9268694310

Committed 28 May 2024 11:45AM UTC coverage: 51.799% (-0.004%) from 51.803%

Build # 9268694310

Build Type

Pull #1647

github

Committed by

web-flow

Commit Message

Merge 2995a272c into 8f0e61f23

Pull Request Pull Request #1647: Improved Efficiency of the DiskUsage Metric

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/avalanche/benchmarks/datasets/torchaudio_wrapper.py

################################################################################
# Copyright (c) 2022 ContinualAI.                                              #
# Copyrights licensed under the MIT License.                                   #
# See the accompanying LICENSE file for terms.                                 #
#                                                                              #
# Author(s): Andrea Cossu                                                      #
# E-mail: contact@continualai.org                                              #
# Website: www.continualai.org                                                 #
################################################################################

""" This module conveniently wraps TorchAudio Datasets for using a clean and
comprehensive Avalanche API."""
import os

try:
    import torchaudio
    from torchaudio.datasets import SPEECHCOMMANDS
except ImportError:
    import warnings

    warnings.warn(
        "TorchAudio package is required to load SpeechCommands. "
        "You can install it as extra dependency with "
        "`pip install avalanche-lib[extra]`"
    )
    SPEECHCOMMANDS = object

from avalanche.benchmarks.utils import _make_taskaware_classification_dataset
from avalanche.benchmarks.datasets import default_dataset_location
import torch


def speech_commands_collate(batch):
    tensors, targets, t_labels = [], [], []
    for waveform, label, rate, sid, uid, t_label in batch:
        tensors += [waveform]
        targets += [torch.tensor(label)]
        t_labels += [torch.tensor(t_label)]
    tensors = [item.t() for item in tensors]
    tensors_padded = torch.nn.utils.rnn.pad_sequence(
        tensors, batch_first=True, padding_value=0.0
    )

    if len(tensors_padded.size()) == 2:  # no MFCC, add feature dimension
        tensors_padded = tensors_padded.unsqueeze(-1)
    targets = torch.stack(targets)
    t_labels = torch.stack(t_labels)
    return [tensors_padded, targets, t_labels]


class SpeechCommandsData(SPEECHCOMMANDS):
    def __init__(self, root, url, download, subset, mfcc_preprocessing):
        os.makedirs(root, exist_ok=True)
        super().__init__(root=root, download=download, subset=subset, url=url)
        self.labels_names = [
            "backward",
            "bed",
            "bird",
            "cat",
            "dog",
            "down",
            "eight",
            "five",
            "follow",
            "forward",
            "four",
            "go",
            "happy",
            "house",
            "learn",
            "left",
            "marvin",
            "nine",
            "no",
            "off",
            "on",
            "one",
            "right",
            "seven",
            "sheila",
            "six",
            "stop",
            "three",
            "tree",
            "two",
            "up",
            "visual",
            "wow",
            "yes",
            "zero",
        ]
        self.mfcc_preprocessing = mfcc_preprocessing

    def __getitem__(self, item):
        wave, rate, label, speaker_id, ut_number = super().__getitem__(item)
        label = self.labels_names.index(label)
        wave = wave.squeeze(0)  # (T,)
        if self.mfcc_preprocessing is not None:
            assert rate == self.mfcc_preprocessing.sample_rate
            # (T, MFCC)
            wave = self.mfcc_preprocessing(wave).permute(1, 0)
        return wave, label, rate, speaker_id, ut_number


def SpeechCommands(
    root=default_dataset_location("speech_commands"),
    url="speech_commands_v0.02",
    download=True,
    subset=None,
    mfcc_preprocessing=None,
):
    """
    root: dataset root location
    url: version name of the dataset
    download: automatically download the dataset, if not present
    subset: one of 'training', 'validation', 'testing'
    mfcc_preprocessing: an optional torchaudio.transforms.MFCC instance
        to preprocess each audio. Warning: this may slow down the execution
        since preprocessing is applied on-the-fly each time a sample is
        retrieved from the dataset.
    """
    dataset = SpeechCommandsData(
        root=root,
        download=download,
        subset=subset,
        url=url,
        mfcc_preprocessing=mfcc_preprocessing,
    )
    labels = [datapoint[1] for datapoint in dataset]
    return _make_taskaware_classification_dataset(
        dataset, collate_fn=speech_commands_collate, targets=labels
    )


