compute sequence mask in model, add tacotron2 relatedfiles

2026-09-12 12:11:47 +08:00 · 2019-03-06 13:14:58 +01:00
parent a2a22d253f
commit b031a65677
4 changed files with 508 additions and 7 deletions
@@ -0,0 +1,69 @@
+import os
+import copy
+import torch
+import unittest
+import numpy as np
+
+from torch import optim
+from torch import nn
+from utils.generic_utils import load_config
+from layers.losses import MSELossMasked
+from models.tacotron2 import Tacotron2
+
+torch.manual_seed(1)
+use_cuda = torch.cuda.is_available()
+device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
+
+file_path = os.path.dirname(os.path.realpath(__file__))
+c = load_config(os.path.join(file_path, 'test_config.json'))
+
+
+class TacotronTrainTest(unittest.TestCase):
+    def test_train_step(self):
+        input = torch.randint(0, 24, (8, 128)).long().to(device)
+        input_lengths = torch.randint(100, 128, (8, )).long().to(device)
+        input_lengths = torch.sort(input_lengths, descending=True)[0]
+        mel_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
+        mel_postnet_spec = torch.rand(8, 30, c.audio['num_mels']).to(device)
+        mel_lengths = torch.randint(20, 30, (8, )).long().to(device)
+        stop_targets = torch.zeros(8, 30, 1).float().to(device)
+
+        for idx in mel_lengths:
+            stop_targets[:, int(idx.item()):, 0] = 1.0
+
+        stop_targets = stop_targets.view(input.shape[0],
+                                         stop_targets.size(1) // c.r, -1)
+        stop_targets = (stop_targets.sum(2) > 0.0).unsqueeze(2).float().squeeze()
+
+        criterion = MSELossMasked().to(device)
+        criterion_st = nn.BCEWithLogitsLoss().to(device)
+        model = Tacotron2(24, c.r).to(device)
+        model.train()
+        model_ref = copy.deepcopy(model)
+        count = 0
+        for param, param_ref in zip(model.parameters(),
+                                    model_ref.parameters()):
+            assert (param - param_ref).sum() == 0, param
+            count += 1
+        optimizer = optim.Adam(model.parameters(), lr=c.lr)
+        for i in range(5):
+            mel_out, mel_postnet_out, align, stop_tokens = model.forward(
+                input, input_lengths, mel_spec)
+            assert torch.sigmoid(stop_tokens).data.max() <= 1.0
+            assert torch.sigmoid(stop_tokens).data.min() >= 0.0
+            optimizer.zero_grad()
+            loss = criterion(mel_out, mel_spec, mel_lengths)
+            stop_loss = criterion_st(stop_tokens, stop_targets)
+            loss = loss + criterion(mel_postnet_out, mel_postnet_spec, mel_lengths) + stop_loss
+            loss.backward()
+            optimizer.step()
+        # check parameter changes
+        count = 0
+        for param, param_ref in zip(model.parameters(),
+                                    model_ref.parameters()):
+            # ignore pre-higway layer since it works conditional
+            # if count not in [145, 59]:
+            assert (param != param_ref).any(
+            ), "param {} with shape {} not updated!! \n{}\n{}".format(
+                count, param.shape, param, param_ref)
+            count += 1