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data.py
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data.py
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# coding=utf-8
# Copyright 2024 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.
"""Provides helper data related functions."""
import tensorflow as tf
import tensorflow_datasets as tfds
from cola import constants
def get_self_supervised_data(dataset=constants.Dataset.LBS,
shuffle_buffer=1000):
"""Reads TFDS data for self-supervised task."""
def _parse_example(audio, _):
return {"audio": tf.cast(audio, tf.float32) / float(tf.int16.max)}
if dataset == constants.Dataset.LBS:
split = "train_clean360"
else:
split = "train"
ds_train = tfds.load(
dataset.value, split=split, as_supervised=True)
ds_train = ds_train.shuffle(shuffle_buffer, reshuffle_each_iteration=True)
ds_train = ds_train.map(
_parse_example, num_parallel_calls=tf.data.experimental.AUTOTUNE)
return ds_train
def get_downstream_dataset(dataset=constants.Dataset.VOXFORGE,
shuffle_buffer=1000):
"""Reads downstream task data from TFDS."""
def _parse_example(audio, label):
audio = tf.cast(audio, tf.float32) / float(tf.int16.max)
return {"audio": audio, "label": label}
(ds_train, ds_test), ds_info = tfds.load(
dataset.value,
split=["train", "test"],
shuffle_files=True,
as_supervised=True,
with_info=True)
ds_train = ds_train.shuffle(
shuffle_buffer, reshuffle_each_iteration=True).map(
_parse_example, num_parallel_calls=tf.data.experimental.AUTOTUNE)
ds_test = ds_test.shuffle(
shuffle_buffer, reshuffle_each_iteration=True).map(
_parse_example, num_parallel_calls=tf.data.experimental.AUTOTUNE)
return (ds_train, ds_test, ds_info.features["label"].num_classes)
def extract_log_mel_spectrogram(waveform,
sample_rate=16000,
frame_length=400,
frame_step=160,
fft_length=1024,
n_mels=64,
fmin=60.0,
fmax=7800.0):
"""Extract frames of log mel spectrogram from a raw waveform."""
stfts = tf.signal.stft(
waveform,
frame_length=frame_length,
frame_step=frame_step,
fft_length=fft_length)
spectrograms = tf.abs(stfts)
num_spectrogram_bins = stfts.shape[-1]
lower_edge_hertz, upper_edge_hertz, num_mel_bins = fmin, fmax, n_mels
linear_to_mel_weight_matrix = tf.signal.linear_to_mel_weight_matrix(
num_mel_bins, num_spectrogram_bins, sample_rate, lower_edge_hertz,
upper_edge_hertz)
mel_spectrograms = tf.tensordot(spectrograms, linear_to_mel_weight_matrix, 1)
mel_spectrograms.set_shape(spectrograms.shape[:-1].concatenate(
linear_to_mel_weight_matrix.shape[-1:]))
mel_spectrograms = tf.clip_by_value(
mel_spectrograms,
clip_value_min=1e-5,
clip_value_max=1e8)
log_mel_spectrograms = tf.math.log(mel_spectrograms)
return log_mel_spectrograms
def extract_window(waveform, seg_length=16000):
"""Extracts a random segment from a waveform."""
padding = tf.maximum(seg_length - tf.shape(waveform)[0], 0)
left_pad = padding // 2
right_pad = padding - left_pad
padded_waveform = tf.pad(waveform, paddings=[[left_pad, right_pad]])
return tf.image.random_crop(padded_waveform, [seg_length])