Fix stopnet training

TTS/tts/layers/losses.py

@@ -246,9 +246,9 @@ class Huber(nn.Module):
 class TacotronLoss(torch.nn.Module):
     """Collection of Tacotron set-up based on provided config."""
 
-    def __init__(self, c, stopnet_pos_weight=10, ga_sigma=0.4):
+    def __init__(self, c, ga_sigma=0.4):
         super().__init__()
-        self.stopnet_pos_weight = stopnet_pos_weight
+        self.stopnet_pos_weight = c.stopnet_pos_weight
         self.ga_alpha = c.ga_alpha
         self.decoder_diff_spec_alpha = c.decoder_diff_spec_alpha
         self.postnet_diff_spec_alpha = c.postnet_diff_spec_alpha
@@ -274,7 +274,7 @@ class TacotronLoss(torch.nn.Module):
             self.criterion_ssim = SSIMLoss()
         # stopnet loss
         # pylint: disable=not-callable
-        self.criterion_st = BCELossMasked(pos_weight=torch.tensor(stopnet_pos_weight)) if c.stopnet else None
+        self.criterion_st = BCELossMasked(pos_weight=torch.tensor(self.stopnet_pos_weight)) if c.stopnet else None
 
     def forward(
         self,
@@ -284,6 +284,7 @@ class TacotronLoss(torch.nn.Module):
         linear_input,
         stopnet_output,
         stopnet_target,
+        stop_target_length,
         output_lens,
         decoder_b_output,
         alignments,
@@ -315,12 +316,12 @@ class TacotronLoss(torch.nn.Module):
         return_dict["decoder_loss"] = decoder_loss
         return_dict["postnet_loss"] = postnet_loss
 
-        # stopnet loss
         stop_loss = (
-            self.criterion_st(stopnet_output, stopnet_target, output_lens) if self.config.stopnet else torch.zeros(1)
+            self.criterion_st(stopnet_output, stopnet_target, stop_target_length)
+            if self.config.stopnet
+            else torch.zeros(1)
         )
-        if not self.config.separate_stopnet and self.config.stopnet:
+        loss += stop_loss
-            loss += stop_loss
         return_dict["stopnet_loss"] = stop_loss
 
         # backward decoder loss (if enabled)

TTS/tts/models/base_tts.py

@@ -119,9 +119,10 @@ class BaseTTS(BaseModel):
                 ), f" [!] total duration {dur.sum()} vs spectrogram length {mel_lengths[idx]}"
                 durations[idx, : text_lengths[idx]] = dur
 
-        # set stop targets view, we predict a single stop token per iteration.
+        # set stop targets wrt reduction factor
         stop_targets = stop_targets.view(text_input.shape[0], stop_targets.size(1) // self.config.r, -1)
         stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze(2)
+        stop_target_lengths = torch.divide(mel_lengths, self.config.r).ceil_()
 
         return {
             "text_input": text_input,
@@ -131,6 +132,7 @@ class BaseTTS(BaseModel):
             "mel_lengths": mel_lengths,
             "linear_input": linear_input,
             "stop_targets": stop_targets,
+            "stop_target_lengths": stop_target_lengths,
             "attn_mask": attn_mask,
             "durations": durations,
             "speaker_ids": speaker_ids,

TTS/tts/models/tacotron.py

@@ -219,6 +219,7 @@ class Tacotron(BaseTacotron):
         mel_lengths = batch["mel_lengths"]
         linear_input = batch["linear_input"]
         stop_targets = batch["stop_targets"]
+        stop_target_lengths = batch["stop_target_lengths"]
         speaker_ids = batch["speaker_ids"]
         d_vectors = batch["d_vectors"]
 
@@ -250,6 +251,7 @@ class Tacotron(BaseTacotron):
             linear_input,
             outputs["stop_tokens"],
             stop_targets,
+            stop_target_lengths,
             mel_lengths,
             outputs["decoder_outputs_backward"],
             outputs["alignments"],

TTS/tts/models/tacotron2.py

@@ -224,6 +224,7 @@ class Tacotron2(BaseTacotron):
         mel_lengths = batch["mel_lengths"]
         linear_input = batch["linear_input"]
         stop_targets = batch["stop_targets"]
+        stop_target_lengths = batch["stop_target_lengths"]
         speaker_ids = batch["speaker_ids"]
         d_vectors = batch["d_vectors"]
 
@@ -255,6 +256,7 @@ class Tacotron2(BaseTacotron):
             linear_input,
             outputs["stop_tokens"],
             stop_targets,
+            stop_target_lengths,
             mel_lengths,
             outputs["decoder_outputs_backward"],
             outputs["alignments"],

TTS/tts/utils/data.py

@@ -27,10 +27,19 @@ def prepare_tensor(inputs, out_steps):
     return np.stack([_pad_tensor(x, pad_len) for x in inputs])
 
 
-def _pad_stop_target(x, length):
+def _pad_stop_target(x: np.ndarray, length: int, pad_val=1) -> np.ndarray:
-    _pad = 0.0
+    """Pad stop target array.
+
+    Args:
+        x (np.ndarray): Stop target array.
+        length (int): Length after padding.
+        pad_val (int, optional): Padding value. Defaults to 1.
+
+    Returns:
+        np.ndarray: Padded stop target array.
+    """
     assert x.ndim == 1
-    return np.pad(x, (0, length - x.shape[0]), mode="constant", constant_values=_pad)
+    return np.pad(x, (0, length - x.shape[0]), mode="constant", constant_values=pad_val)
 
 
 def prepare_stop_target(inputs, out_steps):

tests/data_tests/test_loader.py

@@ -207,7 +207,7 @@ class TestTTSDataset(unittest.TestCase):
                 assert linear_input[1 - idx, -1].sum() == 0
                 assert mel_input[1 - idx, -1].sum() == 0
                 assert stop_target[1, mel_lengths[1] - 1] == 1
-                assert stop_target[1, mel_lengths[1] :].sum() == 0
+                assert stop_target[1, mel_lengths[1] :].sum() == stop_target.shape[1] - mel_lengths[1]
                 assert len(mel_lengths.shape) == 1
 
                 # check batch zero-frame conditions (zero-frame disabled)