433 lines
14 KiB
Python
433 lines
14 KiB
Python
![]() |
# Copyright 2022 The TensorFlow Authors. All Rights Reserved.
|
||
|
#
|
||
|
# 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.
|
||
|
# ==============================================================================
|
||
|
"""Keras audio dataset loading utilities."""
|
||
|
|
||
|
import numpy as np
|
||
|
import tensorflow.compat.v2 as tf
|
||
|
|
||
|
from keras.utils import dataset_utils
|
||
|
|
||
|
# isort: off
|
||
|
from tensorflow.python.util.tf_export import keras_export
|
||
|
|
||
|
|
||
|
try:
|
||
|
import tensorflow_io as tfio
|
||
|
except ImportError:
|
||
|
tfio = None
|
||
|
|
||
|
ALLOWED_FORMATS = (".wav",)
|
||
|
|
||
|
|
||
|
@keras_export("keras.utils.audio_dataset_from_directory", v1=[])
|
||
|
def audio_dataset_from_directory(
|
||
|
directory,
|
||
|
labels="inferred",
|
||
|
label_mode="int",
|
||
|
class_names=None,
|
||
|
batch_size=32,
|
||
|
sampling_rate=None,
|
||
|
output_sequence_length=None,
|
||
|
ragged=False,
|
||
|
shuffle=True,
|
||
|
seed=None,
|
||
|
validation_split=None,
|
||
|
subset=None,
|
||
|
follow_links=False,
|
||
|
):
|
||
|
"""Generates a `tf.data.Dataset` from audio files in a directory.
|
||
|
|
||
|
If your directory structure is:
|
||
|
|
||
|
```
|
||
|
main_directory/
|
||
|
...class_a/
|
||
|
......a_audio_1.wav
|
||
|
......a_audio_2.wav
|
||
|
...class_b/
|
||
|
......b_audio_1.wav
|
||
|
......b_audio_2.wav
|
||
|
```
|
||
|
|
||
|
Then calling `audio_dataset_from_directory(main_directory,
|
||
|
labels='inferred')`
|
||
|
will return a `tf.data.Dataset` that yields batches of audio files from
|
||
|
the subdirectories `class_a` and `class_b`, together with labels
|
||
|
0 and 1 (0 corresponding to `class_a` and 1 corresponding to `class_b`).
|
||
|
|
||
|
Only `.wav` files are supported at this time.
|
||
|
|
||
|
Args:
|
||
|
directory: Directory where the data is located. If `labels` is "inferred",
|
||
|
it should contain subdirectories, each containing audio files for a
|
||
|
class. Otherwise, the directory structure is ignored.
|
||
|
labels: Either "inferred" (labels are generated from the directory
|
||
|
structure), None (no labels), or a list/tuple of integer labels of the
|
||
|
same size as the number of audio files found in the directory. Labels
|
||
|
should be sorted according to the alphanumeric order of the audio file
|
||
|
paths (obtained via `os.walk(directory)` in Python).
|
||
|
label_mode: String describing the encoding of `labels`. Options are:
|
||
|
- 'int': means that the labels are encoded as integers (e.g. for
|
||
|
`sparse_categorical_crossentropy` loss). - 'categorical' means that
|
||
|
the labels are encoded as a categorical vector (e.g. for
|
||
|
`categorical_crossentropy` loss). - 'binary' means that the labels
|
||
|
(there can be only 2) are encoded as `float32` scalars with values 0
|
||
|
or 1 (e.g. for `binary_crossentropy`). - None (no labels).
|
||
|
class_names: Only valid if "labels" is "inferred". This is the explicit
|
||
|
list of class names (must match names of subdirectories). Used to
|
||
|
control the order of the classes (otherwise alphanumerical order is
|
||
|
used).
|
||
|
batch_size: Size of the batches of data. Default: 32. If `None`, the data
|
||
|
will not be batched (the dataset will yield individual samples).
|
||
|
sampling_rate: Audio sampling rate (in samples per second).
|
||
|
output_sequence_length: Maximum length of an audio sequence. Audio files
|
||
|
longer than this will be truncated to `output_sequence_length`. If set
|
||
|
to `None`, then all sequences in the same batch will be padded to the
|
||
|
length of the longest sequence in the batch.
|
||
|
ragged: Whether to return a Ragged dataset (where each sequence has its
|
||
|
own length). Default: False.
|
||
|
shuffle: Whether to shuffle the data. Default: True. If set to False,
|
||
|
sorts the data in alphanumeric order.
|
||
|
seed: Optional random seed for shuffling and transformations.
