mirror of
https://github.com/BoardWare-Genius/jarvis-models.git
synced 2025-12-13 16:53:24 +00:00
374 lines
13 KiB
Python
374 lines
13 KiB
Python
# -*- encoding: utf-8 -*-
|
|
# @Author: SWHL
|
|
# @Contact: liekkaskono@163.com
|
|
import functools
|
|
import logging
|
|
import pickle
|
|
from pathlib import Path
|
|
from typing import Any, Dict, Iterable, List, NamedTuple, Set, Tuple, Union
|
|
import warnings
|
|
|
|
import numpy as np
|
|
import yaml
|
|
from onnxruntime import (GraphOptimizationLevel, InferenceSession,
|
|
SessionOptions, get_available_providers, get_device)
|
|
from typeguard import check_argument_types
|
|
|
|
from .kaldifeat import compute_fbank_feats
|
|
|
|
root_dir = Path(__file__).resolve().parent
|
|
|
|
logger_initialized = {}
|
|
|
|
|
|
class TokenIDConverter():
|
|
def __init__(self, token_path: Union[Path, str],
|
|
unk_symbol: str = "<unk>",):
|
|
check_argument_types()
|
|
|
|
self.token_list = self.load_token(token_path)
|
|
self.unk_symbol = unk_symbol
|
|
|
|
@staticmethod
|
|
def load_token(file_path: Union[Path, str]) -> List:
|
|
if not Path(file_path).exists():
|
|
raise TokenIDConverterError(f'The {file_path} does not exist.')
|
|
|
|
with open(str(file_path), 'rb') as f:
|
|
token_list = pickle.load(f)
|
|
|
|
if len(token_list) != len(set(token_list)):
|
|
raise TokenIDConverterError('The Token exists duplicated symbol.')
|
|
return token_list
|
|
|
|
def get_num_vocabulary_size(self) -> int:
|
|
return len(self.token_list)
|
|
|
|
def ids2tokens(self,
|
|
integers: Union[np.ndarray, Iterable[int]]) -> List[str]:
|
|
if isinstance(integers, np.ndarray) and integers.ndim != 1:
|
|
raise TokenIDConverterError(
|
|
f"Must be 1 dim ndarray, but got {integers.ndim}")
|
|
return [self.token_list[i] for i in integers]
|
|
|
|
def tokens2ids(self, tokens: Iterable[str]) -> List[int]:
|
|
token2id = {v: i for i, v in enumerate(self.token_list)}
|
|
if self.unk_symbol not in token2id:
|
|
raise TokenIDConverterError(
|
|
f"Unknown symbol '{self.unk_symbol}' doesn't exist in the token_list"
|
|
)
|
|
unk_id = token2id[self.unk_symbol]
|
|
return [token2id.get(i, unk_id) for i in tokens]
|
|
|
|
|
|
class CharTokenizer():
|
|
def __init__(
|
|
self,
|
|
symbol_value: Union[Path, str, Iterable[str]] = None,
|
|
space_symbol: str = "<space>",
|
|
remove_non_linguistic_symbols: bool = False,
|
|
):
|
|
check_argument_types()
|
|
|
|
self.space_symbol = space_symbol
|
|
self.non_linguistic_symbols = self.load_symbols(symbol_value)
|
|
self.remove_non_linguistic_symbols = remove_non_linguistic_symbols
|
|
|
|
@staticmethod
|
|
def load_symbols(value: Union[Path, str, Iterable[str]] = None) -> Set:
|
|
if value is None:
|
|
return set()
|
|
|
|
if isinstance(value, Iterable[str]):
|
|
return set(value)
|
|
|
|
file_path = Path(value)
|
|
if not file_path.exists():
|
|
logging.warning("%s doesn't exist.", file_path)
|
|
return set()
|
|
|
|
with file_path.open("r", encoding="utf-8") as f:
|
|
return set(line.rstrip() for line in f)
|
|
|
|
def text2tokens(self, line: Union[str, list]) -> List[str]:
|
|
tokens = []
|
|
while len(line) != 0:
|
|
for w in self.non_linguistic_symbols:
|
|
if line.startswith(w):
|
|
if not self.remove_non_linguistic_symbols:
|
|
tokens.append(line[: len(w)])
|
|
line = line[len(w):]
|
|
break
|
|
else:
|
|
t = line[0]
|
|
if t == " ":
|
|
t = "<space>"
|
