#8374 语音识别阶段出错:无效的值或参数: Expected parameter logits (Tensor of shape (1, 51866)) of distribution Categorical(logits: torch.Size(

语音识别阶段出错:无效的值或参数: Expected parameter logits (Tensor of shape (1, 51866)) of distribution Categorical(logits: torch.Size([1, 51866])) to satisfy the constraint IndependentConstraint(Real(), 1), but found invalid values:
tensor([[nan, nan, nan, ..., nan, nan, nan]], device='cuda:0')[openai-whisper(本地)]:
Traceback (most recent call last):
File "videotrans\task\job.py", line 97, in run
File "videotrans\task\_speech2text.py", line 93, in recogn
File "videotrans\recognition\__init__.py", line 204, in run
File "videotrans\recognition\_base.py", line 70, in run
File "videotrans\recognition\_openai.py", line 71, in _exec
File "whisper\transcribe.py", line 295, in transcribe

result: DecodingResult = decode_with_fallback(mel_segment)

File "whisper\transcribe.py", line 201, in decode_with_fallback

decode_result = model.decode(segment, options)

File "torch\utils\_contextlib.py", line 116, in decorate_context

return func(*args, **kwargs)

File "whisper\decoding.py", line 824, in decode

result = DecodingTask(model, options).run(mel)

File "torch\utils\_contextlib.py", line 116, in decorate_context

return func(*args, **kwargs)

File "whisper\decoding.py", line 737, in run

tokens, sum_logprobs, no_speech_probs = self._main_loop(audio_features, tokens)

File "whisper\decoding.py", line 703, in _main_loop

tokens, completed = self.decoder.update(tokens, logits, sum_logprobs)

File "whisper\decoding.py", line 283, in update

next_tokens = Categorical(logits=logits / self.temperature).sample()

File "torch\distributions\categorical.py", line 73, in init

super().__init__(batch_shape, validate_args=validate_args)

File "torch\distributions\distribution.py", line 72, in init

raise ValueError(

ValueError: Expected parameter logits (Tensor of shape (1, 51866)) of distribution Categorical(logits: torch.Size([1, 51866])) to satisfy the constraint IndependentConstraint(Real(), 1), but found invalid values:
tensor([[nan, nan, nan, ..., nan, nan, nan]], device='cuda:0')