Fx: capture compile error on Windows

README.md

@@ -1,13 +1,20 @@
 ## 🎙️ VoxCPM: Tokenizer-Free TTS for Context-Aware Speech Generation and True-to-Life Voice Cloning
 
 
-[![Project Page](https://img.shields.io/badge/Project%20Page-GitHub-blue)](https://github.com/OpenBMB/VoxCPM/) [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-OpenBMB-yellow)](https://huggingface.co/openbmb/VoxCPM-0.5B) [![Live Playground](https://img.shields.io/badge/Live%20PlayGround-Demo-orange)](https://huggingface.co/spaces/OpenBMB/VoxCPM-Demo) [![Samples](https://img.shields.io/badge/Page-Samples-red)](https://thuhcsi.github.io/VoxCPM/)
+[![Project Page](https://img.shields.io/badge/Project%20Page-GitHub-blue)](https://github.com/OpenBMB/VoxCPM/) [![Hugging Face](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-OpenBMB-yellow)](https://huggingface.co/openbmb/VoxCPM-0.5B) [![ModelScope](https://img.shields.io/badge/ModelScope-OpenBMB-purple)](https://modelscope.cn/models/OpenBMB/VoxCPM-0.5B)  [![Live Playground](https://img.shields.io/badge/Live%20PlayGround-Demo-orange)](https://huggingface.co/spaces/OpenBMB/VoxCPM-Demo) [![Samples](https://img.shields.io/badge/Page-Samples-red)](https://openbmb.github.io/VoxCPM-demopage)
+
 
 
 <div align="center">
   <img src="assets/voxcpm_logo.png" alt="VoxCPM Logo" width="40%">
 </div>
 
+<div align="center">
+
+👋 Contact us on [WeChat](assets/wechat.png)
+
+</div>
+
 ## News 
 * [2025.09.16] 🔥 🔥 🔥  We Open Source the VoxCPM-0.5B [weights](https://huggingface.co/openbmb/VoxCPM-0.5B)!
 * [2025.09.16] 🎉 🎉 🎉  We Provide the [Gradio PlayGround](https://huggingface.co/spaces/OpenBMB/VoxCPM-Demo) for VoxCPM-0.5B, try it now! 
@@ -32,11 +39,6 @@ Unlike mainstream approaches that convert speech to discrete tokens, VoxCPM uses
 
 
 
-
-
-
-
-
 ##  Quick Start
 
 ### 🔧 Install from PyPI
@@ -238,6 +240,13 @@ VoxCPM achieves competitive results on public zero-shot TTS benchmarks:
 
 
 
+## 📝TO-DO List
+Please stay tuned for updates!
+- [ ] Release the VoxCPM technical report.
+- [ ] Support higher sampling rate (next version).
+
+
+
 ## 📄 License
 The VoxCPM model weights and code are open-sourced under the [Apache-2.0](LICENSE) license.
 
@@ -258,10 +267,14 @@ This project is developed by the following institutions:
 - <img src="assets/thuhcsi_logo.png" width="28px"> [THUHCSI](https://github.com/thuhcsi)
 
 
+## ⭐ Star History
+ [![Star History Chart](https://api.star-history.com/svg?repos=OpenBMB/VoxCPM&type=Date)](https://star-history.com/#OpenBMB/VoxCPM&Date)
 
