Datasets:
HenryAI
/
KerasBERTv1-Data

Dataset card Data Studio Files
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Found 6 files belonging to 2 directories
Resample all noise samples to 16000 Hz
command = (
\"for dir in `ls -1 \" + DATASET_NOISE_PATH + \"`; do \"
\"for file in `ls -1 \" + DATASET_NOISE_PATH + \"/$dir/*.wav`; do \"
\"sample_rate=`ffprobe -hide_banner -loglevel panic -show_streams \"
\"$file | grep sample_rate | cut -f2 -d=`; \"
\"if [ $sample_rate -ne 16000 ]; then \"
\"ffmpeg -hide_banner -loglevel panic -y \"
\"-i $file -ar 16000 temp.wav; \"
\"mv temp.wav $file; \"
\"fi; done; done\"
)
os.system(command)
# Split noise into chunks of 16000 each
def load_noise_sample(path):
sample, sampling_rate = tf.audio.decode_wav(
tf.io.read_file(path), desired_channels=1
)
if sampling_rate == SAMPLING_RATE:
# Number of slices of 16000 each that can be generated from the noise sample
slices = int(sample.shape[0] / SAMPLING_RATE)
sample = tf.split(sample[: slices * SAMPLING_RATE], slices)
return sample
else:
print(\"Sampling rate for {} is incorrect. Ignoring it\".format(path))
return None
noises = []
for path in noise_paths:
sample = load_noise_sample(path)
if sample:
noises.extend(sample)
noises = tf.stack(noises)
print(
\"{} noise files were split into {} noise samples where each is {} sec. long\".format(
len(noise_paths), noises.shape[0], noises.shape[1] // SAMPLING_RATE
)
)
6 noise files were split into 354 noise samples where each is 1 sec. long
Dataset generation
def paths_and_labels_to_dataset(audio_paths, labels):
\"\"\"Constructs a dataset of audios and labels.\"\"\"
path_ds = tf.data.Dataset.from_tensor_slices(audio_paths)
audio_ds = path_ds.map(lambda x: path_to_audio(x))
label_ds = tf.data.Dataset.from_tensor_slices(labels)
return tf.data.Dataset.zip((audio_ds, label_ds))
def path_to_audio(path):
\"\"\"Reads and decodes an audio file.\"\"\"
audio = tf.io.read_file(path)
audio, _ = tf.audio.decode_wav(audio, 1, SAMPLING_RATE)
return audio
def add_noise(audio, noises=None, scale=0.5):
if noises is not None:
# Create a random tensor of the same size as audio ranging from
# 0 to the number of noise stream samples that we have.
tf_rnd = tf.random.uniform(
(tf.shape(audio)[0],), 0, noises.shape[0], dtype=tf.int32
)
noise = tf.gather(noises, tf_rnd, axis=0)
# Get the amplitude proportion between the audio and the noise
prop = tf.math.reduce_max(audio, axis=1) / tf.math.reduce_max(noise, axis=1)
prop = tf.repeat(tf.expand_dims(prop, axis=1), tf.shape(audio)[1], axis=1)
# Adding the rescaled noise to audio
audio = audio + noise * prop * scale
return audio
def audio_to_fft(audio):
# Since tf.signal.fft applies FFT on the innermost dimension,
# we need to squeeze the dimensions and then expand them again
# after FFT
audio = tf.squeeze(audio, axis=-1)
fft = tf.signal.fft(
tf.cast(tf.complex(real=audio, imag=tf.zeros_like(audio)), tf.complex64)
)
fft = tf.expand_dims(fft, axis=-1)
# Return the absolute value of the first half of the FFT
# which represents the positive frequencies
return tf.math.abs(fft[:, : (audio.shape[1] // 2), :])
# Get the list of audio file paths along with their corresponding labels
class_names = os.listdir(DATASET_AUDIO_PATH)