1. clean-up

2. update readme and release info

taco2/tts.py

@@ -46,7 +46,7 @@ class TTSModel(object):
             self.waveglow.load_state_dict(wave_params)
             self.waveglow.eval()
         except:
-            self.waveglow = wave_params['model']
+            self.waveglow = wave_params["model"]
             self.waveglow = self.waveglow.remove_weightnorm(self.waveglow)
             self.waveglow.eval()
         # workaround from
@@ -60,7 +60,6 @@ class TTSModel(object):
         self.synth_speech = klepto.safe.inf_cache(cache=self.k_cache)(self.synth_speech)
         self.denoiser = Denoiser(self.waveglow)
 
-
     def synth_speech(self, text):
         sequence = np.array(text_to_sequence(text, ["english_cleaners"]))[None, :]
         sequence = torch.autograd.Variable(torch.from_numpy(sequence)).long()
@@ -78,15 +77,23 @@ class TTSModel(object):
         data = float2pcm(float_data)
         return data.tobytes()
 
-    def synth_speech_algo(self,text,griffin_iters=60):
+    def synth_speech_algo(self, text, griffin_iters=60):
         sequence = np.array(text_to_sequence(text, ["english_cleaners"]))[None, :]
         sequence = torch.autograd.Variable(torch.from_numpy(sequence)).long()
         mel_outputs, mel_outputs_postnet, _, alignments = self.model.inference(sequence)
         from .hparams import HParams
         from .layers import TacotronSTFT
         from .audio_processing import griffin_lim
+
         hparams = HParams()
-        taco_stft = TacotronSTFT(hparams.filter_length, hparams.hop_length, hparams.win_length, n_mel_channels=hparams.n_mel_channels, sampling_rate=hparams.sampling_rate, mel_fmax=4000)
+        taco_stft = TacotronSTFT(
+            hparams.filter_length,
+            hparams.hop_length,
+            hparams.win_length,
+            n_mel_channels=hparams.n_mel_channels,
+            sampling_rate=hparams.sampling_rate,
+            mel_fmax=4000,
+        )
         mel_decompress = taco_stft.spectral_de_normalize(mel_outputs_postnet)
         mel_decompress = mel_decompress.transpose(1, 2).data.cpu()
         spec_from_mel_scaling = 1000
@@ -94,7 +101,11 @@ class TTSModel(object):
         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]), taco_stft.stft_fn, griffin_iters)
+        audio = griffin_lim(
+            torch.autograd.Variable(spec_from_mel[:, :, :-1]),
+            taco_stft.stft_fn,
+            griffin_iters,
+        )
         audio = audio.squeeze()
         audio = audio.cpu().numpy()
 
@@ -102,6 +113,8 @@ class TTSModel(object):
         float_data = resample(slow_data, TTS_SAMPLE_RATE, OUTPUT_SAMPLE_RATE)
         data = float2pcm(float_data)
         return data.tobytes()
+
+
 # adapted from
 # https://github.com/mgeier/python-audio/blob/master/audio-files/utility.py
 def float2pcm(sig, dtype="int16"):
@@ -140,13 +153,6 @@ def float2pcm(sig, dtype="int16"):
     return (sig * abs_max + offset).clip(i.min, i.max).astype(dtype)
 
 
-def display(data):
-    import IPython.display as ipd
-
-    aud = ipd.Audio(data, rate=16000)
-    return aud
-
-
 def player_gen():
     audio_interface = pyaudio.PyAudio()
     _audio_stream = audio_interface.open(
@@ -160,51 +166,22 @@ def player_gen():
     return play_device
 
 
-def synthesize_corpus():
-    tts_model = TTSModel(
-        "/Users/malar/Work/tacotron2_statedict.pt",
-        "/Users/malar/Work/waveglow.pt",
-    )
-    all_data = []
-    for (i, line) in enumerate(open("corpus.txt").readlines()):
-        print(f'synthesizing... "{line.strip()}"')
-        data = tts_model.synth_speech(line.strip())
-        all_data.append(data)
-    return all_data
-
 def repl():
-    tts_model = TTSModel(
-        # "/Users/malar/Work/tacotron2_statedict.pt",
-        # "/Users/malar/Work/tacotron2_80_22000.pt",
-        "/path/to/tacotron2.pt",
-        # "/Users/malar/Work/tacotron2_40_22000.pt",
-        # "/Users/malar/Work/tacotron2_16000.pt",
-        "/path/to/waveglow.pt",
-        # "/Users/malar/Work/waveglow.pt",
-        # "/Users/malar/Work/waveglow_38000",
-    )
+    tts_model = TTSModel("/path/to/tacotron2.pt", "/path/to/waveglow.pt")
     player = player_gen()
+
     def loop():
-        text = input('tts >')
+        text = input("tts >")
         data = tts_model.synth_speech(text.strip())
         player(data)
+
     return loop
 
 
-def play_corpus(corpus_synths):
-    player = player_gen()
-    for d in corpus_synths:
-        player(d)
-
-
 def main():
-    # corpus_synth_data = synthesize_corpus()
-    # play_corpus(corpus_synth_data)
     interactive_loop = repl()
     while True:
         interactive_loop()
-    # import pdb
-    # pdb.set_trace()
 
 
 if __name__ == "__main__":