Audio resample injector
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@ -1,6 +1,7 @@
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import random
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import torch.nn
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import torchaudio.functional
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from kornia.augmentation import RandomResizedCrop
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from torch.cuda.amp import autocast
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@ -569,6 +570,17 @@ class RandomAudioCropInjector(Injector):
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return {self.output: inp[:, :, start:start+self.crop_sz]}
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class AudioResampleInjector(Injector):
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def __init__(self, opt, env):
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super().__init__(opt, env)
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self.input_sr = opt['input_sample_rate']
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self.output_sr = opt['output_sample_rate']
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def forward(self, state):
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inp = state[self.input]
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return {self.output: torchaudio.functional.resample(inp, self.input_sr, self.output_sr)}
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if __name__ == '__main__':
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inj = MelSpectrogramInjector({'in': 'x', 'out': 'y'}, None)
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print(inj({'x':torch.rand(10,1,40800)})['y'].shape)
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