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NVIDIA / tacotron2

Tacotron 2 - PyTorch implementation with faster-than-realtime inference
BSD 3-Clause "New" or "Revised" License
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inference error! please help me #583

Open lunar333 opened 1 year ago

lunar333 commented 1 year ago

when I run the inference code ,it has an error as followed

waveglow_path = '/home/zhonghuihang/tacotron2-master/waveglow_256channels_universal_v5.pt' waveglow = torch.load(waveglow_path)['model'] waveglow.cuda().eval().half() for k in waveglow.convinv: k.float() denoiser = Denoiser(waveglow)

InvalidArgumentsError Traceback (most recent call last) /tmp/ipykernel_1534266/1318601042.py in 1 waveglow_path = '/home/zhonghuihang/tacotron2-master/waveglow_256channels_universal_v5.pt' ----> 2 waveglow = torch.load(waveglow_path)['model'] 3 waveglow.cuda().eval().half() 4 for k in waveglow.convinv: 5 k.float()

~/miniconda3/envs/torch/lib/python3.7/site-packages/torch/serialization.py in load(f, map_location, pickle_module, weights_only, **pickle_load_args)

~/miniconda3/envs/torch/lib/python3.7/site-packages/torch/serialization.py in _legacy_load(f, map_location, pickle_module, **pickle_load_args)

~/miniconda3/envs/torch/lib/python3.7/site-packages/torch/serialization.py in find_class(self, mod_name, name)

~/miniconda3/envs/torch/lib/python3.7/site-packages/glow/init.py in 28 extend_all(functions) 29 ---> 30 from .wgr import * # For backwards compatibility. Avoid showing this import in docs. 31 32 from . import wgr

~/miniconda3/envs/torch/lib/python3.7/site-packages/glow/wgr/init.py in 13 # limitations under the License. 14 ---> 15 from glow.wgr.ridge_reduction import 16 from glow.wgr.ridge_regression import 17 from glow.wgr.logistic_ridge_regression import *

~/miniconda3/envs/torch/lib/python3.7/site-packages/glow/wgr/ridge_reduction.py in 13 # limitations under the License. 14 ---> 15 from .ridge_udfs import * 16 from .model_functions import _is_binary, _prepare_covariates, _prepare_labels_and_warn, _check_model 17 from nptyping import Float, NDArray

~/miniconda3/envs/torch/lib/python3.7/site-packages/glow/wgr/ridge_udfs.py in 13 # limitations under the License. 14 ---> 15 from glow.wgr.model_functions import * 16 from nptyping import Float 17 import pandas as pd

~/miniconda3/envs/torch/lib/python3.7/site-packages/glow/wgr/model_functions.py in 108 # @typechecked -- typeguard does not support numpy array 109 def assemble_block(n_rows: Int, n_cols: Int, pdf: pd.DataFrame, cov_matrix: NDArray[(Any, Any), --> 110 Float], 111 row_mask: NDArray[Any]) -> NDArray[Float]: 112 """

~/miniconda3/envs/torch/lib/python3.7/site-packages/nptyping/base_meta_classes.py in getitem(cls, item) 136 raise NPTypingError(f"Type nptyping.{cls} is already parameterized.") 137 --> 138 args = cls._get_item(item) 139 additional_values = cls._get_additional_values(item) 140 assert hasattr(cls, "args"), "A SubscriptableMeta must have args."

~/miniconda3/envs/torch/lib/python3.7/site-packages/nptyping/ndarray.py in _get_item(cls, item) 67 def _get_item(cls, item: Any) -> Tuple[Any, ...]: 68 cls._check_item(item) ---> 69 shape, dtype = cls._get_from_tuple(item) 70 return shape, dtype 71

~/miniconda3/envs/torch/lib/python3.7/site-packages/nptyping/ndarray.py in _get_from_tuple(cls, item) 108 def _get_from_tuple(cls, item: Tuple[Any, ...]) -> Tuple[Shape, DType]: 109 # Return the Shape Expression and DType from a tuple. --> 110 shape = cls._get_shape(item[0]) 111 dtype = cls._get_dtype(item[1]) 112 return shape, dtype

~/miniconda3/envs/torch/lib/python3.7/site-packages/nptyping/ndarray.py in _get_shape(cls, dtype_candidate) 122 else: 123 raise InvalidArgumentsError( --> 124 f"Unexpected argument '{dtype_candidate}', expecting" 125 " Shape[]" 126 " or Literal[]"

InvalidArgumentsError: Unexpected argument '(typing.Any, typing.Any)', expecting Shape[] or Literal[] or typing.Any.