Create synthesis.py

synthesis.py

@@ -0,0 +1,66 @@
+import torch
+from tqdm import tqdm
+import librosa
+from hparams import hparams
+from wavenet_vocoder import builder
+
+torch.set_num_threads(4)
+use_cuda = torch.cuda.is_available()
+device = torch.device("cuda" if use_cuda else "cpu")
+
+
+def build_model():
+    
+    model = getattr(builder, hparams.builder)(
+        out_channels=hparams.out_channels,
+        layers=hparams.layers,
+        stacks=hparams.stacks,
+        residual_channels=hparams.residual_channels,
+        gate_channels=hparams.gate_channels,
+        skip_out_channels=hparams.skip_out_channels,
+        cin_channels=hparams.cin_channels,
+        gin_channels=hparams.gin_channels,
+        weight_normalization=hparams.weight_normalization,
+        n_speakers=hparams.n_speakers,
+        dropout=hparams.dropout,
+        kernel_size=hparams.kernel_size,
+        upsample_conditional_features=hparams.upsample_conditional_features,
+        upsample_scales=hparams.upsample_scales,
+        freq_axis_kernel_size=hparams.freq_axis_kernel_size,
+        scalar_input=True,
+        legacy=hparams.legacy,
+    )
+    return model
+
+
+
+def wavegen(model, c=None, tqdm=tqdm):
+    """Generate waveform samples by WaveNet.
+    
+    """
+
+    model.eval()
+    model.make_generation_fast_()
+
+    Tc = c.shape[0]
+    upsample_factor = hparams.hop_size
+    # Overwrite length according to feature size
+    length = Tc * upsample_factor
+
+    # B x C x T
+    c = torch.FloatTensor(c.T).unsqueeze(0)
+
+    initial_input = torch.zeros(1, 1, 1).fill_(0.0)
+
+    # Transform data to GPU
+    initial_input = initial_input.to(device)
+    c = None if c is None else c.to(device)
+
+    with torch.no_grad():
+        y_hat = model.incremental_forward(
+            initial_input, c=c, g=None, T=length, tqdm=tqdm, softmax=True, quantize=True,
+            log_scale_min=hparams.log_scale_min)
+
+    y_hat = y_hat.view(-1).cpu().data.numpy()
+
+    return y_hat