added FWGAN weight dumping code

dnn/torch/fwgan/inference.py

@@ -0,0 +1,141 @@
+import os
+import time
+import torch
+import numpy as np
+from scipy import signal as si
+from scipy.io import wavfile
+import argparse
+
+from models import model_dict
+
+parser = argparse.ArgumentParser()
+parser.add_argument('model', choices=['fwgan400', 'fwgan500'], help='model name')
+parser.add_argument('weightfile', type=str, help='weight file')
+parser.add_argument('input', type=str, help='input: feature file or folder with feature files')
+parser.add_argument('output', type=str, help='output: wav file name or folder name, depending on input')
+
+
+########################### Signal Processing Layers ###########################
+
+def preemphasis(x, coef= -0.85):
+
+    return si.lfilter(np.array([1.0, coef]), np.array([1.0]), x).astype('float32')
+
+def deemphasis(x, coef= -0.85):
+
+    return si.lfilter(np.array([1.0]), np.array([1.0, coef]), x).astype('float32')
+
+gamma = 0.92
+weighting_vector = np.array([gamma**i for i in range(16,0,-1)])
+
+
+def lpc_synthesis_one_frame(frame, filt, buffer, weighting_vector=np.ones(16)):
+
+    out = np.zeros_like(frame)
+
+    filt = np.flip(filt)
+
+    inp = frame[:]
+
+
+    for i in range(0, inp.shape[0]):
+
+        s = inp[i] - np.dot(buffer*weighting_vector, filt)
+
+        buffer[0] = s
+
+        buffer = np.roll(buffer, -1)
+
+        out[i] = s
+
+    return out
+
+def inverse_perceptual_weighting (pw_signal, filters, weighting_vector):
+
+    #inverse perceptual weighting= H_preemph / W(z/gamma)
+
+    pw_signal = preemphasis(pw_signal)
+
+    signal = np.zeros_like(pw_signal)
+    buffer = np.zeros(16)
+    num_frames = pw_signal.shape[0] //160
+    assert num_frames == filters.shape[0]
+
+    for frame_idx in range(0, num_frames):
+
+        in_frame = pw_signal[frame_idx*160: (frame_idx+1)*160][:]
+        out_sig_frame = lpc_synthesis_one_frame(in_frame, filters[frame_idx, :], buffer, weighting_vector)
+        signal[frame_idx*160: (frame_idx+1)*160] = out_sig_frame[:]
+        buffer[:] = out_sig_frame[-16:]
+
+    return signal
+
+
+def process_item(generator, feature_filename, output_filename, verbose=False):
+
+    feat = np.memmap(feature_filename, dtype='float32', mode='r')
+
+    num_feat_frames = len(feat) // 36
+    feat = np.reshape(feat, (num_feat_frames, 36))
+
+    bfcc = np.copy(feat[:, :18])
+    corr = np.copy(feat[:, 19:20]) + 0.5
+    bfcc_with_corr =  torch.from_numpy(np.hstack((bfcc, corr))).type(torch.FloatTensor).unsqueeze(0)#.to(device)
+
+    period = torch.from_numpy((0.1 + 50 * np.copy(feat[:, 18:19]) + 100)\
+                            .astype('int32')).type(torch.long).view(1,-1)#.to(device)
+
+    lpc_filters = np.copy(feat[:, -16:])
+
+    start_time = time.time()
+    x1 = generator(period, bfcc_with_corr, torch.zeros(1,320)) #this means the vocoder runs in complete synthesis mode with zero history audio frames
+    end_time = time.time()
+    total_time = end_time - start_time
+    x1 = x1.squeeze(1).squeeze(0).detach().cpu().numpy()
+    gen_seconds = len(x1)/16000
+    out = deemphasis(inverse_perceptual_weighting(x1, lpc_filters, weighting_vector))
+    if verbose:
+        print(f"Took {total_time:.3f}s to generate {len(x1)}  samples ({gen_seconds}s) -> {gen_seconds/total_time:.2f}x real time")
+
+    out = np.clip(np.round(2**15 * out), -2**15, 2**15 -1).astype(np.int16)
+    wavfile.write(output_filename, 16000, out)
+
+
+########################### The inference loop over folder containing lpcnet feature files #################################
+if __name__ == "__main__":
+
+    args = parser.parse_args()
+
+    generator = model_dict[args.model]()
+
+
+    #Load the FWGAN500Hz Checkpoint
+    saved_gen= torch.load(args.weightfile, map_location='cpu')
+    generator.load_state_dict(saved_gen)
+
+    #this is just to remove the weight_norm from the model layers as it's no longer needed
+    def _remove_weight_norm(m):
+        try:
+            torch.nn.utils.remove_weight_norm(m)
+        except ValueError:  # this module didn't have weight norm
+            return
+    generator.apply(_remove_weight_norm)
+
+    #enable inference mode
+    generator = generator.eval()
+
+    print('Successfully loaded the generator model ... start generation:')
+
+    if os.path.isdir(args.input):
+
+        os.makedirs(args.output, exist_ok=True)
+
+        for fn in os.listdir(args.input):
+            print(f"processing input {fn}...")
+            feature_filename = os.path.join(args.input, fn)
+            output_filename = os.path.join(args.output, os.path.splitext(fn)[0] + f"_{args.model}.wav")
+            process_item(generator, feature_filename, output_filename)
+    else:
+        process_item(generator, args.input, args.output)
+
+    print("Finished!")