|
tokens.append(t)
|
|
line = line[1:]
|
|
return tokens
|
|
|
|
def tokens2text(self, tokens: Iterable[str]) -> str:
|
|
tokens = [t if t != self.space_symbol else " " for t in tokens]
|
|
return "".join(tokens)
|
|
|
|
def __repr__(self):
|
|
return (
|
|
f"{self.__class__.__name__}("
|
|
f'space_symbol="{self.space_symbol}"'
|
|
f'non_linguistic_symbols="{self.non_linguistic_symbols}"'
|
|
f")"
|
|
)
|
|
|
|
|
|
class WavFrontend():
|
|
"""Conventional frontend structure for ASR.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
cmvn_file: str = None,
|
|
fs: int = 16000,
|
|
window: str = 'hamming',
|
|
n_mels: int = 80,
|
|
frame_length: int = 25,
|
|
frame_shift: int = 10,
|
|
filter_length_min: int = -1,
|
|
filter_length_max: float = -1,
|
|
lfr_m: int = 1,
|
|
lfr_n: int = 1,
|
|
dither: float = 1.0
|
|
) -> None:
|
|
check_argument_types()
|
|
|
|
self.fs = fs
|
|
self.window = window
|
|
self.n_mels = n_mels
|
|
self.frame_length = frame_length
|
|
self.frame_shift = frame_shift
|
|
self.filter_length_min = filter_length_min
|
|
self.filter_length_max = filter_length_max
|
|
self.lfr_m = lfr_m
|
|
self.lfr_n = lfr_n
|
|
self.cmvn_file = cmvn_file
|
|
self.dither = dither
|
|
|
|
if self.cmvn_file:
|
|
self.cmvn = self.load_cmvn()
|
|
|
|
def fbank(self,
|
|
input_content: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
|
|
waveform_len = input_content.shape[1]
|
|
waveform = input_content[0][:waveform_len]
|
|
waveform = waveform * (1 << 15)
|
|
mat = compute_fbank_feats(waveform,
|
|
num_mel_bins=self.n_mels,
|
|
frame_length=self.frame_length,
|
|
frame_shift=self.frame_shift,
|
|
dither=self.dither,
|
|
energy_floor=0.0,
|
|
window_type=self.window,
|
|
sample_frequency=self.fs)
|
|
feat = mat.astype(np.float32)
|
|
feat_len = np.array(mat.shape[0]).astype(np.int32)
|
|
return feat, feat_len
|
|
|
|
def lfr_cmvn(self, feat: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
|
|
if self.lfr_m != 1 or self.lfr_n != 1:
|
|
feat = self.apply_lfr(feat, self.lfr_m, self.lfr_n)
|
|
|
|
if self.cmvn_file:
|
|
feat = self.apply_cmvn(feat)
|
|
|
|
feat_len = np.array(feat.shape[0]).astype(np.int32)
|
|
return feat, feat_len
|
|
|
|
@staticmethod
|
|
def apply_lfr(inputs: np.ndarray, lfr_m: int, lfr_n: int) -> np.ndarray:
|
|
LFR_inputs = []
|
|
|
|
T = inputs.shape[0]
|
|
T_lfr = int(np.ceil(T / lfr_n))
|
|
left_padding = np.tile(inputs[0], ((lfr_m - 1) // 2, 1))
|
|
inputs = np.vstack((left_padding, inputs))
|
|
T = T + (lfr_m - 1) // 2
|
|
for i in range(T_lfr):
|
|
if lfr_m <= T - i * lfr_n:
|
|
LFR_inputs.append(
|
|
(inputs[i * lfr_n:i * lfr_n + lfr_m]).reshape(1, -1))
|
|
else:
|
|
# process last LFR frame
|
|
num_padding = lfr_m - (T - i * lfr_n)
|
|
frame = inputs[i * lfr_n:].reshape(-1)
|
|
for _ in range(num_padding):
|
|
frame = np.hstack((frame, inputs[-1]))
|
|
|
|
LFR_inputs.append(frame)
|
|
LFR_outputs = np.vstack(LFR_inputs).astype(np.float32)
|
|
return LFR_outputs
|
|
|
|
def apply_cmvn(self, inputs: np.ndarray) -> np.ndarray:
|
|
"""
|
|
Apply CMVN with mvn data
|
|
"""
|
|
frame, dim = inputs.shape
|
|
means = np.tile(self.cmvn[0:1, :dim], (frame, 1))
|
|
vars = np.tile(self.cmvn[1:2, :dim], (frame, 1))
|
|
inputs = (inputs + means) * vars
|
|
return inputs
|
|
|
|
def load_cmvn(self,) -> np.ndarray:
|
|
with open(self.cmvn_file, 'r', encoding='utf-8') as f:
|
|
lines = f.readlines()