__all__ = ["SpeechCommands"]

1	################################################################################
2	# Copyright (c) 2022 ContinualAI. #
3	# Copyrights licensed under the MIT License. #
4	# See the accompanying LICENSE file for terms. #
5	# #
6	# Author(s): Andrea Cossu #
7	# E-mail: contact@continualai.org #
8	# Website: www.continualai.org #
9	################################################################################
10
11	""" This module conveniently wraps TorchAudio Datasets for using a clean and
12	comprehensive Avalanche API."""
13	import os	×
14
15	try:	×
16	import torchaudio	×
17	from torchaudio.datasets import SPEECHCOMMANDS	×
18	except ImportError:	×
19	import warnings	×
20
21	warnings.warn(	×
22	"TorchAudio package is required to load SpeechCommands. "
23	"You can install it as extra dependency with "
24	"`pip install avalanche-lib[extra]`"
25	)
26	SPEECHCOMMANDS = object	×
27
28	from avalanche.benchmarks.utils import _make_taskaware_classification_dataset	×
29	from avalanche.benchmarks.datasets import default_dataset_location	×
30	import torch	×
31
32
33	def speech_commands_collate(batch):	×
34	tensors, targets, t_labels = [], [], []	×
35	for waveform, label, rate, sid, uid, t_label in batch:	×
36	tensors += [waveform]	×
37	targets += [torch.tensor(label)]	×
38	t_labels += [torch.tensor(t_label)]	×
39	tensors = [item.t() for item in tensors]	×
40	tensors_padded = torch.nn.utils.rnn.pad_sequence(	×
41	tensors, batch_first=True, padding_value=0.0
42	)
43
44	if len(tensors_padded.size()) == 2: # no MFCC, add feature dimension	×
45	tensors_padded = tensors_padded.unsqueeze(-1)	×
46	targets = torch.stack(targets)	×
47	t_labels = torch.stack(t_labels)	×
48	return [tensors_padded, targets, t_labels]	×
49
50
51	class SpeechCommandsData(SPEECHCOMMANDS):	×
52	def __init__(self, root, url, download, subset, mfcc_preprocessing):	×
53	os.makedirs(root, exist_ok=True)	×
54	super().__init__(root=root, download=download, subset=subset, url=url)	×
55	self.labels_names = [	×
56	"backward",
57	"bed",
58	"bird",
59	"cat",
60	"dog",
61	"down",
62	"eight",
63	"five",
64	"follow",
65	"forward",
66	"four",
67	"go",
68	"happy",
69	"house",
70	"learn",
71	"left",
72	"marvin",
73	"nine",
74	"no",
75	"off",
76	"on",
77	"one",
78	"right",
79	"seven",
80	"sheila",
81	"six",
82	"stop",
83	"three",
84	"tree",
85	"two",
86	"up",
87	"visual",
88	"wow",
89	"yes",
90	"zero",
91	]
92	self.mfcc_preprocessing = mfcc_preprocessing	×
93
94	def __getitem__(self, item):	×
95	wave, rate, label, speaker_id, ut_number = super().__getitem__(item)	×
96	label = self.labels_names.index(label)	×
97	wave = wave.squeeze(0) # (T,)	×
98	if self.mfcc_preprocessing is not None:	×
99	assert rate == self.mfcc_preprocessing.sample_rate	×
100	# (T, MFCC)
101	wave = self.mfcc_preprocessing(wave).permute(1, 0)	×
102	return wave, label, rate, speaker_id, ut_number	×
103
104
105	def SpeechCommands(	×
106	root=default_dataset_location("speech_commands"),
107	url="speech_commands_v0.02",
108	download=True,
109	subset=None,
110	mfcc_preprocessing=None,
111	):
112	"""
113	root: dataset root location
114	url: version name of the dataset
115	download: automatically download the dataset, if not present
116	subset: one of 'training', 'validation', 'testing'
117	mfcc_preprocessing: an optional torchaudio.transforms.MFCC instance
118	to preprocess each audio. Warning: this may slow down the execution
119	since preprocessing is applied on-the-fly each time a sample is
120	retrieved from the dataset.
121	"""
122	dataset = SpeechCommandsData(	×
123	root=root,
124	download=download,
125	subset=subset,
126	url=url,
127	mfcc_preprocessing=mfcc_preprocessing,
128	)
129	labels = [datapoint[1] for datapoint in dataset]	×
130	return _make_taskaware_classification_dataset(	×
131	dataset, collate_fn=speech_commands_collate, targets=labels
132	)
133
134
135	__all__ = ["SpeechCommands"]	×

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