def aggregate_all_command(n_tracks):
aggrdata_dir = Path(current_app.config['AGGRDATA_DIR'])
aggrdata_dir.mkdir(parents=True, exist_ok=True)
models = get_models()
for model in models.get_all_offline():
aggregate(model, aggrdata_dir / str(model), n_tracks)
def measure_spread_command(output_dir, n_tracks, n_tags):
output_dir = Path(output_dir)
output_dir.mkdir(parents=True, exist_ok=True)
for model in get_models().get_all_offline():
logging.info(f'Analyzing {model}..')
measure_model_spread(model, output_dir / str(model), n_tracks)
measure_tag_spread(model, output_dir / str(model), n_tags)
def index_all_embeddings(n_trees=16, n_tracks=None, dry=False, force=False):
models = get_models()
for model in models.get_combinations():
index_embeddings(
model,
n_trees, n_tracks, dry, force
)
def extract_all(models_dir, dry=False, force=False):
# TODO: incorporate dry properly
from app.processing.essentia_wrappers import get_embeddings, get_melspecs, get_predictors
app = current_app
audio_dir = Path(app.config['AUDIO_DIR'])
data_root_dir = Path(app.config['DATA_DIR'])
models_dir = Path(models_dir)
models = get_models()
predictors = get_predictors(models_dir, models.data['architectures'])
tracks_to_delete = []
for track in tqdm(Track.get_all()):
audio_file = audio_dir / track.path
if force or not _already_extracted(track, models, data_root_dir):
melspecs = get_melspecs(audio_file, models.data['algorithms'])
embeddings = get_embeddings(melspecs, models.data['architectures'],
predictors)
if embeddings is None:
tracks_to_delete.append(track)
else:
for model_name, embedding in embeddings.items():
embeddings_file = data_root_dir / model_name / track.get_embeddings_filename(
)
if force or not embeddings_file.exists():
embeddings_file.parent.mkdir(parents=True,
exist_ok=True)
np.save(embeddings_file, embedding.astype(np.float16))
for track in tracks_to_delete:
track.delete()
def synthesize_all_spread_metrics_command(input_dir, output_file):
results = {}
# only regular models, no projections
for model in get_models().get_combinations():
logging.info(f'Analyzing {model}..')
results[str(model)] = synthesize_spread_metric(model, input_dir)
df = pd.DataFrame.from_dict(results, orient='index', columns=['std'])
df.to_csv(output_file, float_format=FLOAT_FORMAT)
def app():
app = create_app()
models = get_models()
with app.app_context():
database.create_tables(models)
yield app
database.drop_tables(models)
os.unlink(config_.get('DATABASE').get('database'))
def reduce_all(projection=None, n_tracks=None, dry=False, force=False):
app = current_app
data_dir = Path(app.config['DATA_DIR'])
models = get_models()
if projection is not None:
projection_models = models.get_offline_projections(projection)
else:
projection_models = models.get_all_offline_projections()
for model in projection_models:
logging.info(f'Generating {model}')
reduce(
data_dir / str(model.without_projection()),
data_dir / str(model),
model.projection,
n_tracks, dry, force
)
def measure_hubness(n_tracks, output_file, metric, projection, dimensions, n_jobs, random):
from skhubness import Hubness
tracks = Track.get_all(limit=n_tracks, random=random)
models = get_models()
models_iter = models.get_combinations() if projection is None else models.get_offline_projections(projection)
results = []
for model in list(models_iter):
for _dimensions in tqdm(range(2, dimensions+1), desc=str(model)):
embeddings = model.get_embeddings(tracks, dimensions=slice(_dimensions))
embeddings_stacked = np.vstack(embeddings)
hub = Hubness(k=10, metric=metric, return_value='all', n_jobs=n_jobs)
hub.fit(embeddings_stacked[:, :_dimensions])
result = {key: value for key, value in hub.score().items() if key in RETURN_VALUES}
result.update({
'model': f'{model.dataset}-{model.architecture}',
'layer': model.layer,
'dimensions': _dimensions
})
results.append(result)
results_df = pd.DataFrame(results)
results_df.to_csv(output_file, float_format=FLOAT_FORMAT)
def aggregate_command(dataset, architecture, layer, output_file, projection,
n_tracks):
model = Model(get_models(), dataset, architecture, layer, projection)
aggregate(model, Path(output_file), n_tracks)
def measure_model_spread_command(dataset, architecture, layer, output_dir, n_tracks):
model = Model(get_models().data, dataset, architecture, layer, projection=None)
measure_model_spread(model, Path(output_dir), n_tracks)
def plot_dimension_spread_command(dataset, architecture, layer, projection, input_dir):
model = Model(get_models().data, dataset, architecture, layer, projection=projection)
plot_dimension_spread(model, input_dir)