|
||
|
validation_split: Optional float between 0 and 1, fraction of data to
|
||
|
reserve for validation.
|
||
|
subset: Subset of the data to return. One of "training", "validation" or
|
||
|
"both". Only used if `validation_split` is set.
|
||
|
follow_links: Whether to visits subdirectories pointed to by symlinks.
|
||
|
Defaults to False.
|
||
|
|
||
|
Returns:
|
||
|
A `tf.data.Dataset` object.
|
||
|
- If `label_mode` is None, it yields `string` tensors of shape
|
||
|
`(batch_size,)`, containing the contents of a batch of audio files.
|
||
|
- Otherwise, it yields a tuple `(audio, labels)`, where `audio`
|
||
|
has shape `(batch_size, sequence_length, num_channels)` and `labels`
|
||
|
follows the format described
|
||
|
below.
|
||
|
|
||
|
Rules regarding labels format:
|
||
|
- if `label_mode` is `int`, the labels are an `int32` tensor of shape
|
||
|
`(batch_size,)`.
|
||
|
- if `label_mode` is `binary`, the labels are a `float32` tensor of
|
||
|
1s and 0s of shape `(batch_size, 1)`.
|
||
|
- if `label_mode` is `categorical`, the labels are a `float32` tensor
|
||
|
of shape `(batch_size, num_classes)`, representing a one-hot
|
||
|
encoding of the class index.
|
||
|
"""
|
||
|
if labels not in ("inferred", None):
|
||
|
if not isinstance(labels, (list, tuple)):
|
||
|
raise ValueError(
|
||
|
"The `labels` argument should be a list/tuple of integer "
|
||
|
"labels, of the same size as the number of audio files in "
|
||
|
"the target directory. If you wish to infer the labels from "
|
||
|
"the subdirectory names in the target directory,"
|
||
|
' pass `labels="inferred"`. '
|
||
|
"If you wish to get a dataset that only contains audio samples "
|
||
|
f"(no labels), pass `labels=None`. Received: labels={labels}"
|
||
|
)
|
||
|
if class_names:
|
||
|
raise ValueError(
|
||
|
"You can only pass `class_names` if "
|
||
|
f'`labels="inferred"`. Received: labels={labels}, and '
|
||
|
f"class_names={class_names}"
|
||
|
)
|
||
|
if label_mode not in {"int", "categorical", "binary", None}:
|
||
|
raise ValueError(
|
||
|
'`label_mode` argument must be one of "int", "categorical", '
|
||
|
'"binary", '
|
||
|
f"or None. Received: label_mode={label_mode}"
|
||
|
)
|
||
|
|
||
|
if ragged and output_sequence_length is not None:
|
||
|
raise ValueError(
|
||
|
"Cannot set both `ragged` and `output_sequence_length`"
|
||
|
)
|
||
|
|
||
|
if sampling_rate is not None:
|
||
|
if not isinstance(sampling_rate, int):
|
||
|
raise ValueError(
|
||
|
"`sampling_rate` should have an integer value. "
|
||
|
f"Received: sampling_rate={sampling_rate}"
|
||
|
)
|
||
|
|
||
|
if sampling_rate <= 0:
|
||
|
raise ValueError(
|
||
|
"`sampling_rate` should be higher than 0. "
|
||
|
f"Received: sampling_rate={sampling_rate}"
|
||
|
)
|
||
|
|
||
|
if tfio is None:
|
||
|
raise ImportError(
|
||
|
"To use the argument `sampling_rate`, you should install "
|
||
|
"tensorflow_io. You can install it via `pip install "
|
||
|
"tensorflow-io`."
