update readme for modelscope download

README.md

@@ -103,7 +103,7 @@ from voxcpm import VoxCPM
 import soundfile as sf
 
 model = VoxCPM.from_pretrained(
-  "openbmb/VoxCPM2"
+  "openbmb/VoxCPM2",
   load_denoiser=False,
 )
 
@@ -116,6 +116,28 @@ sf.write("demo.wav", wav, model.tts_model.sample_rate)
 print("saved: demo.wav")
 ```
 
+If you prefer downloading from ModelScope first, you can use:
+
+```bash
+pip install modelscope
+```
+
+```python
+from modelscope.hub.snapshot_download import snapshot_download
+from voxcpm import VoxCPM
+import soundfile as sf
+
+local_model_dir = snapshot_download("OpenBMB/VoxCPM2")
+model = VoxCPM.from_pretrained(local_model_dir, load_denoiser=False)
+
+wav = model.generate(
+    text="VoxCPM2 is the current recommended release for realistic multilingual speech synthesis.",
+    cfg_value=2.0,
+    inference_timesteps=10,
+)
+sf.write("demo.wav", wav, model.tts_model.sample_rate)
+```
+
 #### 🎨 Voice Design
 
 Create a voice from a natural-language description — no reference audio needed. **Format:** put the description in parentheses at the start of `text`(e.g. `"(your voice description)The text to synthesize."`):

README_zh.md

@@ -102,7 +102,7 @@ from voxcpm import VoxCPM
 import soundfile as sf
 
 model = VoxCPM.from_pretrained(
-  "openbmb/VoxCPM2"
+  "openbmb/VoxCPM2",
   load_denoiser=False,
 )
 
@@ -115,6 +115,28 @@ sf.write("demo.wav", wav, model.tts_model.sample_rate)
 print("已保存: demo.wav")
 ```
 
+如果你希望先从 ModelScope 下载模型到本地（适用于国内网络访问），可以使用：
+
+```bash
+pip install modelscope
+```
+
+```python
+from modelscope.hub.snapshot_download import snapshot_download
+from voxcpm import VoxCPM
+import soundfile as sf
+
+local_model_dir = snapshot_download("OpenBMB/VoxCPM2")
+model = VoxCPM.from_pretrained(local_model_dir, load_denoiser=False)
+
+wav = model.generate(
+    text="VoxCPM2 是目前推荐使用的多语言语音合成版本。",
+    cfg_value=2.0,
+    inference_timesteps=10,
+)
+sf.write("demo.wav", wav, model.tts_model.sample_rate)
+```
+
 #### 🎨 音色设计
 
 用自然语言描述创建全新音色，无需参考音频。**格式：** 在 `text` 开头用括号写入音色描述（如 `"(音色描述)要合成的文本。"`）：

src/voxcpm/model/voxcpm2.py

@@ -402,18 +402,25 @@ class VoxCPM2Model(nn.Module):
     def _dtype(self):
         return get_dtype(self.config.dtype)
 
-    def _encode_wav(self, wav_path: str, padding_mode: str = "right") -> torch.Tensor:
+    def _encode_wav(
+        self,
+        wav_path: str,
+        padding_mode: str = "right",
+        trim_silence_vad: bool = False,
+    ) -> torch.Tensor:
         """Load, trim, pad and VAE-encode an audio file.
 
         Args:
             wav_path: path to the audio file.
             padding_mode: "right" (default) or "left" padding for alignment.
+            trim_silence_vad: whether to apply VAD-based silence trimming.
 
