added some bwe-related stuff

dnn/torch/osce/losses/td_lowpass.py

+import torch
+import scipy.signal
+
+
+from utils.layers.fir import FIR
+
+class TDLowpass(torch.nn.Module):
+    def __init__(self, numtaps, cutoff, power=2):
+        super().__init__()
+        
+        self.b = scipy.signal.firwin(numtaps, cutoff)
+        self.weight = torch.from_numpy(self.b).float().view(1, 1, -1)
+        self.power = power
+        
+    def forward(self, y_true, y_pred):
+        
+        assert len(y_true.shape) == 3 and len(y_pred.shape) == 3
+        
+        diff = y_true - y_pred
+        diff_lp = torch.nn.functional.conv1d(diff, self.weight)
+        
+        loss = torch.mean(torch.abs(diff_lp ** self.power))
+        
+        return loss, diff_lp
+    
+    def get_freqz(self):
+        freq, response = scipy.signal.freqz(self.b)
+        
+        return freq, response
+        
+        
+        
+        
+        
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dnn/torch/osce/silk_16_to_48.py

+import argparse
+
+from scipy.io import wavfile
+import torch
+import numpy as np
+
+from utils.layers.silk_upsampler import SilkUpsampler
+
+parser = argparse.ArgumentParser()
+parser.add_argument("input", type=str, help="input wave file")
+parser.add_argument("output", type=str, help="output wave file")
+
+if __name__ == "__main__":
+    args = parser.parse_args()
+    
+    fs, x = wavfile.read(args.input)
+
+    # being lazy for now
+    assert fs == 16000 and x.dtype == np.int16
+    
+    x = torch.from_numpy(x.astype(np.float32)).view(1, 1, -1)
+    
+    upsampler = SilkUpsampler()
+    y = upsampler(x)
+    
+    y = y.squeeze().numpy().astype(np.int16)
+    
+    wavfile.write(args.output, 48000, y[13:])
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dnn/torch/osce/utils/layers/fir.py

+import numpy as np
+import scipy.signal
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+
+class FIR(nn.Module):
+    def __init__(self, numtaps, bands, desired, fs=2):
+        super().__init__()
+        
+        if numtaps % 2 == 0:
+            print(f"warning: numtaps must be odd, increasing numtaps to {numtaps + 1}")
+            numtaps += 1
+        
+        a = scipy.signal.firls(numtaps, bands, desired, fs=fs)
+        
+        self.weight = torch.from_numpy(a.astype(np.float32))
+        
+    def forward(self, x):
+        num_channels = x.size(1)
+        
+        weight = torch.repeat_interleave(self.weight.view(1, 1, -1), num_channels, 0)
+        
+        y = F.conv1d(x, weight, groups=num_channels)
+        
+        return y
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