|
||
|
)
|
||
|
|
||
|
if labels is None or label_mode is None:
|
||
|
labels = None
|
||
|
label_mode = None
|
||
|
|
||
|
dataset_utils.check_validation_split_arg(
|
||
|
validation_split, subset, shuffle, seed
|
||
|
)
|
||
|
|
||
|
if seed is None:
|
||
|
seed = np.random.randint(1e6)
|
||
|
|
||
|
file_paths, labels, class_names = dataset_utils.index_directory(
|
||
|
directory,
|
||
|
labels,
|
||
|
formats=ALLOWED_FORMATS,
|
||
|
class_names=class_names,
|
||
|
shuffle=shuffle,
|
||
|
seed=seed,
|
||
|
follow_links=follow_links,
|
||
|
)
|
||
|
|
||
|
if label_mode == "binary" and len(class_names) != 2:
|
||
|
raise ValueError(
|
||
|
'When passing `label_mode="binary"`, there must be exactly 2 '
|
||
|
f"class_names. Received: class_names={class_names}"
|
||
|
)
|
||
|
|
||
|
if subset == "both":
|
||
|
train_dataset, val_dataset = get_training_and_validation_dataset(
|
||
|
file_paths=file_paths,
|
||
|
labels=labels,
|
||
|
validation_split=validation_split,
|
||
|
directory=directory,
|
||
|
label_mode=label_mode,
|
||
|
class_names=class_names,
|
||
|
sampling_rate=sampling_rate,
|
||
|
output_sequence_length=output_sequence_length,
|
||
|
ragged=ragged,
|
||
|
)
|
||
|
|
||
|
train_dataset = prepare_dataset(
|
||
|
dataset=train_dataset,
|
||
|
batch_size=batch_size,
|
||
|
shuffle=shuffle,
|
||
|
seed=seed,
|
||
|
class_names=class_names,
|
||
|
output_sequence_length=output_sequence_length,
|
||
|
ragged=ragged,
|
||
|
)
|
||
|
val_dataset = prepare_dataset(
|
||
|
dataset=val_dataset,
|
||
|
batch_size=batch_size,
|
||
|
shuffle=False,
|
||
|
seed=seed,
|
||
|
class_names=class_names,
|
||
|
output_sequence_length=output_sequence_length,
|
||
|
ragged=ragged,
|
||
|
)
|
||
|
return train_dataset, val_dataset
|
||
|
|
||
|
else:
|
||
|
dataset = get_dataset(
|
||
|
file_paths=file_paths,
|
||
|
labels=labels,
|
||
|
directory=directory,
|
||
|
validation_split=validation_split,
|
||
|
subset=subset,
|
||
|
label_mode=label_mode,
|
||
|
class_names=class_names,
|
||
|
sampling_rate=sampling_rate,
|
||
|
output_sequence_length=output_sequence_length,
|
||
|
ragged=ragged,
|
||
|
)
|
||
|
|
||
|
dataset = prepare_dataset(
|
||
|
dataset=dataset,
|
||
|
batch_size=batch_size,
|
||
|
shuffle=shuffle,
|
||
|
seed=seed,
|
||
|
class_names=class_names,
|
||
|
output_sequence_length=output_sequence_length,
|
||
|
ragged=ragged,
|
||
|
)
|
||
|
return dataset
|
||
|
|
||
|
|
||
|
def prepare_dataset(
|
||
|
dataset,
|
||
|
batch_size,
|
||
|
shuffle,
|
||
|
seed,
|
||
|
class_names,
|
||
|
output_sequence_length,
|
||
|
ragged,
|
||
|
):
|
||
|
dataset = dataset.prefetch(tf.data.AUTOTUNE)
|
||
|
if batch_size is not None:
|
||
|
if shuffle:
|
||
|
dataset = dataset.shuffle(buffer_size=batch_size * 8, seed=seed)
|
||
|
|
||
|
if output_sequence_length is None and not ragged:
|
||
|
dataset = dataset.padded_batch(
|
||
|
batch_size, padded_shapes=([None, None], [])
|
||
|
)
|
||
|
else:
|
||
|
dataset = dataset.batch(batch_size)
|
||
|
else:
|
||
|
if shuffle:
|
||
|
dataset = dataset.shuffle(buffer_size=1024, seed=seed)
