries algorithm (use "gt" for groundtruth)
labels_id: str
Identifier of the labels algorithm (use None to not compute labels)
hier : bool
Whether to compute a hierarchical or flat segmentation.
sonify_bounds: bool
Whether to write an output audio file with the annotated boundaries
or not (only available in Single File Mode).
plot: bool
Whether to plot the boundaries and labels against the ground truth.
n_jobs: int
Number of processes to run in parallel. Only available in collection
mode.
annotator_id: int
Annotator identificator in the ground truth.
config: dict
Dictionary containing custom configuration parameters for the
algorithms. If None, the default parameters are used.
out_bounds: str
Path to the output for the sonified boundaries (only in single file
mode, when sonify_bounds is True.
out_sr : int
Sampling rate for the sonified bounds.
Returns
-------
results : list
List containing tuples of (est_times, est_labels) of estimated
boundary times and estimated labels.
If labels_id is None, est_labels will be a list of -1.
"""
# Seed random to reproduce results
np.random.seed(123)
# Make sure that the features used are correct
assert feature in msaf.AVAILABLE_FEATS
# Set up configuration based on algorithms parameters
if config is None:
config = io.get_configuration(feature, annot_beats, framesync, boundaries_id, labels_id)
config["features"] = None
# Save multi-segment (hierarchical) configuration
config["hier"] = hier
if os.path.isfile(in_path):
# Single file mode
# Get (if they exitst) or compute features
file_struct = msaf.io.FileStruct(in_path)
if not os.path.exists(file_struct.features_file):
# Compute and save features
all_features = featextract.compute_features_for_audio_file(in_path)
msaf.utils.ensure_dir(os.path.dirname(file_struct.features_file))
msaf.featextract.save_features(file_struct.features_file, all_features)
# Get correct features
config["features"] = msaf.io.get_features(in_path, annot_beats=annot_beats, framesync=framesync)
# And run the algorithms
est_times, est_labels = run_algorithms(in_path, boundaries_id, labels_id, config, annotator_id=annotator_id)
if sonify_bounds:
logging.info("Sonifying boundaries in %s..." % out_bounds)
audio_hq, sr = librosa.load(in_path, sr=out_sr)
utils.sonify_clicks(audio_hq, est_times, out_bounds, out_sr)
if plot:
plotting.plot_one_track(file_struct, est_times, est_labels, boundaries_id, labels_id, ds_name)
# Save estimations
msaf.utils.ensure_dir(os.path.dirname(file_struct.est_file))
io.save_estimations(file_struct, est_times, est_labels, boundaries_id, labels_id, **config)
return est_times, est_labels
else:
# Collection mode
file_structs = io.get_dataset_files(in_path, ds_name)
# Call in parallel
return Parallel(n_jobs=n_jobs)(
delayed(process_track)(file_struct, boundaries_id, labels_id, config, annotator_id=annotator_id)
for file_struct in file_structs[:]
)
def process(in_path, annot_beats=False, feature="mfcc", ds_name="*",
framesync=False, boundaries_id="gt", labels_id=None, hier=False,
sonify_bounds=False, plot=False, n_jobs=4, annotator_id=0,
config=None, out_bounds="out_bounds.wav"):
"""Main process to segment a file or a collection of files.
Parameters
----------
in_path: str
Input path. If a directory, MSAF will function in collection mode.
If audio file, MSAF will be in single file mode.
annot_beats: bool
Whether to use annotated beats or not. Only available in collection
mode.
feature: str
String representing the feature to be used (e.g. hpcp, mfcc, tonnetz)
ds_name: str
Prefix of the dataset to be used (e.g. SALAMI, Isophonics)
framesync: str
Whether to use framesync features or not (default: False -> beatsync)
boundaries_id: str
Identifier of the boundaries algorithm (use "gt" for groundtruth)
labels_id: str
Identifier of the labels algorithm (use None to not compute labels)
hier : bool
Whether to compute a hierarchical or flat segmentation.
sonify_bounds: bool
Whether to write an output audio file with the annotated boundaries
or not (only available in Single File Mode).
plot: bool
Whether to plot the boundaries and labels against the ground truth.
n_jobs: int
Number of processes to run in parallel. Only available in collection
mode.
annotator_id: int
Annotator identificator in the ground truth.
config: dict
Dictionary containing custom configuration parameters for the
algorithms. If None, the default parameters are used.
out_bounds: str
Path to the output for the sonified boundaries (only in single file
mode, when sonify_bounds is True.
Returns
-------
results : list
List containing tuples of (est_times, est_labels) of estimated
boundary times and estimated labels.
If labels_id is None, est_labels will be a list of -1.
