Datasets:
HenryAI
/
KerasBERTv1-Data

Dataset card Data Studio Files
xet
Community
text
stringlengths
0
4.99k
DATASET_AUDIO_PATH = os.path.join(DATASET_ROOT, AUDIO_SUBFOLDER)
DATASET_NOISE_PATH = os.path.join(DATASET_ROOT, NOISE_SUBFOLDER)
# Percentage of samples to use for validation
VALID_SPLIT = 0.1
# Seed to use when shuffling the dataset and the noise
SHUFFLE_SEED = 43
# The sampling rate to use.
# This is the one used in all of the audio samples.
# We will resample all of the noise to this sampling rate.
# This will also be the output size of the audio wave samples
# (since all samples are of 1 second long)
SAMPLING_RATE = 16000
# The factor to multiply the noise with according to:
# noisy_sample = sample + noise * prop * scale
# where prop = sample_amplitude / noise_amplitude
SCALE = 0.5
BATCH_SIZE = 128
EPOCHS = 100
Data preparation
The dataset is composed of 7 folders, divided into 2 groups:
Speech samples, with 5 folders for 5 different speakers. Each folder contains 1500 audio files, each 1 second long and sampled at 16000 Hz.
Background noise samples, with 2 folders and a total of 6 files. These files are longer than 1 second (and originally not sampled at 16000 Hz, but we will resample them to 16000 Hz). We will use those 6 files to create 354 1-second-long noise samples to be used for training.
Let's sort these 2 categories into 2 folders:
An audio folder which will contain all the per-speaker speech sample folders
A noise folder which will contain all the noise samples
Before sorting the audio and noise categories into 2 folders,
main_directory/
...speaker_a/
...speaker_b/
...speaker_c/
...speaker_d/
...speaker_e/
...other/
..._background_noise_/
After sorting, we end up with the following structure:
main_directory/
...audio/
......speaker_a/
......speaker_b/
......speaker_c/
......speaker_d/
......speaker_e/
...noise/
......other/
......_background_noise_/
# If folder `audio`, does not exist, create it, otherwise do nothing
if os.path.exists(DATASET_AUDIO_PATH) is False:
os.makedirs(DATASET_AUDIO_PATH)
# If folder `noise`, does not exist, create it, otherwise do nothing
if os.path.exists(DATASET_NOISE_PATH) is False:
os.makedirs(DATASET_NOISE_PATH)
for folder in os.listdir(DATASET_ROOT):
if os.path.isdir(os.path.join(DATASET_ROOT, folder)):
if folder in [AUDIO_SUBFOLDER, NOISE_SUBFOLDER]:
# If folder is `audio` or `noise`, do nothing
continue
elif folder in [\"other\", \"_background_noise_\"]:
# If folder is one of the folders that contains noise samples,
# move it to the `noise` folder
shutil.move(
os.path.join(DATASET_ROOT, folder),
os.path.join(DATASET_NOISE_PATH, folder),
)
else:
# Otherwise, it should be a speaker folder, then move it to
# `audio` folder
shutil.move(
os.path.join(DATASET_ROOT, folder),
os.path.join(DATASET_AUDIO_PATH, folder),
)
Noise preparation
In this section:
We load all noise samples (which should have been resampled to 16000)
We split those noise samples to chuncks of 16000 samples which correspond to 1 second duration each
# Get the list of all noise files
noise_paths = []
for subdir in os.listdir(DATASET_NOISE_PATH):
subdir_path = Path(DATASET_NOISE_PATH) / subdir
if os.path.isdir(subdir_path):
noise_paths += [
os.path.join(subdir_path, filepath)
for filepath in os.listdir(subdir_path)
if filepath.endswith(\".wav\")
]
print(
\"Found {} files belonging to {} directories\".format(
len(noise_paths), len(os.listdir(DATASET_NOISE_PATH))