 
 ## 📚 Citation
 
+The techical report is coming soon, please wait for the release 😊
+
 If you find our model helpful, please consider citing our projects 📝 and staring us ⭐️！
 
 ```bib

app.py

@@ -170,7 +170,7 @@ def create_demo_interface(demo: VoxCPMDemo):
 
         # Pro Tips
         with gr.Accordion("💡 Pro Tips ｜使用建议", open=False, elem_id="acc_tips"):
-            gr.Markdown(f"""
+            gr.Markdown("""
             ### Prompt Speech Enhancement｜参考语音降噪
             - **Enable** to remove background noise for a clean, studio-like voice, with an external ZipEnhancer component.  
               **启用**：通过 ZipEnhancer 组件消除背景噪音，获得更好的音质。
@@ -194,10 +194,6 @@ def create_demo_interface(demo: VoxCPMDemo):
               **调低**：合成速度更快。
             - **Higher** for better synthesis quality.  
               **调高**：合成质量更佳。
-
-            ### Long Text (e.g., >5 min speech)｜长文本 (如 >5分钟的合成语音)
-            While VoxCPM can handle long texts directly, we recommend using empty lines to break very long content into paragraphs; the model will then synthesize each paragraph individually.  
-            虽然 VoxCPM 支持直接生成长文本，但如果目标文本过长，我们建议使用换行符将内容分段；模型将对每个段落分别合成。
             """)
 
         # Main controls
@@ -206,7 +202,7 @@ 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(
@@ -244,14 +240,13 @@ def create_demo_interface(demo: VoxCPMDemo):
                     text = gr.Textbox(
                         value="VoxCPM is an innovative end-to-end TTS model from ModelBest, designed to generate highly realistic speech.",
                         label="Target Text",
-                        info="Default processing splits text on \\n into paragraphs; each is synthesized as a chunk and then concatenated into the final audio."
                     )
                 with gr.Row():
                     DoNormalizeText = gr.Checkbox(
                         value=False,
                         label="Text Normalization",
                         elem_id="chk_normalize",
-                        info="We use WeTextPorcessing library to normalize the input text."
+                        info="We use wetext library to normalize the input text."
                     )
                 audio_output = gr.Audio(label="Output Audio")
 

pyproject.toml

@@ -34,9 +34,9 @@ dependencies = [
     "gradio",
     "inflect",
     "addict",
-    "WeTextProcessing",
+    "wetext",
     "modelscope>=1.22.0",
-    "datasets>=2,<4",
+    "datasets>=3,<4",
     "huggingface-hub",
     "pydantic",
     "tqdm",

src/voxcpm/core.py

@@ -1,11 +1,10 @@
 import torch
 import torchaudio
 import os
+import re
 import tempfile
 from huggingface_hub import snapshot_download
 from .model.voxcpm import VoxCPMModel
-from .utils.text_normalize import TextNormalizer
-
 
 class VoxCPM:
     def __init__(self,
@@ -25,7 +24,7 @@ class VoxCPM:
         """
         print(f"voxcpm_model_path: {voxcpm_model_path}, zipenhancer_model_path: {zipenhancer_model_path}, enable_denoiser: {enable_denoiser}")
         self.tts_model = VoxCPMModel.from_local(voxcpm_model_path)
-        self.text_normalizer = TextNormalizer()
+        self.text_normalizer = None
         if enable_denoiser and zipenhancer_model_path is not None:
             from .zipenhancer import ZipEnhancer
             self.denoiser = ZipEnhancer(zipenhancer_model_path)
@@ -33,8 +32,9 @@ class VoxCPM:
             self.denoiser = None
         print("Warm up VoxCPMModel...")
         self.tts_model.generate(
-            target_text="Hello, this is the first test sentence."
-        ) 
+            target_text="Hello, this is the first test sentence.",
+            max_len=10,
+        )
 
     @classmethod
     def from_pretrained(cls,
@@ -121,10 +121,19 @@ class VoxCPM:
         Returns:
             numpy.ndarray: 1D waveform array (float32) on CPU.