         Returns:
             audio_feat: (T, P, D) tensor of latent patches.
         """
         audio, _ = librosa.load(wav_path, sr=self._encode_sample_rate, mono=True)
         audio = torch.from_numpy(audio).unsqueeze(0)
+        if trim_silence_vad:
             audio = _trim_audio_silence_vad(audio, self._encode_sample_rate, max_silence_ms=200.0)
         patch_len = self.patch_size * self.chunk_size
         if audio.size(1) % patch_len != 0:
@@ -475,6 +482,7 @@ class VoxCPM2Model(nn.Module):
         retry_badcase: bool = False,
         retry_badcase_max_times: int = 3,
         retry_badcase_ratio_threshold: float = 6.0,
+        trim_silence_vad: bool = False,
         streaming: bool = False,
         streaming_prefix_len: int = 4,
     ) -> Generator[torch.Tensor, None, None]:
@@ -495,8 +503,12 @@ class VoxCPM2Model(nn.Module):
             )
             text_length = text_token.shape[0]
 
-            ref_feat = self._encode_wav(reference_wav_path, padding_mode="right")
+            ref_feat = self._encode_wav(
-            prompt_feat = self._encode_wav(prompt_wav_path, padding_mode="left")
+                reference_wav_path,
+                padding_mode="right",
+                trim_silence_vad=trim_silence_vad,
+            )
+            prompt_feat = self._encode_wav(prompt_wav_path, padding_mode="left", trim_silence_vad=trim_silence_vad)
             prompt_audio_length = prompt_feat.size(0)
 
             ref_tokens, ref_feats, ref_t_mask, ref_a_mask = self._make_ref_prefix(ref_feat, text_token.device)
@@ -538,7 +550,11 @@ class VoxCPM2Model(nn.Module):
             )
             text_length = text_token.shape[0]
 
-            ref_feat = self._encode_wav(reference_wav_path, padding_mode="right")
+            ref_feat = self._encode_wav(
+                reference_wav_path,
+                padding_mode="right",
+                trim_silence_vad=trim_silence_vad,
+            )
             ref_tokens, ref_feats, ref_t_mask, ref_a_mask = self._make_ref_prefix(ref_feat, text_token.device)
 
             text_pad_feat = torch.zeros(
@@ -595,7 +611,7 @@ class VoxCPM2Model(nn.Module):
             )
             text_length = text_token.shape[0]
 
-            prompt_feat = self._encode_wav(prompt_wav_path, padding_mode="left")
+            prompt_feat = self._encode_wav(prompt_wav_path, padding_mode="left", trim_silence_vad=trim_silence_vad)
             prompt_audio_length = prompt_feat.size(0)
             prompt_pad_token = torch.zeros(prompt_audio_length, dtype=torch.int32, device=text_token.device)
             text_pad_feat = torch.zeros(
@@ -677,6 +693,7 @@ class VoxCPM2Model(nn.Module):
         prompt_text: str = None,
         prompt_wav_path: str = None,
         reference_wav_path: str = None,
+        trim_silence_vad: bool = False,
     ):
         """
         Build prompt cache for subsequent generation.
@@ -693,6 +710,8 @@ class VoxCPM2Model(nn.Module):
                 Must be paired with ``prompt_text``.
             reference_wav_path: reference audio path for voice cloning
                 (structurally isolated via ref_audio tokens).
+            trim_silence_vad: whether to apply VAD-based silence trimming
+                before encoding prompt/reference audio.
 
         Returns:
             prompt_cache: dict used by ``_generate_with_prompt_cache``.
@@ -705,11 +724,19 @@ class VoxCPM2Model(nn.Module):
         cache = {}
 
         if reference_wav_path:
-            cache["ref_audio_feat"] = self._encode_wav(reference_wav_path, padding_mode="right")
+            cache["ref_audio_feat"] = self._encode_wav(
+                reference_wav_path,
+                padding_mode="right",
+                trim_silence_vad=trim_silence_vad,
+            )
 
         if prompt_wav_path and prompt_text is not None:
             cache["prompt_text"] = prompt_text
-            cache["audio_feat"] = self._encode_wav(prompt_wav_path, padding_mode="left")
+            cache["audio_feat"] = self._encode_wav(
+                prompt_wav_path,
+                padding_mode="left",
+                trim_silence_vad=trim_silence_vad,
+            )
 
         has_ref = "ref_audio_feat" in cache
         has_prompt = "audio_feat" in cache