feat: add adjust_speech_rate function to modify speech speed with mor…

…e durable latents. also missed tts speed implementations added.

TTS/api.py

@@ -283,6 +283,7 @@ def tts(
             style_text=None,
             reference_speaker_name=None,
             split_sentences=split_sentences,
+            speed=speed,
             **kwargs,
         )
         return wav
@@ -330,13 +331,13 @@ def tts_to_file(
                 Additional arguments for the model.
         """
         self._check_arguments(speaker=speaker, language=language, speaker_wav=speaker_wav, **kwargs)
-
         wav = self.tts(
             text=text,
             speaker=speaker,
             language=language,
             speaker_wav=speaker_wav,
             split_sentences=split_sentences,
+            speed=speed,
             **kwargs,
         )
         self.synthesizer.save_wav(wav=wav, path=file_path, pipe_out=pipe_out)

TTS/tts/models/base_tts.py

@@ -4,6 +4,7 @@
 
 import torch
 import torch.distributed as dist
+import torch.nn.functional as F
 from coqpit import Coqpit
 from torch import nn
 from torch.utils.data import DataLoader
@@ -76,6 +77,33 @@ def _set_model_args(self, config: Coqpit):
         else:
             raise ValueError("config must be either a *Config or *Args")
 
+    def adjust_speech_rate(self, gpt_latents, length_scale):
+        if abs(length_scale - 1.0) < 1e-6:
+            return gpt_latents
+
+        B, L, D = gpt_latents.shape
+        target_length = int(L * length_scale)
+
+        assert target_length > 0, f"Invalid target length: {target_length}"
+
+        try:
+            resized = F.interpolate(
+                gpt_latents.transpose(1, 2),
+                size=target_length,
+                mode="linear",
+                align_corners=True
+            ).transpose(1, 2)
+
+            if torch.isnan(resized).any():
+                print("Warning: NaN values detected on adjust speech rate")
+                return gpt_latents
+
+            return resized
+
+        except RuntimeError as e:
+            print(f"Interpolation failed: {e}")
+            return gpt_latents
+
     def init_multispeaker(self, config: Coqpit, data: List = None):
         """Initialize a speaker embedding layer if needen and define expected embedding channel size for defining
         `in_channels` size of the connected layers.

TTS/tts/models/xtts.py

@@ -379,7 +379,7 @@ def get_conditioning_latents(
 
         return gpt_cond_latents, speaker_embedding
 
-    def synthesize(self, text, config, speaker_wav, language, speaker_id=None, **kwargs):
+    def synthesize(self, text, config, speaker_wav, language, speaker_id=None, speed: float = 1.0, **kwargs):
         """Synthesize speech with the given input text.
 
         Args:
@@ -409,14 +409,14 @@ def synthesize(self, text, config, speaker_wav, language, speaker_id=None, **kwa
         settings.update(kwargs)  # allow overriding of preset settings with kwargs
         if speaker_id is not None:
             gpt_cond_latent, speaker_embedding = self.speaker_manager.speakers[speaker_id].values()
-            return self.inference(text, language, gpt_cond_latent, speaker_embedding, **settings)
+            return self.inference(text, language, gpt_cond_latent, speaker_embedding, speed=speed, **settings)
         settings.update({
             "gpt_cond_len": config.gpt_cond_len,
             "gpt_cond_chunk_len": config.gpt_cond_chunk_len,
             "max_ref_len": config.max_ref_len,
             "sound_norm_refs": config.sound_norm_refs,
         })
-        return self.full_inference(text, speaker_wav, language, **settings)
+        return self.full_inference(text, speaker_wav, language, speed=speed, **settings)
 
     @torch.inference_mode()
     def full_inference(
@@ -436,6 +436,7 @@ def full_inference(
         gpt_cond_chunk_len=6,
         max_ref_len=10,
         sound_norm_refs=False,
+        speed: float = 1.0,
         **hf_generate_kwargs,
     ):
         """
@@ -496,6 +497,7 @@ def full_inference(
             top_k=top_k,
             top_p=top_p,
             do_sample=do_sample,
+            speed=speed,
             **hf_generate_kwargs,
         )
 
@@ -569,10 +571,7 @@ def inference(
                 )
 
                 if length_scale != 1.0:
-                    gpt_latents = F.interpolate(
-                        gpt_latents.transpose(1, 2), scale_factor=length_scale, mode="linear"
-                    ).transpose(1, 2)
-
+                    gpt_latents = self.adjust_speech_rate(gpt_latents, length_scale)
                 gpt_latents_list.append(gpt_latents.cpu())
                 wavs.append(self.hifigan_decoder(gpt_latents, g=speaker_embedding).cpu().squeeze())