         """
-        texts = text.split("\n")
-        texts = [t.strip() for t in texts if t.strip()]
-        final_wav = []
-        temp_prompt_wav_path = None 
+        if not text.strip() or not isinstance(text, str):
+            raise ValueError("target text must be a non-empty string")
+        
+        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")
+        
+        text = text.replace("\n", " ")
+        text = re.sub(r'\s+', ' ', text)
+        temp_prompt_wav_path = None
         
         try:
             if prompt_wav_path is not None and prompt_text is not None:
@@ -140,32 +149,25 @@ class VoxCPM:
             else:
                 fixed_prompt_cache = None  # will be built from the first inference
             
-            for sub_text in texts:
-                if sub_text.strip() == "":
-                    continue
-                print("sub_text:", sub_text)
-                if normalize:
-                    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)
+            if normalize:
+                if self.text_normalizer is None:
+                    from .utils.text_normalize import TextNormalizer
+                    self.text_normalizer = TextNormalizer()
+                text = self.text_normalizer.normalize(text)
+            
+            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 torch.cat(final_wav, dim=1).squeeze(0).cpu().numpy()
+            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

@@ -148,12 +148,20 @@ class VoxCPMModel(nn.Module):
 
     
     def optimize(self):
-        if self.device == "cuda":
+        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)
-        else:
+        except Exception as e:
+            print(f"Error: {e}")
+            print("Warning: 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
             self.feat_encoder_step = self.feat_encoder
@@ -275,8 +283,11 @@ class VoxCPMModel(nn.Module):
                 else:
                     break
             else:
-                break
-        return self.audio_vae.decode(latent_pred.to(torch.float32)).squeeze(1).cpu()
+                break   
+                
+        decode_audio = self.audio_vae.decode(latent_pred.to(torch.float32)).squeeze(1).cpu()  
+        decode_audio = decode_audio[..., 640:-640] # trick: trim the start and end of the audio
+        return decode_audio        
     
     @torch.inference_mode()
     def build_prompt_cache(
@@ -314,7 +325,7 @@ 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,
@@ -460,7 +471,8 @@ class VoxCPMModel(nn.Module):
             else:
                 break
         decode_audio = self.audio_vae.decode(latent_pred.to(torch.float32)).squeeze(1).cpu()
-            
+        decode_audio = decode_audio[..., 640:-640] # trick: trim the start and end of the audio
+
         return (
             decode_audio,
             target_text_token,
@@ -572,7 +584,6 @@ class VoxCPMModel(nn.Module):
         pred_feat_seq = torch.cat(pred_feat_seq, dim=1)  # b, t, p, d
 
         feat_pred = rearrange(pred_feat_seq, "b t p d -> b d (t p)", b=B, p=self.patch_size)
-        feat_pred = feat_pred[..., 1:-1] # trick: remove the first and last token
         return feat_pred, pred_feat_seq.squeeze(0).cpu()
 
     @classmethod

src/voxcpm/utils/text_normalize.py

@@ -3,41 +3,8 @@ import re
 import regex
 import inflect
 from functools import partial
-from tn.chinese.normalizer import Normalizer as ZhNormalizer
-from tn.english.normalizer import Normalizer as EnNormalizer
+from wetext import Normalizer
 
-def normal_cut_sentence(text):
-    # 先处理括号内的逗号，将其替换为特殊标记
-    text = re.sub(r'([（(][^）)]*)([,，])([^）)]*[）)])', r'\1&&&\3', text)
-    text = re.sub('([。！，？\?])([^’”])',r'\1\n\2',text)#普通断句符号且后面没有引号
