Add speed control for inference

TTS/tts/models/xtts.py

@@ -530,8 +530,10 @@ class Xtts(BaseTTS):
         top_p=0.85,
         do_sample=True,
         num_beams=1,
+        speed=1.0,
         **hf_generate_kwargs,
     ):
+        length_scale = 1.0 / max(speed, 0.05)
         text = text.strip().lower()
         text_tokens = torch.IntTensor(self.tokenizer.encode(text, lang=language)).unsqueeze(0).to(self.device)
 
@@ -584,6 +586,13 @@ class Xtts(BaseTTS):
                     gpt_latents = gpt_latents[:, :k]
                     break
 
+            if length_scale != 1.0:
+                gpt_latents = F.interpolate(
+                    gpt_latents.transpose(1, 2),
+                    scale_factor=length_scale,
+                    mode="linear"
+                ).transpose(1, 2)
+
             wav = self.hifigan_decoder(gpt_latents, g=speaker_embedding)
 
         return {
@@ -634,8 +643,10 @@ class Xtts(BaseTTS):
         top_k=50,
         top_p=0.85,
         do_sample=True,
+        speed=1.0,
         **hf_generate_kwargs,
     ):
+        length_scale = 1.0 / max(speed, 0.05)
         text = text.strip().lower()
         text_tokens = torch.IntTensor(self.tokenizer.encode(text, lang=language)).unsqueeze(0).to(self.device)
 
@@ -674,6 +685,12 @@ class Xtts(BaseTTS):
 
             if is_end or (stream_chunk_size > 0 and len(last_tokens) >= stream_chunk_size):
                 gpt_latents = torch.cat(all_latents, dim=0)[None, :]
+                if length_scale != 1.0:
+                    gpt_latents = F.interpolate(
+                        gpt_latents.transpose(1, 2),
+                        scale_factor=length_scale,
+                        mode="linear"
+                    ).transpose(1, 2)
                 wav_gen = self.hifigan_decoder(gpt_latents, g=speaker_embedding.to(self.device))
                 wav_chunk, wav_gen_prev, wav_overlap = self.handle_chunks(
                     wav_gen.squeeze(), wav_gen_prev, wav_overlap, overlap_wav_len