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# -*- coding:utf-8 -*-
# @FileName :vadOrtInferSession.py
# @Time :2023/8/9 09:25
# @Author :lovemefan
# @Email :[email protected]
# -*- coding:utf-8 -*-
# @FileName :VadOrtInferSession.py
# @Time :2023/4/3 18:09
# @Author :lovemefan
# @Email :[email protected]
import logging
from pathlib import Path
from typing import List, Union
import numpy as np
from onnxruntime import (GraphOptimizationLevel, InferenceSession,
SessionOptions, get_available_providers, get_device)
from paraformer.runtime.python.utils.singleton import singleton
@singleton
class VadOrtInferRuntimeSession:
def __init__(self, config, root_dir: Path):
sess_opt = SessionOptions()
sess_opt.log_severity_level = 4
sess_opt.enable_cpu_mem_arena = False
sess_opt.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL
cuda_ep = "CUDAExecutionProvider"
cpu_ep = "CPUExecutionProvider"
cpu_provider_options = {
"arena_extend_strategy": "kSameAsRequested",
}
EP_list = []
if (
config["use_cuda"]
and get_device() == "GPU"
and cuda_ep in get_available_providers()
):
EP_list = [(cuda_ep, config[cuda_ep])]
EP_list.append((cpu_ep, cpu_provider_options))
config["model_path"] = root_dir / str(config["model_path"])
self._verify_model(config["model_path"])
logging.info(f"Loading onnx model at {str(config['model_path'])}")
self.session = InferenceSession(
str(config["model_path"]), sess_options=sess_opt, providers=EP_list
)
if config["use_cuda"] and cuda_ep not in self.session.get_providers():
logging.warning(
f"{cuda_ep} is not available for current env, "
f"the inference part is automatically shifted to be "
f"executed under {cpu_ep}.\n "
"Please ensure the installed onnxruntime-gpu version"
" matches your cuda and cudnn version, "
"you can check their relations from the offical web site: "
"https://onnxruntime.ai/docs/execution-providers/CUDA-ExecutionProvider.html",
RuntimeWarning,
)
def __call__(
self, input_content: List[Union[np.ndarray, np.ndarray]]
) -> np.ndarray:
if isinstance(input_content, list):
input_dict = {
"speech": input_content[0],
"in_cache0": input_content[1],
"in_cache1": input_content[2],
"in_cache2": input_content[3],
"in_cache3": input_content[4],
}
else:
input_dict = {"speech": input_content}
return self.session.run(None, input_dict)
def get_input_names(
self,
):
return [v.name for v in self.session.get_inputs()]
def get_output_names(
self,
):
return [v.name for v in self.session.get_outputs()]
def get_character_list(self, key: str = "character"):
return self.meta_dict[key].splitlines()
def have_key(self, key: str = "character") -> bool:
self.meta_dict = self.session.get_modelmeta().custom_metadata_map
if key in self.meta_dict.keys():
return True
return False
@staticmethod
def _verify_model(model_path):
model_path = Path(model_path)
if not model_path.exists():
raise FileNotFoundError(f"{model_path} does not exists.")
if not model_path.is_file():
raise FileExistsError(f"{model_path} is not a file.")
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