Merge branch 'dev' of https://github.com/mozilla/TTS into dev

TTS/bin/synthesize.py

@@ -9,6 +9,7 @@ import string
 import time
 
 import torch
+import numpy as np
 
 from TTS.tts.utils.generic_utils import setup_model, is_tacotron
 from TTS.tts.utils.synthesis import synthesis
@@ -21,6 +22,18 @@ from TTS.vocoder.utils.generic_utils import setup_generator
 def tts(model, vocoder_model, text, CONFIG, use_cuda, ap, use_gl, speaker_fileid, speaker_embedding=None, gst_style=None):
     t_1 = time.time()
     waveform, _, _, mel_postnet_spec, _, _ = synthesis(model, text, CONFIG, use_cuda, ap, speaker_fileid, gst_style, False, CONFIG.enable_eos_bos_chars, use_gl, speaker_embedding=speaker_embedding)
+
+    # grab spectrogram (thx to the nice guys at mozilla discourse for codesnipplet)
+    if args.save_spectogram:
+        spec_file_name = args.text.replace(" ", "_")
+        spec_file_name = spec_file_name.translate(
+            str.maketrans('', '', string.punctuation.replace('_', ''))) + '.npy'
+        spec_file_name = os.path.join(args.out_path, spec_file_name)
+        spectrogram = torch.FloatTensor(mel_postnet_spec.T)
+        spectrogram = spectrogram.unsqueeze(0)
+        np.save(spec_file_name, spectrogram)
+        print(" > Saving raw spectogram to " + spec_file_name)
+
     if CONFIG.model == "Tacotron" and not use_gl:
         mel_postnet_spec = ap.out_linear_to_mel(mel_postnet_spec.T).T
     if not use_gl:
@@ -88,6 +101,11 @@ if __name__ == "__main__":
         '--gst_style',
         help="Wav path file for GST stylereference.",
         default=None)
+    parser.add_argument(
+        '--save_spectogram',
+        type=bool,
+        help="If true save raw spectogram for further (vocoder) processing in out_path.",
+        default=False)
 
     args = parser.parse_args()
 

TTS/vocoder/models/wavegrad.py

@@ -105,8 +105,8 @@ class Wavegrad(nn.Module):
         self.noise_level = self.noise_level.to(y_0)
         if len(y_0.shape) == 3:
             y_0 = y_0.squeeze(1)
-        s = torch.randint(1, self.num_steps + 1, [y_0.shape[0]])
-        l_a, l_b = self.noise_level[s-1], self.noise_level[s]
+        s = torch.randint(0, self.num_steps - 1, [y_0.shape[0]])
+        l_a, l_b = self.noise_level[s], self.noise_level[s+1]
         noise_scale = l_a + torch.rand(y_0.shape[0]).to(y_0) * (l_b - l_a)
         noise_scale = noise_scale.unsqueeze(1)
         noise = torch.randn_like(y_0)