style fixes

TTS/tts/models/base_tacotron.py

@@ -252,12 +252,7 @@ class BaseTacotron(BaseTTS):
 
     def compute_capacitron_VAE_embedding(self, inputs, reference_mel_info, text_info=None, speaker_embedding=None):
         """Capacitron Variational Autoencoder"""
-        (
-            VAE_outputs,
-            posterior_distribution,
-            prior_distribution,
-            capacitron_beta,
-        ) = self.capacitron_vae_layer(
+        (VAE_outputs, posterior_distribution, prior_distribution, capacitron_beta,) = self.capacitron_vae_layer(
             reference_mel_info,
             text_info,
             speaker_embedding,  # pylint: disable=not-callable
@@ -302,4 +297,4 @@ class BaseTacotron(BaseTTS):
             self.decoder.set_r(r)
             if trainer.config.bidirectional_decoder:
                 trainer.model.decoder_backward.set_r(r)
-            print(f"\n > Number of output frames: {self.decoder.r}")
+            print(f"\n > Number of output frames: {self.decoder.r}")

TTS/tts/models/tortoise.py

@@ -29,12 +29,12 @@ def pad_or_truncate(t, length):
     """
     Utility function for forcing <t> to have the specified sequence length, whether by clipping it or padding it with 0s.
     """
+    tp = t[..., :length]
     if t.shape[-1] == length:
-        return t
+        tp = t
     elif t.shape[-1] < length:
-        return F.pad(t, (0, length - t.shape[-1]))
-    else:
-        return t[..., :length]
+        tp = F.pad(t, (0, length - t.shape[-1]))
+    return tp
 
 
 def load_discrete_vocoder_diffuser(
@@ -186,19 +186,12 @@ class TextToSpeech:
 
     def _config(self):
         raise RuntimeError("This is depreciated")
-        return {
-            "high_vram": self.high_vram,
-            "models_dir": self.models_dir,
-            "kv_cache": self.autoregressive.inference_model.kv_cache,
-            "ar_checkpoint": self.ar_checkpoint,
-        }
 
     def __init__(
         self,
         autoregressive_batch_size=None,
         models_dir=MODELS_DIR,
         enable_redaction=True,
-        device=None,
         high_vram=False,
         kv_cache=True,
         ar_checkpoint=None,
@@ -351,6 +344,19 @@ class TextToSpeech:
         )
         self.cvvp.load_state_dict(torch.load(get_model_path("cvvp.pth", self.models_dir)))
 
+    def deterministic_state(self, seed=None):
+        """
+        Sets the random seeds that tortoise uses to the current time() and returns that seed so results can be
+        reproduced.
+        """
+        seed = int(time()) if seed is None else seed
+        torch.manual_seed(seed)
+        random.seed(seed)
+        # Can't currently set this because of CUBLAS. TODO: potentially enable it if necessary.
+        # torch.use_deterministic_algorithms(True)
+
+        return seed
+
     def get_conditioning_latents(
         self,
         voice_samples,
@@ -424,8 +430,7 @@ class TextToSpeech:
 
         if return_mels:
             return auto_latent, diffusion_latent, auto_conds, diffusion_conds
-        else:
-            return auto_latent, diffusion_latent
+        return auto_latent, diffusion_latent
 
     def get_random_conditioning_latents(self):
         # Lazy-load the RLG models.
@@ -723,13 +728,13 @@ class TextToSpeech:
 
                 # Find the first occurrence of the "calm" token and trim the codes to that.
                 ctokens = 0
-                for k in range(codes.shape[-1]):
-                    if codes[0, k] == calm_token:
+                for code in range(codes.shape[-1]):
+                    if codes[0, code] == calm_token:
                         ctokens += 1
                     else:
                         ctokens = 0
                     if ctokens > 8:  # 8 tokens gives the diffusion model some "breathing room" to terminate speech.
-                        latents = latents[:, :k]
+                        latents = latents[:, :code]
                         break
                 with self.temporary_cuda(self.diffusion) as diffusion:
                     mel = do_spectrogram_diffusion(
@@ -763,18 +768,4 @@ class TextToSpeech:
                     voice_samples,
                     conditioning_latents,
                 )
-            else:
-                return res
-
-    def deterministic_state(self, seed=None):
-        """
-        Sets the random seeds that tortoise uses to the current time() and returns that seed so results can be
-        reproduced.
-        """
-        seed = int(time()) if seed is None else seed
-        torch.manual_seed(seed)
-        random.seed(seed)
-        # Can't currently set this because of CUBLAS. TODO: potentially enable it if necessary.
-        # torch.use_deterministic_algorithms(True)
-
-        return seed
+            return res