|
|
|
|
means_list = []
|
|
vars_list = []
|
|
for i in range(len(lines)):
|
|
line_item = lines[i].split()
|
|
if line_item[0] == '<AddShift>':
|
|
line_item = lines[i + 1].split()
|
|
if line_item[0] == '<LearnRateCoef>':
|
|
add_shift_line = line_item[3:(len(line_item) - 1)]
|
|
means_list = list(add_shift_line)
|
|
continue
|
|
elif line_item[0] == '<Rescale>':
|
|
line_item = lines[i + 1].split()
|
|
if line_item[0] == '<LearnRateCoef>':
|
|
rescale_line = line_item[3:(len(line_item) - 1)]
|
|
vars_list = list(rescale_line)
|
|
continue
|
|
|
|
means = np.array(means_list).astype(np.float64)
|
|
vars = np.array(vars_list).astype(np.float64)
|
|
cmvn = np.array([means, vars])
|
|
return cmvn
|
|
|
|
|
|
class Hypothesis(NamedTuple):
|
|
"""Hypothesis data type."""
|
|
|
|
yseq: np.ndarray
|
|
score: Union[float, np.ndarray] = 0
|
|
scores: Dict[str, Union[float, np.ndarray]] = dict()
|
|
states: Dict[str, Any] = dict()
|
|
|
|
def asdict(self) -> dict:
|
|
"""Convert data to JSON-friendly dict."""
|
|
return self._replace(
|
|
yseq=self.yseq.tolist(),
|
|
score=float(self.score),
|
|
scores={k: float(v) for k, v in self.scores.items()},
|
|
)._asdict()
|
|
|
|
|
|
class TokenIDConverterError(Exception):
|
|
pass
|
|
|
|
|
|
class ONNXRuntimeError(Exception):
|
|
pass
|
|
|
|
|
|
class OrtInferSession():
|
|
def __init__(self, config):
|
|
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'] = config['model_path']
|
|
self._verify_model(config['model_path'])
|
|
self.session = InferenceSession(config['model_path'],
|
|
sess_options=sess_opt,
|
|
providers=EP_list)
|
|
|
|
if config['use_cuda'] and cuda_ep not in self.session.get_providers():
|
|
warnings.warn(f'{cuda_ep} is not avaiable for current env, the inference part is automatically shifted to be 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:
|
|
input_dict = dict(zip(self.get_input_names(), input_content))
|
|
try:
|
|
return self.session.run(None, input_dict)
|
|
except Exception as e:
|
|
raise ONNXRuntimeError('ONNXRuntime inferece failed.') from e
|
|
|
|
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.')
|
|
|
|
|
|
def read_yaml(yaml_path: Union[str, Path]) -> Dict:
|
|
if not Path(yaml_path).exists():
|
|
raise FileExistsError(f'The {yaml_path} does not exist.')
|
|
|
|
with open(str(yaml_path), 'rb') as f:
|
|
data = yaml.load(f, Loader=yaml.Loader)
|
|
return data
|
|
|
|
|
|
@functools.lru_cache()
|
|
def get_logger(name='rapdi_paraformer'):
|
|
"""Initialize and get a logger by name.
|
|
If the logger has not been initialized, this method will initialize the
|
|
logger by adding one or two handlers, otherwise the initialized logger will
|
|
be directly returned. During initialization, a StreamHandler will always be
|
|
added.
|
|
Args:
|
|
name (str): Logger name.
|
|
Returns:
|
|
logging.Logger: The expected logger.
|
|
"""
|
|
logger = logging.getLogger(name)
|
|
if name in logger_initialized:
|
|
return logger
|
|
|
|
for logger_name in logger_initialized:
|
|
if name.startswith(logger_name):
|
|
return logger
|
|
|
|
formatter = logging.Formatter(
|
|
'[%(asctime)s] %(name)s %(levelname)s: %(message)s',
|
|
datefmt="%Y/%m/%d %H:%M:%S")
|
|
|
|
sh = logging.StreamHandler()
|
|
sh.setFormatter(formatter)
|
|
logger.addHandler(sh)
|
|
logger_initialized[name] = True
|
|
logger.propagate = False
|
|
return logger
|