Enable ensuring minimum length per token

TTS/tts/models/vits.py

@@ -1082,12 +1082,47 @@ class Vits(BaseTTS):
             return aux_input["x_lengths"]
         return torch.tensor(x.shape[1:2]).to(x.device)
     
-    # JMa: add `length_scale`` to `aux_input` to enable changing length (duration) per each input text (sentence)
+    # JMa: set minimum duration if predicted duration is lower than threshold
+    # Workaround to avoid short durations that cause some chars/phonemes to be reduced
+    # @staticmethod
+    # def _set_min_inference_length(d, threshold):
+    #     d_mask = d < threshold
+    #     d[d_mask] = threshold
+    #     return d
+    
+    def _set_min_inference_length(self, x, durs, threshold):
+        punctlike = list(self.config.characters.punctuations) + [self.config.characters.blank]
+        # Get list of tokens from IDs
+        tokens = x.squeeze().tolist()
+        # Check current and next token
+        n = self.tokenizer.characters.id_to_char(tokens[0])
+        # for ix, (c, n) in enumerate(zip(tokens[:-1], tokens[1:])):
+        for ix, idx in enumerate(tokens[1:]):
+            # c = self.tokenizer.characters.id_to_char(id_c)
+            c = n
+            n = self.tokenizer.characters.id_to_char(idx)
+            if c in punctlike:
+                # Skip thresholding for punctuation
+                continue
+            # Add duration from next punctuation if possible
+            d = durs[:,:,ix] + durs[:,:,ix+1] if n in punctlike else durs[:,:,ix]
+            # Threshold duration if duration lower than threshold
+            if d < threshold:
+                durs[:,:,ix] = threshold
+        return durs
+
     @torch.no_grad()
     def inference(
         self,
         x,
-        aux_input={"x_lengths": None, "d_vectors": None, "speaker_ids": None, "language_ids": None, "durations": None, "length_scale": None},
+        aux_input={"x_lengths": None,
+                   "d_vectors": None,
+                   "speaker_ids": None,
+                   "language_ids": None,
+                   "durations": None,
+                   "length_scale": None,    # JMa: add `length_scale`` to `aux_input` to enable changing length (duration) per each input text (sentence)
+                   "min_input_length": 0    # JMa: set minimum length if predicted length is lower than `min_input_length`
+                  },
     ):  # pylint: disable=dangerous-default-value
         """
         Note:
@@ -1107,6 +1142,9 @@ class Vits(BaseTTS):
             - m_p: :math:`[B, C, T_dec]`
             - logs_p: :math:`[B, C, T_dec]`
         """
+        # JMa: Save input
+        x_input = x
+
         sid, g, lid, durations = self._set_cond_input(aux_input)
         x_lengths = self._set_x_lengths(x, aux_input)
 
@@ -1135,9 +1173,13 @@ class Vits(BaseTTS):
                 logw = self.duration_predictor(
                     x, x_mask, g=g if self.args.condition_dp_on_speaker else None, lang_emb=lang_emb
                 )
+            # JMa: set minimum duration if required
+            # w = self._set_min_inference_length(torch.exp(logw) * x_mask, aux_input["min_input_length"]) if aux_input["min_input_length"] else torch.exp(logw) * x_mask
+            w = self._set_min_inference_length(x_input, torch.exp(logw) * x_mask, aux_input["min_input_length"]) if aux_input["min_input_length"] else torch.exp(logw) * x_mask
             # JMa: length scale for the given sentence-like input
             length_scale = aux_input["length_scale"] if aux_input["length_scale"] else self.length_scale
-            w = torch.exp(logw) * x_mask * length_scale
+            w *= length_scale
+            # w = torch.exp(logw) * x_mask * length_scale
         else:
             assert durations.shape[-1] == x.shape[-1]
             w = durations.unsqueeze(0)

TTS/tts/utils/synthesis.py

@@ -30,7 +30,7 @@ def run_model_torch(
     style_text: str = None,
     d_vector: torch.Tensor = None,
     language_id: torch.Tensor = None,
-    aux_input: Dict = {},
+    aux_input: Dict = {"durations": None, "length_scale": None, "min_input_length": 0},
 ) -> Dict:
     """Run a torch model for inference. It does not support batch inference.
 
@@ -49,22 +49,19 @@ def run_model_torch(
         _func = model.module.inference
     else:
         _func = model.inference
-    outputs = _func(
+    # JMa: propagate `durations``, `length_scale``, and  `min_input_length` to `aux_input`
-        inputs,
+    #      to enable changing length (durations) per each input text (sentence) and to set
-        aux_input={
+    #      minimum allowed length of each input char/phoneme
-            "x_lengths": input_lengths,
-            "speaker_ids": speaker_id,
-            "d_vectors": d_vector,
-            "style_mel": style_mel,
-            "style_text": style_text,
-            "language_ids": language_id,
-            # JMa: add `durations`` and `length_scale`` to `aux_input` to enable changing length (durations) per each input text (sentence)
     #   - `length_scale` changes length of the whole generated wav
     #   - `durations` sets up duration (in frames) for each input text ID
-            "durations": aux_input.get("durations", None),
+    #   -  minimum allowed length (in frames) per input ID (char/phoneme) during inference
-            "length_scale": aux_input.get("length_scale", None),
+    aux_input["x_lengths"] = input_lengths
-        },
+    aux_input["speaker_ids"] = speaker_id
-    )
+    aux_input["d_vectors"] = d_vector
+    aux_input["style_mel"] = style_mel
+    aux_input["style_text"] = style_text
+    aux_input["language_ids"] = language_id
+    outputs = _func(inputs, aux_input)
     return outputs