-    text = re.sub('(\.{6})([^’”])',r'\1\n\2',text)#英文省略号且后面没有引号
-    text = re.sub('(\…{2})([^’”])',r'\1\n\2',text)#中文省略号且后面没有引号
-    text = re.sub('([. ,。！；？\?\.{6}\…{2}][’”])([^’”])',r'\1\n\2',text)#断句号+引号且后面没有引号
-    # 处理英文句子的分隔
-    text = re.sub(r'([.,!?])([^’”\'"])', r'\1\n\2', text)  # 句号、感叹号、问号后面没有引号
-    text = re.sub(r'([.!?][’”\'"])([^’”\'"])', r'\1\n\2', text)  # 句号、感叹号、问号加引号后面的部分
-    text = re.sub(r'([（(][^）)]*)(&&&)([^）)]*[）)])', r'\1，\3', text)
-    text = [t for t in text.split("\n") if t]
-    return text
-
-
-def cut_sentence_with_fix_length(text : str, length : int):
-    sentences = normal_cut_sentence(text)
-    cur_length = 0
-    res = ""
-    for sentence in sentences:
-        if not sentence:
-            continue
-        if cur_length > length or cur_length + len(sentence) > length:
-            yield res
-            res = ""
-            cur_length = 0
-        res += sentence
-        cur_length += len(sentence)
-    if res:
-        yield res
-
- 
 chinese_char_pattern = re.compile(r'[\u4e00-\u9fff]+')
 
 # whether contain chinese character
@@ -195,8 +162,8 @@ def clean_text(text):
 class TextNormalizer:
     def __init__(self, tokenizer=None):
         self.tokenizer = tokenizer
-        self.zh_tn_model = ZhNormalizer(remove_erhua=False, full_to_half=False, remove_interjections=False, overwrite_cache=True)
-        self.en_tn_model = EnNormalizer()
+        self.zh_tn_model = Normalizer(lang="zh", operator="tn", remove_erhua=True)
+        self.en_tn_model = Normalizer(lang="en", operator="tn")
         self.inflect_parser = inflect.engine()
     
     def normalize(self, text, split=False):
@@ -207,38 +174,12 @@ class TextNormalizer:
             text = text.replace("=", "等于") # 修复 ”550 + 320 等于 870 千卡。“ 被错误正则为 ”五百五十加三百二十等于八七十千卡.“
             if re.search(r'([\d$%^*_+≥≤≠×÷?=])', text): # 避免 英文连字符被错误正则为减
                 text = re.sub(r'(?<=[a-zA-Z0-9])-(?=\d)', ' - ', text) # 修复 x-2 被正则为 x负2
-                text = self.zh_tn_model.normalize(text)
-            text = re.sub(r'(?<=[a-zA-Z0-9])-(?=\d)', ' - ', text) # 修复 x-2 被正则为 x负2
             text = self.zh_tn_model.normalize(text)
             text = replace_blank(text)
             text = replace_corner_mark(text)
             text = remove_bracket(text)
-            text = re.sub(r'[，,]+$', '。', text)
         else:
             text = self.en_tn_model.normalize(text)
             text = spell_out_number(text, self.inflect_parser)
         if split is False:
-            return text
-        
-        
-if __name__ == "__main__":
-    text_normalizer = TextNormalizer()
-    text = r"""今天我们学习一元二次方程。一元二次方程的标准形式是：
-ax2+bx+c=0ax^2 + bx + c = 0ax2+bx+c=0 
-其中，aaa、bbb 和 ccc 是常数，xxx 是变量。这个方程的解可以通过求根公式来找到。
-一元二次方程的解法有几种：
-  - 因式分解法：通过将方程因式分解来求解。我们首先尝试将方程表达成两个括号的形式，解决方程的解。比如，方程x2−5x+6=0x^2 - 5x + 6 = 0x2−5x+6=0可以因式分解为(x−2)(x−3)=0(x - 2)(x - 3) = 0(x−2)(x−3)=0，因此根为2和3。
-  - 配方法：通过配方将方程转化为完全平方的形式，从而解出。我们通过加上或减去适当的常数来完成这一过程，使得方程可以直接写成一个完全平方的形式。
-  - 求根公式：我们可以使用求根公式直接求出方程的解。这个公式适用于所有的一元二次方程，即使我们无法通过因式分解或配方法来解决时，也能使用该公式。
-公式：x=−b±b2−4ac2ax = \frac{-b \pm \sqrt{b^2 - 4ac}}{2a}x=2a−b±b2−4ac​​这个公式可以帮助我们求解任何一元二次方程的根。
-对于一元二次方程，我们需要了解判别式。判别式的作用是帮助我们判断方程的解的个数和性质。判别式 Δ\DeltaΔ 由下式给出：Δ=b2−4ac\Delta = b^2 - 4acΔ=b2−4ac 根据判别式的值，我们可以知道：
-  - 如果 Δ>0\Delta > 0Δ>0，方程有两个不相等的实数解。这是因为判别式大于0时，根号内的值是正数，所以我们可以得到两个不同的解。
-  - 如果 Δ=0\Delta = 0Δ=0，方程有一个实数解。这是因为根号内的值为零，导致两个解相等，也就是说方程有一个解。
-  - 如果 Δ<0\Delta < 0Δ<0，方程没有实数解。这意味着根号内的值是负数，无法进行实数运算，因此方程没有实数解，可能有复数解。"""
-    texts = ["这是一个公式 (a+b)³=a³+3a²b+3ab²+b³ S=(a×b)÷2", "这样的发展为AI仅仅作为“工具”这一观点提出了新的挑战，", "550 + 320 = 870千卡。", "解一元二次方程：3x^2+x-2=0", "你好啊"]
-    texts = [text]
-    for text in texts:
-        text = text_normalizer.normalize(text)
-        print(text)
-        for t in cut_sentence_with_fix_length(text, 15):
-            print(t)
+            return text