"""
# Seed random to reproduce results
np.random.seed(123)
# Set up configuration based on algorithms parameters
if config is None:
config = io.get_configuration(feature, annot_beats, framesync,
boundaries_id, labels_id)
config["features"] = None
config["hier"] = hier
if os.path.isfile(in_path):
# Single file mode
# Get (if they exitst) or compute features
# TODO:Modularize!
file_struct = msaf.io.FileStruct(in_path)
if os.path.exists(file_struct.features_file):
feat_prefix = ""
if not framesync:
feat_prefix = "bs_"
features = {}
'''Mi: added the Gammatone featureset'''
features["%shpcp" % feat_prefix], features["%smfcc" % feat_prefix], \
features["%stonnetz" % feat_prefix], features["%scqt" % feat_prefix], \
features["%sgmt" % feat_prefix], features["beats"], dur, \
features["anal"] = msaf.io.get_features(in_path,
annot_beats=annot_beats,
framesync=framesync,
pre_features=None)
else:
# Compute and save features
features = featextract.compute_features_for_audio_file(in_path)
msaf.utils.ensure_dir(os.path.dirname(file_struct.features_file))
msaf.featextract.save_features(file_struct.features_file, features)
config["features"] = features
config["hier"] = hier
# And run the algorithms
est_times, est_labels = run_algorithms(in_path, boundaries_id,
labels_id, config,
annotator_id=annotator_id)
if sonify_bounds:
logging.info("Sonifying boundaries in %s..." % out_bounds)
fs = 44100
audio_hq, sr = librosa.load(in_path, sr=fs)
utils.sonify_clicks(audio_hq, est_times, out_bounds, fs)
if plot:
plotting.plot_one_track(save_plot_path, file_struct, est_times, est_labels,
boundaries_id, labels_id, ds_name)
# Save estimations
msaf.utils.ensure_dir(os.path.dirname(file_struct.est_file))
config["features"] = None
io.save_estimations(file_struct.est_file, est_times, est_labels,
boundaries_id, labels_id, **config)
return est_times, est_labels
else:
# Collection mode
file_structs = io.get_dataset_files(in_path, ds_name)
# Call in parallel
return Parallel(n_jobs=n_jobs)(delayed(process_track)(
file_struct, boundaries_id, labels_id, config,
annotator_id=annotator_id, plot=plot) for file_struct in file_structs[:])
def process(in_path,
annot_beats=False,
feature="pcp",
framesync=False,
boundaries_id=msaf.config.default_bound_id,
labels_id=msaf.config.default_label_id,
hier=False,
sonify_bounds=False,
plot=False,
n_jobs=4,
annotator_id=0,
config=None,
out_bounds="out_bounds.wav",
out_sr=22050,
output_file=None):
"""Main process to segment a file or a collection of files.
Parameters
----------
in_path: str
Input path. If a directory, MSAF will function in collection mode.
If audio file, MSAF will be in single file mode.
annot_beats: bool
Whether to use annotated beats or not.
feature: str
String representing the feature to be used (e.g. pcp, mfcc, tonnetz)
framesync: str
Whether to use framesync features or not (default: False -> beatsync)
boundaries_id: str
Identifier of the boundaries algorithm (use "gt" for groundtruth)
labels_id: str
Identifier of the labels algorithm (use None to not compute labels)
hier : bool
Whether to compute a hierarchical or flat segmentation.
sonify_bounds: bool
Whether to write an output audio file with the annotated boundaries
or not (only available in Single File Mode).
plot: bool
Whether to plot the boundaries and labels against the ground truth.
n_jobs: int
Number of processes to run in parallel. Only available in collection
mode.
annotator_id: int
Annotator identificator in the ground truth.
config: dict
Dictionary containing custom configuration parameters for the
algorithms. If None, the default parameters are used.
out_bounds: str
Path to the output for the sonified boundaries (only in single file
mode, when sonify_bounds is True.
out_sr : int
Sampling rate for the sonified bounds.
Returns
-------
results : list
List containing tuples of (est_times, est_labels) of estimated
boundary times and estimated labels.
If labels_id is None, est_labels will be a list of -1.
"""
# Seed random to reproduce results
np.random.seed(123)
# Set up configuration based on algorithms parameters
if config is None:
config = io.get_configuration(feature, annot_beats, framesync,
boundaries_id, labels_id)
config["features"] = None
# Save multi-segment (hierarchical) configuration
config["hier"] = hier
if not os.path.exists(in_path):
raise NoAudioFileError("File or directory does not exists, %s" %
in_path)
if os.path.isfile(in_path):
# Single file mode
# Get (if they exitst) or compute features
file_struct = msaf.io.FileStruct(in_path)
# Use temporary file in single mode
file_struct.features_file = msaf.config.features_tmp_file
# Get features
config["features"] = Features.select_features(feature, file_struct,
annot_beats, framesync)
# And run the algorithms
est_times, est_labels = run_algorithms(file_struct,
boundaries_id,
labels_id,
config,
annotator_id=annotator_id)
if sonify_bounds:
logging.info("Sonifying boundaries in %s..." % out_bounds)
audio_hq, sr = librosa.load(in_path, sr=out_sr)
utils.sonify_clicks(audio_hq, est_times, out_bounds, out_sr)
if plot:
custom_plotting.plot_one_track(file_struct,
est_times,
est_labels,
boundaries_id,
labels_id,
output_file=output_file)
# TODO: Only save if needed
# Save estimations
msaf.utils.ensure_dir(os.path.dirname(file_struct.est_file))
io.save_estimations(file_struct, est_times, est_labels, boundaries_id,
labels_id, **config)
return est_times, est_labels
else:
# Collection mode
file_structs = io.get_dataset_files(in_path)
return Parallel(n_jobs=n_jobs)(
delayed(process_track)(file_struct,
boundaries_id,
labels_id,
config,
annotator_id=annotator_id)
for file_struct in file_structs[:])
