From dbfc489775844d7ad8270b8062cdc25cdfe77286 Mon Sep 17 00:00:00 2001
From: Eren Golge <egolge@mozilla.com>
Date: Tue, 17 Apr 2018 10:05:50 -0700
Subject: [PATCH] Remove redundant dataset import

---
 train.py | 59 ++++++++++++++++++++++++++++----------------------------
 1 file changed, 29 insertions(+), 30 deletions(-)

diff --git a/train.py b/train.py
index 3786c0ee..7e91a77a 100644
--- a/train.py
+++ b/train.py
@@ -23,7 +23,6 @@ from utils.generic_utils import (Progbar, remove_experiment_folder,
                                  count_parameters, check_update, get_commit_hash)
 from utils.model import get_param_size
 from utils.visual import plot_alignment, plot_spectrogram
-from datasets.LJSpeech import LJSpeechDataset
 from models.tacotron import Tacotron
 from layers.losses import L1LossMasked
 
@@ -296,41 +295,41 @@ def main(args):
     Dataset = getattr(mod, c.dataset+"Dataset")
 
     # Setup the dataset
-    train_dataset = LJSpeechDataset(os.path.join(c.data_path, c.meta_file_train),
-                                    os.path.join(c.data_path, 'wavs'),
-                                    c.r,
-                                    c.sample_rate,
-                                    c.text_cleaner,
-                                    c.num_mels,
-                                    c.min_level_db,
-                                    c.frame_shift_ms,
-                                    c.frame_length_ms,
-                                    c.preemphasis,
-                                    c.ref_level_db,
-                                    c.num_freq,
-                                    c.power,
-                                    min_seq_len=c.min_seq_len
-                                    )
+    train_dataset = Dataset(os.path.join(c.data_path, c.meta_file_train),
+                            os.path.join(c.data_path, 'wavs'),
+                            c.r,
+                            c.sample_rate,
+                            c.text_cleaner,
+                            c.num_mels,
+                            c.min_level_db,
+                            c.frame_shift_ms,
+                            c.frame_length_ms,
+                            c.preemphasis,
+                            c.ref_level_db,
+                            c.num_freq,
+                            c.power,
+                            min_seq_len=c.min_seq_len
+                            )
 
     train_loader = DataLoader(train_dataset, batch_size=c.batch_size,
                               shuffle=False, collate_fn=train_dataset.collate_fn,
                               drop_last=False, num_workers=c.num_loader_workers,
                               pin_memory=True)
 
-    val_dataset = LJSpeechDataset(os.path.join(c.data_path, c.meta_file_val),
-                                  os.path.join(c.data_path, 'wavs'),
-                                  c.r,
-                                  c.sample_rate,
-                                  c.text_cleaner,
-                                  c.num_mels,
-                                  c.min_level_db,
-                                  c.frame_shift_ms,
-                                  c.frame_length_ms,
-                                  c.preemphasis,
-                                  c.ref_level_db,
-                                  c.num_freq,
-                                  c.power
-                                  )
+    val_dataset = Dataset(os.path.join(c.data_path, c.meta_file_val),
+                          os.path.join(c.data_path, 'wavs'),
+                          c.r,
+                          c.sample_rate,
+                          c.text_cleaner,
+                          c.num_mels,
+                          c.min_level_db,
+                          c.frame_shift_ms,
+                          c.frame_length_ms,
+                          c.preemphasis,
+                          c.ref_level_db,
+                          c.num_freq,
+                          c.power
+                          )
 
     val_loader = DataLoader(val_dataset, batch_size=c.eval_batch_size,
                             shuffle=False, collate_fn=val_dataset.collate_fn,