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Audio resample injector

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James Betker 2021-11-10 20:06:33 -07:00
parent 79367f753d
commit f36bab95dd
1 changed files with 12 additions and 0 deletions
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codes/trainer/injectors/base_injectors.py
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@ -1,6 +1,7 @@
import random import random
import torch.nn import torch.nn
import torchaudio.functional
from kornia.augmentation import RandomResizedCrop from kornia.augmentation import RandomResizedCrop
from torch.cuda.amp import autocast from torch.cuda.amp import autocast
@ -569,6 +570,17 @@ class RandomAudioCropInjector(Injector):
return {self.output: inp[:, :, start:start+self.crop_sz]} return {self.output: inp[:, :, start:start+self.crop_sz]}
class AudioResampleInjector(Injector):
def __init__(self, opt, env):
super().__init__(opt, env)
self.input_sr = opt['input_sample_rate']
self.output_sr = opt['output_sample_rate']
def forward(self, state):
inp = state[self.input]
return {self.output: torchaudio.functional.resample(inp, self.input_sr, self.output_sr)}
if __name__ == '__main__': if __name__ == '__main__':
inj = MelSpectrogramInjector({'in': 'x', 'out': 'y'}, None) inj = MelSpectrogramInjector({'in': 'x', 'out': 'y'}, None)
print(inj({'x':torch.rand(10,1,40800)})['y'].shape) print(inj({'x':torch.rand(10,1,40800)})['y'].shape)