|
||
|
|
||
|
# Users may need to reference `class_names`.
|
||
|
dataset.class_names = class_names
|
||
|
return dataset
|
||
|
|
||
|
|
||
|
def get_training_and_validation_dataset(
|
||
|
file_paths,
|
||
|
labels,
|
||
|
validation_split,
|
||
|
directory,
|
||
|
label_mode,
|
||
|
class_names,
|
||
|
sampling_rate,
|
||
|
output_sequence_length,
|
||
|
ragged,
|
||
|
):
|
||
|
(
|
||
|
file_paths_train,
|
||
|
labels_train,
|
||
|
) = dataset_utils.get_training_or_validation_split(
|
||
|
file_paths, labels, validation_split, "training"
|
||
|
)
|
||
|
if not file_paths_train:
|
||
|
raise ValueError(
|
||
|
f"No training audio files found in directory {directory}. "
|
||
|
f"Allowed format(s): {ALLOWED_FORMATS}"
|
||
|
)
|
||
|
|
||
|
file_paths_val, labels_val = dataset_utils.get_training_or_validation_split(
|
||
|
file_paths, labels, validation_split, "validation"
|
||
|
)
|
||
|
if not file_paths_val:
|
||
|
raise ValueError(
|
||
|
f"No validation audio files found in directory {directory}. "
|
||
|
f"Allowed format(s): {ALLOWED_FORMATS}"
|
||
|
)
|
||
|
|
||
|
train_dataset = paths_and_labels_to_dataset(
|
||
|
file_paths=file_paths_train,
|
||
|
labels=labels_train,
|
||
|
label_mode=label_mode,
|
||
|
num_classes=len(class_names),
|
||
|
sampling_rate=sampling_rate,
|
||
|
output_sequence_length=output_sequence_length,
|
||
|
ragged=ragged,
|
||
|
)
|
||
|
|
||
|
val_dataset = paths_and_labels_to_dataset(
|
||
|
file_paths=file_paths_val,
|
||
|
labels=labels_val,
|
||
|
label_mode=label_mode,
|
||
|
num_classes=len(class_names),
|
||
|
sampling_rate=sampling_rate,
|
||
|
output_sequence_length=output_sequence_length,
|
||
|
ragged=ragged,
|
||
|
)
|
||
|
|
||
|
return train_dataset, val_dataset
|
||
|
|
||
|
|
||
|
def get_dataset(
|
||
|
file_paths,
|
||
|
labels,
|
||
|
directory,
|
||
|
validation_split,
|
||
|
subset,
|
||
|
label_mode,
|
||
|
class_names,
|
||
|
sampling_rate,
|
||
|
output_sequence_length,
|
||
|
ragged,
|
||
|
):
|
||
|
file_paths, labels = dataset_utils.get_training_or_validation_split(
|
||
|
file_paths, labels, validation_split, subset
|
||
|
)
|
||
|
if not file_paths:
|
||
|
raise ValueError(
|
||
|
f"No audio files found in directory {directory}. "
|
||
|
f"Allowed format(s): {ALLOWED_FORMATS}"
|
||
|
)
|
||
|
|
||
|
dataset = paths_and_labels_to_dataset(
|
||
|
file_paths=file_paths,
|
||
|
labels=labels,
|
||
|
label_mode=label_mode,
|
||
|
num_classes=len(class_names),
|
||
|
sampling_rate=sampling_rate,
|
||
|
output_sequence_length=output_sequence_length,
|
||
|
ragged=ragged,
|
||
|
)
|
||
|
|
||
|
return dataset
|
||
|
|
||
|
|
||
|
def read_and_decode_audio(
|
||
|
path, sampling_rate=None, output_sequence_length=None
|
||
|
):
|
||
|
"""Reads and decodes audio file."""
|
||
|
audio = tf.io.read_file(path)
|
||
|
|
||
|
if output_sequence_length is None:
|
||
|
output_sequence_length = -1
|
||
|
|
||
|
audio, default_audio_rate = tf.audio.decode_wav(
|
||
|
contents=audio, desired_samples=output_sequence_length
|
||
|
)
|
||
|
if sampling_rate is not None:
|
||
|
# default_audio_rate should have dtype=int64
|
||
|
default_audio_rate = tf.cast(default_audio_rate, tf.int64)
|
||
|
audio = tfio.audio.resample(
|
||
|
input=audio, rate_in=default_audio_rate, rate_out=sampling_rate
|
||
|
)
|
||
|
return audio
|
||
|
|
||
|
|
||
|
def paths_and_labels_to_dataset(
|
||
|
file_paths,
|
||
|
labels,
|
||
|
label_mode,
|
||
|
num_classes,
|
||
|
sampling_rate,
|
||
|
output_sequence_length,
|
||
|
ragged,
|
||
|
):
|
||
|
"""Constructs a fixed-size dataset of audio and labels."""
|
||
|
path_ds = tf.data.Dataset.from_tensor_slices(file_paths)
|
||
|
audio_ds = path_ds.map(
|
||
|
lambda x: read_and_decode_audio(
|
||
|
x, sampling_rate, output_sequence_length
|
||
|
),
|
||
|
num_parallel_calls=tf.data.AUTOTUNE,
|
||
|
)
|
||
|
|
||
|
if ragged:
|
||
|
audio_ds = audio_ds.map(
|
||
|
lambda x: tf.RaggedTensor.from_tensor(x),
|
||
|
num_parallel_calls=tf.data.AUTOTUNE,
|
||
|
)
|
||
|
|
||
|
if label_mode:
|
||
|
label_ds = dataset_utils.labels_to_dataset(
|
||
|
labels, label_mode, num_classes
|
||
|
)
|
||
|
audio_ds = tf.data.Dataset.zip((audio_ds, label_ds))
|
||
|
return audio_ds
|