Pine/VoxCPM
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

update readme for modelscope download

Browse Source
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
Download Patch File Download Diff File Expand all files Collapse all files
src/voxcpm/model/voxcpm2.py
+35 -8
View File
@@ -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