5
chat.load(compile=False)加载模型后提示使用cuda:0。执行示例代码的Zero shot下的chat.sample_audio_speaker时，会提示
stft input and window must be on the same device but got self on cpu and window on cuda:0错误。需要修改ChatTTS\ChatTTS\model\dvae.py文件，在第200行处插入
audio = audio.to('cuda')才可解决。但是当chat.load()载入时，如检测到显存不足，使用了cpu。则需要把这行删除才能正常运行。否则会报stft的输入使用了gpu，但是window使用了cpu。
RuntimeError Traceback (most recent call last)
Cell In[5], line 3
1 from tools.audio import load_audio
----> 3 spk_smp = chat.sample_audio_speaker(load_audio("zm.mp3", 24000))
4 print(spk_smp) # save it in order to load the speaker without sample audio next time
6 params_infer_code = ChatTTS.Chat.InferCodeParams(
7 spk_smp=spk_smp,
8 txt_smp="每到夏天就有一个特别有名的小吃，就是冰粉。现在的冰粉从最传统的红糖冰粉已经发展到了现在的糍粑冰粉跟玫瑰冰粉各种各样的冰粉然后今天的天气也很。",
9 )
File D:\ChatTTS\ChatTTS\core.py:163, in Chat.sample_audio_speaker(self, wav, device)
162 def sample_audio_speaker(self, wav: Union[np.ndarray, torch.Tensor], device: str = 'cuda:0') -> str:
--> 163 return self.speaker.encode_prompt(self.dvae.sample_audio(wav))
File D:\ChatTTS\.conda\lib\site-packages\torch\utils\_contextlib.py:115, in context_decorator.<locals>.decorate_context(*args, **kwargs)
112 @functools.wraps(func)
113 def decorate_context(*args, **kwargs):
114 with ctx_factory():
--> 115 return func(*args, **kwargs)
File D:\ChatTTS\ChatTTS\model\dvae.py:292, in DVAE.sample_audio(self, wav, device)
290 if isinstance(wav, np.ndarray):
291 wav = torch.from_numpy(wav)
--> 292 return self(wav, "encode").squeeze_(0)
File D:\ChatTTS\ChatTTS\model\dvae.py:248, in DVAE.__call__(self, inp, mode)
245 def __call__(
246 self, inp: torch.Tensor, mode: Literal["encode", "decode"] = "decode"
247 ) -> torch.Tensor:
--> 248 return super().__call__(inp, mode)
File D:\ChatTTS\.conda\lib\site-packages\torch\nn\modules\module.py:1532, in Module._wrapped_call_impl(self, *args, **kwargs)
1530 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1531 else:
-> 1532 return self._call_impl(*args, **kwargs)
File D:\ChatTTS\.conda\lib\site-packages\torch\nn\modules\module.py:1541, in Module._call_impl(self, *args, **kwargs)
1536 # If we don't have any hooks, we want to skip the rest of the logic in
1537 # this function, and just call forward.
1538 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1539 or _global_backward_pre_hooks or _global_backward_hooks
1540 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1541 return forward_call(*args, **kwargs)
1543 try:
1544 result = None
File D:\ChatTTS\.conda\lib\site-packages\torch\utils\_contextlib.py:115, in context_decorator.<locals>.decorate_context(*args, **kwargs)
112 @functools.wraps(func)
113 def decorate_context(*args, **kwargs):
114 with ctx_factory():
--> 115 return func(*args, **kwargs)
File D:\ChatTTS\ChatTTS\model\dvae.py:255, in DVAE.forward(self, inp, mode)
250 @torch.inference_mode()
251 def forward(
252 self, inp: torch.Tensor, mode: Literal["encode", "decode"] = "decode"
253 ) -> torch.Tensor:
254 if mode == "encode" and hasattr(self, "encoder") and self.vq_layer is not None:
--> 255 mel = self.preprocessor_mel(inp)
256 x: torch.Tensor = self.downsample_conv(
257 torch.div(mel, self.coef.view(100, 1).expand(mel.shape), out=mel),
258 ).unsqueeze_(0)
259 del mel
File D:\ChatTTS\ChatTTS\model\dvae.py:197, in MelSpectrogramFeatures.__call__(self, audio)
196 def __call__(self, audio: torch.Tensor) -> torch.Tensor:
--> 197 return super().__call__(audio)
File D:\ChatTTS\.conda\lib\site-packages\torch\nn\modules\module.py:1532, in Module._wrapped_call_impl(self, *args, **kwargs)
1530 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1531 else:
-> 1532 return self._call_impl(*args, **kwargs)
File D:\ChatTTS\.conda\lib\site-packages\torch\nn\modules\module.py:1541, in Module._call_impl(self, *args, **kwargs)
1536 # If we don't have any hooks, we want to skip the rest of the logic in
1537 # this function, and just call forward.
