Remove segment text logic

app.py

@@ -206,7 +206,7 @@ def create_demo_interface(demo: VoxCPMDemo):
                 prompt_wav = gr.Audio(
                     sources=["upload", 'microphone'],
                     type="filepath",
-                    label="Prompt Speech",
+                    label="Prompt Speech (Optional, or let VoxCPM improvise)",
                     value="./examples/example.wav",
                 )
                 DoDenoisePromptAudio = gr.Checkbox(

src/voxcpm/core.py

@@ -120,9 +120,16 @@ class VoxCPM:
         Returns:
             numpy.ndarray: 1D waveform array (float32) on CPU.
         """
-        texts = text.split("\n")
+        if not text.strip() or not isinstance(text, str):
-        texts = [t.strip() for t in texts if t.strip()]
+            raise ValueError("target text must be a non-empty string")
-        final_wav = []
+        
+        if prompt_wav_path is not None:
+            if not os.path.exists(prompt_wav_path):
+                raise FileNotFoundError(f"prompt_wav_path does not exist: {prompt_wav_path}")
+        
+        if (prompt_wav_path is None) != (prompt_text is None):
+            raise ValueError("prompt_wav_path and prompt_text must both be provided or both be None")
+        
         temp_prompt_wav_path = None
         
         try:
@@ -139,35 +146,25 @@ class VoxCPM:
             else:
                 fixed_prompt_cache = None  # will be built from the first inference
             
-            for sub_text in texts:
+            if normalize:
-                if sub_text.strip() == "":
+                if self.text_normalizer is None:
-                    continue
+                    from .utils.text_normalize import TextNormalizer
-                print("sub_text:", sub_text)
+                    self.text_normalizer = TextNormalizer()
-                if normalize:
+                text = self.text_normalizer.normalize(text)
-                    if self.text_normalizer is None:
-                        from .utils.text_normalize import TextNormalizer
-                        self.text_normalizer = TextNormalizer()
-                    sub_text = self.text_normalizer.normalize(sub_text)
-                wav, target_text_token, generated_audio_feat = self.tts_model.generate_with_prompt_cache(
-                                target_text=sub_text,
-                                prompt_cache=fixed_prompt_cache,
-                                min_len=2,
-                                max_len=max_length,
-                                inference_timesteps=inference_timesteps,
-                                cfg_value=cfg_value,
-                                retry_badcase=retry_badcase,
-                                retry_badcase_max_times=retry_badcase_max_times,
-                                retry_badcase_ratio_threshold=retry_badcase_ratio_threshold,
-                            )
-                if fixed_prompt_cache is None:
-                    fixed_prompt_cache = self.tts_model.merge_prompt_cache(
-                        original_cache=None,
-                        new_text_token=target_text_token,
-                        new_audio_feat=generated_audio_feat
-                    )
-                final_wav.append(wav)
             
-            return torch.cat(final_wav, dim=1).squeeze(0).cpu().numpy()
+            wav, target_text_token, generated_audio_feat = self.tts_model.generate_with_prompt_cache(
+                            target_text=text,
+                            prompt_cache=fixed_prompt_cache,
+                            min_len=2,
+                            max_len=max_length,
+                            inference_timesteps=inference_timesteps,
+                            cfg_value=cfg_value,
+                            retry_badcase=retry_badcase,
+                            retry_badcase_max_times=retry_badcase_max_times,
+                            retry_badcase_ratio_threshold=retry_badcase_ratio_threshold,
+                        )
+        
+            return wav.squeeze(0).cpu().numpy()
         
         finally:
             if temp_prompt_wav_path and os.path.exists(temp_prompt_wav_path):

src/voxcpm/model/voxcpm.py

@@ -151,11 +151,16 @@ class VoxCPMModel(nn.Module):
         try:
             if self.device != "cuda":
                 raise ValueError("VoxCPMModel can only be optimized on CUDA device")
+            try:
+                import triton
+            except:
+                raise ValueError("triton is not installed")
             self.base_lm.forward_step = torch.compile(self.base_lm.forward_step, mode="reduce-overhead", fullgraph=True)
             self.residual_lm.forward_step = torch.compile(self.residual_lm.forward_step, mode="reduce-overhead", fullgraph=True)
             self.feat_encoder_step = torch.compile(self.feat_encoder, mode="reduce-overhead", fullgraph=True)
             self.feat_decoder.estimator = torch.compile(self.feat_decoder.estimator, mode="reduce-overhead", fullgraph=True)
-        except:
+        except Exception as e:
+            print(e)
             print("VoxCPMModel can not be optimized by torch.compile, using original forward_step functions")
             self.base_lm.forward_step = self.base_lm.forward_step
             self.residual_lm.forward_step = self.residual_lm.forward_step
@@ -317,7 +322,7 @@ class VoxCPMModel(nn.Module):
             audio = torch.nn.functional.pad(audio, (0, patch_len - audio.size(1) % patch_len))
 
         # extract audio features
-        audio_feat = self.audio_vae.encode(audio.cuda(), self.sample_rate).cpu()
+        audio_feat = self.audio_vae.encode(audio.to(self.device), self.sample_rate).cpu()
 
         audio_feat = audio_feat.view(
             self.audio_vae.latent_dim,