Formating and printing more about the model

config.json

@@ -3,7 +3,7 @@
     "audio_processor": "audio",
     "num_mels": 80,
     "num_freq": 1025,
-    "sample_rate": 22050,
+    "sample_rate": 22000,
     "frame_length_ms": 50,
     "frame_shift_ms": 12.5,
     "preemphasis": 0.97,
@@ -21,7 +21,7 @@
     "eval_batch_size":-1,
     "r": 5,
 
-    "griffin_lim_iters": 60,
+    "griffin_lim_iters": 50,
     "power": 1.5,
 
     "num_loader_workers": 8,

config_kusal.json

@@ -3,7 +3,7 @@
     "audio_processor": "audio",
     "num_mels": 80,
     "num_freq": 1025,
-    "sample_rate": 22050,
+    "sample_rate": 22000,
     "frame_length_ms": 50,
     "frame_shift_ms": 12.5,
     "preemphasis": 0.97,
@@ -21,7 +21,7 @@
     "eval_batch_size":-1,
     "r": 5,
 
-    "griffin_lim_iters": 60,
+    "griffin_lim_iters": 50,
     "power": 1.5,
 
     "num_loader_workers": 8,

tests/tacotron_tests.py

@@ -62,8 +62,8 @@ class TacotronTrainTest(unittest.TestCase):
         for param, param_ref in zip(model.parameters(),
                                     model_ref.parameters()):
             # ignore pre-higway layer since it works conditional
-            if count not in [148, 59]:
+            # if count not in [145, 59]:
-                assert (param != param_ref).any(
+            assert (param != param_ref).any(
-                ), "param {} with shape {} not updated!! \n{}\n{}".format(
+            ), "param {} with shape {} not updated!! \n{}\n{}".format(
-                    count, param.shape, param, param_ref)
+                count, param.shape, param, param_ref)
-            count += 1
+            count += 1

train.py

@@ -37,7 +37,7 @@ def train(model, criterion, criterion_st, data_loader, optimizer, optimizer_st,
     avg_step_time = 0
     print(" | > Epoch {}/{}".format(epoch, c.epochs), flush=True)
     n_priority_freq = int(3000 / (c.sample_rate * 0.5) * c.num_freq)
-    batch_n_iter = len(data_loader.dataset) / c.batch_size
+    batch_n_iter = int(len(data_loader.dataset) / c.batch_size)
     for num_iter, data in enumerate(data_loader):
         start_time = time.time()
 
@@ -321,13 +321,14 @@ def evaluate(model, criterion, criterion_st, data_loader, ap, current_step):
     # test sentences
     ap.griffin_lim_iters = 60
     for idx, test_sentence in enumerate(test_sentences):
+        try:
         wav, linear_spec, alignments = synthesis(model, ap, test_sentence,
                                                  use_cuda, c.text_cleaner)
-        try:
+        wav_name = 'TestSentences/{}'.format(idx)
-            wav_name = 'TestSentences/{}'.format(idx)
+        tb.add_audio(
-            tb.add_audio(
+            wav_name, wav, current_step, sample_rate=c.sample_rate)
-                wav_name, wav, current_step, sample_rate=c.sample_rate)
         except:
+            print(" !! Error as creating Test Sentence -", idx)
             pass
         align_img = alignments[0].data.cpu().numpy()
         linear_spec = plot_spectrogram(linear_spec, ap)

utils/audio.py

@@ -23,6 +23,7 @@ class AudioProcessor(object):
                  max_mel_freq,
                  griffin_lim_iters=None):
 
+        print(" > Setting up Audio Processor...")
         self.sample_rate = sample_rate
         self.num_mels = num_mels
         self.min_level_db = min_level_db
@@ -36,11 +37,12 @@ class AudioProcessor(object):
         self.max_mel_freq = max_mel_freq
         self.griffin_lim_iters = griffin_lim_iters
         self.n_fft, self.hop_length, self.win_length = self._stft_parameters()
+        if preemphasis == 0:
+            print(" | > Preemphasis is deactive.")
 
     def save_wav(self, wav, path):
         wav *= 32767 / max(0.01, np.max(np.abs(wav)))
-        librosa.output.write_wav(
+        librosa.output.write_wav(path, wav.astype(np.int16), self.sample_rate)
-            path, wav.astype(np.int16), self.sample_rate)
 
     def _linear_to_mel(self, spectrogram):
         global _mel_basis
@@ -64,6 +66,10 @@ class AudioProcessor(object):
         n_fft = (self.num_freq - 1) * 2
         hop_length = int(self.frame_shift_ms / 1000.0 * self.sample_rate)
         win_length = int(self.frame_length_ms / 1000.0 * self.sample_rate)
+        hop_length = 256
+        win_length = 1024
+        print(" | > fft size: {}, hop length: {}, win length: {}".format(
+            n_fft, hop_length, win_length))
         return n_fft, hop_length, win_length
 
     def _amp_to_db(self, x):
@@ -123,13 +129,11 @@ class AudioProcessor(object):
         return self._normalize(S)
 
     def _stft(self, y):
-        n_fft, hop_length, win_length = self._stft_parameters()
         return librosa.stft(
-            y=y, n_fft=n_fft, hop_length=hop_length, win_length=win_length)
+            y=y, n_fft=self.n_fft, hop_length=self.hop_length, win_length=self.win_length)
 
     def _istft(self, y):
-        _, hop_length, win_length = self._stft_parameters()
+        return librosa.istft(y, hop_length=self.hop_length, win_length=self.win_length)
-        return librosa.istft(y, hop_length=hop_length, win_length=win_length)
 
     def find_endpoint(self, wav, threshold_db=-40, min_silence_sec=0.8):
         window_length = int(self.sample_rate * min_silence_sec)