import datetime
import h5py
import librosa
import numpy as np
import os
import sys
import time
import localmodule
# Define constants.
data_dir = localmodule.get_data_dir()
dataset_name = localmodule.get_dataset_name()
sample_rate = localmodule.get_sample_rate()
args = sys.argv[1:]
aug_str = args[0]
instance_id = int(args[1])
instance_str = str(instance_id)
unit_str = args[2]
if aug_str == "original":
instanced_aug_str = aug_str
else:
instanced_aug_str = "-".join([aug_str, instance_str])
pcen_settings = localmodule.get_pcen_settings()
# Print header.
start_time = int(time.time())
print(str(datetime.datetime.now()) + " Start.")
print("Computing per-channel energy normalization (PCEN) for " +\
dataset_name + " clips, with domain-specific librosa parameters.")
print("Unit: " + unit_str + ".")
def multiplex_lms_with_background(aug_kind_str, fold_units, n_input_hops,
batch_size):
# Define constants.
aug_dict = localmodule.get_augmentations()
data_dir = localmodule.get_data_dir()
dataset_name = localmodule.get_dataset_name()
tfr_name = "_".join([dataset_name, "clip-logmelspec"])
tfr_dir = os.path.join(data_dir, tfr_name)
bg_name = "_".join([dataset_name, "clip-logmelspec-backgrounds"])
bg_dir = os.path.join(data_dir, bg_name)
T_str = "T-" + str(bg_duration).zfill(4)
T_dir = os.path.join(bg_dir, T_str)
# Parse augmentation kind string (aug_kind_str).
if aug_kind_str == "none":
augs = ["original"]
elif aug_kind_str == "pitch":
augs = ["original", "pitch"]
elif aug_kind_str == "stretch":
augs = ["original", "stretch"]
elif aug_kind_str == "all-but-noise":
augs = ["original", "pitch", "stretch"]
else:
noise_augs = ["noise-" + unit_str for unit_str in fold_units]
if aug_kind_str == "all":
augs = noise_augs + ["original", "pitch", "stretch"]
elif aug_kind_str == "noise":
augs = noise_augs + ["original"]
# Loop over augmentations.
streams = []
for aug_str in augs:
# Define instances.
aug_dir = os.path.join(tfr_dir, aug_str)
if aug_str == "original":
instances = [aug_str]
else:
n_instances = aug_dict[aug_str]
instances = [
"-".join([aug_str, str(instance_id)])
for instance_id in range(n_instances)
]
# Define bias.
if aug_str[:5] == "noise":
bias = np.float32(-17.0)
else:
bias = np.float32(0.0)
# Loop over instances.
for instanced_aug_str in instances:
# Loop over units.
for unit_str in fold_units:
# Define path to time-frequency representation.
lms_name = "_".join(
[dataset_name, instanced_aug_str, unit_str])
lms_path = os.path.join(aug_dir, lms_name + ".hdf5")
# Define path to background.
bg_name = "_".join([
dataset_name, "background_summaries", unit_str,
T_str + ".hdf5"
])
bg_path = os.path.join(T_dir, bg_name)
# Define pescador streamer.
stream = pescador.Streamer(yield_lms_and_background, lms_path,
n_input_hops, bias, bg_path)
streams.append(stream)
# Multiplex streamers together.
mux = pescador.Mux(streams,
k=len(streams),
lam=None,
with_replacement=True,
revive=True)
# Create buffered streamer with specified batch size.
buffered_streamer = pescador.BufferedStreamer(mux, batch_size)
return pescador.maps.keras_tuples(buffered_streamer,
inputs=["X_spec", "X_bg"],
outputs=["y"])