Spaces:
Runtime error
Runtime error
File size: 4,965 Bytes
2c0f55c |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 |
"""Base dataset classes."""
import logging
import math
import random
import numpy as np
import pandas as pd
import torch
import torchaudio
from torch.utils.data import Dataset
from torch.utils.data.dataset import T_co
LOGGER = logging.getLogger(__name__)
SAMPLING_RATE = 16_000
APPLY_NORMALIZATION = True
APPLY_TRIMMING = True
APPLY_PADDING = True
FRAMES_NUMBER = 480_000 # <- originally 64_600
SOX_SILENCE = [
# trim all silence that is longer than 0.2s and louder than 1% volume (relative to the file)
# from beginning and middle/end
["silence", "1", "0.2", "1%", "-1", "0.2", "1%"],
]
class SimpleAudioFakeDataset(Dataset):
def __init__(
self,
subset,
transform=None,
return_label: bool = True,
return_meta: bool = False,
):
self.transform = transform
self.samples = pd.DataFrame()
self.subset = subset
self.allowed_attacks = None
self.partition_ratio = None
self.seed = None
self.return_label = return_label
self.return_meta = return_meta
def split_samples(self, samples_list):
if isinstance(samples_list, pd.DataFrame):
samples_list = samples_list.sort_values(by=list(samples_list.columns))
samples_list = samples_list.sample(frac=1, random_state=self.seed)
else:
samples_list = sorted(samples_list)
random.seed(self.seed)
random.shuffle(samples_list)
p, s = self.partition_ratio
subsets = np.split(
samples_list, [int(p * len(samples_list)), int((p + s) * len(samples_list))]
)
return dict(zip(["train", "test", "val"], subsets))[self.subset]
def df2tuples(self):
tuple_samples = []
for i, elem in self.samples.iterrows():
tuple_samples.append(
(str(elem["path"]), elem["label"], elem["attack_type"])
)
self.samples = tuple_samples
return self.samples
def __getitem__(self, index) -> T_co:
if isinstance(self.samples, pd.DataFrame):
sample = self.samples.iloc[index]
path = str(sample["path"])
label = sample["label"]
attack_type = sample["attack_type"]
if type(attack_type) != str and math.isnan(attack_type):
attack_type = "N/A"
else:
path, label, attack_type = self.samples[index]
waveform, sample_rate = torchaudio.load(path, normalize=APPLY_NORMALIZATION)
real_sec_length = len(waveform[0]) / sample_rate
waveform, sample_rate = apply_preprocessing(waveform, sample_rate)
return_data = [waveform, sample_rate]
if self.return_label:
label = 1 if label == "bonafide" else 0
return_data.append(label)
if self.return_meta:
return_data.append(
(
attack_type,
path,
self.subset,
real_sec_length,
)
)
return return_data
def __len__(self):
return len(self.samples)
def apply_preprocessing(
waveform,
sample_rate,
):
if sample_rate != SAMPLING_RATE and SAMPLING_RATE != -1:
waveform, sample_rate = resample_wave(waveform, sample_rate, SAMPLING_RATE)
# Stereo to mono
if waveform.dim() > 1 and waveform.shape[0] > 1:
waveform = waveform[:1, ...]
# Trim too long utterances...
if APPLY_TRIMMING:
waveform, sample_rate = apply_trim(waveform, sample_rate)
# ... or pad too short ones.
if APPLY_PADDING:
waveform = apply_pad(waveform, FRAMES_NUMBER)
return waveform, sample_rate
def resample_wave(waveform, sample_rate, target_sample_rate):
waveform, sample_rate = torchaudio.sox_effects.apply_effects_tensor(
waveform, sample_rate, [["rate", f"{target_sample_rate}"]]
)
return waveform, sample_rate
def resample_file(path, target_sample_rate, normalize=True):
waveform, sample_rate = torchaudio.sox_effects.apply_effects_file(
path, [["rate", f"{target_sample_rate}"]], normalize=normalize
)
return waveform, sample_rate
def apply_trim(waveform, sample_rate):
(
waveform_trimmed,
sample_rate_trimmed,
) = torchaudio.sox_effects.apply_effects_tensor(waveform, sample_rate, SOX_SILENCE)
if waveform_trimmed.size()[1] > 0:
waveform = waveform_trimmed
sample_rate = sample_rate_trimmed
return waveform, sample_rate
def apply_pad(waveform, cut):
"""Pad wave by repeating signal until `cut` length is achieved."""
waveform = waveform.squeeze(0)
waveform_len = waveform.shape[0]
if waveform_len >= cut:
return waveform[:cut]
# need to pad
num_repeats = int(cut / waveform_len) + 1
padded_waveform = torch.tile(waveform, (1, num_repeats))[:, :cut][0]
return padded_waveform
|