1538 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1539 or _global_backward_pre_hooks or _global_backward_hooks
1540 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1541 return forward_call(*args, **kwargs)
1543 try:
1544 result = None
File D:\ChatTTS\ChatTTS\model\dvae.py:200, in MelSpectrogramFeatures.forward(self, audio)
199 def forward(self, audio: torch.Tensor) -> torch.Tensor:
--> 200 mel: torch.Tensor = self.mel_spec(audio)
201 features = torch.log(torch.clip(mel, min=1e-5))
202 return features
File D:\ChatTTS\.conda\lib\site-packages\torch\nn\modules\module.py:1532, in Module._wrapped_call_impl(self, *args, **kwargs)
1530 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1531 else:
-> 1532 return self._call_impl(*args, **kwargs)
File D:\ChatTTS\.conda\lib\site-packages\torch\nn\modules\module.py:1541, in Module._call_impl(self, *args, **kwargs)
1536 # If we don't have any hooks, we want to skip the rest of the logic in
1537 # this function, and just call forward.
1538 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1539 or _global_backward_pre_hooks or _global_backward_hooks
1540 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1541 return forward_call(*args, **kwargs)
1543 try:
1544 result = None
File D:\ChatTTS\.conda\lib\site-packages\torchaudio\transforms\_transforms.py:619, in MelSpectrogram.forward(self, waveform)
611 def forward(self, waveform: Tensor) -> Tensor:
612 r"""
613 Args:
614 waveform (Tensor): Tensor of audio of dimension (..., time).
(...)
617 Tensor: Mel frequency spectrogram of size (..., `n_mels, time).
618 """
--> 619 specgram = self.spectrogram(waveform)
620 mel_specgram = self.mel_scale(specgram)
621 return mel_specgram
File D:\ChatTTS\.conda\lib\site-packages\torch\nn\modules\module.py:1532, in Module._wrapped_call_impl(self, *args, **kwargs)
1530 return self._compiled_call_impl(*args, **kwargs) # type: ignore[misc]
1531 else:
-> 1532 return self._call_impl(*args, **kwargs)
File D:\ChatTTS\.conda\lib\site-packages\torch\nn\modules\module.py:1541, in Module._call_impl(self, *args, **kwargs)
1536 # If we don't have any hooks, we want to skip the rest of the logic in
1537 # this function, and just call forward.
1538 if not (self._backward_hooks or self._backward_pre_hooks or self._forward_hooks or self._forward_pre_hooks
1539 or _global_backward_pre_hooks or _global_backward_hooks
1540 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1541 return forward_call(*args, **kwargs)
1543 try:
1544 result = None
File D:\ChatTTS\.conda\lib\site-packages\torchaudio\transforms\_transforms.py:110, in Spectrogram.forward(self, waveform)
100 def forward(self, waveform: Tensor) -> Tensor:
101 r"""
102 Args:
103 waveform (Tensor): Tensor of audio of dimension (..., time).
(...)
108 Fourier bins, and time is the number of window hops (n_frame).
109 """
--> 110 return F.spectrogram(
111 waveform,
112 self.pad,
113 self.window,
114 self.n_fft,
115 self.hop_length,
116 self.win_length,
117 self.power,
118 self.normalized,
119 self.center,
120 self.pad_mode,
121 self.onesided,
122 )
File D:\ChatTTS\.conda\lib\site-packages\torchaudio\functional\functional.py:126, in spectrogram(waveform, pad, window, n_fft, hop_length, win_length, power, normalized, center, pad_mode, onesided, return_complex)
123 waveform = waveform.reshape(-1, shape[-1])
125 # default values are consistent with librosa.core.spectrum._spectrogram
--> 126 spec_f = torch.stft(
127 input=waveform,
128 n_fft=n_fft,
129 hop_length=hop_length,
130 win_length=win_length,
131 window=window,
132 center=center,
133 pad_mode=pad_mode,
134 normalized=frame_length_norm,
135 onesided=onesided,
136 return_complex=True,
137 )
139 # unpack batch
140 spec_f = spec_f.reshape(shape[:-1] + spec_f.shape[-2:])
File D:\ChatTTS\.conda\lib\site-packages\torch\functional.py:665, in stft(input, n_fft, hop_length, win_length, window, center, pad_mode, normalized, onesided, return_complex)
663 input = F.pad(input.view(extended_shape), [pad, pad], pad_mode)
664 input = input.view(input.shape[-signal_dim:])
--> 665 return _VF.stft(input, n_fft, hop_length, win_length, window, # type: ignore[attr-defined]
666 normalized, onesided, return_complex)
RuntimeError: stft input and window must be on the same device but got self on cpu and window on cuda:0