rename glow-tts-2 to glow-tts (removing git submodule)

glow-tts-jaywalnut/glow-tts/init.py

@@ -0,0 +1,58 @@
+import os
+import json
+import argparse
+import math
+import torch
+from torch import nn, optim
+from torch.nn import functional as F
+from torch.utils.data import DataLoader
+
+from data_utils import TextMelLoader, TextMelCollate
+import models
+import commons
+import utils
+from text.symbols import symbols
+                            
+
+class FlowGenerator_DDI(models.FlowGenerator):
+  """A helper for Data-dependent Initialization"""
+  def __init__(self, *args, **kwargs):
+    super().__init__(*args, **kwargs)
+    for f in self.decoder.flows:
+      if getattr(f, "set_ddi", False):
+        f.set_ddi(True)
+
+
+def main():
+  hps = utils.get_hparams()
+  logger = utils.get_logger(hps.model_dir)
+  logger.info(hps)
+  utils.check_git_hash(hps.model_dir)
+
+  torch.manual_seed(hps.train.seed)
+
+  train_dataset = TextMelLoader(hps.data.training_files, hps.data)
+  collate_fn = TextMelCollate(1)
+  train_loader = DataLoader(train_dataset, num_workers=8, shuffle=True,
+      batch_size=hps.train.batch_size, pin_memory=True,
+      drop_last=True, collate_fn=collate_fn)
+
+  generator = FlowGenerator_DDI(
+      len(symbols), 
+      out_channels=hps.data.n_mel_channels,
+      **hps.model).cuda()
+  optimizer_g = commons.Adam(generator.parameters(), scheduler=hps.train.scheduler, dim_model=hps.model.hidden_channels, warmup_steps=hps.train.warmup_steps, lr=hps.train.learning_rate, betas=hps.train.betas, eps=hps.train.eps)
+   
+  generator.train()
+  for batch_idx, (x, x_lengths, y, y_lengths) in enumerate(train_loader):
+    x, x_lengths = x.cuda(), x_lengths.cuda()
+    y, y_lengths = y.cuda(), y_lengths.cuda()
+
+    _ = generator(x, x_lengths, y, y_lengths, gen=False)
+    break
+
+  utils.save_checkpoint(generator, optimizer_g, hps.train.learning_rate, 0, os.path.join(hps.model_dir, "ddi_G.pth"))
+
+                            
+if __name__ == "__main__":
+  main()