added glow as module

2. rename private methods

taco2/tts.py

@@ -17,7 +17,7 @@ from .audio_processing import griffin_lim, postprocess_audio
 
 OUTPUT_SAMPLE_RATE = 22050
 GL_ITERS = 30
-VOCODER_MODEL = "wavglow"
+VOCODER_WAVEGLOW, VOCODER_GL = "wavglow", "gl"
 
 # config from
 # https://github.com/NVIDIA/waveglow/blob/master/config.json
@@ -74,11 +74,11 @@ class TTSModel(object):
                 self.waveglow, n_mel_channels=hparams.n_mel_channels
             )
             self.synth_speech = klepto.safe.inf_cache(cache=self.k_cache)(
-                self.synth_speech
+                self._synth_speech
             )
         else:
             self.synth_speech = klepto.safe.inf_cache(cache=self.k_cache)(
-                self.synth_speech_fast
+                self._synth_speech_fast
             )
         self.taco_stft = TacotronSTFT(
             hparams.filter_length,
@@ -89,7 +89,7 @@ class TTSModel(object):
             mel_fmax=4000,
         )
 
-    def generate_mel_postnet(self, text):
+    def _generate_mel_postnet(self, text):
         sequence = np.array(text_to_sequence(text, ["english_cleaners"]))[None, :]
         if torch.cuda.is_available():
             sequence = torch.autograd.Variable(torch.from_numpy(sequence)).cuda().long()
@@ -102,14 +102,14 @@ class TTSModel(object):
         return mel_outputs_postnet
 
     def synth_speech_array(self, text, vocoder):
-        mel_outputs_postnet = self.generate_mel_postnet(text)
+        mel_outputs_postnet = self._generate_mel_postnet(text)
 
-        if vocoder == "wavglow":
+        if vocoder == VOCODER_WAVEGLOW:
             with torch.no_grad():
                 audio_t = self.waveglow.infer(mel_outputs_postnet, sigma=0.666)
                 audio_t = self.denoiser(audio_t, 0.1)[0]
             audio = audio_t[0].data
-        elif vocoder == "gl":
+        elif vocoder == VOCODER_GL:
             mel_decompress = self.taco_stft.spectral_de_normalize(mel_outputs_postnet)
             mel_decompress = mel_decompress.transpose(1, 2).data.cpu()
             spec_from_mel_scaling = 1000
@@ -122,7 +122,7 @@ class TTSModel(object):
             audio = griffin_lim(
                 torch.autograd.Variable(spec_from_mel[:, :, :-1]),
                 self.taco_stft.stft_fn,
-                60,
+                GL_ITERS,
             )
             audio = audio.squeeze()
         else:
@@ -130,36 +130,28 @@ class TTSModel(object):
         audio = audio.cpu().numpy()
         return audio
 
-    def synth_speech(
+    def _synth_speech(
         self, text, speed: float = 1.0, sample_rate: int = OUTPUT_SAMPLE_RATE
     ):
-        audio = self.synth_speech_array(text, VOCODER_MODEL)
+        audio = self.synth_speech_array(text, VOCODER_WAVEGLOW)
 
         return postprocess_audio(
-            audio, src_rate=self.hparams.sampling_rate, dst_rate=sample_rate, tempo=speed
+            audio,
+            src_rate=self.hparams.sampling_rate,
+            dst_rate=sample_rate,
+            tempo=speed,
         )
 
-    def synth_speech_fast(
+    def _synth_speech_fast(
         self, text, speed: float = 1.0, sample_rate: int = OUTPUT_SAMPLE_RATE
     ):
-        mel_outputs_postnet = self.generate_mel_postnet(text)
+        audio = self.synth_speech_array(text, VOCODER_GL)
-
-        mel_decompress = self.taco_stft.spectral_de_normalize(mel_outputs_postnet)
-        mel_decompress = mel_decompress.transpose(1, 2).data.cpu()
-        spec_from_mel_scaling = 1000
-        spec_from_mel = torch.mm(mel_decompress[0], self.taco_stft.mel_basis)
-        spec_from_mel = spec_from_mel.transpose(0, 1).unsqueeze(0)
-        spec_from_mel = spec_from_mel * spec_from_mel_scaling
-        audio = griffin_lim(
-            torch.autograd.Variable(spec_from_mel[:, :, :-1]),
-            self.taco_stft.stft_fn,
-            GL_ITERS,
-        )
-        audio = audio.squeeze()
-        audio = audio.cpu().numpy()
 
         return postprocess_audio(
-            audio, tempo=speed, src_rate=self.hparams.sampling_rate, dst_rate=sample_rate
+            audio,
+            tempo=speed,
+            src_rate=self.hparams.sampling_rate,
+            dst_rate=sample_rate,
         )