fix glow-tts inference()

TTS/tts/models/glow_tts.py

@@ -290,7 +290,10 @@ def decoder_inference(
         return outputs
 
     @torch.no_grad()
-    def inference(self, x, x_lengths, cond_input={"d_vectors": None}):  # pylint: disable=dangerous-default-value
+    def inference(
+        self, x, cond_input={"x_lengths": None, "d_vectors": None}
+    ):  # pylint: disable=dangerous-default-value
+        x_lengths = cond_input["x_lengths"]
         g = cond_input["d_vectors"] if cond_input is not None and "d_vectors" in cond_input else None
         if g is not None:
             if self.d_vector_dim:

TTS/tts/utils/synthesis.py

@@ -1,8 +1,10 @@
 import os
+from typing import Dict
 
 import numpy as np
 import pkg_resources
 import torch
+from torch import nn
 
 from .text import phoneme_to_sequence, text_to_sequence
 
@@ -65,9 +67,34 @@ def compute_style_mel(style_wav, ap, cuda=False):
     return style_mel
 
 
-def run_model_torch(model, inputs, speaker_id=None, style_mel=None, d_vector=None):
+def run_model_torch(
+    model: nn.Module,
+    inputs: torch.Tensor,
+    speaker_id: int = None,
+    style_mel: torch.Tensor = None,
+    d_vector: torch.Tensor = None,
+) -> Dict:
+    """Run a torch model for inference. It does not support batch inference.
+
+    Args:
+        model (nn.Module): The model to run inference.
+        inputs (torch.Tensor): Input tensor with character ids.
+        speaker_id (int, optional): Input speaker ids for multi-speaker models. Defaults to None.
+        style_mel (torch.Tensor, optional): Spectrograms used for voice styling . Defaults to None.
+        d_vector (torch.Tensor, optional): d-vector for multi-speaker models    . Defaults to None.
+
+    Returns:
+        Dict: model outputs.
+    """
+    input_lengths = torch.tensor(inputs.shape[1:2]).to(inputs.device)
     outputs = model.inference(
-        inputs, cond_input={"speaker_ids": speaker_id, "d_vector": d_vector, "style_mel": style_mel}
+        inputs,
+        cond_input={
+            "x_lengths": input_lengths,
+            "speaker_ids": speaker_id,
+            "d_vectors": d_vector,
+            "style_mel": style_mel,
+        },
     )
     return outputs
 

tests/inference_tests/test_synthesizer.py

@@ -1,13 +1,14 @@
 import os
 import unittest
 
-from tests import get_tests_output_path
 from TTS.config import load_config
 from TTS.tts.models import setup_model
 from TTS.tts.utils.io import save_checkpoint
 from TTS.tts.utils.text.symbols import make_symbols, phonemes, symbols
 from TTS.utils.synthesizer import Synthesizer
 
+from .. import get_tests_output_path
+
 
 class SynthesizerTest(unittest.TestCase):
     # pylint: disable=R0201

tests/tts_tests/test_tacotron2_model.py

@@ -259,9 +259,7 @@ def test_train_step():
         stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
         criterion = MSELossMasked(seq_len_norm=False).to(device)
         criterion_st = nn.BCEWithLogitsLoss().to(device)
-        model = Tacotron2(num_chars=24, r=c.r, num_speakers=5, d_vector_dim=55, use_gst=True, gst=c.gst).to(
-            device
-        )
+        model = Tacotron2(num_chars=24, r=c.r, num_speakers=5, d_vector_dim=55, use_gst=True, gst=c.gst).to(device)
         model.train()
         model_ref = copy.deepcopy(model)
         count = 0

tests/tts_tests/test_tacotron2_train.py

@@ -8,7 +8,6 @@
 config_path = os.path.join(get_tests_output_path(), "test_model_config.json")
 output_path = os.path.join(get_tests_output_path(), "train_outputs")
 
-
 config = Tacotron2Config(
     r=5,
     batch_size=8,