BUG fixes and more visualization changes

datasets/LJSpeech.py

@@ -29,7 +29,7 @@ class LJSpeechDataset(Dataset):
 
     def load_wav(self, filename):
         try:
-            audio = librosa.load(filename, sr=self.sample_rate)
+            audio = librosa.core.load(filename, sr=self.sample_rate)
             return audio
         except RuntimeError as e:
             print(" !! Cannot read file : {}".format(filename))
@@ -43,7 +43,7 @@ class LJSpeechDataset(Dataset):
         text = self.frames.ix[idx, 1]
         text = np.asarray(text_to_sequence(text, [self.cleaners]), dtype=np.int32)
         wav = np.asarray(self.load_wav(wav_name)[0], dtype=np.float32)
-        sample = {'text': text, 'wav': wav}
+        sample = {'text': text, 'wav': wav, 'item_idx': self.frames.ix[idx, 0]}
         return sample
 
     def get_dummy_data(self):
@@ -55,33 +55,36 @@ class LJSpeechDataset(Dataset):
         if isinstance(batch[0], collections.Mapping):
             keys = list()
 
+            wav = [d['wav'] for d in batch]
+            item_idxs = [d['item_idx'] for d in batch]
             text = [d['text'] for d in batch]
+
             text_lenghts = np.array([len(x) for x in text])
             max_text_len = np.max(text_lenghts)
-            wav = [d['wav'] for d in batch]
 
             # PAD sequences with largest length of the batch
             text = prepare_data(text).astype(np.int32)
             wav = prepare_data(wav)
 
-            magnitude = np.array([self.ap.spectrogram(w) for w in wav])
+            linear = np.array([self.ap.spectrogram(w).astype('float32') for w in wav])
-            mel = np.array([self.ap.melspectrogram(w) for w in wav])
+            mel = np.array([self.ap.melspectrogram(w).astype('float32') for w in wav])
+            assert mel.shape[2] == linear.shape[2]
             timesteps = mel.shape[2]
 
             # PAD with zeros that can be divided by outputs per step
-            if timesteps % self.outputs_per_step != 0:
+            # if timesteps % self.outputs_per_step != 0:
-                magnitude = pad_per_step(magnitude, self.outputs_per_step)
+            linear = pad_per_step(linear, self.outputs_per_step)
             mel = pad_per_step(mel, self.outputs_per_step)
 
             # reshape jombo
-            magnitude = magnitude.transpose(0, 2, 1)
+            linear = linear.transpose(0, 2, 1)
             mel = mel.transpose(0, 2, 1)
 
             text_lenghts = torch.LongTensor(text_lenghts)
             text = torch.LongTensor(text)
-            magnitude = torch.FloatTensor(magnitude)
+            linear = torch.FloatTensor(linear)
             mel = torch.FloatTensor(mel)
-            return text, text_lenghts, magnitude, mel
+            return text, text_lenghts, linear, mel, item_idxs[0]
 
         raise TypeError(("batch must contain tensors, numbers, dicts or lists;\
                          found {}"

train.py

@@ -94,14 +94,16 @@ def main(args):
 
     optimizer = optim.Adam(model.parameters(), lr=c.lr)
 
-    try:
+    if args.restore_step:
         checkpoint = torch.load(os.path.join(
-            CHECKPOINT_PATH, 'checkpoint_%d.pth.tar' % args.restore_step))
+            args.restore_path, 'checkpoint_%d.pth.tar' % args.restore_step))
         model.load_state_dict(checkpoint['model'])
         optimizer.load_state_dict(checkpoint['optimizer'])
         print("\n > Model restored from step %d\n" % args.restore_step)
+        start_epoch = checkpoint['step'] // len(dataloader)
 
-    except:
+    else:
+        start_epoch = 0
         print("\n > Starting a new training")
 
     model = model.train()
@@ -119,7 +121,7 @@ def main(args):
     #lr_scheduler = ReduceLROnPlateau(optimizer, factor=c.lr_decay,
     #                               patience=c.lr_patience, verbose=True)
     epoch_time = 0
-    for epoch in range(c.epochs):
+    for epoch in range(0, c.epochs):
 
         print("\n | > Epoch {}/{}".format(epoch, c.epochs))
         progbar = Progbar(len(dataset) / c.batch_size)
@@ -214,6 +216,7 @@ def main(args):
                 save_checkpoint({'model': model.state_dict(),
                                  'optimizer': optimizer.state_dict(),
                                  'step': current_step,
+                                 'epoch': epoch,
                                  'total_loss': loss.data[0],
                                  'linear_loss': linear_loss.data[0],
                                  'mel_loss': mel_loss.data[0],
@@ -238,8 +241,13 @@ def main(args):
                 audio_signal = linear_output[0].data.cpu().numpy()
                 dataset.ap.griffin_lim_iters = 60
                 audio_signal = dataset.ap.inv_spectrogram(audio_signal.T)
+                try:
                     tb.add_audio('SampleAudio', audio_signal, current_step,
                                  sample_rate=c.sample_rate)
+                except:
+                    print("\n > Error at audio signal on TB!!")
+                    print(audio_signal.max())
+                    print(audio_signal.min())
 
         #lr_scheduler.step(loss.data[0])
         tb.add_scalar('Time/EpochTime', epoch_time, epoch)
@@ -250,6 +258,8 @@ if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--restore_step', type=int,
                         help='Global step to restore checkpoint', default=0)
+    parser.add_argument('--restore_path', type=str,
+                        help='Folder path to checkpoints', default=0)
     parser.add_argument('--config_path', type=str,
                        help='path to config file for training',)
     args = parser.parse_args()

utils/audio.py

@@ -5,7 +5,6 @@ import numpy as np
 from scipy import signal
 
 _mel_basis = None
-global c
 
 
 class AudioProcessor(object):
@@ -37,7 +36,7 @@ class AudioProcessor(object):
         return np.dot(_mel_basis, spectrogram)
 
 
-    def _build_mel_basis(self):
+    def _build_mel_basis(self, ):
         n_fft = (self.num_freq - 1) * 2
         return librosa.filters.mel(self.sample_rate, n_fft, n_mels=self.num_mels)
 
@@ -50,7 +49,7 @@ class AudioProcessor(object):
         return (np.clip(S, 0, 1) * -self.min_level_db) + self.min_level_db
 
 
-    def _stft_parameters(self):
+    def _stft_parameters(self, ):
         n_fft = (self.num_freq - 1) * 2
         hop_length = int(self.frame_shift_ms / 1000 * self.sample_rate)
         win_length = int(self.frame_length_ms / 1000 * self.sample_rate)
@@ -102,7 +101,7 @@ class AudioProcessor(object):
 
     def melspectrogram(self, y):
         D = self._stft(self.apply_preemphasis(y))
-        S = self._amp_to_db(self._linear_to_mel(np.abs(D)))
+        S = self._amp_to_db(self._linear_to_mel(np.abs(D))) - self.ref_level_db
         return self._normalize(S)
 
 

utils/data.py

@@ -3,6 +3,7 @@ import numpy as np
 
 def pad_data(x, length):
     _pad = 0
+    assert x.ndim == 1
     return np.pad(x, (0, length - x.shape[0]),
                   mode='constant',
                   constant_values=_pad)