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Running
on
Zero
import torch | |
import torch.nn as nn | |
from torchaudio import transforms as T | |
class PadCrop(nn.Module): | |
def __init__(self, n_samples, randomize=True): | |
super().__init__() | |
self.n_samples = n_samples | |
self.randomize = randomize | |
def __call__(self, signal): | |
n, s = signal.shape | |
start = 0 if (not self.randomize) else torch.randint(0, max(0, s - self.n_samples) + 1, []).item() | |
end = start + self.n_samples | |
output = signal.new_zeros([n, self.n_samples]) | |
output[:, :min(s, self.n_samples)] = signal[:, start:end] | |
return output | |
def set_audio_channels(audio, target_channels): | |
if target_channels == 1: | |
# Convert to mono | |
audio = audio.mean(1, keepdim=True) | |
elif target_channels == 2: | |
# Convert to stereo | |
if audio.shape[1] == 1: | |
audio = audio.repeat(1, 2, 1) | |
elif audio.shape[1] > 2: | |
audio = audio[:, :2, :] | |
return audio | |
def prepare_audio(audio, in_sr, target_sr, target_length, target_channels, device): | |
audio = audio.to(device) | |
if in_sr != target_sr: | |
resample_tf = T.Resample(in_sr, target_sr).to(device) | |
audio = resample_tf(audio) | |
audio = PadCrop(target_length, randomize=False)(audio) | |
# Add batch dimension | |
if audio.dim() == 1: | |
audio = audio.unsqueeze(0).unsqueeze(0) | |
elif audio.dim() == 2: | |
audio = audio.unsqueeze(0) | |
audio = set_audio_channels(audio, target_channels) | |
return audio |