File size: 3,743 Bytes
85ba398 |
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 |
import numpy as np
import torch
from fairseq.data.audio import rand_uniform
from fairseq.data.audio.dataset_transforms import (
AudioDatasetTransform,
register_audio_dataset_transform,
)
from fairseq.data.audio.waveform_transforms.noiseaugment import (
NoiseAugmentTransform,
)
_DEFAULTS = {
"rate": 0.25,
"mixing_noise_rate": 0.1,
"noise_path": "",
"noise_snr_min": -5,
"noise_snr_max": 5,
"utterance_snr_min": -5,
"utterance_snr_max": 5,
}
@register_audio_dataset_transform("noisyoverlapaugment")
class NoisyOverlapAugment(AudioDatasetTransform):
@classmethod
def from_config_dict(cls, config=None):
_config = {} if config is None else config
return NoisyOverlapAugment(
_config.get("rate", _DEFAULTS["rate"]),
_config.get("mixing_noise_rate", _DEFAULTS["mixing_noise_rate"]),
_config.get("noise_path", _DEFAULTS["noise_path"]),
_config.get("noise_snr_min", _DEFAULTS["noise_snr_min"]),
_config.get("noise_snr_max", _DEFAULTS["noise_snr_max"]),
_config.get("utterance_snr_min", _DEFAULTS["utterance_snr_min"]),
_config.get("utterance_snr_max", _DEFAULTS["utterance_snr_max"]),
)
def __init__(
self,
rate=_DEFAULTS["rate"],
mixing_noise_rate=_DEFAULTS["mixing_noise_rate"],
noise_path=_DEFAULTS["noise_path"],
noise_snr_min=_DEFAULTS["noise_snr_min"],
noise_snr_max=_DEFAULTS["noise_snr_max"],
utterance_snr_min=_DEFAULTS["utterance_snr_min"],
utterance_snr_max=_DEFAULTS["utterance_snr_max"],
):
self.rate = rate
self.mixing_noise_rate = mixing_noise_rate
self.noise_shaper = NoiseAugmentTransform(noise_path)
self.noise_snr_min = noise_snr_min
self.noise_snr_max = noise_snr_max
self.utterance_snr_min = utterance_snr_min
self.utterance_snr_max = utterance_snr_max
def __repr__(self):
return (
self.__class__.__name__
+ "("
+ ", ".join(
[
f"rate={self.rate}",
f"mixing_noise_rate={self.mixing_noise_rate}",
f"noise_snr_min={self.noise_snr_min}",
f"noise_snr_max={self.noise_snr_max}",
f"utterance_snr_min={self.utterance_snr_min}",
f"utterance_snr_max={self.utterance_snr_max}",
]
)
+ ")"
)
def __call__(self, sources):
for i, source in enumerate(sources):
if np.random.random() > self.rate:
continue
pri = source.numpy()
if np.random.random() > self.mixing_noise_rate:
sec = sources[np.random.randint(0, len(sources))].numpy()
snr = rand_uniform(self.utterance_snr_min, self.utterance_snr_max)
else:
sec = self.noise_shaper.pick_sample(source.shape)
snr = rand_uniform(self.noise_snr_min, self.noise_snr_max)
L1 = pri.shape[-1]
L2 = sec.shape[-1]
l = np.random.randint(0, min(round(L1 / 2), L2)) # mix len
s_source = np.random.randint(0, L1 - l)
s_sec = np.random.randint(0, L2 - l)
get_power = lambda x: np.mean(x**2)
if get_power(sec) == 0:
continue
scl = np.sqrt(get_power(pri) / (np.power(10, snr / 10) * get_power(sec)))
pri[s_source : s_source + l] = np.add(
pri[s_source : s_source + l], np.multiply(scl, sec[s_sec : s_sec + l])
)
sources[i] = torch.from_numpy(pri).float()
return sources
|