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# coding=utf-8
# Copyright 2022 The Google Research Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
"""Creates a set of audio files to test FAD calculation."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import errno
import os
from absl import app
from absl import flags
import numpy as np
import scipy.io.wavfile
_SAMPLE_RATE = 16000
FLAGS = flags.FLAGS
flags.DEFINE_string(
"test_files", "", "Directory where the test files should be located"
)
def create_dir(output_dir):
"""Ignore directory exists error."""
try:
os.makedirs(output_dir)
except OSError as exception:
if exception.errno == errno.EEXIST and os.path.isdir(output_dir):
pass
else:
raise
def add_noise(data, stddev):
"""Adds Gaussian noise to the samples.
Args:
data: 1d Numpy array containing floating point samples. Not necessarily
normalized.
stddev: The standard deviation of the added noise.
Returns:
1d Numpy array containing the provided floating point samples with added
Gaussian noise.
Raises:
ValueError: When data is not a 1d numpy array.
"""
if len(data.shape) != 1:
raise ValueError("expected 1d numpy array.")
max_value = np.amax(np.abs(data))
num_samples = data.shape[0]
gauss = np.random.normal(0, stddev, (num_samples)) * max_value
return data + gauss
def gen_sine_wave(freq=600, length_seconds=6, sample_rate=_SAMPLE_RATE, param=None):
"""Creates sine wave of the specified frequency, sample_rate and length."""
t = np.linspace(0, length_seconds, int(length_seconds * sample_rate))
samples = np.sin(2 * np.pi * t * freq)
if param:
samples = add_noise(samples, param)
return np.asarray(2**15 * samples, dtype=np.int16)
def main(argv):
del argv # Unused.
for traget, count, param in [
("reference", 50, 0.0),
("paired", 50, 0.001),
("unpaired", 25, 0.001),
]:
output_dir = os.path.join(FLAGS.test_files, "example", traget)
create_dir(output_dir)
print("output_dir:", output_dir)
frequencies = np.linspace(100, 1000, count).tolist()
for freq in frequencies:
samples = gen_sine_wave(freq, param=param)
filename = os.path.join(output_dir, "sin_%.0f.wav" % freq)
print("Creating: %s with %i samples." % (filename, samples.shape[0]))
scipy.io.wavfile.write(filename, _SAMPLE_RATE, samples)
if __name__ == "__main__":
os.makedirs("example", exist_ok=True)
app.run(main)
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