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update readme for modelscope download

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This commit is contained in:
Labmem-Zhouyx
2026-04-08 11:29:19 +08:00
parent 9adfaf6996
commit df38f0a167
3 changed files with 81 additions and 10 deletions
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README.md
+23 -1
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@@ -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
+23 -1
View File
@@ -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
+35 -8
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@@ -402,19 +402,26 @@ 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)
audio = _trim_audio_silence_vad(audio, self._encode_sample_rate, max_silence_ms=200.0)
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:
padding_size = patch_len - audio.size(1) % patch_len
@@ -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")
prompt_feat = self._encode_wav(prompt_wav_path, padding_mode="left")
ref_feat = self._encode_wav(
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