Comment Tacotron2 model

TTS/tts/models/tacotron.py

@@ -24,7 +24,7 @@ class Tacotron(BaseTacotron):
             a multi-speaker model. Defaults to None.
     """
 
-    def __init__(self, config: Coqpit, speaker_manager: SpeakerManager=None):
+    def __init__(self, config: Coqpit, speaker_manager: SpeakerManager = None):
         super().__init__(config)
 
         self.speaker_manager = speaker_manager

TTS/tts/models/tacotron2.py

@@ -1,5 +1,6 @@
 # coding: utf-8
 
+from typing import Dict
 import torch
 from coqpit import Coqpit
 from torch import nn
@@ -38,7 +39,7 @@ class Tacotron2(BaseTacotron):
             Speaker manager for multi-speaker training. Uuse only for multi-speaker training. Defaults to None.
     """
 
-    def __init__(self, config: Coqpit, speaker_manager: SpeakerManager=None):
+    def __init__(self, config: Coqpit, speaker_manager: SpeakerManager = None):
         super().__init__(config)
 
         self.speaker_manager = speaker_manager
@@ -132,11 +133,11 @@ class Tacotron2(BaseTacotron):
         """Forward pass for training with Teacher Forcing.
 
         Shapes:
-            text: [B, T_in]
-            text_lengths: [B]
-            mel_specs: [B, T_out, C]
-            mel_lengths: [B]
-            aux_input: 'speaker_ids': [B, 1] and  'd_vectors':[B, C]
+            text: :math:`[B, T_in]`
+            text_lengths: :math:`[B]`
+            mel_specs: :math:`[B, T_out, C]`
+            mel_lengths: :math:`[B]`
+            aux_input: 'speaker_ids': :math:`[B, 1]` and  'd_vectors': :math:`[B, C]`
         """
         aux_input = self._format_aux_input(aux_input)
         outputs = {"alignments_backward": None, "decoder_outputs_backward": None}
@@ -199,9 +200,9 @@ class Tacotron2(BaseTacotron):
     def inference(self, text, aux_input=None):
         """Forward pass for inference with no Teacher-Forcing.
 
-         Shapes:
-            text: :math:`[B, T_in]`
-            text_lengths: :math:`[B]`
+        Shapes:
+           text: :math:`[B, T_in]`
+           text_lengths: :math:`[B]`
         """
         aux_input = self._format_aux_input(aux_input)
         embedded_inputs = self.embedding(text).transpose(1, 2)
@@ -236,12 +237,12 @@ class Tacotron2(BaseTacotron):
         }
         return outputs
 
-    def train_step(self, batch, criterion):
+    def train_step(self, batch:Dict, criterion:torch.nn.Module):
         """A single training step. Forward pass and loss computation.
 
         Args:
-            batch ([type]): [description]
-            criterion ([type]): [description]
+            batch ([Dict]): A dictionary of input tensors.
+            criterion ([type]): Callable criterion to compute model loss.
         """
         text_input = batch["text_input"]
         text_lengths = batch["text_lengths"]
@@ -296,6 +297,7 @@ class Tacotron2(BaseTacotron):
         return outputs, loss_dict
 
     def _create_logs(self, batch, outputs, ap):
+        """Create dashboard log information."""
         postnet_outputs = outputs["model_outputs"]
         alignments = outputs["alignments"]
         alignments_backward = outputs["alignments_backward"]
@@ -321,6 +323,7 @@ class Tacotron2(BaseTacotron):
     def train_log(
         self, batch: dict, outputs: dict, logger: "Logger", assets: dict, steps: int
     ) -> None:  # pylint: disable=no-self-use
+        """Log training progress."""
         ap = assets["audio_processor"]
         figures, audios = self._create_logs(batch, outputs, ap)
         logger.train_figures(steps, figures)

TTS/tts/models/vits.py

@@ -1,5 +1,4 @@
 import math
-import os
 import random
 from dataclasses import dataclass, field
 from itertools import chain

TTS/tts/utils/ssim.py

@@ -23,8 +23,10 @@ def _ssim(img1, img2, window, window_size, channel, size_average=True):
     mu1 = F.conv2d(img1, window, padding=window_size // 2, groups=channel)
     mu2 = F.conv2d(img2, window, padding=window_size // 2, groups=channel)
 
-    mu1_sq = mu1.pow(2)
-    mu2_sq = mu2.pow(2)
+    # TODO: check if you need AMP disabled
+    # with torch.cuda.amp.autocast(enabled=False):
+    mu1_sq = mu1.float().pow(2)
+    mu2_sq = mu2.float().pow(2)
     mu1_mu2 = mu1 * mu2
 
     sigma1_sq = F.conv2d(img1 * img1, window, padding=window_size // 2, groups=channel) - mu1_sq